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COMMON PROPERTY RESOURCES, POVERTY AND ENVIRONMENTAL DEGRADATION:
A CASE STUDY IN WEST BENGAL THESIS SUBMITTED FOR THE FULFILMENT OF THE
DEGREE OF PHILOSOPHY IN ARTS (ECONOMICS) AT THE UNIVERSITY OF BURDWAN, WEST BENGAL
By Soma Saha
Department of Economics The University of Burdwan
February, 2014
Pravat Kumar Kuri Phone: 0342-2556566 Ext 438 Associate Professor Email: Department of Economics [email protected] University of Burwan Golapbag, Burdwan Date: West Bengal-713104
TO WHOM IT MAY CONCERN This is to certify that Ms. Soma Saha has duly completed her research work for the thesis entitled “Common Property Resources, Poverty and Environment Degradation: A Case Study in West Bengal” under my supervision. I have approved the thesis and permitted her to submit it for the Ph.D. degree in Economics to the University of Burdwan. Further, I certify that neither this dissertation nor any part thereof was submitted to this or any other University in this country or abroad for Ph.D. or any other degree. It may also be noted that Ms. Soma Saha had delivered two seminar lectures on this research work on 30th October, 2009 and 22nd November, 2011, at the Department of Economics, Burdwan University, in partial fulfilment of the requirement for the submission of the Ph.D. thesis. She has also complied with other relevant conditions specified in the regulations of the University of Burdwan including the residential requirements.
Pravat Kumar Kuri
i
Acknowledgement
I have accumulated many debts of gratitude in the course of my research studies.
Foremost, I would like to express my humble gratitude to my mentor and supervisor
Prof. Pravat Kumar Kuri for his scholarly guidance and constructive supervision. His
invaluable advice, critical revision, perpetual inspiration and consistent
encouragement were necessary for me to complete my dissertation in time. Heartfelt
thanks to him for his invaluable comments and suggestions. In addition, I express my
sincere gratitude to Prof. Arup Chattopadhyay, Head of the Department, Economics
and all the teachers of the Department of Economics, Burdwan University for their
kind support at various phases of my work. I remain thankful to my Principal, Dr.
Amal Kanta Hati and all my colleagues of Tarakeswar Degree College for their
support and good wishes.
Since empirical study is solely dependent on the access to the data, I would like to
acknowledge the help that I had received during my fieldwork from the forest officers
of Bankura and Purulia districts; panchayat office bearers and Head Masters of our
surveyed villages. I am also grateful to all the village respondents of the study area
who took out time to answer the lengthy questionnaires. Without their active
participation and support, the research would never have taken this shape. My special
appreciation to Mr. Suvendu Chel, part-time professor of Bankura Women’s College
and local students for their jovial assistance while I was working at the forest villages.
No research is possible without the library, the centre of learning resources. For the
secondary data sources of my research study, I have consulted many libraries viz.
Central Library of Burdwan University; National Library; Central library of Indian
Statistical Institute, Kolkata. I am indebted to the authorities and staffs of these
institutions for their active cooperation and services. I am very much privileged to
have learned effective use of several econometric software packages from the
Workshop conducted on Research Methodology, by Department of Economics,
University of Burdwan. In this matter my guide, Prof. Pravat Kumar Kuri was a great
help to me in various ways.
I owe a lot to my parents, Sri Rebati Mohan Saha and Smt Anjali Saha; my in-laws,
Sri. J. K. Saha and Smt. Dipali Saha who always encouraged and helped me at every
stage of my personal and academic life and longed to see this achievement come true.
ii
Most gratefully, I acknowledge my immense debt to my dear husband, Jayanta, who
gave me whole hearted support during the research work. In spite of his busy official
schedule, he gave me his helping hand whenever I was in need. I am also grateful to
my daughter Sukrita and son Soumil for the love and encouragement they offered me
while carrying out my studies. I also acknowledge the well wishes of my elder sister,
Supta Manna; sister-in-law, Jayashree Poddar and my dear friend, Suparna Pal.
Finally, I beg to be apologised for any shortcomings.
Soma Saha Department of Economics
The University of Burdwan
Contents
iii
Contents Page Number Acknowledgement i-ii List of Contents iii-vii List of Tables viii-xi List of Figures & Maps xii Chapter 1: Introduction 1-7 Chapter 2: Review of Literature 8-34 2.1: Pioneering Works on Common Property Resources 8 2.2: CPR, Poverty and Environmental Degradation 10 2.3: Agricultural Risk and CPR 17 2.4: Common Forest and Participatory Management 20 2.5: Common Property Resources and Gender 30 Chapter 3: Objectives, Data Source and Methodology 35-51 3.1: Objective of the Study 35 3.2: Data Source 36 3.3: Methodology 40
3.3.1: Conceptual Framework 40 3.3.2: Econometric and Statistical Specification 46
3.4: Hypothesis Tested 51
Contents
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Page Number
Chapter 4: Common Property Forest Resources: 52-93 Contribution and Crisis 4.1: Introduction 52 4.2: Data and Methodology 55 4.2.1: Conceptual Framework 55 4.2.2: The Empirical Model Specification 60 4.2.2.A: Determinants of CPR: Multiple Regression Model 60 4.2.2.B: Determinants of Poverty: Poverty Environment 62 Nexus 4.3: Results and Discussion 64 4.3.1: Nature of Dependency on CPRs 64 4.3.2: CPRs and the Contribution to the Household 68 Income 4.3.3: CPRs and the Contribution to the Consumption 71 Expenditure 4.3.4: CPRs and the Contribution to the Employment 73 Generation 4.3.5: Household Energy Consumption and the Extent 74 of Dependency on CPRs 4.3.6: CPRs and Animal Grazing 76 4.4: CPRs and Rural Poverty 78 4.5: Determinants of CPR Extraction 81 4.6: Poverty-Environment Nexus- Logit Model 85 4.6.1: Determinants of Poverty 87 4.7: Conclusion 92
Contents
v
Page Number Chapter 5: Agricultural Risk and Common Property 94-119 Resources 5.1: Introduction 94 5.2: Data and Methodology 97 5.2.1: Conceptual Framework 98 5.2.2: The Empirical Model Specification 102 5.3: Results and Discussion 105 5.3.1: Nature of Agriculture in Bankura & Purulia 105 District 5.3.2: Agricultural Productivity 108 5.3.3: Labour Allocation in CPR Collection 110 5.3.4: Agricultural Risk and CPR 111 5.3.5: Association between CPR Extraction and 116 Agricultural Risk: Count Data Regression Model 5.4: Conclusion 119 Chapter 6: Common Forest and Participatory 120-150 Management 6.1: Introduction 120 6.2: Data and Methodology 121 6.3: Forest Cover and its Management in India: An 122 Interstate Analysis 6.3.1: Forest Cover in India 122 6.3.2: Introduction of Joint Forest Management in India 126 6.4: Forest Cover and Joint Forest Management in 128 West Bengal 6.4.1: Forest Cover in West Bengal 128
Contents
vi
Page Number 6.4.2: Joint Forest Management in West Bengal 131 6.5: Forest Management in the Study Area 133 6.6: Collective Action in Joint forest Management 138 6.7: Collective Action and Forest Conservation 145 6.8: Conclusion 150 Chapter 7: Women’s Participation in CPR 151-170 Management 7.1: Introduction 151 7.2: Role of Women in CPR Collection in the Study Area 154 7.3: Women’s Participation in Forest Resource Management 160 7.4: Women’s Participation in JFM and Sustainability in 163 Forest Resources 7.4.1: Empirical Model Specification 163 7.4.2: Results and Discussions 166 7.5: Conclusion 170 Chapter 8: Summary, Conclusion and Policy 171-187
Suggestions 8.1: Summary 171 8.2: Conclusion 182 8.3: Suggestions and Policy Implications 184
Contents
vii
Page Number Bibliography 188-204 Appendices 205-230 Appendix- I: Village Characteristics of Study Area 205 1.1: Characteristics of the Population in the 210 Study Area in Bankura and Purulia 1.1.1: Family Size and Literacy Rate 210 1.1.2: Caste Composition 211 1.1.3: Occupation 211 Appendix -II: Price List of CPRs 212 Appendix-III: Summary Statistics 213 Appendix-IV: Land Ownership Pattern 215 Appendix-V: Property Rights and ‘The Tragedy of 218 the Commons’ Appendix-VI: Joint Forest Management in India 223
List of Tables
viii
List of Tables Page Number Chapter 4: Common Property Forest Resources: Contribution and Crisis Table 4.1: Description of Variables of Multiple Regression 61 Model Table 4.2: Household Collection of Common Property 65 Resources in Last 1 year (Rs) Table 4.3: Annual Income from Various Sources (Rs) 68 Table 4.4: CPRs and its Contribution to Total Consumption 72 Expenditure (Rs) Table 4.5: Employment Provided by CPR Based Activity 73 (in last 1 year) Table 4.6: Household Energy Consumption and the Extent 75 of Dependency on CPR Table 4.7: Dependency on CPRs for Animal Grazing 77 (in last 1 month) Table 4.8A: Distribution of Monthly per Capita Income of 78 the Sample Households in Bankura District (including income derived from CPRs) Table 4.8B: Distribution of Monthly per Capita Income of 79 the Sample Households in Purulia District (including income derived from CPRs) Table 4.9A: Distribution of Monthly per Capita Income of 80 the Sample Households in Bankura District (excluding income derived from CPRs)
List of Tables
ix
Page Number Table 4.9B: Distribution of Monthly per Capita Income of the Sample Households in Purulia District 80 (excluding income derived from CPRs) Table 4.10A: Determinants of CPR Extraction-Bankura District 81 Table 4.10B: Variance Inflation Factors (Bankura District) 82 Table 4.11A: Determinants of CPR Extraction-Purulia District 82 Table 4.11B: Variance Inflation Factors (Purulia District) 83 Table 4.12: Extent of Depletion of Common Property Resources 86 (during 1990-2010) Table 4.13: Description of Variables of Logit Regression Model 88 Table 4.14A: Determinants of Poverty-Bankura District 89 Table 4.14B: Determinants of Poverty-Purulia District 90 Chapter 5: Agricultural Risk and Common Property Resources Table 5.1: Description of Variables in Count Data Model 104 Table 5.2: Crop Productivity 108 Table 5.3: Agricultural Implements Used 109 Table 5.4: Agricultural Labour 110 Table 5.5: Labour Allocation in CPR Collection 110 Table 5.6: Agricultural Production and CPR Collection 112 Table 5.7: Agricultural Shortfall and CPR Collection 114 Table 5.8: Forest Collection as a Function of Agricultural 116 Risk (Bankura District)
List of Tables
x
Page Number Table 5.9: Forest Collection as a Function of 117 Agricultural Risk (Purulia district) Chapter 6: Common Forest and Participatory Management Table 6.1: Forest Density Classification 122 Table 6.2: Forest Cover in India 124 Table 6.3: Forest Cover in States/UT of India 125 Table 6.4: Status of JFM in Different States in India 127 Table 6.5: District Wise Forest Cover in West Bengal 129 Table 6.6: Degraded Notified Forest Land in West Bengal 130 Table 6.7: Forest Protection Committees in West Bengal 131 Table 6.8: Status of JFM Committees in West Bengal 132 Table 6.9: JFM Participation in the Study Area of Bankura 136 and Purulia district Table 6.10: Forest Management and Enforcement of Forest 137 Protection Scheme Table 6.11: Description of Variables in Censored Tobit Model 141 Table 6.12: Determinants of Collective Action-Bankura 142 District Table 6.13: Determinants of Collective Action-Purulia District 143 Table 6.14: Description and Hypothesis in Logit Regression 146 Model Table 6.15: Determinants of Forest Degradation –Bankura 147 District Table 6.16: Determinants of Forest Degradation –Purulia 148 District
List of Tables
xi
Page Number Chapter 7: Women’s Participation in CPR Management Table 7.1: Women Headed Household and CPR Collection 157 Table 7.2: Women and CPR Collection in Last One Month 158 of the Date of Survey
Table 7.3: Participation of Household Members of Study 161 Area in JFM Table 7.4: Description of Variables in Binary Probit Model 165 Table 7.5: Determinants of Forest Sustainability-Bankura 166 Table 7.6: Determinants of Forest Sustainability- Purulia 167 Appendices Appendix-I: Village Characteristics of Study Area Table A1.1: Village Wise Characteristics 210 Table A1.2: Village Wise Occupation Composition of 211 the Surveyed Population Appendix-II: Price List of CPRs Table A2.1: Price List of Common Property Resources 212 in the Surveyed Villages Appendix-III: Summary Statistics Table A3.1: Descriptive Statistics of Quantitative and 213 Dummy Variables for Bankura District Table A3.2: Descriptive Statistics of Quantitative and 213 Dummy Variables for Purulia District Appendix-IV: Land Ownership Pattern Table A4.1: Land Ownership Pattern 215 Table A4.2: Land Ownership Pattern (Own land) 216 Table A4.3: Land Ownership Pattern (Operated land) 217 Appendix-V: Property Rights and ‘The Tragedy of the Commons’ Table A5.1: Prisoner’s Dilemma 222
List of Figures & Maps
xii
List of Figures & Maps Page Number Chapter 3: Objectives, Data Source and Methodology Photo 1: Interview with the household head at Panjhoria 39 Photo 2: Interaction with a villager at Dulaltora 39 Chapter 4: Common Property Forest Resources: Contribution and Crisis Photo 3: Villagers collecting leaves from common forest area in Seolibona 67 Figure 4.1: Percentage contribution to household income by different 69 income generation activities in the study area of Bankura and Purulia districts Figure 4.2: Percentage contribution to household income by different 70 income generation activities in Bankura district Figure 4.3: Percentage contribution to household income by different 70 income generation activities in Purulia district Photo 4: Villagers at Jiyathole using fuelwood 71 Photo 5: Common forest area in Jiyathole 71 Chapter 5: Agricultural Risk and Common Property Resources Figure 5.1: Index number of agricultural production (Cereals) 107 Photo 6 &7: CPR collection by rural households at Ramjibanpur 111 Figure 5.2: Agricultural production & CPR collection in survey area 113 of Bankura district Figure 5.3: Agricultural production & CPR collection in survey area 113 of Purulia district Figure 5.4: Agricultural shortfall and CPR collection in Bankura district 115 Figure 5.5: Agricultural shortfall and CPR collection in Purulia district 115 Chapter 6: Common Forest and Participatory Management Map 6.1: Forest cover map of India 123 Map 6.2: Forest cover map of West Bengal 128 Chapter 7: Women’s Participation in CPR Management Photo 8: Interaction with rural women in Jiyathole 156 Photo 9: Women collecting cowdung in Baldanga 156 Appendix-I: Village Characteristics of Study area Map A1.1: Map of West Bengal- Bankura 206 Map A1.2: District map of Bankura 206 Map A1.3: Map of West Bengal- Purulia 209 Map A1.4: District map of Purulia 209 Appendix-V: Property Rights and ‘The Tragedy of the Commons’ Figure A5.1: Relationship among effort, cost and revenue 220
Introduction
1
CHAPTER 1
INTRODUCTION
The concept of Property rights has an important implication to the use of natural
resources, rural poverty, degradation and conservations. Bromley (1990) describes
property, not as a natural resource but as a benefit stream that arises from that
resource. With property, comes the right to use or access, which can be defined as
one’s claim to a benefit stream. Based on the different access regime and the rights
and duties governing them, the different resource regimes are i) State property ii)
Private property iii) Common property and iv) Open access resource (Bromley and
Cochrane, 1994). In the state property regime, the state has full ownership and control
over the property, while in private property regime it is privately used and controlled
by individuals. In the case of common property, individuals within a group have
access, rights and duties and all others are excluded from its use and decision making
(Ciriacy-Wantrup and Bishop, 1975). Here the group of people who have the right to
its collective use is well defined, and the rules that govern their use of it are set out
clearly and followed universally. In an open access regime, nobody owns as there is
no property right and hence everybody has access to it.
Ostrom (1990) uses the term "common pool resources" to denote natural resources
used by many individuals in common, such as fisheries, groundwater basins and
irrigation systems. She sets a ‘design principles’ which includes clearly defined
boundaries, monitors who are either resource users or accountable to them, graduated
sanctions, and mechanisms dominated by the users themselves to resolve conflicts and
to alter the rules. Ostrom observes that the biggest challenge in a common property
regime is to foster contingent self-commitment among the members.
It is now well established that Common Property Resources (CPRs) are the natural
resources belonging to every community that each member could access purposefully
with specific obligations since no one could exercise their own right exclusively over
them monopolising them as their own property (Jodha, 1986). An identifiable
community alone holds the power to access and manage these resources collectively
Introduction
2
and to which no individual has exclusive property rights. In rural India, the commonly
seen resources endowed by nature such as the abundant lands in the form of village
pastures and grazing grounds, common forest areas in the form of village forests,
protected and un-classed forests, ponds, rivers, rivulets and waste lands used for
agricultural practices form the first and foremost property of the rural common man.
The Common Property Resources are the singular source of human sustenance in the
households that constitute a large section of rural India. CPRs are integral part of the
social and institutional arrangements made to meet the day to day requirements of the
rural poor. The rural poor, especially the landless, are highly dependent on the CPRs
for their subsistence. Earlier studies have also suggested that both the poor and not so
poor also depend on the CPRs for their livelihood. CPRs not only act as a buffer
during the economic crisis arising due to crop failure but also act as an additional
source of income during normal times. Forests have provided ample resource in the
form of Non Timber Forest Products (NTFPs) for the subsistence of the rural poor.
The rural poor collect several NTFPs in the form of fuel wood, shrubs, dry leaves
which are used by them for cooking and heating. The bamboo and cane are used for
construction of house, while the wild grasses and shrubs are used as animal fodder.
The forest is also a rich source for several medicinal plants used for curing diseases.
Fruits, vegetables and roots are collected by the rural poor for consumption and sale.
The critical role of natural resources in the sustenance of the rural livelihood can be
traced to time immemorial.
However, efficient use of the natural resources and a critical balance between stock
and flow of resources is essential. Indiscriminate use of natural resources leads to over
exploitation and then scarcity. The concept of over exploitation of common natural
resources was first published by Hardin (1968) in the article titled ‘The Tragedy of the
Commons’. The parable demonstrates that free access and unrestricted demand for a
finite resource ultimately results in the depletion of the resource through over-
exploitation. Here the author advocates that individuals with a group, acting
independently and rationally according to one’s own interest and with no regard for
others leads to depletion of the shared natural resources, despite their understanding
Introduction
3
that the depletion of the common natural resource is contrary to the group’s long term
best interest.
Hardin introduces a hypothetical example of a pasture shared by local herdsmen. Each
herdsman will try to maximize his yield and will therefore increase the size of his
herd whenever possible. The utility of each additional animal has both a positive and
negative component; Positive: the herdsman receives all of the proceeds from each
additional animal; Negative: the pasture is slightly degraded by each additional
animal. The division of these costs and benefits is unequal: the individual herdsman
gains all of the advantage, but the disadvantage is shared among all herdsmen using
the pasture. An individual herdsman therefore continues to add additional animals to
his herd. Since all herdsmen reach the same rational conclusion, overgrazing and
degradation of the pasture is its long-term fate. Since this sequence of events follows
predictably from the behaviour of the individuals concerned, the author describes it as
a ‘Tragedy’. The metaphor illustrates the argument that free access and unrestricted
demand for a finite resource ultimately dooms the resource through over-exploitation.
This occurs because the benefits of exploitation accrue to individuals or groups, each
of whom is motivated to maximize use of the resource to the point in which they
become dependent on it, while the costs of the over exploitation are borne by all those
to whom the resource is available. This, in turn, causes demand for the resource to
increase, which causes the problem to snowball to the point that the resource is
exhausted. The rate at which exhaustion of the resource is realized depends primarily
on three factors: the number of users wanting to consume the commons, the
consumptiveness of their uses, and the relative robustness of the commons. The
author also addresses potential management solutions to the problems of the
commons through resource management solution like privatization, polluter pays, and
regulation. The author argues against relying on conscience as a means of policing the
commons, suggesting that this favours selfish individuals – often known as free riders
– over those who are more selfless.
However, Hardin’s theory of ‘The Tragedy of the Commons’ has been severely
criticised by social scientists for his failure to recognise that the local commons were
most often CPRs and not open-access. Further the decline of the commons system
Introduction
4
was the result of a variety of factors like abuse of the rules governing the commons,
land ‘reforms’, improved agricultural techniques, and the effects of the industrial
revolution, all these having little to do with the system's inherent worth (Cox, 1985).
Dasgupta (1982) had postulated that in a dynamic model, the open access renewal
resources like the tropical rain forest, fishes in the open sea, etc. could not be ruined if
the cost of extraction was large relative to the value of the resource itself.
In developing countries, the role of common property resources is very widely spread
especially in the rural areas, where there is coexistence of both community ownership
of the natural resources and private property rights. The collection of common
property resources not only helps to sustain their livelihood, but also helps to generate
additional income. Collection of common property resources by the rural poor have
therefore helped to mitigate poverty to large extent. The rural poor in Southern
Zimbabwe in African continent depend heavily on the collection of natural resources
for their subsistence and the income generated from the collection of the natural
resources contribute about 35 percent of the household income (Cavendish, 1999). In
South Eastern Nigeria, a 10 percent increase in income from forest collections has
helped in about 4.9 percent decline in the number of households living in poverty
(Fonta et al. 2010).
In India, the extent of dependency of CPRs ranges from 15 percent to 29 percent
(Chopra, Kadekodi and Murty, 1989; Jodha, 1986; Singh et al., 1996). The survey
data from the National Sample Survey (NSS) 54th round, suggest that 48 percent of
the rural household collect CPRs. Further, studies on poverty with relation to CPR
collection from forest indicate that poverty increases by as much as 28 percent, when
income from forest is set to zero in poverty calculations (Reddy and Chakravarty,
1999). Based on the study of agro-ecological zones of West Bengal, Beck & Ghosh
(2000) postulated that CPRs constitute about 12 percent of the total income of the
poor household. The field surveys in the state of Himachal Pradesh by Dasgupta
(2006) suggest that the rural poor collect Non Timber Forest Products from the
common forests both for sale as well as for self-consumption. Indiscriminate use of
common property resources leads to decline in CPR land. Further, rampant
deforestation has led to ecological degradation in the forest areas as well. Field survey
Introduction
5
of dry regions in India has shown a decline of 31 percent in the CPR land during the
period 1950-52 to 1982-84 (Jodha, 1986). There has been 30 percent decline of the
total CPR area in Haryana during the period from 1970-71 and 1986-87 (Chopra et
al., 1989). Based on the findings of field survey in Karnataka, Pasha (1992),
concludes that the total land available and used for CPRs showed a reduction of 1.9
percent primarily due to encroachment by the rural rich households, CPR land taken
up for development by the Government and CPRs distributed to the poor for crop
cultivation, housing, etc. as part of Anti-Poverty programme initiated by the
Government.
Collection of Common Property Resources in the form of NTFPs has played a critical
role to mitigate the agricultural risks of the rural poor (Pattanayak & Sills, 2001). The
CPRs act as a safety net and provide the desirable consumption insurance to the rural
poor (Baland & Francois, 2004). Investigating the safety net function of collection of
forest products during crop risks, Delacote (2009) suggest that risk aversion is
positively correlated to forest cover and hence risk reduction policies should be
combined with environmental and forest management policies. Kochar (1999)
suggests that the rural poor in order to smooth their income during agricultural shocks
increase their market hours of work. Sustainable agricultural policies of the
government can help to increase the agricultural income and employment
opportunities for the rural poor, thus reducing their dependence on the CPRs. For
sustainable development of the rural livelihood, management of the common property
resource is crucial. An institutional approach to the study of self-organisation and
self-governance in CPR situations was put forth by Ostrom (1999). According to the
author, active participation in collective management by the rural poor depends on
expected benefits, expected costs, internal norms and discount rates. Berkes (2006)
examining the coastal resource management in the marine observes that there is need
to deal with multiple levels of governance and external drivers of change. The success
of Common Property Resource Management depends on the size of the group,
homogeneity among the group members, effective enforcement mechanisms and past
experiences of cooperation (Baland & Platteau, 1996).
Introduction
6
The National Sample Survey Organisation (NSSO) report (Report number 452:
Common Property Resources in India, Jan–June 1998, NSS 54th Round) gives an
estimate of the Inter-State variation in availability of CPR land. It is observed that the
average area of CPR land available to a household varies over a wide range from 0.01
ha (in Tripura) to 4.37 ha (in Mizoram). The north eastern states apart, CPR land per
household was the highest in Rajasthan (2.04 ha), followed by Madhya Pradesh (0.74
ha) and Gujarat (0.72 ha). The percentage of CPR land to geographical area,
observed, varies from 1 percent (in Tripura) to 32 percent (in Rajasthan) across the
states. West Bengal does not have the relatively large areas of common land as
compared to other regions of India. In West Bengal, CPR land per household is 0.03
ha and the percentage of CPR land to geographical area is 2. It is further observed that
the CPRs in West Bengal are declining at an alarming rate mainly due to agricultural
intensification, commercialisation of CPRs, environmental degradation and
population growth. CPRs are largely the work of women and girl. However, where
CPRs are commercialised, it is observed that men always take greater control over
managing the CPRs. Further studies on CPRs reveal that since women are largely
dependent on CPRs for their livelihood and accessing CPRs adds to the women status
within the household, loss of control over CPRs may lead to reduction in the status.
This affects them both economically and socially.
The study of common property resources is an emerging area of research in West
Bengal. In the economically backward region of the state a significant proportion of
the population is highly dependent on common property resources specially the
common property forest resources. This dependency on common property forest
resources is much higher among the scheduled castes and scheduled tribes population
of the state. Accordingly, the extraction of common property resources has serious
implications to rural household income, employment, economic inequalities, poverty
and more so to natural environment. The property rights to resources have an
important bearing on productivity. Participation in forest management plays a critical
role in resource utilisation and conservation. Further gender equity in participation in
forest management impacts upon sustainable governance of the forest resources.
However to our knowledge, no comprehensive study has yet been conducted to deal
Introduction
7
with these complex issues of poverty, environmental degradation and collective action
in common property resource management in West Bengal.
Based on a primary survey in Bankura and Purulia district of West Bengal, our study
fills the gap of knowledge in this area of research.
There are several dimensions to the study of CPRs. This study is primarily intended to
focus on the following dimensions of common property resources in an integrated
manner:
i) Dependency of rural poor on Common Property Resources (specially
forest resources), Rural Poverty and the Environment
ii) Agricultural Risk and Common Property Resources
iii) Common Forest and Participatory Management
iv) Gender discrimination of CPR dependency and their role in CPR
management
More specifically, this study intends to examine the nature and extent of CPR, the
extent of dependency of rural poor households on CPR with a clear focus on its
gender dimension, its impact on poverty and environmental degradation and the role
of Common Property Forest Management in the State of West Bengal.
Review of Literature
8
CHAPTER 2
REVIEW OF LITERATURE
The subject on Common Property Resources (CPRs) has received considerable
attention both in theoretical as well as in empirical research. Various studies by
distinguished scholars have broadened the understanding of the subject. In accordance
with the dimensions of our study we have reviewed different existing studies by
broadly classifying them into five major sub-themes: i) Pioneering works on Common
Property Resources; ii) CPR, poverty and environmental degradation; iii) CPRs and
its role in mitigating agricultural risk; iv) Common Forest and Participatory
Management and v) Gender dimensions of CPR use and management. Attempts have
been made to make an extensive review of the existing literature on each subject. For
convenience of our understanding, existing literature on each sub-themes have been
arranged in two dimensions: works on the theme outside India and works in India.
This helps to identify new ways to interpret and shed light on any gaps in previous
research, resolve conflicts amongst apparently contradictory previous studies and
suggest the way forward for further research. In conformity with the above sequence
the review of existing literature is given below:
2.1 Pioneering works on Common Property Resources Gordon (1954) was among the first to deal on the economic theory of optimum
utilisation of natural resources. He believed in the conservative dictum that
everybody’s property is nobody’s property and therefore the common natural
resources were free goods for the individual but scarce for the society in large. He
advocated that regulation of the natural resources is possible only through conversion
of the common property into private property or public (Government) property.
The concept of ownership of property rights was first examined by Demsetz (1967).
He advocated that there are three ideal types of ownership: i) Communal ownership
where all members of community can exercise this right; neither citizen nor state can
interfere; ii) Private (owner can exclude others) and iii) State (state can exclude
anyone from using the property).
Review of Literature
9
Hardin (1968) was the first scholar to publish an article on the concept of over
exploitation of common natural resources, titled ‘The Tragedy of the Commons’.
Here the author focuses on the depletion of a shared natural resource by individuals in
a group who acts independently and rationally according to each one’s self-interest, in
spite of the fact that they understand that the depletion of the resource is contrary to
the group’s long term best interests. As the access to the natural resource is shared by
all, the benefit of using it goes to the individual user while the consequence of misuse
of the natural resource gets disbursed to the entire community. The reason for this
over exploitation of the natural resource, according to the author, is free access and
unrestricted demand of the finite resource. The author asserts that this problem can be
resolved either through privatisation, polluter pays or regulation.
The argument of Hardin on the common natural resources was criticised by several
authors. The scholars emphasised that Hardin had confused between common
property and open access and had failed to distinguish between ‘collective property’
and ‘no property’ (Ciriacy-Wantrup and Bishop, 1975). The scholars were of the view
that the example cited by Hardin was more appropriate for national rangelands and
parks. The decline of the traditional common system was not due to any inherent
flawed land use policy but was primarily due to variety of other reasons like abuse of
rules governing the commons, improved agricultural techniques and effects of
industrial revolution (Cox, 1985).
Breaking the myth of the ‘Tragedy of the Commons’, Berkes (1989), defines
Common Property Resources as a ‘class of resources for which exclusion is difficult
and joint use involves subtractability’. The common property regime would be
effective only if it has efficiency, stability, resiliency and equitability. Thus a
decentralised collective management of the common property resources by their users
would help to mitigate their depletion. The authority system may be centralised and
diffused to varying degree so as to provide the common property resource users
assurance about the expected behaviour of other users and thereby enable
coordination and minimise ‘free riding’(Runge,1986).
Review of Literature
10
2.2 CPR, Poverty and Environmental Degradation Forests constitute a large part of CPRs and the existing literature on CPR dependence
is essentially centred on forest dependence. Forest resources play a crucial role in the
income and subsistence of the rural poor. Cavendish (1999) had analysed the
collection of natural resources by the rural poor in Africa. The author conducted
surveys in Shindi Ward in Southern Zimbabwe using a random sample of 197
households in 29 villages. Majority of the households depend heavily on common
natural resources. The resource dependence varies systematically with income. The
most important finding of the study was that the natural resources contribute
significantly to the income of the rural poor with about 35 percent of the household
income coming from collection of the natural resources.
Based on empirical results of the survey of 313 households in 8 villages in Pahang,
peninsular Malaysia, Schwade et al. (2006) have highlighted that the collection of
Non Timber Forest products (NTFPs) is an important activity for the rural
households. 65 percent of collectors in the study area sold NTFPs for income. The
empirical results also suggest that the households those are poor or farther away from
the market are more dependent on the collections of NTFPs for income or subsistence
as compared to the wealthier households. It is further noted that the diversity in NTFP
collection helps to lower the risk in income generation.
Sapkota & Odén (2008) have analysed the household characteristics and the high
dependence of the rural households on community forest in the Terai region of Nepal.
The study was mainly based on the primary data through survey of 52 households in
Rupandehi district in western region of Nepal popularly known as ‘Terai’. The
empirical results suggest that there is high socio-economic heterogeneity among the
rural households. According to the authors, the forest collection by the households
depends on their wealth, proximity to the common forest area, landholding size and
labour allocation. The authors conclude that in order to prevent over exploitation of
the common forest, the poor households should involve in other income generating
activities like cultivation of Non Timber Forest Products inside the common forest
area.
Review of Literature
11
The dependence of the rural poor on the community forest in South Eastern Nigeria
and the impact of forest income to the total household income were studied by Fonta
et al. (2010) based on the report of empirical findings on the survey of 1457 heads of
the households from 18 communities. According to the authors, a 10 percent increase
in income from forest collections has helped to decline in the number of households in
poverty by about 4.9 percent. The study advocates the need for change in the policy in
order to ensure reduction in income inequalities for households who are heavily
dependent on the forest.
Several works have been carried out in India to explore the nature and pattern of
CPRs and its associated linkages to poverty, environmental conservation and
sustainability (Jodha 1985a, 1985b, 1986, 1990; Pasha, 1992; Singh et al., (1996);
Iyengar and Shukla, 1999; Beck and Ghosh, 2000). The empirical findings of Jodha
(1986) were based on field survey of 82 villages covering 21 arid and semi-arid
districts in 7 states of India viz. Andhra Pradesh, Gujarat, Karnataka, Madhya
Pradesh, Maharashtra, Rajasthan and Tamil Nadu. As per the findings, 84-100 percent
of the poor households gather food, fuel, and fodder from the CPRs. The rural poor
use the common pastures for grazing of their herds. CPRs also greatly contribute to
employment and income of the rural people. In almost all the villages in the study
area, the income from CPRs account for 15-23 percent of the total income of the
households.
The important role of forest in the livelihood of the rural poor was highlighted by
Conroy (1991). Based on the primary survey of Panchmahals district of Eastern
Gujarat state, the author observes that the local tribal are highly dependent on the
forest for fuel wood, house construction and manufacturing agricultural implements.
The social forestry programme initiated by the government encouraged the local poor
to grow eucalyptus trees, which made them self-sufficient in fuel wood. Moreover, it
also saved a lot of time of the women and children who gathered fire wood from
distant places. The plantation helped the poor to make agricultural implements and
also earn additional income through sale of timber in a restricted manner.
Review of Literature
12
Based on a field survey of 15 villages in Gujarat, Iyengar & Shukla (1999) asserts that
CPRs make up 0.1 percent to 11 percent of the consumption expenditure for farm
households and 1 percent to 22 percent for non-farm households. A study on
utilisation and development of CPRs in the Kandi area of Punjab was undertaken by
Singh et al. (1996). Based on field survey of eight villages in Dasuya-Langerpur
watershed, Hoshiarpur district, Punjab during the period from 1990-91 to 1992-93,
the authors suggest that the rural poor in the study area are highly dependent on CPRs
for their subsistence and it is also a very important source of income contributing 23
percent of their total income. More than 60 percent of the rural households use CPR
land for grazing their livestock and 90 percent collect fuel wood from the common
forest.
On the basis of field survey of Sirmour district in Himachal Pradesh lying in the outer
western Himalayan range, Bon (2000) observed that the communal forest not only
provides timber for households and agricultural implements but also fodder, grass,
food and medicines. The common pastures and wastelands and river beds are used by
the rural poor for grazing. Privatisation and nationalisation of the CPRs have greatly
affected the lives of the poor. The author feels that collective action through social
bindings could be an efficient alternative to privatisation and nationalisations of the
CPRs.
Beck & Ghosh (2000) have studied the common property resources based on a village
survey carried out in agro-ecological zones of West Bengal. The field work was
carried out in seven villages in the district of Purulia, Bardhaman, Birbhum,
Jalpaiguri, North 24 Pargana and South 24 Pargana. The empirical findings suggest
that the CPRs constitute about 12 percent of the total income of the poor household.
Women and girls were primarily involved in the collection of the CPRs with the
proportion of women’s share ranging from 70-78 percent of the total collection.
Agricultural intensification and commoditisation of the CPRs have resulted in the
poor being denied access to the common property resource area.
The importance of common property resources in different agro-climatic zones of
India was analysed by Menon & Vadivelu (2006). The study was based on the
Review of Literature
13
secondary data of National Sample Survey (NSS) 54th round on CPRs. According to
the survey data, 48 percent of the household collect CPRs. In the Eastern Plateau and
Hills, the collection of CPRs by the households is highest (71 percent) whereas in the
western dry regions it is the lowest (13 percent). The average annual household value
of CPR collections at the all-India level is Rs 693. It is highest in the western
Himalayas (Rs 1939) and lowest in Trans Gangetic plain (Rs 230). At all India level,
around 58 percent of the household collect firewood and 25 percent collect fodder
from the common forest.
The literatures reviewed above have shown the high dependence of the rural poor on
CPRs. However, there are many studies where the non-poor also benefit from CPRs.
In fact, while the poor benefit more in relative terms, the rich benefit more in absolute
terms (Nadkarni et al. 1989; Pasha 1992; Singh et al. 1996). Further, there is also
evidence that CPRs are often extracted by the rich or allocated in ways that privilege
the needs of the rich (Karanth 1992).
The study of Chopra & Dasgupta (2003) is primarily centred on the dependence of
forest for rural households and seeks to understand whether this dependence is a
consequence of absence of alternative options or a choice made in presence of it.
Based on secondary data samples of approximately 78,000 households in four states,
Bihar, Karnataka, Madhya Pradesh and Maharashtra from NSS Report (54th Round),
the authors conclude that the non-poor households collect NTFPs for sale, provided
they have access and property rights. Collection of NTFPs has been a source of
income generation for the rural households. There has been market driven over-
exploitation of the CPRs.
Based on the field survey covering 447 households of 19 villages from 2 districts of
Arunachal Pradesh; Kuri (2005) opines that 24.89 percent of the total consumption
expenditure of the non-poor households is supported by the CPRs as compared to
24.18 percent for the poor households, which signifies that CPRs play a crucial role in
the livelihood for both poor and non-poor households. The role of common property
resources as a sustainable source of income for the rural household was investigated
by Dasgupta (2006). The field survey was conducted in 15 villages in the Changar
Review of Literature
14
belt of Kangra district in the state of Himachal Pradesh. The empirical findings
suggest that rural households collect Non Timber Forest Products from the common
forests both for sale as well as for self-consumption.
A micro level analysis of CPRs and the rural poor was conducted by Pasha (1992).
The author examines the extent to which the rural household have access to the
Common Property Resources on the basis of survey findings of 14 villages in the state
of Karnataka. According to the author, in absolute terms, the contribution from CPRs
to the gross income of the rural non-poor is much more than in the case of poor
families. But in relative terms whatever the poor get from CPRs is very important and
crucial in their household economies.
Based on the study of a single village Rajapura in Magadi ‘taluk’ of Bangalore (rural)
district in Karnataka, Karanth (1992), opines that CPR lands have been privatised in
order to bring land under cultivation for maximum revenue. According to the author,
even when specific schemes were meant to benefit the rural poor, the net benefit was
actually accrued by the non-poor households. Further, encroachment of the common
property was a common phenomenon which benefited the rich and dominant
households of the village.
The dependence of the rural poor on the common natural resources has however led to
its over-exploitation in many cases [Jodha (1986); Singh et al. (1996); Chopra and
Dasgupta (2003)]. Jodha (1986) observed that the CPR land declined by 31 percent in
some states to 55 percent in others during the period from 1950-52 to 1982-84. This
decline in the area and quality of CPR land was primarily due to indiscriminate CPR
collection and changes in the institutional arrangements, including the legal status,
underlying these resources. In another study Chopra et al. (1989) established that the
size of CPRs (including forests) had reduced by 4 percent in the state of Maharashtra
and by 30 percent of the total CPR area in Haryana in the period from 1970-71 to
1986-87.
Review of Literature
15
Poverty is often associated with environmental degradation. The rural poor are the
major victims of environment degradation because of their heavy dependence on
natural resources and less alternate source of income. Various studies undertaken by
eminent scholars have broadened the relationship between poverty and environment.
The different environmental factors that affect the poverty of the rural poor have been
put forth by Bucknall et al. (2000). Examining the poverty-environment links, the
authors, conclude that environmental degradation leads to decline in opportunity for
the poor as they are unable to extract CPRs for their subsistence; lowering of capacity
due to ill health and finally loss of security due to natural calamities.
Brocklesby & Hinshelwood (2001) explored the linkages between poverty and
environment by analysing the findings of 23 Participatory Poverty Assessments (PPA)
covering 14 countries in Asia, Europe, Africa and Latin America. Due to long term
environmental trends like deforestation, pollution, soil infertility, salination and
erosion, etc. poor people are more dependent on common property resources for their
livelihood. Poor people, while trying to manage their livelihood, largely resort to
adapting, mitigating and coping strategies like reduced food consumption, substitution
with less nutritious food and use of Common Property Resources like wild food, etc.
The impact of Common Property Resources in the poor people’s livelihood due to its
deteriorating condition was analysed by Biswas (2006). The findings were based on
field survey conducted in two villages: Rakshachwok and Santipur in the district of
Purba Medinipore of West Bengal. Both the villages are adversely affected by the fly
ash disposal of the Kolaghat Thermal Power Plant which is located in their vicinity.
There has been considerable shrinkage of the CPRs and other lands due to dumping of
fly ash in the fallow land, waste land, marshy land, pools, ditches, pits, ponds. Thus
the quality and quantity of fishes and vegetables have drastically reduced, resulting in
the adverse effect on the subsistence of the rural poor. Mukherjee et al. (2009)
examined the connection between tribal household and environmental degradation in
a study on the backward tribal communities of Purulia district in West Bengal in
terms of their physical environment, society, economy and culture. Due to limited
natural resources, the poor tribal communities are under severe pressure to support
their livelihood. High level of poverty has led to environmental degradation. The
Review of Literature
16
authors stresses on the need for education as the key for fostering the well beings of
the tribal.
Saha & Kuri (2012, 2013) analysed the critical role of CPRs in sustainable
development of the rural poor. Based on the field surveys of 300 households in the
districts of Bankura and Purulia, the authors conclude that the rural poor, especially
the landless, are heavily dependent on the CPRs for their subsistence. The quantum of
extraction of CPRs for a household depends on several factors relating to household
and the village level characteristics which have far reaching implications to rural
poverty and environmental degradation.
The relationship between poverty and environment is an emerging area of research in
India in recent times. There is contradictory view on the nexus between poverty and
environment. While Dasgupta and Mäler (1994) assert that the rural poor are most
vulnerable to environmental degradation; Duraiappah (1996) infers that poverty is the
main cause of environmental degradation. However, poverty is not the sole cause of
environmental degradation. Increased pressure on CPRs due to population growth,
low income and ignorance of future benefits of CPR has led to environmental
degradation (Jodha, 1990; Somanathan, 1991; Beck and Ghosh, 2000). There are
several literatures which indicate the positive association between wealth and
extraction of CPRs. Singh et al. (1996) observed that the extraction of CPRs in Punjab
was directly proportional to the ownership of private resources.
Similar study on the size, status and use of Common Property Land Resources was
conducted by Iyengar (1989). The author surveyed 25 villages located in five different
geo-physical regions in Gujarat and observed that the CPR land has reduced
substantially over the last 25-30 years (1960 to 1985-86) in the study villages
primarily due to privatisation through encroachment. Further the CPR land has also
deteriorated due to over use and over exploitation and this degrades the environment.
Pasha (1992) attributes the reduction in CPRs lands primarily to encroachment by the
rural rich households, common land taken up for development under social forestry
programme initiated by the Government and CPRs distributed to the poor for crop
cultivation, housing, etc. by the Government as part of Anti-Poverty programme.
Review of Literature
17
2.3 Agricultural Risk and CPR Extraction of forest products helps the rural poor to mitigate income shocks. Tropical
forest helps the local rural households by providing ‘natural insurance’. Pattanayak &
Sills (2001) have examined the critical role of collection of NTFPs to mitigate the
agricultural risks of the rural poor by systematically measuring the forest collection
trips. Based on the survey of rural households living on the margin of Tapajós
National Forest in the Brazilian Amazon, the authors observed that agriculture is
always subject to infinite risks due to weather, crop disease, pests, fire, seasonal
flooding, unpredictable and variable soil quality, variation in precipitation pattern, etc.
The forest collection trips of the rural poor increase with agricultural shocks or
expected agricultural risks, thus suggesting that the rural households rely on the forest
to mitigate agricultural risks.
On similar lines, based on a study of the empirical data from the survey of three
villages in Southern Malawi, Fisher & Shively (2003) observed that the rural
households were heavily dependent on the common forest for collection of firewood,
timber and bamboo. The authors conclude that during income shocks due to
unpredictable agricultural production, the households save out of transitory income by
accumulating physical assets in order to reduce their dependence on the forest. Fisher
(2004) advocates that the forest income reduces income inequality i.e. inequality
increases 12 percent when income from forest is not considered.
Based on the field survey of 300 riverine rural households in eight villages in Pacaya-
Samiria National Reserve (PSNR) in north-eastern Peru, Takasaki et al. (2002)
analysed their asset holdings during covariate flood and major health shocks.
Gathering of forest products, hunting and fishing are the primary risk coping
strategies adopted by the households. The other coping strategies are precautionary
savings in the form of food stocks or asset disposition and informal insurance in the
form of borrowing, remittance and mutual insurance. According to Baland & Francois
(2004), although the private properties are efficient, the rural poor tend to favour the
common property resources as they provide the informal consumption insurance to
them during agricultural risk. In spite of its low efficiency gain, the commons are
more likely to influence rural livelihood.
Review of Literature
18
The extraction of natural resources as a coping strategy is shaped by local
environmental endowments. Based on the survey of 116 rural households of
Tawahka communities in Eastern Honduras, McSweeny (2004) observes that the rural
poor sell forest products not only to smooth their income but also to meet sudden cash
requirements during any medical emergency. The use of forest resources for
subsistence acts as a ‘safety net’ for the poor and any earnings from the sale of forest
products helps to mitigate the loss of income due to agricultural crisis.
According to Dercon (2002), risk-management has an important role to play in
mitigating income risks during any agricultural shortfall or illness since it is extremely
difficult to implement intra-village credit or any other type of insurance system for the
rural households for coping with the strategies. If the need for insurance is quite large
due to economic crisis, then the less skilled households will devote most of their time
in NTFP collection, thus depriving them of any other development opportunities and
in turn leading to deforestation (Delacote, 2009). The rural households could also use
the livestock as a risk management strategy. Agricultural risk and safety net due to
extraction of forest products have severe impact on deforestation, poverty trap and
environmental degradation of the common property resources. Based on the study in
Honduras, Godoy et al. (2002) argued that in spite of collection of NTFP that has a
low annual value, it plays a critical role to provide insurance in times of economic
crisis.
In the Indian context also, several literatures have highlighted the role of CPRs during
agricultural shocks. Jodha (1978) had attempted to examine the effectiveness of
different adjustment mechanism adopted by the rural household during natural
calamities like drought. Based on the data on rural households in different drought
prone regions in India, the author indicates that the different adjustment mechanism
adopted by the households are reduction in consumption levels, asset depletion &
replenishment, periodic out migration and traditional informal cooperation. However,
maintaining minimum consumption and retaining minimum production potential are
not only inadequate to overcome drought risk but also very expensive. Thus they
depend heavily on CPRs as a source of consumption and income during agricultural
risks.
Review of Literature
19
Agarwal (1990) examined the rural poor households in India so as to determine how
they coped up with food insecurity during agricultural shortfall and calamities like
drought and famine. According to the author, in order to overcome seasonal variation
in the crop cycle, the rural poor adopt to diversify their income source, collect
common property resources, borrow money from kin, adjust their consumption
pattern or mortgage their assets. The landless and poor households’ attempts to
diversify their income by seeking available alternate employment, multiple cropping
and intercropping, keeping variety of livestock and poultry, trading and seasonal
immigration of either individuals or families.
The hours of work responses of the rural households to agricultural shocks was
examined by Kochar (1999). Based on the household data of three villages in central
India: Aurepalle (Karnataka), Shirapur & Kanzara (Maharashtra) made available from
International Crop Research Institute of the Semi-arid Tropics (ICRISAT), the author
opines that the rural poor in order to smooth their income during agricultural shocks
increase their market hours of work. The author suggests that interventions in the
labour market through public works program can help the households to improve their
economic security during any economic crisis.
Chakrabarthy (2001) studied the household characteristics of 18 villages from the
districts of Birbhum and Burdwan in West Bengal and suggests that any agrarian
development adversely affects the supply of CPRs, which in turn affects the
livelihood of the rural poor who are largely dependent on them for their subsistence.
On the flip side, these developments also results in increase of income and job
opportunities for the rural poor, which in turn helps to reduce their dependence on
CPRs and thus prevent its over exploitation. The author concludes that sustainable
agricultural policies of the government should help to increase the agricultural income
and employment opportunities for the rural poor, thus reducing their dependence on
the CPRs. Chopra & Dasgupta (2002) analysed the critical role played by common
property resources as a supplement to rural livelihood by acting as the safety net
provider in times of agricultural crisis. Based on the secondary data of NSS 54th
round, the authors opines that CPRs not only play an important role for sustainable
livelihood of the rural poor but also acts as a safety net during agricultural crisis.
Review of Literature
20
2.4 Common Forest and Participatory Management Since the publication of the article ‘Tragedy of the Commons’ there has been a
noticeable proliferation of literature on property rights and common property
resources and environmental degradation. The primary goal of managing natural
resources is to maximize the long-term economic benefit for the sustainable
development of the rural poor. Broadly three different schools of thought have
emerged on the property rights. The school of property rights advocates that the
problem of over-exploitation and degradation of common property resources (CPRs)
can be resolved only by creating and enforcing private property rights (Demsetz,
1967; Johnson, 1972; Smith, 1981; Cheung, 1970). According to them, private
property is the most efficient way to overcome over-exploitation of the natural
resources. The second school of thought believes that ‘state property’ regime can
effectively help to reduce the over-exploitation of CPRs (Hardin, 1968). The third
school of thought recommends that community based institution can manage the
common resources in a most effective manner and prevent the 'tragedy of commons'
(Berkes,1989; Wade, 1987; Jodha, 1986; Chopra et al., 1989, Ostrom, 1990).
Defining Private and Common Property Rights, Ostrom (1990) asserts that private
property rights depend upon the existence and enforcement of a set of rules that define
as to who has the right to undertake which activity. Rules are required to establish,
monitor and enforce a property system. The importance of private property rights was
exemplified by Smith (1981), Bromley (1992) and Berkes (1996). According to Smith
(1981), for common property resource, the private property rights are far superior to
state or public property rights. The private property owners not only have
unambiguous exclusivity but also there is a direct and immediate incentive for them to
manage their property well. In case of ‘Private property’ where an individual or
corporation has the right to exclude others, Bromley (1992) maintains that private
property appear to be stable and adaptive because they have the social and legal
sanction to exclude excess population, and effectively to resist unwanted intrusions.
Berkes (1996) affirms that state property regimes do not necessarily guarantee a
sustainable use of resources because their decision-makers do not share the same time
horizon or values of nature as resource users would, whereas private property regime
Review of Literature
21
provides the institutional arrangement for a successful exclusion because it is more
effective in making the government enforce their rights.
The property rights school has been criticised by several scholars. The most important
criticism has come from the Marxian theory, which advocates that the institution of
the property rights is established by the state (Bromley 1989). In case of ‘State
property’ regimes where the resources are held entirely by the state, it can control the
inputs and outputs of the natural environment in various ways through taxes or
subsidies to the natural resource use (Ciriacy-Wantrup and Bishop, 1975). According
to Wade (1987), Common Property Resources are to be understood as a subset of
public goods. CPRs are public goods in the sense that they are open to everyone, but
unlike public goods, CPRs have finite benefits, which may lead to their overuse,
depletion, or degradation. The author argues that both private and state property
regimes are expensive to make effective.
Participatory resource management is viewed as a solution to a number of problems
linked to state management of natural resources. Management of the common
property resources are now becoming an integral part of sustainable development.
Ostrom (1990) advocates that the success of the community based management
depends on the well-defined boundaries, congruence between appropriation and
provision rules, graduated sanctions, efficient conflict-resolution mechanisms and
effective monitoring. The author elaborated on an institutional approach to the study
of self-organisation and self-governance in CPR situations. The study further reveals
that individuals always tend to discount future benefits. These discount rates however
are affected by the levels of physical and economic security faced by the rural poor.
There are four internal variables- expected benefits, expected costs, internal norms
and discount rates that affect an individual’s choice of strategies.
Randall (1981) asserts that property rights specify the relationships among people
with respect to the use of things and the penalties for violation of those relationships.
So, there must be an institution to enforce the claims and decide which claim is valid.
Bromley (1992) also clarifies the traditional confusion between open access resources
and common property resources and stresses the need to use the term ‘common
Review of Literature
22
property regimes’ in place of ‘common property resources’. All the institutional
arrangements are man-made and hence the natural resources can either be private
property, common property or public (state) property. He further emphasised that the
property regime is basically an authority system which defines the rule for use of the
natural resources.
Bromley & Cernea (1989) observes that the common property resources in the
developing countries are degrading primarily due to dissolution of local community
management and non-establishment of more effective institutions and the success of
any resource management institution depends on the efforts it puts to address the
social arrangement among the rural poor using the natural resources rather than on the
commodities. Further the institution needs to formulate and implement the system of
incentives and sanctions for influencing the behaviour of the rural poor. Richerson et
al. (2001) describes the evolution theory of management of the commons. According
to the authors, there exist a parallel between the sophisticated bounded rationality
models necessary to account for the behaviour of people towards commons and dual
inheritance or gene-culture co-evolutionary theory. In a social institution, there exists
a complex cultural tradition of social behaviour, wherein the disposition to cooperate
varies considerably from person to person, society to society and time to time. Berkes
(2006) examining the coastal resource management in the marine observed that the
commons management may be considered as a management of complex systems with
emphasis of scale, self-organisation, uncertainty and resilience. The common resource
management therefore needs to deal with multiple levels of governance and external
drivers of change.
The factors that contribute to the effectiveness of local organisations as resource
managers were examined by Hobley & Shah (1996). Based on the evidence from
Nepal, the authors are of the view that effectiveness of the community management
depends on the divisibility of the natural resources and its short terms and long term
benefits. It is further important for the community to adapt to opportunities in both
production and marketing.
Review of Literature
23
Balland & Platteau (1999) examined the impact of inequality on the ability of the
rural households to undertake a successful collective action by analysing the
individual incentive to contribute. The contribution of the rural households is
dependent on the benefits and the costs involved in the participation. The authors
argue that either privatisation of the common property resources or regulation by state
authority tends to eliminate the implicit entitlements enjoyed by the rural households.
Richards et al. (1999) studied four Forest User Groups (FUGs) in Dhankuta and
Terhathum Districts in the Koshi Hills of the Eastern region of Nepal in order to
examine the ways to improve their equity and to understand its socio-economic
impact. Based on the result findings, the authors developed a replicable participatory
economic methodology by which FUG stakeholder groups could calculate the returns
to community forestry.
Bardhan & Dayton-Johnson (2001) examined the relationship between socio-
economic heterogeneity and distribution implications of common property resource
management. The authors observe that a U-shaped relationship exists between
inequality and commons management, with very low and very high levels of
inequality associated with better performance, while mid-levels of inequality
associated with poor performance.
Poteete & Ostrom (2002) emphasises the need for having policies that enhances the
capabilities of the rural poor to form local management communities or institutions
that have strong legal bindings and that they ensure proper monitoring, sanctioning
and conflict resolution. Baland et al. (2010) reiterates that inequality among the rural
communities discourages the poorer individuals to participate in collective action and
thus reduces its efficiency.
Agrawal & Gibson (1999) asserts that the success of the natural resource conservation
can be ascertained by examining its development and conservation by focussing on
the different interests and actors within communities, their decision making and the
internal and external institutions that shape the decision making process. Tiwari
(2004) makes an attempt to analyse the policy, institutional and management designs
Review of Literature
24
of community forest for a sustainable forest management in South and Southeast
Asian countries. The author concludes that forest management is a useful structure for
managing the forests with the help of local villagers.
According to Moretto & Rosato (2002), there has been substantial reduction in the use
of common property resources, primarily due to privatization. However, in those
regions where the local community has been firmly rooted and the privatization has
not been that rampant, the use of CPRs has survived. The success of the communities
of common property users can be attributed to the adoption of appropriate rules for
the users and the ability to enforce the same.
Springate- Baginski et al. (2003) examines 14 sites in the Koshi hills in Nepal and
concludes that the local people can become effective managers for protection of the
forest; robust and efficient planning and decision making makes the community
effective; Community forestry has a generally beneficial impact on household
livelihoods. However, conflict resolution, collective decision making and limited
capacity of the forest department are limitation for its continued existence.
The success and sustainability of the community based natural resource management
(CBNRM) in Botswana was examined by Mbaiwa (2004). The study based on
empirical findings from the survey of 124 household representatives of Okavango
Delta located in north-western Botswana, in south-central Africa concludes that
CBNRM has helped the community to develop a positive approach towards
conservation of the high valued natural resources which has led to several socio-
economic benefits like participation in decision-making, employment and income
generation. Based on the findings of the household survey conducted in 31 villages
adjacent to Chimaliro and Liwonde forest reserves in Malawi in southern central
Africa, Jumbe & Angelsen (2006) observe that forest participation is high in those
locations where the forest dependency of the rural poor is high and it acts as safety net
during economic crisis. However, with increased commercialization of the forest and
a more heterogeneous social context, the forest participation has reduced. The authors
conclude that in addition to forest co-management programme there has to be
Review of Literature
25
provision for supplementary income sources for the participants and also increase in
the incentive for participation.
Adhikari (2004) investigated the relevance of transaction costs in determining the
performance of the common property forest management. Based on the survey
analysis of 309 households from the Middle-Hills of Nepal, the author concludes that
transaction costs are higher for poorer households (14 percent) than those for Middle-
wealth (12 percent) or rich households (9 percent). The author suggests that the
institutional structures for different forest regimes and their associated transaction
costs are important determinants for the success of the resource management.
Shrestha & McManus (2008) advocates that in spite of the fact that community forest
management in Nepal has helped to reduce the forest degradation to a large extent; it
has not produced any significant conservation of bio diversity. Since the benefits from
the community forest management remain very limited and unfairly distributed
among the users dependent on the forest products for their subsistence, there is need
for greater understanding of the political ecology of the community forestry.
From the Indian forest history, it is evident that forest resources have influenced the
livelihood of the forest communities (Cavendish, 1999). During the colonial period,
there was need for restriction on the collection of forest products as it was used both
for consumption and commercial purposes. Even after independence, the situation
livelihood did not change very much. In order to restrain the rampant extraction of
forest products, the Government of India, enforced several rules and regulations
(Saxena, 2003). From the late 1970s and early 1990s it was realised that the reversal
in forest degradation was not possible without the active involvement of the local
forest communities. Under this situation, the Joint Forest Management in India
evolved (Joshi, 1983; Kumar, 2002). Jodha & Bhatia (1998) discusses that there is
need for rebuilding community stakes in CPRs; control of the CPRs given to the local
communities and use of local perspective and knowledge to ensure sustainable use of
the natural resources.
Based on the study of participatory institutions in Sukhomajri, a village in the
foothills of the Siwalik range of Himalayas, Chopra et al. (1989) observe that there
Review of Literature
26
has been considerable reduction in soil erosion, forest denudation and declining land
productivity due to efficient resource management. Further several sustainable
development projects were initiated through people’s participation. Although the
Sukhomajri model has been a great success, the authors conclude that the latent
conflict and interference from the local panchayat and the urbanisation / consumerism
threatens the institution of participation.
Poffenberger (1993) surveyed households in the Chandana and Harinakuri villages in
the state of West Bengal and Mahapada village and Budhikamari in Orissa and opines
that wherever the state forest departments are supportive, community forest
management groups often are able to sustain protection effectively, even under
pressure from other communities and the private sector. The local government body
could play a vital role in conflict resolution among communities or between
communities and the forest department. Based on the study of common lands
belonging to 293 villages bordering the Aravali Hills in the districts of Faridabad,
Gurgaon, Mohindergarh, Rewari and Bhiwani in Haryana, Babu, P.V.S.C and S.
Chandra (1998) using a mathematical programing model, concludes that clear benefits
have been derived from common property regimes, which have been used to examine
institutional development at the village level.
Prasad (1999) observed that the objectives of the JFM to ensure access to the forest,
payment of fair wages, elimination of intermediaries, prevent exploitation of the
NTFP collectors, ensure better socio-economic conditions, maintenance of benefit
sharing and gender equity, sustainable harvesting, forest protection, natural
regeneration, etc. are not fully met. The author concludes that state control of the
NTFPs have often resulted in restricted access and non- remunerative returns to the
collectors and this may in future jeopardise the achievements of sustainable forest
management. Based on the household data on socio-economic variables obtained
through primary survey of 385 household in 32 villages in three states of India (127 in
Haryana, 123 in Bihar, 135 in Uttar Pradesh), Lise (2000) observes that participation
in JFM increases when the condition of forest is good or the poor are highly
dependent on the forest for their subsistence. Educational level of the family has a
negative correlation with people’s participation in JFM. There is need for healthy
Review of Literature
27
interaction between the forest officials and the rural households in order to bring in
transparency for the overall success of the JFM.
Conroy (2001) analysed the constraints to the adaptation of participatory Forest
management in semi-arid India as negative attitudes of the state forest bureaucracy
with a rigidity approach and the strained relationship between forest officials and
villagers; motivation and benefits of the stakeholders; access rights of the villagers to
the forest and its products. The author concludes that there is need for greater
devolution of power to the communities; shift to multi-purpose forest management;
greater benefits for women and poor; and extending participatory forest management
beyond degraded forests. Conroy et al. (2002) examined the self-initiated community
forest management in Orissa. Based on the survey of 43 forest dependent
communities in Orissa, the authors observed that Community forest management has
immensely contributed to the regeneration and sustainable management of the forests
and argue that the formal balance of control of forests be shifted further towards
communities. The authors conclude that a customised approach in place of a
standardised approach is critical for the sustained growth of JFM and recommends
several reforms.
According to Balooni (2002), the need for participatory community forest
management was felt by the policy makers because there was continuous
deforestation and the degradation of forests leading to a huge decline in forest cover
in India, primarily due to misdirected forest policies. The author concludes that for the
success of JFM, we need to ensure equity in representation and participation of the
poor and the women, equitable benefit sharing among the forest department and the
village communities. Singh (2002) probed the status of utilisation and resource
management in Chenani watershed of Jammu & Kashmir. Based on the primary data
of 300 households in 37 villages falling under 30 micro-watersheds and secondary
data, the author is of the view that common forest lands are being converted into
agricultural lands leaving very little scope for their rehabilitation as agricultural lands
are more prone to soil erosion problems. The author concludes that the natural
resources in the study area is badly affected and hence conservation, regeneration,
Review of Literature
28
awareness, use and benefit sharing of the resources and effective management can
help to improve its status and conditions in the long run.
The study of Geevan et al. (2003) broadly supports the views of Singh (2002). The
study was carried out in Banni and Naliya in district Kachchh, Gujarat where there
was large scale degradation of the grassland. The authors conclude that there is need
for restructuring of the property and resource management regimes in order to bring
greater efficiency in conservation of the natural resources. The authors also suggest
review of the biodiversity conservation strategy in order to make both the forest
department officials and the local communities fully responsible for achieving its
biodiversity conservation goals. On similar lines, Kuri (2005) studied 447 households
of 19 villages from two districts namely Papumpare and Upper Siang of Arunachal
Pradesh and opined that JFM seems to succeed in only those areas where the level of
deforestation is very acute. The high level of deforestation has almost threatened the
survival of the rural poor living in the forest fringes and thus they have an incentive to
cooperate with the Government in order to bring about afforestation and actively
participate in the Joint Forest Management.
Based on the study of Adimaly, Neriamangalam, Munnar & Marayur Forest Ranges
in South Western Ghats in Idukki district of Kerala State in South India,
Aravindakshan (2011) observes that the Joint Forest Management committee, now
known as Village Forest Council and also termed as Vana Samrakshana Samithis
(VSS) in Southern states was established to bridge the gap between people and the
forest department and have paid dividends with respect to long term conservation of
the forest and development of the local communities. However ambiguity with respect
to recognition and legal status of VSS could hamper the effectiveness of sustainable
utilization of natural resources.
Based on evidence from Western Midnapore division of West Bengal, Sarker & Das
(2006) discusses the role of Forest Protection Committees (FPCs) in long term
sustainability of the JFM system. Based on secondary data on four FPCs (Ambisole,
Bansiasole, Kasia and Kadokata) in West Midnapore district, the authors observe that
the NTFPs play a critical role in the sustenance of the poor. The authors conclude that
Review of Literature
29
there is need for restructuring of the present JFM models in order to ensure
optimisation of the NTFPs and livelihood benefits for the poor.
Joshi (1999) examined the role played by different stakeholders responsible for the
success of JFM. Based on the study of factors that influenced the emergence and
success of JFM in southwest Bengal, the author observes that although the leadership
of few senior forest officials and the community initiatives were important, the
supportive role provided by the association of front-line workers of the forest
department was critical and thus that there was need for internalisation of the JFM
concept within state bureaucracies for its sustainability.
Roy et al. (2001) illustrated the negotiation systems and design of incentives in JFM
in West Bengal. The study is based on survey of three districts of West Bengal viz.
Midnapore, Bankura and Jalpaiguri to critically examine the performance of JFM as
an institutional system. The authors suggest that there is need for redefining the role
of the Forest Department as a facilitator and introduce equity in sharing of the rights
and responsibilities amongst all sections of the socio-economic groups.
An analysis of the environment- economic interface pertaining to CPR institutions in
West Bengal was carried out by Bhattacharya (2002). The findings of the survey of
forest resources in Belemath in the district of Burdwan and Matha in the district of
Purulia and water bodies in Hazamdihi in the district of Bankura and two fishermen’s
co-operative in Bon Hooghly and Charcharia in Calcutta, suggest that that the
intervention of the state could be reshaped to institutionalize collaboration between
state administration and local resource users.
Basu (2010) examined the deforestation and people’s participation in JFM in the
district of Bankura. Based on the empirical findings of the survey of 65 households in
villages of Kalaberia and Kalyanpur in the district of Bankura, West Bengal, the
author observes that the control of the forestry by the community through JFM has
helped to reduce illegal felling of trees and encroachment. The FPCs under JFM have
helped to protect and manage the common forest area for sustainable forest
management by restricting over extraction of firewood and NTFPs.
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30
2.5 Common Property Resources and Gender The impact of gender difference in property rights in the functioning of the natural
resource management was studied by Meinzen-Dick et.al. (1997). The report of
IUCN-‘The World Conservation Union’ on gender difference in sustainable forest
management suggests that there is need for gender-responsive forest policies by
taking into account the opinions, needs and interests of both men and women.
Bridging the gender inequalities ensures formation of effective sustainable resource
management policies and increased food security, employment opportunities and
household income. It can further avoid conflicts among men and women and thereby
promote equal access of women to land ownership and other resources required for
effective socio-economic participation.
Dealing with the potentials of Common Property Resources in improving the status of
the rural women in Nigerian rainforest ecosystem, Oyerinde (2008) observes that rural
women face unequal access to productive resources and services. Collection of NTFP
by the women is specialised occupation requiring thorough understanding of the
ecology of the forests. The author concludes that Government policies should be
based on social institutions with full participation of the rural women.
The issue of forest tenure and forest governance in Indonesia was dealt by Siscawati
& Mahaningtyas (2012). According to the authors, gender justice is possible through
increasing the voices of women in decision making, adopting gender justice principles
in community organising processes and systematic capacity building on gender justice
in forest tenure and governance.
Jamisolamin (2012) has put forth his views on the reforms in the women led natural
resource management in Philippines. According to the author, existing conflicts from
the management and conservation of natural resources significantly and differently
affect the rural women and give rise to different set of challenges. Women contribute
to development and peace processes as they are able to negotiate and mediate in
conflict situation that leads to resolution. The author concludes that if women are
given consistent recognition in the community and their voices are heard in decision
Review of Literature
31
making process then they acts as key players in leading environmental protection and
conservation efforts.
Giri (2012) dealt on the topic of gender in forest tenure by highlighting the pre-
requisite for sustainable forest management in Nepal. The author observes that
women’s right to common forest resource not only depends on their access to and
benefits accrued from the resources but also the right and authority to take decisions
in the forest management. The author concludes that enacting gender equity through
forest tenure is a multifaceted social and political process that also involves
progressive sensitization and education for the women.
The WWF-UK report (2012) on forest management and gender acknowledges that
ignoring gender issues in forest management leads to improper management and
potential loss of essential ecosystems which has different impact on women and men.
There is need for including women’s voice for decision making in forest management
as it will benefit from their knowledge and skills.
Forest plays an important role in livelihood support system for the rural poor women
in India. The role of women’s participation in the decentralised governance of the
community forest in India was studied by several scholars. Based on the data obtained
from the survey of 641 JFM forest protection committees in the state of Madhya
Pradesh, India, Agrawal et al. (2006) are of the view that participation of women in
forest management has significant effects both on the resource-related outcomes and
at the level of institutional effectiveness and therefore the local community should be
designed to ensure women’s participation in forest governance and protection. The
authors conclude that greater involvement of women in forest management in local
decision-making related to forest governance and protection not only leads to greater
control of illicit harvesting of forest products but also improves regeneration in the
forest.
The survival strategies of the tribal women and their role in forest management were
illustrated by Yadama et.al. (1997). Based on the field study of tribal households
conducted in the high altitude area zone of the Eastern Ghats region of Andhra
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32
Pradesh in the Vishakhapatnam district, the authors conclude that women can play a
greater role in co-management of forests by increasing their collective bargaining
power. Moreover the state needs to play a critical role in bringing out forest
management policies ensuring that the interests of women are protected in the long
run.
Women’s participation in forest management decisions in upper Kullu valley in
Manali in Himachal Pradesh was illustrated by Bingeman (2003). The author observes
that the Mahila Mandals in the Manali region have established a tradition for
women’s participation in forest management and have played an active role in
involving them in actively monitoring, protecting and managing the common forest
area.
Based on the survey of 240 female respondents in 24 villages in Tamil Nadu,
Murugesan & Namasivayam (2012) advocates that the economic factors viz.
occupation, female income to total household income, earner-dependent ratio, female
wealth to total household wealth and female CPR income to total household income
have greater influence on different levels of women empowerment as compared to the
personal factors viz. age, religion, caste, type of family, size of the family and
education in the study area. The authors conclude that it is important for the
Government to use women Self-Help Groups (SHGs) to maintain and protect the
common forest at the village level, which would not only help to generate income but
also empower the women.
Despite the fact that women are the major users of CPRs, their involvement in
participatory management is negligible (Singh, 2010, Patricia Jeffery, et al., 1998,
Agarwal, 2002). Based on the studies of four states in India, Gujarat, Orissa, Madhya
Pradesh and Andhra Pradesh, Patricia Jeffery, et al.,(1998) asserts that women have
been marginal in the establishment of Joint Management Agreements and village
level forest protection committees. Although the forest department made all efforts to
ensure women’s active participation, their involvement has been insignificant. Singh
(2010) surveyed 200 women respondents in 20 villages in two hilly regions (Shivalik
and Aravalli hills) of Haryana State in order to understand the level of women
Review of Literature
33
participation in forest restoration. According to the author, the role of women in forest
management is very important as the collection of forest products to meet subsistence
need is mainly the responsibility of women. Gender equity in benefit sharing should
be ensured so as to improve women’s participation in forest management.
On similar lines, Godbole (2002), asserts that social and cultural constrains have led
to the low participation of women in Joint Forest Management programmes. Women
have little ownership or control over resources such as land and property, education,
technical skills and market information. Hence women are always dependent and
disempowered position as compared to men and therefore run a greater risk of
being excluded from their homes and livelihoods.
Das & Sarker (2008) examines the impact of social capital in a gender sensitive
planning on JFM in West Bengal. The social capital is measured in terms of
productivity, equity and sustainability along with World Bank’s six common
dimensions (groups and networks, trust and solidarity, collective action and
cooperation, information and communication, social cohesion and inclusion, and
empowerment and political action). Based on the survey of Forest Protection
Committees (FPCs) in West Bengal, the authors opine that communal solidarity,
mutual trust and coordinated actions exits in JFM villages, thereby increasing the
social capital. The study further reveals that in female FPC villages there is higher
level of social capital as compared to FPC villages in general. This implies that in
those FPCs where social cohesion and community solidarity is weak, effective
leadership and local support can bring about in improving the social capital.
According to Agarwal (2002), there is gender bias during membership into Forest
Protection Committee in JFM in West Bengal. The General Body (GB) membership
is predominantly for the male household head. However, the woman automatically
becomes a member by virtue of her husband being a member; it is he who is seen as
the primary member. Sarin (1998) studied 20 Community Forest Groups in West
Bengal and observed that 60 percent had no women, and only 8 percent of the 180
Executive Committee members were women. In West Bengal’s Bankura district, the
District Forest Officer had issued a circular stipulating that there should be a
Review of Literature
34
minimum of 30 percent women in the general body, which had resulted in raising the
female membership in several villages (Viegas & Menon, 1993).
Based on the survey of FPCs in a remote Block of South-West Midnapore in West
Bengal, viz., Nayagram, Dasgupta et al. (2006) observe that women participation in
JFM can be enhanced by imparting a simple and low-cost technology based training
programme. The constraints observed in women participation can be attributed to lack
of security, lack of confidence, social and cultural restrictions, etc. which can be
overcome through a model that tackles these gender related issues.
The study of common property resources is an important area of research world over.
In rural India, there is high dependency on common property resources, specially the
common property forest resources. This dependency on common property forest
resources is mainly among the forest fringe dwellers. In fact, the extraction of
common property resources affects the rural household income, employment,
economic inequalities, poverty and environment. As the rural poor are the major users
of the common forest resources, they greatly value the forest conservation and
participate in forest management. Further gender equity in participation in forest
management enhances sustainability of the forest resources. As per our knowledge, no
systematic study on common property resources dealing on all these issues have been
carried out in West Bengal. Further from the survey of a good number of existing
studies on common property resources, we observe that the gender aspect of CPR has
also been neglected. Our study fills-in the gap of knowledge in this area of research.
Objectives, Data Source and Methodology
35
CHAPTER 3
OBJECTIVES, DATA SOURCE AND METHODOLOGY
3.1 Objectives of the Study
In the backdrop of the existing literature survey, we have observed that Common
Property Resources (CPRs) play a critical role not only in the livelihood of the rural
poor but also in overall rural development. The rural poor, mainly women, are highly
dependent on the collection of the CPRs from the common forest for their subsistence.
Rural households gather forest products as a supplement to cultivation. Rural
households collect CPRs from the forest to smooth their income and consumption and
this helps to alleviate their poverty. CPRs not only provide a source of income and
employment to the rural poor but also act as a safety net during agricultural shocks.
However indiscriminate gathering of CPRs leads to forest degradation. Rural poor are
now conscious about long term consequences of forest degradation and they
participate in effective forest management to achieve sustainable development.
This study examines how Common Property Resources affect the livelihood pattern
of the rural poor in the study area and its linkage with poverty, gender, environmental
degradation and forest conservation. It specifically addresses the following research
questions;
1) What is the extent of dependency of rural poor on CPRs in West Bengal?
2) To what extent CPRs provide employment and income to rural poor?
3) What is the implication of CPR extraction on rural poverty?
4) What are the factors responsible for CPR extraction?
5) Does the extraction of CPRs lead to environmental degradation?
6) To what extent agricultural risk affects the collection of common property
resources?
7) Is there any relationship between forest dependency and participation in
forest management?
8) What is the impact of participation of forest management on alleviating
environmental degradation?
9) How the women’s active participation affect the sustainability of forest
resources?
Objectives, Data Source and Methodology
36
Consistent to the above research questions, the specific objectives of this study are the
following:
i) To assess the extent of dependency of rural poor on CPRs and to examine
the impact of CPR collections on income, employment and poverty of the
rural poor in the state of West Bengal.
ii) To examine the impact of CPR extraction on environmental degradation.
iii) To examine the interrelationship among agricultural risk, non-timber forest
collection and the extent of rural poverty.
iv) To assess the nature of participation in forest management and examine
the relationship between forest dependency and participation in forest
management.
v) To investigate the impact of participation of forest management on
alleviating the environmental degradation in the study area.
vi) To assess the role of women in CPR collection and to examine the
performance of women in forest management committee.
vii) To investigate empirically the effect of women’s active participation in
management on forest resource conservation.
3.2 Data Source
The study is based mainly on a primary survey on Common Property Resources in
West Bengal conducted in 2011. Multistage sampling technique has been used for the
collection of primary data. We have purposively chosen two economically backward
districts of West Bengal viz. Bankura and Purulia since both of them are covered by
vast forest area and the rural poor are highly dependent on Common Property
Resources for their subsistence.
Bankura is a district of West Bengal in India, located in the western part of the state.
It is part of Bardhaman Division of the state and is included in the area known as
‘Rarh’ in Bengal. As per the Census 2011, the geographical area of the district is
625900 hectare. It has a population of 3,596,292 and occupies 13th position in terms
of population of the state. Bankura is one of the least urbanised districts of the State
having less than 8.33 percent of its total population in urban areas. The literacy rate
of district is 70.95 percent which is much lower than the state average of 77.08
Objectives, Data Source and Methodology
37
percent (Census 2011). Based on the physiographic terrain, the Bankura district can
be broadly divided into 3 regions i) hilly areas in the west, ii) connecting undulating
track in the middle and iii) level alluvial plains to the east. The climate of Bankura is
tropical, dry and sub humid. Bankura district is well known as a drought prone district
of the State.
The economy of Bankura district is primarily based on agriculture with nearly 62
percent of the main workers of the district engaged in the agricultural sector. The
district has 150,465.3 hectares of land under forest. Out of the total forest area
131,203.19 hectares of land is under protected forest and 3889.07 hectares of land is
under reserved forest. Paddy is the main agricultural crop and is produced in 90
percent of the total cultivable land of the district. The district is characterized by low
industrialization with high dependence on agriculture. The cottage and small scale
industry contribute to the economy of the district. In many parts of Bankura District,
the rural poor are largely dependent on the common property resources for their
subsistence as compared to the non-poor. The high dependence of the rural poor on
the common property resources is primarily due to uncertainty in agriculture and low
cost of wage labour.
Purulia is the westernmost district of West Bengal under the Burdwan division. The
geographical area of the district is 625900 hectare. It has a population of 2,927,965
and population density of 486 per sq. km as per the Census 2011. It is inhabited by a
large number of tribal people. Some of the major tribes of Purulia district are Santals,
Bhumij, Kheria, Shabar and others. Among them Santals are the highest population
concentrations in the district. Purulia conjures up a vision of hills, plain lands,
rivulets, forests and wild birds. The south west monsoon is the main source of rainfall
with an average annual rainfall varying between 1100 mm to 1500 mm. The district is
covered mostly by residual soil formed by weathering of bed rocks.
The agriculture is the fundamental occupation in the district though the agricultural
activity faces a tough hindrance due to the irregularity and paucity of rainfall, infertile
red lateritic soil etc. Paddy is the main crop of the district. The crop is mainly
cultivated under rain fed condition. Purulia district is covered by vast forest area. The
Objectives, Data Source and Methodology
38
forest is covered by an area of 115226 hectare. The natural forests of the district are
mostly of mixed nature and restricted to northwest part of the district.
In both Bankura and Purulia districts, forest species like Sal, Palash, Kusum, Mahua,
Neem, Kend are major source for timber, pole, small wood, NTFP, firewood,
medicinal plant to rural poor. Medicinal plants like Aswagandha, Satmuli, and
Vrigoraj are available in these forests. Haritaki, Amla, Bahera, Karanj, Neem, Sal
seeds, Bamboo, Kendu leaves are major Non-Timber Forest Produce found in this
district. A large number of rural poor living in and around forest directly or indirectly
depends on this forest for fodder, fuel wood, small timber and other tangible or
intangible benefits. The common land is used by the households for grazing of their
livestock. The households also collect fodder from the common forest area for the
livestock. The household hunts animals to some extent in the common forest area and
also fish in the common ponds. They collect small fishes viz. gorou, chunamach, etc.
from the common property resources like ponds and lakes. The households also
collect wild mushrooms (called karan chatu in local dialect) from the common forest
area. The detailed village characteristics of the study area is described in Appendix-I.
In order to address the research problem, we have chosen two blocks viz. Saltora in
Bankura district and Santuri in Purulia district. The blocks have been selected taking
into consideration the dominance of CPR based economic activities. In Bankura
district, from Saltora block, we have chosen six villages viz. Panjhoria, Ramjibanpur
(Bandhghat), Seolibona, Baldanga, Dulaltora and Tantirdanga. Three villages viz.
Jiyathole, Marbediya and Ambari have been selected from Santuri block in Purulia
district. The primary survey was carried out in 300 households in 9 villages of
Bankura and Purulia districts. These villages were in close proximity to the forest
area and the people residing in these villages are highly dependent on forest resources
for their livelihood.
In the surveyed villages, we have collected information from households through
complete enumeration method. In the field work carried out in the above mentioned
villages of Bankura and Purulia districts, two types of structured questionnaire were
used for survey of both village level and household level. The village schedule was
Objectives, Data Source and Methodology
39
administered to Panchayat office bearers, Head Masters and other educated persons in
the village. The village schedule covered the type of forest land available in the
villages, forest management, customary rules of CPR management, extent of
environmental degradation in terms of forest area, common village area and grazing
land, etc. The information about the reasons and remedies for CPR depletion and
forest degradation were sought from the respondents. For the household survey,
information on caste/religion, demography, education, occupation, land holding,
annual income, asset ownership, collection of CPRs, monthly expenditure, etc. was
collected from all the households in each village and the importance of CPRs have
been examined in detail. We have collected information by interviewing mainly
household head, senior female members and persons knowledgeable about the forests
in the study villages. The information collected through field surveys has been
presented in tabular form to quantify the various objectives. We have also utilised the
available secondary data sources of NSS report on Common property resources,
Census of India, Government reports viz. India State Forest Report, Forest Survey of
India Report and various Statistical Abstracts of the Government of West Bengal.
PHOTO 1 PHOTO 2
Interview with a household head at Panjhoria Interaction with a villager at Dulaltora
The collection of primary data, especially the quantitative data, in rural West Bengal
is a complex one. Most of the household heads are illiterate and are not acquainted
with the modern quantitative measurements. They measure the agricultural outputs
using traditional methods; not in terms of weight but in terms of volume. For
example, paddy is measured in terms of the number of sacks (1 sack ~ 80 Kg). Land
is measured in terms of bighas (1 bigha = 0.1338 hectare or 0.3306 acre or 1333 m2).
Objectives, Data Source and Methodology
40
The collection of fodder and fuel wood is measured in terms of bojha (1 bojha ~5 Kg
for fodder and ~10 Kg for fuel wood). Most of the households do not keep record of
their age, income, wealth, consumptions, expenditure, savings, etc. and hence the
information gathered about household characteristics is prone to error. Although, we
have covered almost all the households in our surveyed villages, few people refused
to take part in the survey. Generally, we have interviewed the household head or any
senior male / female member of the household. We have not questioned all the
members of the family and their views may be different from that of the respondents.
In few cases, the households did not give accurate information about CPR collections.
In order to overcome few of the problems mentioned above, we have taken the help of
local students residing near the study villages. The problems of quantification during
the survey have been resolved to some extent through participatory approach and
cross verifications of the relevant socio-economic variables.
3.3 Methodology
In our study, we have applied the empirical methodology for statistical estimation and
econometric analysis. We have summarised the methodology as follows:
3.3.1 Conceptual Framework:
I) Common Property Resource Dependency: A Household utility
maximization framework The study has conceptualised the problems of Common Property Resource extraction
under the maximising behaviour of the household where the household maximises the
utility subject to a number of resource constraints. The conceptualisation of the
problem has been done in the framework of Bardhan et al. (2002). Household derive
utility by means of commodity consumption including consumption of goods
collected from the common land and forest, consumption of produced goods, size and
composition of the household and the leisure time.
The utility function ( HKU ) of the household H in village K can be shown as:
( , , , )HK HK HK HK HKU u C A L S=
Objectives, Data Source and Methodology
41
Where HKC = Consumption of goods collected from the CPR
HKA = Consumption of produced goods
HKL = Leisure
HKS = Family size and composition
Household maximises utility subject to two constraints: i) Budget Constraints and ii)
Time Constraints. The optimisation of utility function subject to the budget and time
constraints would give us implicitly the determinants of the collection of the CPR
goods ( HKC ) by the households.
This can be approximated as
( , , , )HK HK HK HK HK HKC C Y S t v=
where HKY =given level of expenditure
HKS =family size and composition
HKt = time taken to collect one unit of CPR and
HKv = village characteristics, i.e. land holding pattern, livestock,
education, distance between house and common forest, sex / gender
The comparative static outcome of the model enables us to identify the factors
determining the extraction of Common Property Resources. The extraction of
products from the common forests is dependent on a host of factors relating to
household and village characteristics. Some factors relating to demographic features
like size of the household, sex composition, availability of working population, etc.;
some are economic e.g. individual land holding pattern, livestock unit, occupational
structure, the existence of market; some are social like education, male headed /
female headed households, etc.; some are geographical like the size of the village,
distance from the residence to CPR field, distance to the nearest market, etc.
We have used a multiple regression model to examine the determinants of CPR
extraction:
0 1 2 3 4 5( )C HKP C FSIZE FEMPER AVRAGE AVRSCH OWNLANDα α α α α α⋅ = + + + + +
6 7 8 9LIVESTOCK FORESTDIST POVR DISMα α α α+ + + + +∈
(Details discussed in Chapter 4 in Section 4.2.1 and 4.2.2.A)
Objectives, Data Source and Methodology
42
II) Linkage between Poverty and Environment The main arguments found in the literature indicate that poverty leads to
environmental degradation; while other studies argue that environmental degradation
leads to low productivity and therefore increase poverty. The rural poor in their
strenuous effort to live are driven to environmental degradation which has long term
influences on rural livelihood. Again environmental degradation lowers the rural
poor’s ability to generate income. Thus poverty and environment has a two-way
relationship. To examine the determinants of poverty, an empirical model especially
the qualitative response model (logit model) has been employed in the study.
Following Deininger & Minten (1999), we have hypothesised that poverty is a
function of environmental degradation alongside other socio-economic variable which
capable us to indicate the rural poverty conditions as well as the extent of
environmental degradation in our surveyed villages.
The probability that a household will be poor can be specified as:
( ) ( )1
1 ii i XP F Xe α βα β− +
= + =+
……………………….(i)
Where iP is the probability that thi household will be poor given iX , where X is a
vector of explanatory variables and e is the natural logarithm. Equation (i) can be
written as:
( )1 1iXiP e α β− + + =
where 1
1
log1i
PxP
α β
+ = − and 1
11P
P− is the odds ratio, whose log gives the odds
that a household is poor.
The model to be estimated is specified as
1 2 3 4log1
ii i i i
i
P FSIZE AVRAGE AVRSCH OWNLANDP
α β β β β
= + + + + −
5 6 7i i iLIVESTOCK FORSTDIST ENVDGRβ β β+ + + +∈
(Details discussed in Chapter 4 in Section 4.2.2.B)
Objectives, Data Source and Methodology
43
III) Agricultural Risk and Common Property Resources In this section, we have made an effort to build up a household model to analyse the
impact of expected agricultural risk and unexpected agricultural shortfall on the
collection of common property resources following the principles of ‘new home
economies’ (Barnum and Squire 1979; Pattanayak and Sills 2001). In our surveyed
villages the main occupation of the households is farming. They primarily depend on
agriculture. However, in time of agricultural crop failures they are bound to depend
on forest products. In poor agrarian economy the natural forest resources act as
insurance as they smoothen household’s income and consumption. In good harvesting
period, they gather surplus forest products which play the role of natural insurance to
mitigate agricultural risk.
Household maximises utility ( )u subject to the production constraint, time constraints
and budget constraints. We have used lagrangian method to solve the maximising
problem and identify the household forest collection in a reduced form of labour
demand equation.
( ), , ,dF N CN n P X H Fξ=
where NP = Opportunity cost of time as measured by off-farm wages in a
complete market
ξ = Agricultural risks
X = Exogenous income
CH = Household characteristics
F = Forest quality
We have hypothesised a positive relationship between agricultural risk and forest
collection labour.
We have considered the Count Data Model to focus on the relationship between CPR
dependency and agricultural risk. Our specified model is
0 1 2 3 4 5FCL AGEHEAD SQAGEH FAMSIZE AVRSCH LIVESTOCKα α α α α α= + + + + + 6 7 8 9FORESTDIST AGRSHLFALL AGRIRISK WAGEα α α α+ + + + +∈
(Detail discussed in Chapter 5 in Section 5.2.1 and 5.2.2)
Objectives, Data Source and Methodology
44
IV) Property Rights and Common Property Forest Management Most of the rural poor in our surveyed villages of Bankura and Purulia districts are
very much dependent on Common Property Resources, mainly forest resources. The
shadow value of these forest resources is zero because there is no barrier to
accessibility of the resources and their availability is also abundant. However,
continuous over-exploitation of the forest products leads to scarcity of the resources.
Therefore, forest product turns to have an economical value and shadow value
becomes greater than zero. Rural poor collects the forest products as long as the value
of the products equal to cost of collection. To protect the forest from massive
collection of trees and forest patches, it is necessary to patrol and monitor the
common forest. The local people do this job in a better way than the forest department
officials. Therefore in order to conserve the forest resources local rural community
should join the Joint Forest Management (JFM), otherwise they have to travel a
longer distance to collect their necessary forest products. Though agriculture is their
main occupation, it is a seasonal activity. Therefore household allocate their
empowered labour between agricultural activities and forest protection activities.
Now, the proper management of forest resources depend on collective action which is
measured by the amount of labour devoted by the households for managing the forest
resources. The extent of collective action of the household depends on variety of
socio-economic factors. We estimate the following empirical model to examine the
determinants of collective action:
12
i
n
M i ij ijj
L a b Y=
= + +∈∑
Here ML is the dependent variable defined as the amount of labour household
contributions to local management, 1ia denotes community dummies, ijY is set of
explanatory variables including the index of organisational intensity, individual
organisational experience, hectares planted, livelihood activities, socio-cultural
heterogeneity, age, gender, household size, wealth, forest condition and ∈ is the error
term.
We have considered censored Tobit Model to examine the impact of forest
dependency on collective action in JFM.
Objectives, Data Source and Methodology
45
Our specified model is
0 1 2 3 4 5iML a b FSIZE b FEMPER b AVRAGE b AVRSCH b PERAGRIN= + + + + +
6 7 8b PERCPRIN b PERCPRCSM b WEALTH+ + + +∈
(Details discussed in Chapter 6 under Section 6.6)
V) Nexus between Forest Management and Forest Degradation Active Forest management lowers the degree of over-exploitation of forest resources
and hence help to reduce the forest degradation. To examine the impact of better
forest management on forest degradation, we have used logit regression model.
1 2 3 4log( )1
i
i
P a b AVRAGE b AVRSCH b FSIZE b OWNLANDP
= + + + +−
5 6 7b LIVESTOCK b POVR b FMACT+ + +
(Details discussed in Chapter 6 in Section 6.7)
VI) Women’s participation in CPR Management The surveyed households in Bankura and Purulia districts are basically poor.
Moreover these villages are very near to forest area and hence collection of CPRs is
very high in these villages. We have observed from our survey that rural women are
mainly responsible for collection of forest products in order to meet their subsistence
needs. Hence they are very much aware of the forest conservation programme. As
women are major users of forest products, sustainability of forest resources depend on
their active participation in Joint Forest Management.
In this section we have identified the factors within rural household and villages
determining the sustainability of forest resources ( SFOREST ) which can be written
as:
SFOREST = f (Family size, Sex ratio, Active participation of women in JFM,
Gender gap in education, Number of male household head, market pressures)
Objectives, Data Source and Methodology
46
We have used a Binary Probit regression model to examine the determinants of
sustainability of forest resources to justify the role of women’s participation in CPR
management.
Our specified model is:
0 1 2 3 4 5SFOREST SEXR FHEAD FLIT DEPR PCATTLEα α α α α α= + + + + +
6 7 8WACTPM DISM PUNSHMα α α+ + + +∈
(Details discussed in Chapter 7 in the Section 7.4.1)
3.3.2 Econometric and Statistical Specification Statistical analyses are applied to represent a clear idea of our hypothesis of the study.
We have used both Statistical and Econometric techniques to analyse our specific
objectives. The several Econometric and Statistical specification applied are as
follows:
I) Multiple Linear Regression Analysis We have used Multiple Linear Regression Model in our study to examine the
determinants of Common Property Resource extraction. The general form of the
multiple regression models is
1 2( , ,...., )ny f x x x= +∈
1 1 2 2 ..... n nx x xβ β β= + + + +∈
where y is the dependent or explained variable and 1 2, ,..., nx x x are the independent
or explanatory variables. ∈ is the disturbance term which arises mainly to capture the
influence of omitted factors on an economic variable. In our study the dependent
variable is household income from common forest and few village and household
characteristics i.e. family size, female percentage in household, age, education, own
land, livestock, poverty, distance to the nearest forest and market are the explanatory
variables. We have explained the relationship between dependent and explanatory
variables through Multiple Regression Model. We have used R-squared to confirm the
goodness of fit of the model and statistical significance can be checked by an F-test of
the overall fit, followed by t-test of individual parameters.
Objectives, Data Source and Methodology
47
II) Logit Regression Model In statistics logistic regression is a type of regression analysis which is used for
predicting the consequence of a categorical dependent variable based on one or more
predictor variables i.e. it is used in estimating empirical values of the parameters in a
qualitative response model. In binary logistic regression, the outcome generally coded
as ‘0’ or ‘1’. If a particular mentioned outcome for the dependent variable is the
notable possible outcome it is usually coded as ‘1’ and contrary outcome as ‘0’.
Logistic regression is employed to anticipate the odds of being a case based on the
values of the explanatory variables. The odds are defined as the probability that a
particular outcome is a case divided by the probability that it is a non-case. In our
study we have applied binary logistic distribution to examine the determinant of
poverty.
The probability that a household will be poor can be specified as:
( ) ( )11
1 ii i Xi
P E Y F XX e α βα β− +
= = = + = +
Where iP is the probability that thi household will be poor given iX , where X is a
vector of explanatory variables and e is the natural logarithm.
Since the actual estimation of the logit model is done using Maximum Likelihood
Estimation (MLE), conventional R2 is inadequate measure of goodness of fit; we have
used McFadden R-squared measure
2
int
ln ( )1
ln ( )
full
ercept
L MR
L M
∧
∧= −
where fullM = Model with predictors
int erceptM = Model without predictors
III) Count Data Model In statistics, count data is a type of statistical data in which the observations can take
only the non-negative integer values (0, 1,2,3,.…) where these integers originate from
counting rather than ranking. We have used count data model to capture the impact of
agricultural risk on CPR collection. Here the dependent variable is forest collection
Objectives, Data Source and Methodology
48
labour which is measured by the number of major forest collection trips. We have
considered household and village characteristics with agricultural risk and shortfall as
explanatory variables. The Poisson Regression Model has been used to study count
data.
Assuming that the annual number of NTFP collection trips ( in ) follows a Poisson
distribution, with observed frequencies iN , the probability density function of the
Poisson is described by equation (A1), with parameter iλ (Greene, 2003)
( )!
i ini
i ii
eP n NN
λ λ−
= = ………………………………….….…….[A1]
In the Poisson Count Data model, the characteristic of probability density function is
equidispersion with single parameter iλ equal to both mean and variance of the trip.
An adjustment to the variance covariance matrix, where k = number of observations
and l =number of parameters, is used to determine the Statistical significance of the
coefficients in the Poisson model. 1 1
2' ( )1' 'var( ) i
i i
nx xk li i
x x x xλβ λλ
λ λ− −
− = −
∑ ∑ ∑ ∑ ……….[A2]
As the count model very often detect over dispersion or variance greater than the
mean, it is easier to estimate the parameter with maximum likelihood techniques.
The common alternative of Poisson regression model is negative binomial. As the
distribution of forest collection trips has a large concentration of households taking
zero trips as well as large number of trips, it is better to use Negative-Binomial model
which is a mixture distribution of the Poisson with gamma heterogeneity. For the
Negative Binomial model, the densities for positive and zero trips are given in
equation [A3] and [A4] (Green, 2003).
Objectives, Data Source and Methodology
49
'
( )( )( ) ( 1)
in
iNB i i
i
n iP n Nn
i i
θ
θθ
λθθ λ θ λ
−
Γ += = Γ Γ +
+ +
…………………...[A3]
( 0)NB iP n
i
θ
θθ λ
= = +
…………………………………………………….[A4]
As assumed for a negative binomial model, our response variable is a count variable
and the variance of the response variable is greater than the mean of the response
variable. In statistic a zero-inflated model is a statistical model based on a zero-
inflated probability distribution i.e. a distribution that allows frequent zero value
observation. In our study we have applied Zero Inflated Negative Binomial Model
(ZINB) because the number of households taking zero trips and more than zero trips
can be explained in a better way through this model.
The expected count is conveyed as a combination of the two processes i.e.
household’s CPR collection trip against no trip.
E (to take a trip) = prob (not take any trip) * 0 + prob (take any trips) * (E y x=
take any trip)
IV) Censored Tobit Model The Tobit model is a statistical model proposed by James Tobin to describe the
relationship between a non-negative dependent variable and an independent variable.
The Tobit model is also named Censored Tobit Regression Model because few
observations on the dependent variable are censored. In Censored Data model, when
the dependent variable is censored, values in a certain range are all transformed to a
single value (Greene, 2003). We have applied censored Tobit model to explain the
relationship between forest dependency and active forest management. Here the
dependent variable is the number of man days per year a household involves in forest
management activities.
Objectives, Data Source and Methodology
50
From the surveyed data, we observe that about 25 percent of the total respondent in
Bankura district and 15 percent in Purulia district allocated zero man-days to JFM
activities. Since the dependant variable is censored from below, we observe some
estimation problems. Censoring implies expected value of ( )ijME L is non-linear in Y
and constant partial derivatives and the expected value of the dependent variable for
most combinations of parameter estimates and explanatory variable may be negative.
Here we have applied maximum likelihood estimation to estimate a censored Tobit
model. The regression is obtained by making the mean in the preceding correspond to
a classical regression model.
The general formulation is usually given in terms of an index function.
iM iL Yb= +∈
0iML = if 0
iML ≤
*i iM ML L= if 0
iML >
( ) exp( )i viVar vλ∈ =
V) Binary Probit Model In statistics, a Binary Probit Model is a type of regression where the dependent
variable can take only two values and is used to estimate the probability that an
observation with particular characteristics will fall into a specific one of the
categories.
The functional form of the Probit Model is 1 2 2
212
ixz
P e dzβ β
π
+−
−∞
= ∫
We used this model to analysis the determinants of sustainability of forest resources.
Here the dependent variable is sustainability of forest resources where we coded ‘Yes’
response as ‘1’ and ‘No’ response as ‘0’.
Objectives, Data Source and Methodology
51
3.4 Hypothesis Tested
In order to examine the crucial role of Common Property Resources in rural
livelihood, we have tested the following hypothesis in our study:
H1: Common Property Resources (CPRs) have a positive impact on income and employment of rural poor in Bankura and Purulia districts of West Bengal.
H2: CPRs have an immense role in alleviating rural poverty in the study
villages of Bankura and Purulia districts. H3: High extraction of CPRs in the surveyed villages has resulted in
degradation of the environment which further aggravates poverty. H4: CPRs acts as a safety net especially in times of agricultural crisis. H5: There is a strong relationship between forest dependency and
participation in forest management. H6: Active participation in forest management plays a positive role in
alleviating the environmental degradation. H7: Women’s participation in forest management improves the
sustainability of forest resources.
Common Property Forest Resources: Contribution and Crisis
52
CHAPTER 4
COMMON PROPERTY FOREST RESOURCES: CONTRIBUTION AND CRISIS
4.1 Introduction
Common Property Resources (CPRs) can be defined as a ‘community’s natural
resources, where every member has access and usage facility with specified
obligations, without having an exclusive property right over them’(Jodha, 1985b).
Common Property Resources are those resources that are accessible to and
collectively owned or held or managed by an identifiable community and on which no
individual has exclusive property rights. Accessibility to this resource is determined
either by legal status or by convention.
The earliest literature on Common Property Resources (CPR) can be credited to
Hardin for his celebrated work ‘Tragedy of the Commons’ (Hardin, 1968). It
demonstrates that free access and unrestricted demand for a finite resource ultimately
results in the depletion of the resource through over-exploitation. Cox (1985) in the
literature “No Tragedy on the Commons” questions the Hardin’s theory and states that
the decline of the commons system was not due to an inherent flawed land-use policy
but a widespread abuse of the rules governing them, land reforms, improved
agricultural techniques and the effect of industrial revolution.
CPRs are integral part of the social and institutional arrangements for the user
community. The rural poor, especially the landless, are highly dependent on the CPRs
for their subsistence. Several empirical studies have been carried out in India dealing
with the subject of poverty, inequality and dependency of rural households on CPRs
(Jodha, 1986, 1990, 1995; Beck, 1994, 1998; Singh et al., 1996, Iyengar and Shukla,
1999). These studies have postulated that rural poor depend heavily on the CPRs and
these resources provide a source of consumption and income (Ostrom, 1990; Brara
1987, Dasgupta 2006). In fact, in rural areas there are complementarities among
livelihood opportunities like agricultural and livestock incomes and protection of
Common Property Forest Resources: Contribution and Crisis
53
upper catchments for fodder collection and common water resources for irrigation
(Chopra, Kadekodi and Murty, 1989).
The rural poor are highly dependent on the forest for their subsistence. In India,
studies on poverty with relation to CPR collection from forest indicate that when
income from forest is set to zero in poverty calculations, poverty increases by as much
as 28 percent (Reddy and Chakravarty, 1999). Forests contribute a large part of CPRs
especially with the collection of Non Timber Forest Products (Chopra and Gulati,
2001). CPRs supplement rural livelihood and act as safety net for the poor especially
in the time of agricultural crises. Beck and Ghosh (2000) carried out field survey of
seven villages across the agro-ecological zones of West Bengal in India and based on
their findings they estimated that CPR adds about US $ 5 billion a year to the income
of the rural poor or about 12 percent of the household income of the rural poor.
Poverty is often associated with environmental degradation. Roughly half of the
world’s poor live in highly degraded environment. The World Commission on
Environment and Development (Brundtland Commission) wrote (1987): ‘Poverty is a
major cause and effect of global environmental problems. It is therefore futile to
attempt to deal with environmental problems without a broader perspective that
encompasses the factors underlying world poverty’. In recent times, there has been
increasing recognition that the relationship between poverty and environment is
complex and is strongly influenced by economic, social, local demographic,
institutional and cultural factors.
Rural poor are heavily dependent on forest. The household labour allocation decisions
and extraction of forest products are dictated by various socio-economic and
demographic variables. Environmental degradation deepens today’s poverty, whereas
today’s poverty makes it extremely difficult to care for or restore the agricultural base,
to find alternates to deforestation and control soil erosion. Forest degradation refers to
reduction in optimum capability or productivity of the forest. Grazing of domestic
cattle, goat, sheep, etc. causes major problem in forest areas. Over grazing also
adversely affects the soil properties. The adverse effects on soil cause formation of
gullies, loss of top soil and reduction of porosity.
Common Property Forest Resources: Contribution and Crisis
54
The environment matters a lot to the rural poor. The well-being of the poor is strongly
related to the environment in terms of their health, security and earning capacity. The
environment not only provides the sources of livelihood to the rural poor but also
affects their health and influences their vulnerability. Poverty also affects
environment by forcing the rural poor to degrade the environment. Environmental
degradation largely affects the livelihood of the poor. The rural poor are most
vulnerable to environmental degradation because they depend heavily on natural
resources, have less alternative resource, and most often exposed to environmental
hazards, and are least capable of coping to environmental risks (Dasgupta and Mäler,
1994; Lopez Roman, 1997). There is a widely held view that poverty is the main
cause of environmental degradation, because the rural poor are not in a position to use
the natural resources available to them in a sustainable manner (Duraiappah 1996).
This degradation further leads to aggravate the rural poverty.
Under this backdrop, this chapter attempts to explore the nature and pattern of CPR in
Bankura and Purulia districts of West Bengal and the implication of CPR extractions
on rural poverty and environmental degradation.
Specifically, this chapter attempts to explore the
i) nature of dependency of the rural poor on CPRs
ii) determinants of CPR income
iii) role of CPR in poverty reduction
iv) environmental impact of poverty by analysing the relationship among CPR
extraction, rural poverty and environmental degradation
Common Property Forest Resources: Contribution and Crisis
55
4.2 Data and Methodology
The evidence presented in this chapter is based on the primary data collected from
two districts of West Bengal in 2011. The field survey was undertaken in 6 villages in
the district of Bankura in West Bengal, India; viz. Panjhoria, Ramjibanpur
(Bandhghat), Seolibona, Baldanga, Dulaltora and Tantirdanga and 3 villages in the
district of Purulia in West Bengal, viz. Jiyathole, Marbediya and Ambari. Total 300
households were surveyed. These villages were selected for the survey because they
were economically highly backward and the households residing in these villages are
highly dependent on forest resources for their livelihood.
Two types of questionnaire were used in the survey: household schedule for sample
households and village schedule which was administered to Panchayat office bearers,
Head Masters and other educated persons in the village for obtaining village specific
information. For the household survey, information on caste/religion, demography,
education, occupation, land holding, annual income, asset ownership, collection of
CPRs, monthly expenditure, etc. was collected from all the households in each
village. We have examined the importance of CPR in all the study villages in detail.
We have classified the households into two groups; viz. ‘poor’ if household belong to
BPL (Below Poverty Line) and ‘non-poor’ if the household belongs to APL (Above
Poverty Line). Out of the 300 surveyed households, 240 households are considered as
poor and 60 are non-poor. An attempt has been made to quantify the level of
dependency of the rural poor households on common forest resources through tabular
method. The extraction of common property products has serious implications to
poverty. The determinants of CPR extraction have been analysed using multiple
regression technique while the association between poverty and environmental
degradation has been estimated using logit regression method.
4.2.1 Conceptual Framework: In this section attempts have been made to conceptualise the problem of association
among rural poverty and extraction of CPR. The conceptualisation of the problem has
been done following the framework of Bardhan et al. (2002). Households derive
utility by means of commodity consumption. However consumption bundle depends
Common Property Forest Resources: Contribution and Crisis
56
on a variety of goods and services including the consumption of goods collected from
the common land and forests, consumption of produced / market goods, size and
composition of the households and the leisure time. Usually the villagers use the CPR
for their own consumption, but in few cases they sell it in the market or to their
neighbours.
The utility function of the household H in village K can be shown as:
( ), , ,HK HK HK HK HKU u C A L S= ……………………………………………….(1)
where HKC = Consumption of goods collected from the CPR
HKA = Consumption of produced goods
HKL = Leisure
HKS = Family size and composition
It is to be noted that the consumption of goods collected from the common forest
depends on the time spent in collection and sale activities.
Thus,
( ),HK HK HKC F R T= ………………………….……………………..(1a)
where HK CHK SHKT T T= +
= Total time spent in collection activities,
i.e. for Consumption purpose and for the purpose of sale
CHKHK
HK
TRT
= = Proportion of time spent in collecting CPRs for self-
consumption out of the total time spent in collection activities.
It is often found that the use of CPR is guided by the well-defined rules and
regularities of the community. If any individual member overuses the village
commons, he/she is punished according to the provision of the customary rule.
Certainly, the provision of punishment affects the household’s utility. To incorporate
this in our utility function, we have assumed that in normal circumstances an
individual can extract CPR to the level maximum up to the village average extraction
Common Property Forest Resources: Contribution and Crisis
57
rate. If he/she violates the village norms, he/she will be punished. The severity of
punishment depends on the gap between actual rate of collection HKC and the village
average K
K
CN
.
Thus the household utility function can be written as
( ), , ,HK HK HK HK HK EU u C A L S Sα= − ⋅ …………………..…………………(2)
where ES = Quantity of over extraction from CPR. The magnitude of ES is
determined by the village norms.
α = a positive constant exogenously determined by the management
(cost of over-extraction).
In our model, it is assumed that the prices of produced goods ( )KP , collected goods
( )CP and CPR consumption of the rest of the village are fixed and given. The indirect
utility function for the household family size and composition HKS can be represented
as:
( ), , , ,HKHK HK K HK HK EV v C L P Y S Sα= − ⋅ ……………..……………(3)
where (.)v is obtained by maximising (.)u subject to K HK HKP A Y⋅ ≤
The cost of using the CPR depends on the time it takes to collect and the opportunity
cost of this time for the rural household. The opportunity cost of time to collect CPR
depends on the household’s asset, employment opportunities tasks.
Household maximise utility (.)v subject to two constraints:
i) Budget constraints and
ii) Time constraints
The opportunity cost of time to collect CPR depends on the household’s alternate uses
of time. The income of household is earned by allocating the family labour into
different occupations. The different occupations denoted by 1,2,3,4,5i = respectively
are (i) Self-employment in agriculture 1( )HKS , ii) Wage labour in agriculture 2( )HKS ,
(iii) Wage labour in non-agriculture 3( )HKS , (iv) Self-employment in non-agriculture
4( )HKS and (v) Self-employment in livestock grazing 5( )HKS . Besides the above
Common Property Forest Resources: Contribution and Crisis
58
occupational activities, time is allocated to CPR collection for consumption ( )CHKT
and sale ( )SHKT and Leisure ( )HKL .
Now total time taken for CPR collection ( )HKT can be shown as:
HK HK HK CHK HK SHK HKT t C t C t C= ⋅ = ⋅ + ⋅ ……………………………(4)
were HKt = time taken to collect one unit of CPR. The labour allocated to occupation i
is denoted by iHKS .
Now the time constraint is 5
1
iHK HK HK HK
iT S S L T
=
= = + +∑ …………………….………(5)
It is to be noted that total time available for different activities depends on the family
size and composition ( )HKS .
The budget constraints show that the households’ expenditure must be less than the
sum of net income ( )HKY from different sources. In equality form we can write:
Net Income = Expenditure = Income from agriculture 1( )β + Income from non-farm
business 4( )β + Income from livestock activity 5( )β + Income from CPR products
( )C HKP C⋅ + Wage income from agriculture 2( )w + Wage income from non-agriculture
3( )w . We can thus rewrite the equation as follows:
1 1 1 2 2 3 3 4 4 4( ; , , , ) ( , , , , )HK HK HK HK K K K HK K HK HK HK HK K KY S v d P I W S W S S v d P Iβ β= + + +
5 5 5( ; , , )HK HK K K C HKS v P I P Cβ+ + ⋅ ……………………………..(6)
where 1β =Returns to agriculture which depends on labour allocated in agriculture1( )HKS , land 1( )HKv , education ( )HKd , price of non-collected goods ( )KP , village
infrastructure ( )KI
2 2K HKW S =Income of wage labour engaged in agricultural activities where wage
rate is 2KW
Common Property Forest Resources: Contribution and Crisis
59
3 3K HKW S =Income of wage labour engaged in non-agricultural activities where
wage rate is 3KW
4β = Returns to non-farm business which depends on labour allocated in non-
farm business 4( )HKS , non-farm business assets 4( )HKv , education ( )HKd , price of
non-collected goods ( )KP and village infrastructure ( )KI
5β = Returns to livestock activity which depends on labour engaged in
livestock activity 5( )HKS , livestock owned by the household 5( )HKv , price of non-
collected goods ( )KP and village infrastructure ( )KI
C HKP C⋅ =Income from CPR products ( )HKC by selling at the price rate CP .
Now the problem of the household is to select CPR ( )HKC and labour allocation HKL ,
, 1, 2,..5,iHKS i = to maximise its utility (2) subject to constraints (5) and (6).
The optimization of utility function subject to the budget and time constraints would
give us implicitly the determinants of the collection of the CPR goods ( )HKC by the
households. This can be approximated as
( , , , )HK HK HK HK HK HKC C Y S t v=
where HKY =given level of expenditure
HKS =family size and composition
HKt = time taken to collect one unit of CPR and
HKv = village level characteristics, i.e. land holding pattern, livestock,
education, distance between house and common forest, sex / gender, etc.
In this chapter, we have tested the following hypothesis:
H1: Common Property Resources (CPRs) have a positive impact on income and employment of rural poor in Bankura and Purulia districts of West Bengal.
H2: CPRs have an immense role in alleviating rural poverty in the study
villages of Bankura and Purulia districts. H3: High extraction of CPRs in the surveyed villages has resulted in
degradation of the environment which further aggravates poverty.
Common Property Forest Resources: Contribution and Crisis
60
4.2.2 The Empirical Model Specification CPRs are the life support system in rural Bengal. CPRs act as not only a regular
source of income and employment but also a safety net in periods of drought and
other natural calamities. The extractions of CPRs are invariably linked with the
characteristics of household under a specific socio-economic condition. In this section
an attempt has been made to identify the factors determining the extent of forest
product collection. The extractions of products from the common forest depend on
the factors relating to household and village characteristics, i.e., family size and
composition, household labour, land holding pattern, livestock unit, occupational
structure, opportunity cost, economic status, the existence of the market, technology,
education, age, distance between forest and house and institutional factors like
management rule, civil law, etc.
4.2.2.A Determinants of CPR: Multiple Regression Model A multiple regression model has been used to examine the determinants of CPR
extraction.
0 1 2 3 4 5( )C HKP C FSIZE FEMPER AVRAGE AVRSCH OWNLANDα α α α α α⋅ = + + + + +
6 7 8 9LIVESTOCK FORESTDIST POVR DISMα α α α+ + + + +∈
Here dependent variable is C HKP C⋅ i.e. household income from community forest.
The explanatory variables are:
FSIZE = Family size of the household
FEMPER = Percentage of female members in the household
AVRAGE = Experience in collecting CPR product
AVRSCH = Average years (number of years) of schooling of household
OWNLAND = Total land owned (in hectare)
LIVESTOCK = Total number of livestock condensed into animal units (numbers)
FORESTDIST = Distance between house and common forest (in km)
POVR = Poverty of the household
DISM = Distance to the nearest market
Common Property Forest Resources: Contribution and Crisis
61
Here 0α = constant;
iα ( 1, 2,...,8i = ) are the coefficients associated with the explanatory
variables and ∈ is the random disturbance term.
In the following table 4.1, we have presented dependent and explanatory variables,
their expected sign and description.
TABLE 4.1
Description of Variables of Multiple Regression Model Variables Expected
Sign
Variable description
Dependent variable
C HKP C⋅
Household Income from community forest
i.e. CPR income
Explanatory Variables
FSIZE + Average number of population of the
household (size of the family)
FEMPER + Percentage of female members in the
household
AVRAGE + Average age of household, i.e. experience in
collecting CPR product
AVRSCH _ Average years (number of years) of schooling
of household
OWNLAND _ Total land owned by the household (in hectare)
LIVESTOCK + Total livestock of the households converted
into animal units
FORESTDIST _ Distance of the CPR field from the residence of
the households (km)
POVR + Poverty =1, if Household belongs to BPL
Poverty = 0, if household belongs to APL
DISM _ Distance to the nearest market (in km)
Common Property Forest Resources: Contribution and Crisis
62
4.2.2.B Determinants of Poverty: Poverty Environment Nexus The rural poor in their strenuous effort to survive are driven to environmental
degradation which has long term influences on rural livelihood. Due to environmental
degradation, the rural poor’s ability to generate income gradually diminishes. Thus
poverty and environment has a two way relationship. Following Deininger and
Minten (1999), a qualitative response model like logit model is proposed in this study
to examine the determinants of poverty. We consider a range of socio-economic as
well as environmental variable which enable us to express the rural poverty conditions
as well as the extent of environmental degradation in the region.
The probability that a household will be poor can be specified as:
( ) ( )11
1 ii i Xi
P E Y F XX e α βα β− +
= = = + = +
……….………….(i)
where iP is the probability that thi household will be poor given iX , where X is a
vector of explanatory variables and e is the natural logarithm.
Equation (i) can be written as:
( )1 1iXiP e α β− + + = ……………………………….………………..(ii)
From equation (i), we can write
11i ZP
e−=+
where iZ Xα β= +
( ) 11
Z Z
ZZ Z
e eee e−
= =++
Therefore 1
Z
i Z
ePe
=+
……………………………………….…………….(iii)
If iP is the probability that the thi household will be poor, then ( )1 iP− is the
probability that the thi household will not be poor.
1 11 11 1 1
Z Z Z
i Z Z Z
e e ePe e e
+ −− = − = =
+ + +
Therefore, we can write,
( )1111
i
ZZ XZi
i Z
eP e e eP
e
α β++= = =−
+
Common Property Forest Resources: Contribution and Crisis
63
This can be approximated as
log1
ii
i
P XP
α β
= + −
Now 1
i
i
PP−
is simply the odd ratio whose log gives the odds that a household is poor.
We have used logit regression model to analyse the relationship between poverty and
environmental degradation.
The model to be estimated is specified as
1 2 3 4log1
ii i i i
i
P FSIZE AVRAGE AVRSCH OWNLANDP
α β β β β
= + + + + −
5 6 7i i iLIVESTOCK FORSTDIST ENVDGRβ β β+ + + +∈
where
FSIZE = Family size of the household
AVRAGE = Experience in collecting CPR product
AVRSCH = Average years (number of years) of schooling of household
OWNLAND = Total land owned by the household
LIVESTOCK = Total number of livestock condensed into animal units (numbers)
FORESTDIST = Distance between house and common forest (in km)
ENVDGR = Dummy for extent of environmental degradation
ENVDGR =1; if environment is more degraded
ENVDGR =0; if environment is less degraded
α = constant;
iβ ( 1, 2,...,8i = ) are coefficients associated with the explanatory variable and ∈ is the
random disturbance term.
Common Property Forest Resources: Contribution and Crisis
64
4.3 RESULTS AND DISCUSSION 4.3.1 Nature of Dependency on CPRs
The rural poor in the study villages are largely dependent on the common property
resources for their subsistence as compared to the non-poor. The poor households
depend on the CPRs collected from the common forest area, rivers, ponds and the
common grazing lands. It is observed that the not so poor households also collect
CPRs in various degrees. In order to get a better insight into the CPRs collections
amongst the households, all the surveyed households have been classified into two
groups: BPL (Below Poverty Line) and APL (Above Poverty Line). Since we intend
to examine the relative role of common property resources in the extent of poverty,
we have estimated the income of the households by including as well as excluding the
income from common property resources.
In the study area, we have surveyed 300 households in 6 villages in the district of
Bankura and 3 villages in the district of Purulia. Common Property Resources act like
a life support for all the households in all the villages in the study area. The rural poor
have access to various common property resources like fuel wood, dry leaves, shrubs,
dung cakes, etc. which are mainly used for cooking and heating; bamboos, canes, logs
from trees, dry leaves are used for construction of houses; shrubs and grasses are used
as fodder for the animals; fruits, vegetables, fishes, root, meat from hunted birds and
animals are used for consumptions as well as for sale. Few plants and roots are also
used for medicinal purposes for curing several ailments. These common property
resources are means of subsistence for all the households in the study villages.
Common property resources, mainly forest resources have a critical role in rural
livelihood in our study villages. Forest communities are very much dependent on the
collection of forest products for their domestic as well as commercial needs. The rural
households in the surveyed villages collect fodder/grasses, fuel wood, cow dung,
herbal medicine, bamboo, timber, fruits, honey, vegetables, fish, birds and broom.
The household collection of CPRs in the last 1 year in the surveyed villages is shown
in Table 4.2 below.
Common Property Forest Resources: Contribution and Crisis
65
TAB
LE 4
.2
Dis
tric
t B
lock
Nam
e of
vi
llage
Val
ue o
f Fo
dder
co
llect
ed fr
om
Com
mon
P
rope
rty
in la
st
1 ye
ar
Val
ue o
f Fu
elw
ood
colle
cted
fr
om
Com
mon
P
rope
rty
in
last
1 y
ear
Val
ue o
f C
owdu
ng
colle
cted
fr
om
com
mon
pr
oper
ty in
la
st 1
yea
r
Val
ue o
f H
erba
l M
edic
ine
colle
cted
fr
om
Com
mon
P
rope
rty
in
last
1 y
ear
Val
ue o
f B
ambo
o / S
al
& o
ther
leav
es
colle
cted
from
C
omm
on
Pro
pert
y in
last
1
year
Val
ue o
f Ti
mbe
r co
llect
ed fr
om
Com
mon
P
rope
rty
in la
st
1 ye
ar
Val
ue o
f Fr
uits
/F
low
ers
/Hon
ey
colle
cted
fr
om
Com
mon
P
rope
rty
in
last
1 y
ear
Val
ue o
f H
unte
d bi
rds/
an
imal
s/
snai
ls in
last
1
year
Val
ue o
f V
eget
able
s co
llect
ed
from
C
omm
on
Pro
pert
y in
la
st 1
yea
r
Val
ue o
f Fi
sh
colle
cted
fr
om
Com
mon
P
rope
rty
in
last
1 y
ear
Val
ue o
f br
oom
co
llect
ed
from
co
mm
on
prop
erty
in
last
1 y
ear
Tota
l val
ue
colle
cted
from
C
PR
in la
st 1
ye
ar
BP
L22
3050
875
651
1104
011
760
2234
3300
4464
2260
039
550
1763
243
2022
3059
AP
L4
3000
7500
300
940
100
076
850
026
5022
4091
018
908
Tota
l26
3350
883
151
1134
012
700
2334
3300
5232
2310
042
200
1987
252
3024
1967
BP
L15
1820
048
975
8520
8940
3240
1200
6560
1910
028
000
1392
036
0016
0255
AP
L5
2905
9250
560
1580
00
960
700
250
780
480
1746
5To
tal
2021
105
5822
590
8010
520
3240
1200
7520
1980
028
250
1470
040
8017
7720
BP
L47
8132
014
7850
2844
028
680
1118
024
0014
174
5540
096
095
4200
013
160
5206
99A
PL
714
2885
6394
065
00
092
541
027
5064
00
1630
6To
tal
5482
748
1564
1329
380
2933
011
180
2400
1509
955
810
9884
542
640
1316
053
7005
BP
L7
6240
1900
057
0033
1029
400
1520
7300
6300
3600
1400
5731
0A
PL
00
00
00
00
00
00
0To
tal
762
4019
000
5700
3310
2940
015
2073
0063
0036
0014
0057
310
BP
L18
1784
047
000
1950
091
5055
400
4720
2100
032
900
1344
070
0017
8090
AP
L0
00
00
00
00
00
00
Tota
l18
1784
047
000
1950
091
5055
400
4720
2100
032
900
1344
070
0017
8090
BP
L24
3135
070
759
1244
012
090
6780
022
5420
500
3095
010
480
2350
1999
53A
PL
10
2500
022
00
00
00
050
032
20To
tal
2531
350
7325
912
440
1231
067
800
2254
2050
030
950
1048
028
5020
3173
BP
L65
8021
019
2400
1728
023
600
1074
00
1139
068
400
6797
038
086
9455
5195
31A
PL
1632
300
4350
021
2517
4013
200
1780
1800
4965
2980
1560
9407
0To
tal
8111
2510
2359
0019
405
2534
012
060
013
170
7020
072
935
4106
611
015
6136
01B
PL
1527
000
5750
077
4053
1024
000
2390
1500
015
300
9360
3015
1450
15A
PL
1011
730
2675
062
585
020
00
360
2400
2650
1240
1600
4840
5To
tal
2538
730
8425
083
6561
6026
000
2750
1740
017
950
1060
046
1519
3420
BP
L27
4129
596
250
6720
7430
6360
045
9023
600
2150
013
040
4150
2249
35A
PL
1726
293
5400
052
011
5090
00
016
0027
8796
010
2589
235
Tota
l44
6758
815
0250
7240
8580
7260
045
9025
200
2428
714
000
5175
3141
70B
PL
133
1854
5840
9235
8564
073
930
3191
469
0033
692
1459
0023
3795
1010
7231
830
1339
366
AP
L17
7333
2781
318
0033
9010
00
2653
1610
5650
3660
1890
5589
9To
tal
150
1927
9143
7048
8744
077
320
3201
469
0036
345
1475
1023
9445
1047
3233
720
1395
265
BP
L10
714
8505
3461
5031
740
3634
019
500
018
370
1070
0010
4770
6048
616
620
8894
81A
PL
4370
323
1242
5032
7037
4024
200
2140
5800
1040
251
8041
8523
1710
Tota
l15
021
8828
4704
0035
010
4008
021
920
020
510
1128
0011
5172
6566
620
805
1121
191
BP
L24
033
3963
7553
8511
7380
1102
7051
414
6900
5206
225
2900
3385
6516
1558
4845
022
2884
7A
PL
6077
656
1520
6350
7071
3025
200
4793
7410
1605
288
4060
7528
7609
Tota
l30
041
1619
9074
4812
2450
1174
0053
934
6900
5685
526
0310
3546
1717
0398
5452
525
1645
6S
ourc
e: F
ield
Sur
vey,
201
1
Pur
ulia
Tot
al
Gra
nd T
otalM
arbe
diya
Am
bari
Ban
kura
Tot
al
Pur
ulia
San
turi
Jiya
thol
e
Hou
seho
ld c
olle
ctio
n of
Com
mon
Pro
pert
y R
esou
rces
in la
st 1
yea
r (R
s)
Tant
irdan
ga
Ram
jiban
pur
Bal
dang
a
Hou
seho
ld
cate
gory
(no.
of H
H)
Pan
jhor
ia
Ban
kura
Sal
tora
Seo
libon
a
Dul
alto
ra
Common Property Forest Resources: Contribution and Crisis
66
The economic importance of Non Timber Forest Products in the livelihood of the
rural poor can be analysed in two different dimensions: i) The rural poor collect
NTFPs for their own consumption which they get it free of cost, but have to pay
money if they purchase those products from the market; ii) The rural households
collect NTFPs for commercial purposes, which is a source of income for them utilised
for various purposes. The price of individual items collected as part of CPR is shown
in the Appendix-II. Almost all the households in the surveyed villages collect fuel
wood annually of about 180 bojha (1 bojha ~10 Kg) from the CPRs. As per Table 4.2,
the value of fuel wood collected in last 1 year amounts to Rs 907448 in the study
villages. The fuel wood is used primarily for cooking purposes. It is also used for
drying the paddy. The households collect fodder in the form of grass or leaves from
the common forest areas in order to feed their livestock. We observe from the Table
4.2, that the total collection of fodder in last 1 year in the study area is valued Rs
411619. The household collect a special type of grass called Surgunda from the forest
areas which is used for making of the brooms. The households also collect several
medicinal plants from the CPRs. The common medicinal plants collected are neem
leaves, basak leaves, kalmegh, etc.
Our field survey data reveals that in Bankura and Purulia districts the forest
communities use the forest products for their own consumption as well as for
commercial purposes.
The rural poor of Jiyathole, Bablu Mudikora, a villager of Santuri block in Purulia
district had the following to say:
“We collect forest products like fodder, firewood, sal and kendu leaves,
mohua flower & seeds, etc. on a regular basis. Whenever there is any need for
something which is available in the forest, we rush to collect the product. As
we reside near the forest fringes, at least one member of every household goes
to the forest for collection of forest products every day. The collection is
mainly for our consumption, but occasionally we go to the market to sell the
products.” (Dated 18th December, 2011, medium of language was Santhali and
an interpreter was used)
Common Property Forest Resources: Contribution and Crisis
67
Most of the households hunt birds or animals to some extent although hunting is
prohibited. They collect snails very often. The households collect small fishes viz.
gorou, chunamach, etc. from the common property resources like ponds and lakes.
The households collect wild mushrooms (called karan chatu in local dialect) from the
common forest area. The market price of this mushroom is Rs 100 per Kg. The
households collect leafy vegetables (shak) from the CPRs. The leafy vegetables
mainly collected are Shushni shak and Kulakhara shak. The yearly collection of these
leafy vegetables by the household is about 365 tara (1 tara ≈ 250gms).
Forest is not only an important source of income and consumption; it also plays a vital
role in the social, cultural and religious life of the rural poor. The use of forest
products is evident in the forest communities from their birth to death.
PHOTO 3
Villagers collecting leaves from common forest area in Seolibona
The villagers of Dulaltora of Saltora block in Bankura district, Ram Mandi and Ravi
Murmu, have explained their high dependence on the forest product in their cultural
life:
“We use neem, sal, mohua leaves and flowers for our ceremonies like
marriage, festivals or birth of a new born. We use the mahua flowers to
prepare liquor, which we all drink during these occasions” (Dated 4th July,
2011, Medium of language was Bengali / Santhali)
Common Property Forest Resources: Contribution and Crisis
68
4.3.2 CPRs and the contribution to the Household income The objective of this study is to examine and estimate the contribution of CPRs to the
income of the household in the study villages. The classification of households
according to their economic activities reveals that there are five sources of income of
the households in our study area; agricultural income, wage labour income, Business
income, Service income and income derived from CPRs. The income from CPRs as
percentage of the total income gives a fair estimate of the dependency of the rural
households on CPRs. Table 4.3 below depicts the annual income from various
sources.
TABLE 4. 3
District Block Name of village
Agricultural income
Wage Labour income
Business Income
Service income
Income from CPRs
Total Income
(Including CPR
income)
Percentage of Income
from CPRs to theTotal income
BPL 22 100900 378000 0 0 223059 701959 31.78APL 4 18300 36000 0 540000 18908 613208 3.08Total 26 119200 414000 0 540000 241967 1315167 18.40BPL 15 92400 249600 0 0 160255 502255 31.91APL 5 36600 126000 0 700000 17465 880065 1.98Total 20 129000 375600 0 700000 177720 1382320 12.86BPL 47 309600 1077000 24000 20000 520699 1951299 26.68APL 7 31900 36000 0 774000 16306 858206 1.90Total 54 341500 1113000 24000 794000 537005 2809505 19.11BPL 7 25500 135000 0 22400 57310 240210 23.86APL 0 0 0 0 0 0 0 0Total 7 25500 135000 0 22400 57310 240210 23.86BPL 18 120900 357000 0 6000 178090 661990 26.90APL 0 0 0 0 0 0 0 0Total 18 120900 357000 0 6000 178090 661990 26.90BPL 24 124560 512000 0 16000 199953 852513 23.45APL 1 4200 24000 0 36000 3220 67420 4.78Total 25 128760 536000 0 52000 203173 919933 22.09BPL 65 352800 1025700 0 0 519531 1898031 27.37APL 16 85500 280000 0 696000 94070 1155570 8.14Total 81 438300 1305700 0 696000 613601 3053601 20.09BPL 15 67500 250800 0 12000 145015 475315 30.51APL 10 76500 290400 0 36000 48405 451305 10.73Total 25 144000 541200 0 48000 193420 926620 20.87BPL 27 137250 422800 0 28000 224935 812985 27.67APL 17 118125 474000 0 716000 89235 1397360 6.39Total 44 255375 896800 0 744000 314170 2210345 14.21BPL 133 773860 2708600 24000 64400 1339366 4910226 27.28APL 17 91000 222000 0 2050000 55899 2418899 2.31Total 150 864860 2930600 24000 2114400 1395265 7329125 19.04BPL 107 557550 1699300 0 40000 889481 3186331 27.92APL 43 280125 1044400 0 1448000 231710 3004235 7.71Total 150 837675 2743700 0 1488000 1121191 6190566 18.11BPL 240 1331410 4407900 24000 104400 2228847 8096557 27.53APL 60 371125 1266400 0 3498000 287609 5423134 5.30Total 300 1702535 5674300 24000 3602400 2516456 13519691 18.61
Source: Field Survey, 2011
Annual Income from Various Sources (Rs)
Household category
(no. of HH)
Panjhoria
Ramjibanpur
Saltora
Seolibona
Baldanga
Dulaltora
Tantirdanga
Bankura
Grand Total
Marbediya
Ambari
Bankura Total
Purulia Total
Purulia Santuri
Jiyathole
Common Property Forest Resources: Contribution and Crisis
69
In the surveyed 150 villages in Bankura district, the average percentage of income
from CPRs to the total income is 19.04 percent whereas for the 150 villages surveyed
in Purulia district it is 18.11 percent (Table 4.4). The average percentage of CPR
income to total income in Bankura and Purulia district for BPL is 27.28 percent and
27.92 percent respectively whereas for APL households it is 2.31 percent and 7.71
percent respectively. In total, the percentage of income from CPR to the total income
is 18.61 percent. This implies that the BPL households enjoy a greater proportion of
income from CPRs, both in relative as well as absolute terms. The high percentage of
income from CPRs can be primarily attributed to the extent of poverty in the said
households.
Figure 4.1 below shows the percentage contribution to household income by different
income generation activities weighted by number of household engaged in the activity
in the study areas of Bankura and Purulia districts.
FIGURE 4.1
Common Property Forest Resources: Contribution and Crisis
70
FIGURE 4.2
FIGURE 4.3
Common Property Forest Resources: Contribution and Crisis
71
From the above figures, it is evident that CPR contributes a major portion of
household income in our study area of West Bengal. It is further observed that the
percentage of income from CPRs to total income is almost similar in both the districts
of Bankura and Purulia.
4.3.3 CPRs and the contribution to the Consumption
expenditure: Due to high dependency of CPRs for subsistence in the surveyed villages, attempt has
been made to estimate the proportion of CPR product consumed in the total
consumption expenditure. The CPR products consumed by the rural poor households
consists of roots, leafy vegetables, mushrooms, fish, hunted birds and animals. Fuel
wood is collected from the common forest for cooking purposes.
PHOTO 5 PHOTO 6
Villagers at Jiyathole using fuelwood Common forest area in Jiyathole
Due to high dependency of CPRs for subsistence in the surveyed villages, attempt has
been made to estimate the proportion of CPR product consumed in the total
consumption expenditure as depicted in Table 4.4 below.
Common Property Forest Resources: Contribution and Crisis
72
The CPR products consumed by the rural poor households consists of roots, leafy
vegetables, mushrooms, fish, hunted birds and animals. Analysis of the data from the
surveyed villages of Bankura and Purulia districts shows that percentage of the value
of CPR consumed to the total monthly consumption expenditure is 22.29 percent and
20.17 percent respectively with an average percentage of 21.28 percent (Table 4.4).
Thus the household in the surveyed villages of both the districts are highly dependent
on CPRs for consumption.
TABLE 4.4
District Block Name of village
Value of CPR product
consumed (Monthly)
Total consumption expenditure
(Monthly)
Percentage of value of CPRs consumed
to the total consumption expenditure
BPL 22 15078.08 58489.58 25.78APL 4 1425.12 13326.33 10.69Total 26 16503.20 71815.91 22.98BPL 15 10756.25 41266.08 26.07APL 5 1270.45 29107.50 4.36Total 20 12026.70 70373.58 17.09BPL 47 36163.25 139815.00 25.87APL 7 1277.29 23735.88 5.38Total 54 37440.54 163550.88 22.89BPL 7 4035.00 17611.00 22.91APL 0 0 0 0Total 7 4035.00 17611.00 22.91BPL 18 12380.00 47097.67 26.29APL 0 0 0 0Total 18 12380 47097.67 26.29BPL 24 15781.17 66945.50 23.57APL 1 235.19 4142.00 5.68Total 25 16016.36 71087.50 22.53BPL 65 39627.17 146883.83 26.98APL 16 6675.23 52605.67 12.69Total 81 46302.4 199489.50 23.21BPL 15 8940.83 37092.67 24.10APL 10 3401.24 31116.50 10.93Total 25 12342.07 68209.17 18.09BPL 27 14641.67 66949.67 21.87APL 17 6781.33 62349.67 10.88Total 44 21423.00 129299.34 16.57BPL 133 94193.75 371224.83 25.37APL 17 4208.05 70311.71 5.98Total 150 98401.8 441536.54 22.29BPL 107 63209.67 250926.17 25.19APL 43 16857.8 146071.84 11.54Total 150 80067.47 396998.01 20.17BPL 240 157403.42 622151 25.30APL 60 21065.85 216383.55 9.74Total 300 178469.27 838534.55 21.28
Source: Field Survey, 2011
Jiyathole
Dulaltora
Tantirdanga
Bankura Saltora
Purulia Santuri
Seolibona
Purulia Total
Grand Total
Marbediya
Ambari
Bankura Total
Baldanga
Ramjibanpur
CPRs and its Contribution to Total consumption Expenditure (Rs)
Household category
(no. of HH)
Panjhoria
Common Property Forest Resources: Contribution and Crisis
73
4.3.4 CPRs and the contribution to the Employment
generation: From the results of the surveyed villages it is observed that CPRs has played an
important role in employment generation. Due to economic backwardness of the
villages surveyed, high level of poverty and illiteracy and non-development in the
secondary and tertiary sectors there is no scope of providing gainful employment to
the surplus rural population engaged in agriculture. Moreover the rural poor lack the
requisite technical skills to get employment in other non-agricultural sectors.
Therefore the economic activity through the collection of CPRs is an important source
of employment to the rural poor.
In order to estimate the contribution of CPRs in employment generation, we have
calculated the number of employment man days generated from the various CPR
based activities (Table 4.5).
The employment man days have been generated by analysing the average time spent
by the households in collection of CPR products in the last 1 year. From the survey
data, it is observed that the time spent on collection of CPR products not only varies
between the members of the household but also between households. The time spent
in the collection of the CPRs depends upon the distance of the forest area from the
residence. Moreover the number of household members also influences the decision
TABLE 4.5
BPL APL Total BPL APL Total BPL APL TotalPanjhoria 22 4 26 1026 981 1019 128 123 127Ramjibanpur 15 5 20 1373 390 1127 172 49 141Seolibona 47 7 54 890 744 871 111 93 109Baldanga 7 0 7 985 0 985 123 - 123Dulaltora 18 0 18 922 0 922 115 - 115Tantirdanga 24 1 25 819 540 808 102 68 101Jiyathole 65 16 81 868 741 843 108 93 105Marbediya 15 10 25 665 730 691 83 91 86Ambari 27 17 44 658 647 654 82 81 82
133 17 150 963 684 932 120 85 116107 43 150 786 701 762 98 88 95240 60 300 884 696 847 111 87 106
Source: Field Survey, 2011
Purulia TotalGrand Total
Bankura Total
Employment provided by CPR based activity (in last 1 year)
Estimated total employment mandays
Household category (no. of HH)District Block Name of
village
Average no. of hours spent on CPR collection per
household (Hours)
Bankura Saltora
Purulia Santuri
Common Property Forest Resources: Contribution and Crisis
74
on the allotment of labour time for collection of CPRs. Children play a critical in
collection of CPRs. We have assumed that two hours of labour by the children is
equivalent to 1 hour of labour by the adult. Thus the average employment man days of
the households in last one year have been estimated by assuming eight hours of work
as an employment man day. It is observed that the estimated average employment
man days in Bankura and Purulia districts are 116 and 95 respectively in last one year.
Further, Ramjibanpur in Bankura district and Jiyathole in Purulia district have higher
employment man days as compared to other villages. An average BPL household in
the study areas derived 111 man days of employment annually as compared to 87 man
days of employment by an average APL household. Hence we can conclude that the
BPL household spend more time in CPR based activities as compared to the APL
household.
4.3.5 Household Energy consumption and the extent of
dependency on CPRs: In the study villages, the households have very little access to commercial fuels like
coal, kerosene, electricity and cooking gas. The commercial fuels are very costly and
beyond the reach of the poor households. So the poor households rely mainly on fuel
wood gathered from the CPRs for cooking. The fuel wood is the major source of
household energy. Almost all the households in the study area collect fuel wood from
the common forest area. There is no restriction in collection of fire wood from the
common forest area. Further it is also observed that both the BPL and APL
households use the fuel wood gathered from CPRs for the purpose of cooking, leading
to total exploitation of the common forest. In the surveyed villages, the households
gather fuel wood, dung cake for household energy from the CPRs.
The household energy consumption from various sources in the last 1 month and the
dependency of CPR products for the Household energy is shown in Table 4.6 below:
Common Property Forest Resources: Contribution and Crisis
75
TA
BL
E 4
.6
Dis
tric
t B
lock
Na
me
of
vil
lag
e
Va
lue
of
fue
lwo
od
co
nsu
me
d
fro
m C
PR
in
la
st 1
m
on
th
(Rs)
Va
lue
of
Du
ng
ca
ke
co
nsu
me
d
in t
he
la
st 1
m
on
th (
Rs)
Ex
pe
nd
itu
re
on
pu
rch
ase
o
f K
ero
sen
e
in l
ast
1
mo
nth
(R
s)
Ex
pe
nd
itu
re
on
ele
ctr
ic
ch
arg
es
in t
he
la
st 1
mo
nth
(R
s)
Ex
pe
nd
itu
re
on
p
urc
ha
se
of
co
al
in t
he
la
st 1
mo
nth
(R
s)
To
tal
En
erg
y
ex
pe
nd
itu
re
(Rs)
Va
lue
of
CP
R
pro
du
cts
u
sed
as
HH
e
ne
rgy (
Rs)
Pe
rce
nta
ge
o
f th
e v
alu
e
of
CP
R t
o t
he
to
tal
en
erg
y
ex
pe
nd
itu
re
BP
L22
6,3
04
920
1568
00
8792
7224
82.1
7A
PL
4625
25
364
0270
1014
650
64.1
0T
ota
l26
6929
945
1932
0270
9806
7874
80.3
0B
PL
15
4,0
81
710
672
382
05845
4791
81.9
7A
PL
5771
47
252
660
660
1730
818
47.2
7T
ota
l20
4852
757
924
1042
660
7575
5609
74.0
5B
PL
47
12,3
21
2,3
70
2116
1088
017895
14691
82.1
0A
PL
7714
78
288
840
425
1920
792
41.2
5T
ota
l54
13034
2448
2404
1928
425
19815
15483
78.1
4B
PL
71,5
83
475
256
00
2314
2058
88.9
4A
PL
0-
-
0
00
00
0T
ota
l7
1583
475
256
00
2314
2058
88.9
4B
PL
18
3,9
17
1,6
25
736
025
6278
5542
88.2
8A
PL
0-
-
0
00
00
0T
ota
l18
3917
1625
736
025
6278
5542
88.2
8B
PL
24
5,8
97
1,0
37
1060
958
50
8951
6933
77.4
6A
PL
1208
-
32
100
0340
208
61.2
1T
ota
l25
6105
1037
1092
1058
50
9292
7142
76.8
6B
PL
65
16,0
33
1,4
40
3216
2781
023470
17473
74.4
5A
PL
16
3,6
25
177
928
598
05328
3802
71.3
6T
ota
l81
19658
1617
4144
3379
028798
21275
73.8
8B
PL
15
4,7
92
645
816
340
06593
5437
82.4
7A
PL
10
2,2
29
52
736
288
20
3305
2281
69.0
2T
ota
l25
7021
697
1552
628
20
9898
7718
77.9
8B
PL
27
8,0
21
560
1232
422
010235
8581
83.8
4A
PL
17
4,5
00
43
928
450
05921
4543
76.7
3T
ota
l44
12521
603
2160
872
016156
13124
81.2
3B
PL
133
34103
7137
6408
2428
75
50076
41240
82.3
5A
PL
17
2318
150
936
1600
1355
5004
2468
49.3
2T
ota
l150
36421
7287
7344
4028
1430
55079
43707
79.3
5B
PL
107
28846
2645
5264
3543
040298
31491
78.1
5A
PL
43
10354
272.5
2592
1336
20
14555
10627
73.0
1T
ota
l150
39200
2918
7856
4879
20
54853
42118
76.7
8B
PL
240
62949
9782
11672
5971
75
90373
72730
80.4
8A
PL
60
12672
423
3528
2936
1375
19558
13094
66.9
5T
ota
l300
75621
10204
15200
8907
1450
109932
85825
78.0
7S
ou
rce
: F
ield
Su
rve
y,
2011
Gra
nd
To
talA
mbari
Pu
rulia
To
tal
Ba
nk
ura
To
tal
Pu
ruli
a
Sa
ntu
ri
Jiyath
ole
Marb
ediy
a
Ho
us
eh
old
en
erg
y c
on
su
mp
tio
n a
nd
th
e e
xte
nt
of
de
pe
nd
en
cy
on
CP
R
Ho
use
ho
ld
ca
teg
ory
(no
. o
f H
H)
Panjh
oria
Ram
jibanpur
Tantird
anga
Seolib
ona
Bald
anga
Dula
ltora
Ba
nku
ra
Sa
lto
ra
Common Property Forest Resources: Contribution and Crisis
76
In the surveyed villages, it is observed that the households use different sources of
household energy: Fuel wood, Dung cake, Kerosene, Electricity and Coal. Out of
these sources of household energy, the households gather the fuel wood, dung cake
from the CPRs. It is observed that in Bankura and Purulia district on an average 79.35
percent & 76.78 percent respectively (Table 4.6) of the total monthly household
energy consumption were met by the CPR products collected from the common forest
area. In total, an average of 78.07 percent of the total monthly household energy
consumption was met by the CPR products. It is further observed that the gender of
the household member plays an important role in collection of the fuel wood. The
female household members and the children collect fuel wood from the forest area as
compared to adult male household member.
4.3.6 CPRs and Animal Grazing All the households use the common forest area for grazing of the animals. The total
grazing time per household depends on the number of livestock owned by the
household. The common livestock owned by the household in the surveyed villages
are: Cattle, Bullocks, Goats, Pigs, Hens, Chicken, Ducks, Pigeon, etc. The Bullock is
used for ploughing in the agricultural field. The cattle are used for giving milk. The
chicken and ducks are used by the households for laying eggs, while the pigs are
domesticated for meat.
The dependency of CPRs for animal grazing is shown in Table 4.7 below. It is evident
that the APL households have larger number of livestock as compared to the BPL
households. In order to estimate the extent of animal grazing on the CPRs, attempt has
been made to calculate the average number of animal unit grazing days per household
on CPRs. A grazing day implies that an animal has remained in the grazing field of
the CPR at least for 5 hours in a day and the animal has not been fed separately.
Animal grazing days (number of animals x number of grazing days) has been
calculated by changing the number of animals into animal units as per the basis as
shown below:
1 animal unit=1 bullock /cow or 4 goats or 4 pigs or 100 chicken/hens/ducks.
Common Property Forest Resources: Contribution and Crisis
77
From the Table 4.7 it is apparent that in the study villages of Bankura and Purulia
district an average of 88 and 114 animal unit grazing days per household respectively
were observed in the last 1 month. This data signifies the high dependency of the rural
poor on the CPRs for animal grazing.
TABLE 4.7
District Block Name of village
Number of cows
Number of
Bullocks
Number of goats
Number of pigs
Number of hens/
chickens/ducks
Average number of
animal unit grazing days
per household on
CPR #
BPL 22 32 32 23 12 90 100APL 4 6 8 5 1 24 118Total 26 38 40 28 13 114 103BPL 15 22 22 32 0 62 109APL 5 7 6 8 0 12 91Total 20 29 28 40 0 74 105BPL 47 63 86 72 2 209 108APL 7 1 4 2 0 13 24Total 54 64 90 74 2 222 98BPL 7 2 10 5 1 32 68APL 0 0 0 0 0 0 0Total 7 2 10 5 1 32 68BPL 18 8 24 14 1 38 64APL 0 0 0 0 0 0 0Total 18 8 24 14 1 38 64BPL 24 10 36 21 0 54 64APL 1 0 2 0 0 4 61Total 25 10 38 21 0 58 64BPL 65 39 112 92 0 181 80APL 16 11 24 27 0 59 77Total 81 50 136 119 0 240 80BPL 15 2 18 6 1 22 40APL 10 4 6 4 0 58 59Total 25 6 24 10 1 80 47BPL 27 5 40 19 2 143 57APL 17 22 30 19 3 95 103Total 44 27 70 38 5 238 75BPL 133 137 210 167 16 485 91APL 17 14 20 15 1 53 68Total 150 151 230 182 17 538 88BPL 107 46 170 117 3 346 69APL 43 37 60 50 3 212 83Total 150 83 230 167 6 558 114BPL 240 183 380 284 19 831 81APL 60 51 80 65 4 265 79Total 300 234 460 349 23 1096 81
Source: Field Survey, 2011
Note:# A grazing day implies that an animal has remained in the grazing field of the CPR at least for 5 hours in a dayand the animal has not been fed separately. Animal grazing days (number of animals x number of grazing days)has been calculated by changing the no. of animals into animal units as per the basis as shown below:1 animal unit=1 bullock /cow or 4 goats or 4 pigs or 100 chicken/hens/ducks
Dependency on CPRs for Animal Grazing (in last 1 month)
Household category
(no. of HH)
Panjhoria
Dulaltora
Tantirdanga
Bankura Saltora
Seolibona
Baldanga
Ramjibanpur
Grand Total
Marbediya
Ambari
Bankura Total
Purulia Santuri
Jiyathole
Purulia Total
Common Property Forest Resources: Contribution and Crisis
78
4.4 CPRs and Rural Poverty: The objective of this study is to analyse the extent of Poverty in the study villages and
to examine if the income derived from CPRs had any role to play to alleviate the
poverty. The monthly per capita income including the income from CPRs for the 150
households in the study villages have been arranged in the ascending order and then
grouped into 10 classes. As per the Tendulkar Committee report published in
November 2009 and now approved by the planning commission, the revised poverty
line (rural) with the base year 2004-2005 for West Bengal is Rs 445.38. The estimated
monthly per capita income of the households in the study area has been compared
with the revised poverty line for the state. In order to analyse the extent of poverty
and to quantify the role of income from extraction of CPR products in poverty
mitigation, we have used two sets of data i) per capita income of the households
including the income derived from common property resources and ii) per capita
income of the households excluding the income derived from the common property
resources.
Table 4.8A & 4.8B shows the distribution of monthly per capita income including the
income derived from CPRs for Bankura and Purulia district.
TABLE 4.8 A
Sl No. Range of Income per capita per
month
Frequency (No. of Household)
Percentage of (3) to the total
household
Cummulative frequency
Monthly Per Capita
Income (Rs)
Percentage of (6) to the
total
Cummulative total
1 2 3 4 5 6 7 81 0- 400 5 3.33 3.33 1827 1.30 1.302 400-500 38 25.33 28.67 17857 12.73 14.033 500-600 14 9.33 38.00 9461 6.74 20.774 600-700 23 15.33 53.33 15023 10.71 31.485 700-800 20 13.33 66.67 14833 10.57 42.056 800-900 15 10.00 76.67 12666 9.03 51.077 900-1000 8 5.33 82.00 7537 5.37 56.448 1000-1500 12 8.00 90.00 14152 10.09 66.539 1500-2000 6 4.00 94.00 10399 7.41 73.94
10 2000 and above 9 6.00 100.00 36568 26.06 100.00Total 150 100.00 140323 100.00
Source: Field Survey 2011
Distribution of monthly per capita income of the sample households in Bankura district( including income derived from CPRs)
Common Property Forest Resources: Contribution and Crisis
79
If we consider the planning commission estimates of poverty line for West Bengal,
then 28.67 percent in Bankura district and 40 percent in Purulia district are lying
below the poverty line (Table 4.8A & 4.8B). Moreover there is substantial inequality
in the distribution of income.
In the case of Bankura district, the bottom 38 percent of the household has only 20.77
percent of the total income whereas the upper class 6 percent of the household has
26.06 percent of the total income (Table 4.8A). However in the case of Purulia
district, 24.03 percent the total income is generated by 57.33 percent of the total
household whereas the upper class 14.67 percent of the household generates 55.10
percent of the total income (Table 4.8B).
We now exclude the income derived from CPRs from the per capita monthly income.
Table 4.9 shows the distribution of monthly per capita income of the sample
households excluding the income generated through collection of CPR products in
Bankura and Purulia district.
TABLE 4.8 B
Sl No. Range of Income per capita per
month
Frequency (No. of Household)
Percentage of (3) to the total
household
Cummulative frequency
Monthly Per Capita
Income (Rs)
Percentage of (6) to the
total
Cummulative total
1 2 3 4 5 6 7 81 0- 400 20 13.33 13.33 6873 4.25 4.252 400-500 40 26.67 40.00 17885 11.05 15.293 500-600 26 17.33 57.33 14139 8.73 24.034 600-700 20 13.33 70.67 12981 8.02 32.045 700-800 10 6.67 77.33 7343 4.54 36.586 800-900 1 0.67 78.00 879 0.54 37.127 900-1000 4 2.67 80.67 3773 2.33 39.458 1000-1500 6 4.00 84.67 6946 4.29 43.749 1500-2000 1 0.67 85.33 1878 1.16 44.90
10 2000 and above 22 14.67 100.00 89198 55.10 100.00Total 150 100.00 161895 100.00
Source: Field Survey 2011
Distribution of monthly per capita income of the sample households in Purulia district( including income derived from CPRs)
Common Property Forest Resources: Contribution and Crisis
80
As we exclude the income derived from extraction of common property resources
from the data of the surveyed villages, we observed that the extent of poverty in both
the districts have increased by about 26 percent [Poverty level in Bankura district has
increased from 28.67 percent to 55.33 percent (Table 4.8A & 4.9A), whereas in
Purulia district it has increased from 40 percent to 66.67 percent (Table 4.8B &
4.9B)]. This data clearly shows the importance of common property resources in the
subsistence of the poor household and signifies the immense role played by the CPRs
in alleviating the poverty in the study villages.
TABLE 4.9 A
Sl No. Range of Income per capita per
month
Frequency (No. of Household)
Percentage of (3) to the total household
Cummulative frequency
Monthly per capita
Income (Rs)
Percentage of (6) to the total
Cummulative total
1 2 3 4 5 6 7 81 0- 400 46 30.67 30.67 15927 14.28 14.282 400-500 37 24.67 55.33 16934 15.18 29.463 500-600 24 16.00 71.33 13168 11.80 41.264 600-700 13 8.67 80.00 8586 7.70 48.965 700-800 6 4.00 84.00 4562 4.09 53.056 800-900 5 3.33 87.33 4288 3.84 56.907 900-1000 6 4.00 91.33 5900 5.29 62.198 1000-1500 3 2.00 93.33 4075 3.65 65.849 1500-2000 1 0.67 94.00 1933 1.73 67.5710 2000 and above 9 6.00 100.00 36173 32.43 100.00
Total 150 100.00 111546 100.00Source: Field Survey 2011
Distribution of monthly per capita income of the sample households in Bankura district( excluding income derived from CPRs)
TABLE 4.9 B
Sl No. Range of Income per capita per
month
Frequency (No. of Household)
Percentage of (3) to the total household
Cummulative frequency
Monthly per capita
Income (Rs)
Percentage of (6) to the total
Cummulative total
1 2 3 4 5 6 7 81 0- 400 71 47.33 47.33 21784 21.23 21.232 400-500 29 19.33 66.67 13144 12.81 34.033 500-600 10 6.67 73.33 5435 5.30 39.334 600-700 8 5.33 78.67 5010 4.88 44.215 700-800 3 2.00 80.67 2289 2.23 46.446 800-900 2 1.33 82.00 1669 1.63 48.077 900-1000 1 0.67 82.67 938 0.91 48.988 1000-1500 12 8.00 90.67 14869 14.49 63.479 1500-2000 7 4.67 95.33 12616 12.29 75.7610 2000 and above 7 4.67 100.00 24873 24.24 100.00
Total 150 100.00 102627 100.00Source: Field Survey 2011
Distribution of monthly per capita income of the sample households in Purulia district( excluding income derived from CPRs)
Common Property Forest Resources: Contribution and Crisis
81
4.5 Determinants of CPR Extraction We have assumed that collection of CPR is determined by socio-economic and
demographic variables. We have explained the determinants of CPR extraction
through econometric analysis to give an understanding of the relationship between
forest dependency (measured by the household income from the community forest)
and socio-economic variables.
Regression Results and Discussion
The regression model has been tested using the household level data through field
survey in Bankura and Purulia district. Multiple regression analysis has been made
using EViews 7 computer package. The result for the determinants of the household
income from the community forest for Bankura and Purulia district is given in the
Table 4.10 & 4.11 below:
TABLE 4.10 A
Determinants of CPR extraction-Bankura District
Source: Estimated by EViews7 computer software using field survey data of 2011 Note: 1.*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level 2. Summary statistics has been presented in Table A3.1 in Appendix III
Dependent Variable: CPRIN Method: Least Squares Sample: 1 150; Incl obsn: 150
Variable Coefficient Std. Error t-Statistic Prob. C 7282.105 1355.591 5.371904* 0.0000
FSIZE 639.8347 102.3828 6.249433* 0.0000 FEMPER 26.95335 9.081700 2.967874* 0.0035 AVRAGE -10.22113 15.76346 -0.648407 0.5178 AVRSCH -544.4105 97.37766 -5.590712* 0.0000 OWNLAND -299.6904 74.47889 -4.023829* 0.0001 LIVESTOCK 269.3362 133.2933 2.020628** 0.0452 FORESTDIST -459.9032 208.8591 -2.201979** 0.0293 POVR 2372.728 594.3089 3.992415* 0.0001 DISM -106.2043 184.3037 -0.576246 0.5654
R-squared 0.651121 Mean dependent var 11047.09
Adjusted R-squared 0.628694 S.D. dependent var 3309.353 S.E. of regression 2016.551 Akaike info criterion 18.12051 Sum squared resid 5.69E+08 Schwarz criterion 18.32121 Log likelihood -1349.038 Hannan-Quinn criter. 18.20205 F-statistic 29.03176 Durbin-Watson stat 1.685457 Prob(F-statistic) 0.000000
Common Property Forest Resources: Contribution and Crisis
82
TABLE 4.10 B Variance Inflation Factors Sample: 1 150; Included observations: 150
Coefficient Uncentered Centered
Variable Variance VIF VIF C 1837627. 67.78446 NA
FSIZE 10482.25 9.950002 1.432938 FEMPER 82.47728 8.324282 1.057154 AVRAGE 248.4866 8.778397 1.178498 AVRSCH 9482.409 3.107897 1.337570 OWNLAND 5547.106 3.409295 1.411520 LIVESTOCK 17767.11 7.490170 1.795150 FORESTDIST 43622.10 6.080978 1.555698 POVR 353203.1 11.55201 1.309228 DISMK 33967.85 6.750837 1.062677
Source: Estimated by EViews7 computer software using field survey data of 2011
TABLE 4.11 A Determinants of CPR extraction-Purulia District Dependent Variable: CPRIN; Method: Least Squares Sample: 1 150; Included observations: 149
Variable Coefficient Std. Error t-Statistic Prob. C 9326.052 813.2588 11.46751* 0.0000
FSIZE 150.9987 57.41028 2.630167* 0.0095 FEMPER 7.078460 6.320566 1.119909 0.2647 AVRAGE 23.96721 8.194546 2.924776* 0.0040 AVRSCH -214.1267 58.26268 -3.675194* 0.0003 OWNLAND 133.9483 35.06343 3.8201718* 0.0002 LIVESTOCK 56.54811 68.55396 0.824870 0.4109 FORESTDIS -665.4332 187.7843 -3.543605* 0.0005 POVR 911.0631 291.9778 3.120317* 0.0022 DISM -225.5848 130.6185 -1.727051*** 0.0864
R-squared 0.689904 Mean dependent var 9558.933
Adjusted R-squared 0.669825 S.D. dependent var 2125.682 S.E. of regression 1221.434 Akaike info criterion 17.11819 Sum squared resid 2.07E+08 Schwarz criterion 17.31980 Log likelihood -1265.306 Hannan-Quinn criter. 17.20010 F-statistic 34.36086 Durbin-Watson stat 1.566364 Prob(F-statistic) 0.000000
Source: Estimated by EViews 7 computer software using field survey data of 2011 Note: 1.*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level 2. Summary statistics has been presented in Table A3.2 in Appendix III
Common Property Forest Resources: Contribution and Crisis
83
TABLE- 4.11 B Variance Inflation Factors Sample: 1 150 Included observations: 149
Coefficient Uncentered Centered
Variable Variance VIF VIF C 621752.8 61.21651 NA
FSIZE 3344.084 8.191516 1.983340 FEMPER 40.54354 9.305583 1.092243 AVRAGE 68.26892 7.520626 1.079849 AVRSCH 3408.983 11.19958 2.693264 OWNLAND 1248.145 3.836449 1.245577 LIVESTOCK 4759.068 4.052396 1.503749 FORESTDIS 35407.74 16.18120 2.638796 POVR 85116.27 5.174459 1.979491 DISM 47466.12 3.167887 1.020527
Source: Estimated by EViews 7 computer software using field survey data of 2011 The R-square is as high as 65 percent in Bankura district and 68 percent in Purulia
district. The F-statistics for overall goodness of fit of the model is highly significant in
both the districts. Tables 4.10B & 4.11B represents Variance Inflation Factor (VIF)
for explanatory variable. We have observed from our results that VIF is very low i.e.
around 1 in all the cases. Hence we conclude that we do not have multicollinearity
problem. It is evident from the analysis that most of the important variables are
significant with the expected sign. The CPR collection is a labour intensive activity.
Household members collect a lot of CPR products viz. fuel wood, fodder, vegetables,
bamboo, etc. from the forest. They also hunt animals and catch fishes from the
community forest area. All these activities take a lot of time. The larger the family
size, the higher is the labour time available for the collection of community forest
products. In both the districts of Bankura and Purulia, the family size ( FSIZE ) has a
positive impact on community forest income. In Bankura and Purulia districts, the p-
value indicates that the association is significant.
From the regression analysis, we observed that there is a positive relationship between
average age of the household ( AVRAGE ) i.e. experience in collecting CPR products
and CPR income. It is easier for an experienced household member to collect more
Common Property Forest Resources: Contribution and Crisis
84
CPR and thus smooth their consumption and livelihood. In line with our expectation,
the result is positive and significant in Purulia district. However the result is negative
and insignificant in Bankura district. Further it is observed that household with larger
size of livestock ( LIVESTOCK ) collect more fodder to feed the animals. They also
require higher quantity of fuel wood to prepare concentrated food for the animals. So
there is a positive relation between the CPR income and the size of the livestock. The
impact of the number of livestock on CPR income is significant in Bankura district,
but insignificant in Purulia district.
We have hypothesised a positive relationship between female percentage in the
household ( FEMPER ) and CPR income. From our study area in Bankura district, we
observe that female members are mainly involved in collection of CPRs. So the
households with higher percentage of female members collect more CPR products
and hence earn higher income as compared to households with lower percentage of
female members. As per the result, though the percentage of female members in the
household is found to be positively associated with the household level income from
the community forest, the p-value indicates that the association is statistically
significant only in Bankura district.
Education ( AVRSCH ) i.e. average years of schooling of the household has a negative
impact on the income from CPR. Further, the p-value for Bankura and Purulia
districts indicates that it has a significant impact on the income from CPR. Household
members who are better educated get better job opportunities and therefore are less
interested in collection of CPR products. We have hypothesised a negative
relationship between land ownership ( OWNLAND ) and CPR income as household
who own more agricultural land spend more time in cultivation and less time in CPR
collection. In line with our expectation we have observed coefficient of OWNLAND is
negative and significant in Bankura district. However, we observe contradictory
results in case of Purulia district. Here the households with larger ownership of
cultivated land ( OWNLAND ) collect more fuel wood. This is mainly due to the fact
that land in Purulia district is mostly infertile and so mere ownership of land fails to
reduce the dependency on CPR. It is also observed that the forest distance
( FORESTDIST ) i.e. distance between the residence and the common forest area has a
Common Property Forest Resources: Contribution and Crisis
85
negative impact on the CPR income. From the survey, it indicates that households
nearest to the forest area extract more CPR and get more income from the forest
products as compared to the households far away from the forest area. In both the
districts the impact is significant. In the case of distance to the nearest market
( DISM ), the motivation for extraction of forest products is greater when the market
is nearer. This is due to the fact that the rural poor are able to sell the CPR products in
the nearest market and generate income easily. As expected, DISM has a negative
impact on the extraction of CPR product in both the districts. However, the result is
significant only in Purulia district.
The most important finding of this study is impact of poverty on CPR extraction. The
coefficient of dummy variable ( POVR ) is positive and highly significant in both the
districts, which implies that BPL household are more dependent on CPR as compared
to the APL household. Household with low income extract more community forest
product for their consumption purposes and also sell it to increase their income.
Hence in the case of the poor, the income from community forest has a higher
percentage as compared to that of the ‘not so poor’.
4.6 Poverty-Environment Nexus-Logit Model Several literatures have discussed the inter-linkage between poverty and
environmental degradation. While some authors argue that poverty leads to
environmental degradation, others argue that environmental degradation leads to low
productivity which results in increase in poverty. Several concepts have been put forth
by authors to illustrate the over-exploitation in fisheries, forests, over grazing, air and
water pollution, abuse of public land and problems of resource misallocation
(Stevenson, 1991). The inter-linkage between poverty and environmental degradation
has been defined as a ‘vicious circle’ by Dasgupta and Maler (1994). In their study,
the authors argue that the rural poor pushed by increase in population and poverty,
extend their cropping onto fragile marginal lands resulting in further degradation. This
results in reduced yield and this further impoverishes them. It can also be stated that
the environmental degradation adversely affect poverty, because the poor are the most
vulnerable to environmental degradation, due to their heavy dependence on natural-
resource base, and limited resource to cope with adverse environmental effect.
Common Property Forest Resources: Contribution and Crisis
86
We have made an attempt to quantify the diminution of common property resources
and the extent of degradation of common forest in the study villages. We have
gathered information from Panchayat Pradhan, Head Master, Public leaders and
household head about the relative position of forest resources comparing present state
with that of twenty years back.
We have shown the extent of depletion of common property resources during 1990-
2010 in our surveyed villages in the following Table 4.12 below:
From our field survey report we have observed that the forest area had declined as
compared to earlier times of 1990s in both the districts. However, the extent of
depletion of forest area is high in Bankura district as compared to Purulia district.
Severe deforestation was detected in three villages viz. Ramjibanpur, Seolibona and
Baldanaga of Bankura district and Marbediya of Purulia district. Village Ramjibanpur
TABLE 4.12
Common Forest area (in Sq. Km)
Common Village land (in Sq. Km)
Common grazing land (in Sq. Km)
Source: Field Survey, 2011
Bankura Saltora
Purulia Santuri
Bankura Total
Baldanga
Panjhoria
Ramjibanpur
Purulia Total
Grand Total
Jiyathole
Marbediya
Extent of Depletion of Common Property Resources (during 1990-2010)
13.25 12.81 10.75
Ambari
Dulaltora
Tantirdanga
Seolibona
Percentage Decline District Block Name of
village
17.13
16.87
9.24
8.18
13.25
13.75
10.53
6.25
12.99
10.18
12.05
8.28 7.53
8.78 7.05
10.69 8.93
8.31 7.65
14.35
11.76 9.63
11.72
14.05 10.86
8.57 8.64
7.62 4.78
10.43 8.85
9.8811.64
Common Property Forest Resources: Contribution and Crisis
87
of Bankura district witnessed the highest depletion of common forest resources.
Interestingly, no noticeable deforestation was found in Jiyathole village of Purulia
district. The common forest area declined around 12.05 percent in the study area
during 1990-2010.
The villagers of Ramjibanpur of Saltora block in Bankura district, Haren Kora and
Anil Mudikora, have raised their concern on depletion of the common forest area and
have shared their view:
“We now have to walk 2 – 2.5 km more as compared to earlier period (1990s)
in order to gather fire wood and fodder and graze our livestock. This has
resulted in lot of time and energy being spent for reaching the forest area.
Also, several medicinal herbs have become almost rare at the common forest
area” (Dated 28th July, 2011, Medium of language was Bengali / Santhali).
The depletion of common forest resources viz., the forest resources in both the
districts are mainly responsible for absence of any alternative income or employment
opportunities, growing commercialisation of CPR products, emergence of market, and
presence of middle man for market sale. We have also found an enormous pressure on
common forest in our study villages because of the illicit collection of fuel wood,
fodder and small timber by the households. The problem has been tackled by the
formation of Forest Protection Committee under the purview of Joint Forest
Management in our study area of both the districts.
4.6.1 Determinants of Poverty To examine the determinants of poverty a qualitative response model like logit model
is proposed in this study. We consider a range of socio-economic as well as
environmental variable which enable us to express the rural poverty conditions as well
as the extent of environmental degradation in the region. In our study we have
considered forest degradation as a measurement of environmental degradation. The
logit regression model has been tested using the household level data through field
survey in Bankura and Purulia districts. Specification of the regression model has
been presented in the following table where the expected sign and description of the
dependent and explanatory variables are explained.
Common Property Forest Resources: Contribution and Crisis
88
TABLE 4.13
Description of Variables of Logit Regression Model Variables Expected
Sign
Variable description
Dependent variable
POVR
Poverty of the household
POVR =1; if the household belongs to BPL
POVR =0; if the household belongs to APL
Explanatory Variables
FSIZE + Average number of population of the household
(size of the family)
AVRAGE _ Average age of household, i.e. experience in
collecting CPR product
AVRSCH _ Average years (number of years) of schooling of
household
OWNLAND _ Total land owned by the household
LIVESTOCK _ Total livestock of the households converted into
animal units
FORESTDIST + Distance of the CPR field from the residence of
the households (km)
*ENVDGR +
Dummy for extent of environmental degradation
ENVDGR =1; if environment is more degraded
ENVDGR =0; if environment is less degraded
*Measuring Environmental degradation
In this study, we have considered forest degradation as a measurement of
environmental degradation. Forest Degradation is measured on the basis of the data
collected in the village survey from three different variables:
i) Extent of forest damage visually seen (FD)
ii) Condition of the forest informed by the respondents as compared to that of
earlier times (FC)
iii) Forest use penetration i.e. the depth into the forest from the village
boundary where use pressure was evident (FP)
Common Property Forest Resources: Contribution and Crisis
89
For the purpose of regression analysis we have constructed a composite measure
called Forest Degradation Index (FDI) which is sum of the above three variables i.e.
FDI=FD+FC+FP. All these three variables (FD, FC, FP) are coded (using four point
scale) so that increasing values shows more forest degradation. Each variable has a
substantive impact on the index and they are positively correlated.
The regression result is given in the following Table 4.14A and 4.14B below.
TABLE 4.14 A
Determinants of Poverty-Bankura District Dependent Variable: POVR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob.
C 12.39520 7.864797 1.576035 0.1150 FSIZE 1.568055 0.886717 1.768382*** 0.0770 AVRAGE -0.412058 0.253940 -1.622658 0.1047 AVRSCH 0.817163 0.649429 1.258280 0.2083 OWNLAND -2.371136 1.274703 -1.860148*** 0.0629 LIVESTOCK -2.836798 1.566963 -1.810380*** 0.0702 FORESTDIST 2.721305 1.708830 1.592496 0.1113 ENVDGR 20.12960 10.71125 1.879296*** 0.0602
McFadden R-squared 0.910470 Mean dependent var 0.766667
S.D. dependent var 0.424370 S.E. of regression 0.134763 Akaike info criterion 0.203945 Sum squared resid 2.578856 Schwarz criterion 0.364512 Log likelihood -7.295869 Hannan-Quinn criter. 0.269178 Deviance 14.59174 Restr. deviance 162.9818 Restr. log likelihood -81.49092 LR statistic 148.3901 Avg. log likelihood -0.048639 Prob(LR statistic) 0.000000
Obs with Dep=0 17 Total obs 150
Obs with Dep=1 133 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level
Source: Estimated by EViews 7 computer software using field survey data of 2011
Common Property Forest Resources: Contribution and Crisis
90
TABLE 4.14 B
Determinants of Poverty-Purulia District Dependent Variable: POVR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C 4.426351 2.256543 1.961563** 0.0498
FSIZE 0.026525 0.201712 0.131501 0.8954 AVRAGE -0.000489 0.030479 -0.016039 0.9872 AVRSCH -0.053825 0.200660 -0.268240 0.7885 OWNLAND -0.586099 0.172234 -3.402931* 0.0007 LIVESTOCK -0.260669 0.186535 -1.397428 0.1623 FORESTDIST 1.014146 0.532792 1.903457*** 0.0570 ENVDGR 2.466093 1.176092 2.096854** 0.0360
McFadden R-squared 0.691794 Mean dependent var 0.620000
S.D. dependent var 0.487013 S.E. of regression 0.232767 Akaike info criterion 0.516004 Sum squared resid 7.693599 Schwarz criterion 0.676571 Log likelihood -30.70031 Hannan-Quinn criter. 0.581237 Deviance 61.40061 Restr. deviance 199.2192 Restr. log likelihood -99.60962 LR statistic 137.8186 Avg. log likelihood -0.204669 Prob(LR statistic) 0.000000
Obs with Dep=0 43 Total obs 150
Obs with Dep=1 107 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level
Source: Estimated by EViews 7 computer software using field survey data of 2011 From the above Tables 4.14A & 4.14B we observe that in most of the cases, the
results are consistent with our hypothesis. As expected, i.e. experience in collecting
CPR products ( AVRAGE ) has been found to be a negative influence on poverty
( POVR ) in both the districts. The experienced elder members of the household can
earn more income as compared to the younger one thus helping to reduce poverty.
However, the result is insignificant in both the districts. In line with our expectation
households with more own land ( OWNLAND ) and larger number of livestock
( LIVESTOCK ) have a negative impact on poverty. This indicates that wealthier
households have several options to earn money and therefore the probability of the
Common Property Forest Resources: Contribution and Crisis
91
incidence of poverty is low. In Bankura district, both have significant impact on
poverty; however in Purulia district we have observed significant result only in the
case of OWNLAND . We have hypothesized that educated people ( AVRSCH ) get
better job opportunities and thus help to reduce the probability of incidence of
poverty. In Purulia district although the result is consistent with our hypothesis, the
impact is insignificant. In Bankura district, the result is contradictory but insignificant
and therefore can be ignored. It is obvious that households residing closer to the forest
collect more CPR and thus reduce their poverty level. As expected, Forest distance
( FORESTDIST ) has positive impact on poverty ( POVR ) in both districts. However,
the result is significant only in Purulia district. The households with larger family size
( FSIZE ) the probability of incidence of poverty is high. In fact large family size puts
additional financial burden on the earning member which in turn increases their
poverty level. The result is consistent with our hypothesis in both the district.
However the impact is significant only in Bankura district.
The coefficient of environmental degradation is positive and significant which imply
that with more environmental degradation the probability of the incidence of poverty
increases. In fact, households in the study villages live under acute poverty and hence
they are heavily dependent on the natural resources for their subsistence. Under the
circumstances, the level of extraction of the CPRs is higher than their regenerating
process and thus the environment gets further degraded. However, poverty is not the
sole cause for degradation. There are several other factors like rising population,
growing commercialisation of agricultural and forest products and emergence of
market, etc. which leads to forest degradation.
Common Property Forest Resources: Contribution and Crisis
92
4.7 Conclusion
This study investigates the dependency of the rural poor on CPRs and the relationship
between rural poor and environmental degradation. The result of the survey of 300
households in Bankura and Purulia districts indicates a very high dependency of the
rural poor on CPR products. The field survey data shows that the average percentage
of CPR income to total income in Bankura and Purulia district is 19.04 percent and
18.11 percent respectively. For BPL households, 27.28 percent in Bankura district and
27.92 percent in Purulia district of the total household income comes from CPR based
activities whereas in case of APL households it is 2.31 percent and 7.71 percent
respectively. Hence poor households enjoy a greater proportion of income from CPRs
both in relative as well as absolute terms. The survey data further reveals that 21.28
percent of the total consumption expenditure of household is supported by CPRs,
which indicates high dependency of the rural households on CPRs for their
consumption.
CPR has also played an important role in employment generation. As per the survey
data, in an average, rural household generate around 116 and 95 employment man
days per annum from CPR based activities in Bankura and Purulia district
respectively. The households collect fuel wood and cow dung from the common
forest area for the purpose of cooking. As per the survey report, an average of 79.35
percent and 76.78 percent of the total household energy consumption were met by the
CPR products collected from the common forest area in Bankura and Purulia districts
respectively. Here we observed that both the poor and not so poor households use fuel
wood gathered from CPRs to cater to their household energy needs. Our survey data
confirms a high dependency of the rural households on the CPRs for animal grazing
with an average of 88 and 114 animal unit grazing days per household in Bankura and
Purulia districts respectively in the last 1 month.
The survey data also shows the immense role of CPRs in alleviating the poverty in the
study villages. As we exclude the income derived from CPRs from the total income of
the households, we observe that the extent of poverty in both the districts have
increased by around 26 percent.
Common Property Forest Resources: Contribution and Crisis
93
The most important finding of our multiple regression model is the impact of poverty
on CPR extraction and the result indicate that the coefficient of dummy variable
( POVR ) is positive and significant in both the districts. Therefore we can conclude
that BPL households are more dependent on CPR as compared to the APL
households.
The high extraction of CPRs in the surveyed villages has resulted in degradation of
the environment. Environmental degradation has adversely affected poverty because
of high dependence of the rural poor on the natural resources and this further
impoverishes them. We have used logit regression model to analyse the relationship
between poverty and environmental degradation. The important finding of this
analysis is that the coefficient of environmental degradation is positive and significant
in both the districts which show a strong positive relationship between poverty and
environmental degradation. It is thus desirable to ensure conservation of CPRs in
order to maintain the sustainable livelihood of the rural poor.
We have hypothesised that CPRs have a positive impact on income and employment
of rural poor and have an immense role in alleviating rural poverty. We further
hypothesised that high extraction of CPR has resulted in degradation of the
environment which further aggravates rural poverty. Our survey results are consistent
with our hypothesis. Further, our findings on the high dependency of the rural poor on
CPRs and the subsequent degradation of the environment are in conformity with the
results of similar studies carried out in other Indian states viz. Rajasthan, Karnataka,
(Jodha, 1986); Gujarat (Conroy, 1991); etc. However, our empirical results goes
against the findings of Chopra and Dasgupta (2003) and Kuri (2005) who postulated
that non-poor are equally benefited by extraction of CPRs and are also responsible for
forest degradation.
We can conclude that dependence of the rural poor on Common Property Resources
is a basic necessity and cannot be denied.
Agricultural Risk and Common Property Resources
94
CHAPTER 5
AGRICUTURAL RISK AND COMMON PROPERTY RESOURCES
5.1 Introduction
Agriculture is the predominant economic activity in West Bengal. However there is
wide fluctuation in the agricultural productivity in many parts of West Bengal. The
problem of fluctuating crop yield can be attributed to vagaries of the weather and land
degradation in the form of soil erosion, salinity, water logging, etc. The yield is also
affected by outbreak of diseases, pests and other hazards like flood, droughts and fire.
In our study villages in the districts of Bankura and Purulia in West Bengal, the rural
households are heavily dependent on agriculture. These districts have the problem of
‘dry land’ which have low moisture retaining capacity in the soil, thereby leading to
fluctuating crop productivity. Water storage facilities which are the ‘lifeline’ of
irrigation are also lacking in many parts of both the districts. Crop failure also occurs
due to low rainfall. The instability in the crop production raises the agricultural risk
which adversely affects the livelihood and income of the rural households and in turn
also effects their decision to use high technology in farming, thereby impeding the
development process. The fluctuating crop production also affects the price stability.
The rural households have to incur costs for smoothening consumption across income
shocks. The households therefore look for safety net to mitigate the income shocks.
In our study villages of Bankura and Purulia, most of the households live below the
poverty line. The main occupation of the household is agriculture, although they also
engage in off farm wage labour. There are several risks associated with the harvesting
of crops such as weather, seasonal flooding, unpredictable soil quality, crop diseases,
price shocks and forest pests. Under these circumstances, forest acts as a security
especially against crop failure. Households having limited credit and insurance
facility, extract Non Timber Forest Products (NTFP) which not only reduce their
agricultural risk but also help to smooth out their consumption. Hence NTFP has a
supporting role in the wellbeing of the rural poor in the form of ‘natural insurance’.
Agricultural Risk and Common Property Resources
95
By collecting NTFP, rural households smooth their income as well as consumption in
the period of agricultural shortfall.
The collection of forest products is a common phenomenon to support the livelihood
of the rural poor. In rural area, Common Property Resources (CPRs) are critical
resources for the poor households. CPRs are vital resources for the poor primarily
because the cost of using the CPRs are low and these involve only human labour as
the input. CPRs supplement the rural livelihood and act as safety net for the poor
seasonally or during the agricultural crisis.
Several literatures have discussed the importance of Common Property Resources as
insurance. Based on the survey of rural households living on the margin of Tapajós
National Forest in the Brazilian Amazon, Pattanayak and Sills (2001) had put forth a
positive correlation between collection of NTFP, shortfall in agriculture and the
expected agricultural risk. The advantage of common property resources arises
because of its superior insurance properties which tend to provide income
maintenance to the rural poor (Baland and Francois, 2004). The study on the potential
impact of extraction of NTFP on land use choice was carried out by Delacote (2009).
The study revealed that the rural poor collect forest products in order to reduce the
agricultural risk. If the agricultural risk is reduced, then the households reduce the size
of the safety activity thereby being less dependent on the forest products as compared
to agricultural activities.
Informal insurance arrangements are affected by sustainability constraints, often
excluding the poor from these arrangements (Dercon 2002). Both ‘forest dependent’
and ‘forest related’ people depend on forest for a supplementary source of income and
there is a varied nature of relationships of people to forest and forest products (Byron
and Arnold, 1999). In Sierra Leone, a country in West Africa, the farmers in order to
cope agricultural shocks arising due to irregular rainfall often resort to mortgaging or
pledging cocoa or coffee trees or selling timber to buy food (Leach 1990). Tropical
forest resources are known to reduce the vulnerability of the rural poor to income
shocks. Based on the field study of rural households in Eastern Honduras in Central
America, McSweeney (2004) emphasised that the nature and intensity of the calamity
Agricultural Risk and Common Property Resources
96
experienced by the rural households as well as the household attributes in the form of
capital and land, affects the extent to which the forest resources are used by them to
insure against the risk.
In a study in Honduras, Godoy et al. (2002) emphasised that in spite of the fact that
extraction of NTFP brings low annual earnings, it still plays an important role in
mitigating the unexpected loss due to agricultural risks. There is increased recognition
to the concept of ‘Natural Insurance’ where even a small amount of earnings from the
forest help to bridge the income gap and thereby acts as a safety net. Hence several
initiatives are taken by the forest communities to promote sustainable use of forest
resources. Takasaki et al. (2002) examined the vulnerability and responses to
covariate flood shock among peasant households in the Amazonian tropical forests.
The study reveals that forest extraction acts as a coping strategy against shocks. The
study also discussed that conservation and development initiatives can help to
mitigate shocks by targeting and deploying contingent support.
Several Indian literatures have also supported the view that common forest products
provide security or insurance against contingencies. Based on survey of semi-arid
regions in India, Jodha (1978) observes that the rural households adopt different
adjustment mechanism like reduction in consumption levels, asset depletion &
replenishment, periodic out migration and traditional informal cooperation. However,
the shortfall in agricultural production is mitigated by collection of common forest
products. Dasgupta and Mäler (1994) had emphasised that the common property
resources provide the rural poor with partial protection in time of unusual economic
stress. In the study of tribal groups of Bihar, Agarwal, (1991) revealed that
communally held forests provided the only means of subsistence during income
shocks. This natural insurance brings an important twist to the discussion by
connecting rural poverty in risky environments with environmental degradation
(Dasgupta, 1993; Duraiappah, 1996). During a localised drought in eastern Gujarat,
majority of the population who experienced acute shortage of food sold trees to buy
food and meet their subsistence (Conroy 1991). The smoothing of the income of the
households arising due to agricultural shocks is done by the labour markets allowing
the households to shift labour from farm to off-farm employment (Kochar 1999).
Agricultural Risk and Common Property Resources
97
The role of CPRs in rural livelihood is very critical and it act as safety nets especially
in times of agricultural crisis. In our study area of Bankura and Purulia districts of
West Bengal, agricultural activities are subjected to low fertility of soil, scarcity of
water and high dependence on weather. This results in wide variability in production
and productivity in agriculture. Due to the non-availability of alternate income
opportunities, the farmers in the study area fall back on Common Property Resources
to mitigate the agricultural risk.
Under this circumstance, the objective of our study is to determine how and to what
extent agricultural shortfall affects the collection of common forest resources.
Another important objective is to examine the inter relationship among agricultural
risk, non-timber forest collection and the extent of rural poverty.
5.2 Data and Methodology The study is based on primary data collected from field survey on Common Property
Resources conducted in Bankura and Purulia district in 2011. The field survey was
undertaken in 6 villages in the district of Bankura in West Bengal, India; viz.
Panjhoria, Ramjibanpur (Bandhghat), Seolibona, Baldanga, Dulaltora and
Tantirdanga and 3 villages in the district of Purulia in West Bengal, India; viz.
Jiyathole, Marbediya and Ambari. Total 300 households were surveyed.
Majority of the household respondents have listed agriculture as one of their primary
sources of income. Paddy is their main crop. Almost all the households use forest
products. Most of the households (80 percent) made major collection trips to the
forest in the surveyed year. On an average, the distance between common forest and
village community is 2-3 km and the households spend around 2.5 hours to collect the
forest products. During the survey, the households have indicated that they are very
much dependent on nature for all agricultural activities. The households face
agricultural risks which are primarily due to weather risk i.e. rising temperature,
erratic rainfall pattern and increase in severity of drought, flood and cyclones. The
rural households therefore depend on forest products to reduce the risk inherent to
subsistence agriculture. They are therefore dependent on the forest for food or
Agricultural Risk and Common Property Resources
98
medicine which they cannot produce or purchase. They also sell forest products such
as fuel wood, honey, fish and fruits in the market. The rural households also resort to
wage labour as an alternate source of income. However this is done only for a short
period of time. Moreover the rural households do not get the facilities of insurance
and credit. Therefore the rural households having limited credit and insurance
facilities have to depend on common property resources at the time of agricultural
crisis. These characteristic of the surveyed area makes it an ideal setting for testing
the hypothesis on natural insurance.
We have measured agricultural production in terms of rice equivalent production. We
have collected the data on agricultural production for the current year and last four
consecutive years to determine the agricultural shortfall and risk. We have also
collected the data on different types of CPR products which are collected by the rural
households and the total time spent for the collection of the forest products by each
family unit (monthly basis on an average) for the same period. We have used a tabular
method to quantify the pattern of agriculture and the relationship between agricultural
risk and common property resources in the surveyed area. The importance of CPR as
a safety net during agricultural risk has been analysed using count data model
technique.
5.2.1 Conceptual Framework
Based on the stylized features of the backward rural agrarian economy of Bankura
and Purulia district of West Bengal, an attempt has been made in this section to
develop a conceptual framework to examine the inter relationship between
agricultural crop failures and the collection of common property forest products
following the principles of ‘new home economies’ (Barnum and Squire 1979;
Pattanayak and Sills 2001). In other words, it attempts to examine the effect of
unexpected agricultural shortfall and the expected agricultural risks on the collection
of Non timber Forest Products. Efforts have also been made in this section to examine
the insurance strategy which causes over exploitation of the resources and finally
leads to a poverty trap.
Agricultural Risk and Common Property Resources
99
In poor agrarian economy the objective of the household is the maximization of utility
subject to full income constraint. Full income includes income from agricultural
production, off-farm wage income, collection of forest products and the opportunity
cost of leisure time (Pattanayak and Sills (2001). The households primarily depend on
agriculture (A). The off-farm wage income (W) and the collections of common forest
products (F) act as a supportive secondary livelihood activities to the rural poor.
The utility function of the household depends on the consumption of agricultural
products ( AC ), consumption of forest products ( FC ), leisure ( LC ) and a numeraire
( NC ) that represents all other commodities and the household characteristics affecting
preferences ( CH ).
The utility function is given by
( ), , ,A F L N Cu f C C C C H= ………………………………………………….(1)
Household maximize utility ( u ) subject to three constraints.
(i) Production function which assume that Agriculture and Forest products
depend on labour input ( AN and FN ), Capital input ( AK and FK ) and
household characteristics ( CH ) such as age, education, experience in
agricultural activities and collection of forest products. Hence the
agricultural and forest production function takes the form
( ), ,A A CA f N K H= …………………………..………………………..(2)
( ), ,F F CF f N K H= ………………………………………………..….(3)
Since the rural poor are very much dependent on nature for agricultural
activities, they are very often subjected to agricultural risk in the form of
drought, flood or attack of pests.
Hence our agricultural production function at equation (2) can be rewritten
as
( ), ,A A CA f N K Hξ= ………………………………………..………...(4)
where ξ measures agricultural risk
Agricultural Risk and Common Property Resources
100
(ii) The rural poor engage in different household activities in the form of
agriculture, forest collection, off-farm wage labour and leisure or other
household activities. A time constraint implies that sum of households’
allocation of labour into agriculture ( AN ), forestry ( FN ), off-farm wages
( WN ) and leisure or other household activities ( LN ) cannot exceed the
household endowment ( N ).
A F W LN N N N N+ + + ≤ ………………………………………….…….(5)
(iii) The budget constraint which shows the households’ expenditure measured
using existing prices must be less than the sum of net income (Y ) from
agriculture, forest collection, leisure, off-farm wage income, exogenous
income ( X ) and savings ( S ).
A A F F L NC P C P C C Y⋅ + ⋅ + + ≤ ……………………………………..…..(6)
A F L L W WA P F P N P N P X S Y⇒ ⋅ + ⋅ + ⋅ + ⋅ + + ≤ ……………………..(6a)
In equality form, we can rewrite the equation (5a) as follows:
( ) ,, , ( , )A A C A F F C F L L W WY A N K H P F N K H P N P N P X Sξ= ⋅ + ⋅ + ⋅ + ⋅ + +……………………………………………………………………….….(7)
( )1
1
t
t i Ai A Fi F Li Nii
S y C P C P C C X−
=
= − ⋅ − ⋅ − − −∑ …………….……….….(7a)
where y = net income
( ), ,A A C AA N K H Pξ ⋅ = income from agriculture
,( , )F F C FF N K H P⋅ = income from forest
W WN P⋅ = off-farm wage income
L LN P⋅ = income from leisure
X = exogenous income
S = Savings of the previous period
The poor inhabitants collect NTFPs not only to reduce the impact of future
agricultural shocks but also to smooth the income by adding it to current year’s
savings. In poor forest economy, forest collection appears to be a shock absorption
mechanism especially under the situation of crop failures. The poor inhabitants move
Agricultural Risk and Common Property Resources
101
to the forest for the collection of NTFP. However, in the good harvesting period, they
intend to generate surplus (savings) which act as an insurance for mitigating the future
agricultural risk.
Household maximises utility ( )u subject to the production constraints, time constraints
and budget constraints. Solving this maximising problem using the lagrangian
method, we can describe household forest collection in a reduced form of labour
demand equation.
( ), , ,dF N CN n P X H Fξ= …...………………………………….…(8)
where NP = Opportunity cost of time as measured by off-farm wages in a
complete market
ξ = Agricultural risks
X = Exogenous income
CH = Household characteristics
F = Forest quality
Demand is downward sloping in NP which reflects the opportunity cost of time and
the response to off-farm opportunities will be conditional upon , , CX Hξ and F .
In this chapter, we have tested the following hypothesis:
H1: CPRs act as a safety net especially in times of agricultural crisis, i.e.
there is a positive correlation between forest collection labour and
agricultural risk
Agricultural Risk and Common Property Resources
102
5.2.2 The Empirical Model Specification
CPRs, mainly forest products, play a vital role in the livelihood of the rural poor
residing in forest fringe areas. The extraction of forest products depend on the factors
relating to household and village characteristics i.e. family size, age, education, own
land, livestock unit, distance between forest and houses, etc. as well as agricultural
risk and shortfall of that year.
In the previous chapter, the reduced form of labour demand equation
( ), , ,dF N CN n P X H Fξ= allows us to test the signs and significance of the
coefficients. Our intention is to examine the impact of agricultural risk on the
collection of forest products.
To capture the impact of agricultural risk on CPR collection, we have considered the
Count Data model (for details see Chapter 3, Section 3.3.2). The Forest Collection
Labour is measured by the number of major forest collection trips during the survey
year. As the number of trips to collect forest products is a nonnegative, integer valued
variable, we have applied Count Data Model using STATA computer software
package. As the count model very often detect over dispersion or variance greater
than the mean, it is easier to estimate the parameter with maximum likelihood
techniques. First we have applied Poisson Regression model. The common alternative
of Poisson regression model is negative binomial which is a mixer distribution of the
Poisson with gamma heterogeneity.
In our survey area, majority of the households collect forest products. However in the
case of wealthier households, they are not themselves involved in forest collection
trips i.e. they take zero trips to the forest. Thus the number of zeros may be inflated
and the number of household taking zero trips cannot be explained in the same
manner as the number of households taking more than zero trips. A Standard Negative
Binomial model would not distinguish between these two processes, but a zero
inflated model allows for and accommodates this complication. To analyse such type
of data set, a Zero Inflated Negative Binomial model should be considered. A zero
Inflated Negative Binomial model assumes that zero outcomes is due to two different
Agricultural Risk and Common Property Resources
103
processes. In our case the two processes are that the household taking the trip to
collect CPR versus not taking any trip. If not taking any trip, the only possible
outcome is zero. If taking trips to collect CPR, it is then a count process. In zero
inflated Negative Binomial model we use the logistic distribution for the first stage
and Binomial distribution for the second stage. Here we have explained whether a
household takes any trips, as well as additional variables likely to explain whether a
household correctly reports whether it takes any trips. The expected count is
expressed as a combination of the two process.
E (to take a trip) = prob (not take any trip)*0 + prob (take any trips)* (E y x= take
any trip)
The Count Data regression model is specified as follows:
0 1 2 3 4 5FCL AGEHEAD SQAGEH FAMSIZE AVRSCH LIVESTOCKα α α α α α= + + + + + 6 7 8 9FORESTDIST AGRSHLFALL AGRIRISK WAGEα α α α+ + + + +∈
where FCL (Forest Collection Labour) is dependent Variable which is measured by
the number of major forest collection trips during the survey year. Here 0α is the
constant, iα are coefficients associated with the explanatory variables and ∈ is the
random disturbance term.
Explanatory variables, their description and expected correlations of the dependent
and independent variables are given in Table 5.1 below:
Agricultural Risk and Common Property Resources
104
TABLE 5.1
Description of Variables in Count Data Model Variable Name Variable Description Expected
sign
Dependent
Variable
1. FCL
Forest Collection labour which is measured by the
total number of major forest collection trips during
the survey year
Explanatory
Variables
1. AGEHEAD
Age of household head (in years) +
2. SQAGEH Square age of household head which is a measure
of experience in collecting CPR +
3. FAMSIZE Average number of population of the household
(Size of the family) +
4. AVRSCH Education which is measured by the average years
of schooling (number of years) of household -
5. LIVESTOCK Number of livestock owned by the household
converted into animal units
-
6. FORESTDIST Distance of the CPR field from the residence of
the household (km) as measure of forest quality -
7. AGRSHTFALL
Agricultural shortfall which we measure from the
actual agricultural production in terms of rice (Rs)
in the survey year and Mean agricultural
production in one normal year, i.e.
+
8. AGRIRISK Coefficient of variation of agricultural prod. over
the last 5 years is a measure of agricultural risk
+
9. WAGE Annual Wage income of the household (in Rs) -
Agricultural Risk and Common Property Resources
105
5.3 Results and Discussion
5.3.1 Nature of Agriculture in Bankura & Purulia district Bankura district is part of the Burdwan Division in the state of West Bengal with an
area of 688200 hectare. It has primarily two Agro Climatic Zones, viz. undulating red
& lateritic zone and Vindhyan Alluvial Zone. The red & lateritic zone has a tropical
dry sub-humid climate with rainfall ranging from 1100 mm to 1400 mm. Here the
soils are well drained and susceptible to soil erosion. Agriculture in this zone is
mainly dependent on rain. This region has undulating moulds interspersed with rocky
hillocks, with much of the rural area covered with scrub jungles and sal woods. In the
Vindhyan Alluvial zone, the soil is deep, texturally medium fine and moderately well
drained. In this region more than one crop is harvested by utilizing canal irrigation
and ground water. The seasons in Bankura are generally distributed as ‘hot summer’
(April-May), ‘monsoon’ (June-September) and ‘cold season’ (November-
February). The humidity is usually medium to high throughout the year and the
rainfall, though not heavy, is usually well distributed. The rainy months are generally
July and August.
Agriculture accounts for almost 70 percent of the district's income. Most of the
farmers are small & marginal. A vast area of Bankura district is not cultivable due to
undulation of land. However, some land is fertile and due to availability of sufficient
water from canal or deep tube wells, cultivation is done. Several small artificial water
reservoirs (barrage) are also available. Bankura district has a net cultivable land of
around 4.3 lakhs hectare and around 4.47 lakhs cultivators. About 46 percent of the
net cropped area is covered under irrigation. The principal crop of Bankura district is
paddy, wheat, oil seeds and vegetables. The different varieties of paddy cultivated are
Aus, Aman and Boro. Aman is the largest variety of paddy cultivated in an area of
311403 hectare. Most of the pre-kharif and kharif rice are grown in rain fed condition.
Wheat is second most important cereal crop in the district and it is cultivated in
limited irrigated areas. Rape seed, mustard and sesame are important oil seeds grown
in this district. Sesame is cultivated in 3 seasons while Rape & Mustard is cultivated
during Rabi season. Potato is also cultivated in large parts of the district. Agriculture
is largely dependent on the vagaries of monsoon.
Agricultural Risk and Common Property Resources
106
Drought constitutes a major hazard in the district. Intermittent gaps of precipitation
and moisture stress during the monsoon gives rise to serious setback in production
during the kharif, which is the main stay of agriculture in the district.
Purulia district has all India significance because of its tropical location and funnel
shape which funnels the tropical monsoon current from the Bay to the sub-tropical
parts of North West India. Agriculture is the main source of livelihood in the district
of Purulia. About 70 percent of the total agricultural holdings belong to small and
marginal farmers. Most rural households practice subsistence farming under adverse
and risky environmental conditions. The natural resource base can be characterized as
poorly suited to agriculture due to climatic, water resource, and soil conditions. Due
to the topography of the districts, the rivers Kanshabati, Damodar and Dwarakeshwar
flowing provide little irrigation facilities.
Soil erosion and erratic and scanty rainfall are the major stumbling block in successful
irrigation in the district. Irrigation is mainly done through with the help of tanks and
bundhs, which are embankments of accumulated run-off rain water. Cultivation is
predominantly done on a single crop. Paddy is the main crop of the district. Around
50 percent of the total land is under net-cropped area. Almost 77 percent of the net-
cropped area is under the cultivation of Aman paddy. The other varieties of rice
grown are Aus and Boro. Besides paddy, maize, sugarcane, groundnut and pulses are
other important crops grown in this district. The growth in production of cereals for
the period 1980-1987 was 42 percent. In the production of pulses, there has been a
growth of 12 percent in the same period. However, the high potentiality of pulse is
marred by its poor yield (http://www.puruliazp.in).
The agricultural production in Bankura and Purulia districts vis a vis the state of West
Bengal for the year 2008-09, 2009-10 and 2010-11 is illustrated in Figure 5.1 below:
Agricultural Risk and Common Property Resources
107
FIGURE 5.1
Index Number of Agricultural Production (Cereals) (Base: Triennium ending crop year 1981-82 =100)
Source: Bureau of Applied Economics and Statistics, Government of West Bengal
The agricultural production (cereals) have been depicted in terms of Index number
with a base of Triennium ending crop year 1981-82=100. As is evident from the
figure above, the production of cereals has shown a downward trend for the years
2008-09, 2009-10 and 2010-11 in the districts of Bankura and Purulia. Similar trend
in production of cereals is also observed in the state of West Bengal. The drastic
lowering of production of cereals during the period 2010-11 in the districts of
Bankura and Purulia can be attributed to meager rainfall during the said period. It is
further observed that during the year 2010-11, the production of cereals in Bankura
and Purulia is much lower as compared to that for the State average. This is primarily
due to the fact that compared to other districts; Bankura and Purulia are dependent
entirely on rainfall for cultivation.
2008-09 2009-10 2010-11Bankura 229.81 225.69 115.49Purulia 303.31 244.43 110.11West Bengal 234.40 225.40 211.50
229.81 225.69
115.49
303.31
244.43
110.11
234.40
225.40 211.50
0
50
100
150
200
250
300
350In
dex
No.
of A
gric
ultu
ral P
rodu
ctio
n (C
erea
ls)
Year
Bankura
Purulia
West Bengal
Agricultural Risk and Common Property Resources
108
Land ownership pattern of the surveyed villages and the levels of inequality in the
distribution of ownland and operated land have been shown in Appendix –IV.
5.3.2 Agricultural Productivity Paddy is the main crop cultivated in the study area. Besides paddy, oil seeds and few
vegetables like potato are also grown. The surveyed villages are prone to drought;
their lands are infertile; and they lack proper irrigation facilities. Hence the villagers
cultivate only single crop annually. The agricultural productivity in the surveyed
villages in the district of Bankura and Purulia is illustrated in Table 5.2 below:
In agriculture, crop yield (Y), also known as "agricultural output", refers to the
measure of the yield of a crop per unit area of land cultivation. The unit by which the
yield of a crop is measured is kilogram per hectare.
From the Table 5.2, we observe that in the 6 villages of Bankura the yield ranges from
2037 to 2213 kg/hectare. The average yield in the surveyed villages of Bankura is
2144 kg/hectare. In contrast, in the 3 villages of Purulia the yield ranges from 1254-
TABLE 5.2
District Block Name of village
Total area of operated land (L)
(Hectare)
Total Agricultural Production in 2010 (P)
in terms of Paddy (Kg)
Crop Yield (Y) = P / L
(Kg/Hectare)
Panjhoria 7.06 14,610 2,069Ramjibanpur 5.74 12,000 2,091Seolibona 15.59 34,440 2,209Baldanga 1.60 3,500 2,188Dulaltora 6.20 13,720 2,213Tantirdanga 6.48 13,200 2,037Jiyathole 20.09 34,680 1,726Marbediya 8.83 11,074 1,254Ambari 12.72 20,606 1,620
42.67 91,470 2,14441.64 66,360 1,59484.31 157,830 1,872
Source: Field Survey, 2011300
Bankura TotalPurulia TotalGrand Total
2544150150
182581
Crop Productivity
Household category
(no. of HH)
2620547
Bankura
Purulia
Saltora
Santuri
Agricultural Risk and Common Property Resources
109
1726 kg/hectare. The average yield in the surveyed villages of Purulia is 1594
kg/hectare. From the Table we can infer that the average yield of Purulia is lower than
that of Bankura. The average yield of the 9 surveyed villages is 1872 kg/hectare,
which is significantly low as compared to the state average of 2708 kg/hectare in
2010-11.
The various agricultural implements used by the households in the surveyed villages
of Bankura and Purulia are shown in Table 5.3 shown below:
From the Table 5.3, we can infer that most of the households in the villages surveyed
in the district of Bankura and Purulia own wooden plough and a pair of bullock. The
households that do not own the wooden plough or bullock hire them for use as
agricultural implements. Power tiller and pump set are available with very few
households. Majority of the households have hired the thresher. From the Table we
can conclude that majority of households are so poor that they cannot afford to
purchase mechanised agricultural implements like Power tiller and pump set. The
households are totally dependent on wooden plough and bullocks for cultivation.
The engagement of labour in the process of sowing, weeding and harvesting in the
surveyed villages is tabulated in the Table 5.4 is shown below:
TABLE 5.3
Owned Hired Owned Hired Owned Hired Owned Hired Owned Hired110 29 214 52 0 5 10 5 21 161 2118 24 236 47 0 0 3 1 26 98 1228 53 450 99 0 5 13 6 47 259 3
Source: Field Survey, 2011
150150300
BANKURAPURULIA
TOTAL
District Others
Agricultural Implements used
Household category
(no. of HH)
Wooden Plough Bullock Power tiller /
Tractor Pumpset Thresher
Agricultural Risk and Common Property Resources
110
As is evident from the data on agricultural labour (Table 5.4) in the surveyed villages
in the district of Bankura and Purulia, both men and women participate in agriculture
in process of sowing, weeding and harvesting. Further it is also observed that children
are also involved in agricultural labour in several surveyed villages. It is also seen that
several households hire labour for sowing, weeding and harvesting.
5.3.3 Labour allocation in CPR collection The labour allocation in CPR collection in the surveyed villages of Bankura and
Purulia district is shown in Table 5.5 below:
TABLE 5.4
Male Female Child Hired Male Female Child Hired Male Female Child Hired
Total 150 188 141 21 48 187 129 21 38 187 113 19 39
Total 150 201 156 19 19 201 138 19 16 201 101 19 13
Total 300 389 297 40 67 388 267 40 54 388 214 38 52Source: Field Survey, 2011
Bankura Total
Grand Total
Purulia Total
Agricultural Labour
Household category
(no. of HH)
Labour engaged in Sowing Labour engaged in Weeding Labour engaged in HarvestingDistrict
TABLE 5.5
Male Female Child Male Female Child
Source: Field Survey, 2011
1445
180 63.77
46 20722 1862 65.4337087
57.06417
74.33
90 51.84
147 47.98
10521
17406
19681
4425
1545 2460
Purulia Total 150 196 262 12 8996
388 478
Purulia
Grand Total 300
Bankura Total 150 192 216 34 11726
Ambari 44
Marbediya 25 34 43 2
59 73 8 2145
110 75.7620 26 3 1350
170 68.41
2252
5306
1911 2776
Jiyathole 81
Tantirdanga 25
Dulaltora 18
103 146 2
31 35 5
7080 1165 68.79
0 79.710 615Baldanga 7 9 8 740
24 0 1828Ramjibanpur 20
74 84 26 4412Seolibona 54
Santuri
Labour allocation in CPR collection
District Block Name of village
No. of Households
No. of Household members involved in
CPR collection
Monthly collection time in CPR collection
(in Hours)
Average Monthly CPR collection time
per member (in Hours)
32 39 0 1610Panjhoria 26
Bankura Saltora
2764
4069 0 79.99
0 91.8426
Agricultural Risk and Common Property Resources
111
From the data on labour allocation in CPR collection (Table 5.5) surveyed villages in
the district of Bankura and Purulia, we observe that both male and female household
members are involved in the CPR collection. It is further observed that the female
members are more involved as compared to the male in CPR collection. In few
households, the child member is also involved in CPR collection. In the year 2010,
the average monthly CPR collection time per household member was 74.33 hours (i.e.
an average of 2.5 hours per day per household member) in Bankura district and 57.06
hours. (i.e. close to 2 hours per day per household member) in Purulia district, which
is higher than normal. Due to low rain fall in 2010, the agricultural production was
less. Hence we can presume that the labour time normally allotted to agriculture was
utilised in CPR collection.
5.3.4 Agricultural Risk and CPR CPR extractions play an important role in agricultural risk management. In order to
justify the relationship between agricultural risk and CPR extraction, we have
collected data on agricultural production for three years 2008, 2009 and 2010 and
measured the agricultural shortfall. The fluctuation in agricultural production is
observed to have an immediate impact on CPR collection.
PHOTO 6 PHOTO 7
CPR collection by villagers of Ramjibanpur in Bankura district
The level of agricultural production and collection of CPR in the study area over the
three years period is shown in Table 5.6 below:
Agricultural Risk and Common Property Resources
112
The agricultural production is taken in terms of production of rice. The average cost
of rice is taken as Rs 20/- per kg. From the Table 5.6 we can infer that the annual
production of rice in the surveyed villages of Bankura district for the year 2008 is
around Rs 26.3 lakhs, for 2009 it is around Rs 24.9 lakhs and for the year 2010 it is
around Rs 18.2 lakhs. In Purulia district the annual average agricultural production for
the year 2008, 2009 and 2010 is around Rs 18.1 lakhs, 17 lakhs and 13.2 lakhs
respectively. The agricultural production for the year 2010 is drastically lower than
that for the year 2008. This is primarily due to the fact that the rainfall during the year
2010 was much below normal. The percentage of CPRs collection with respect to the
agricultural production in the surveyed villages of Bankura district for the year 2008,
2009 and 2010 is 35 percent, 42 percent and 76 percent respectively and for Purulia it
is 43 percent, 53 percent and 84 percent respectively . We can therefore conclude that
due to low agricultural production in the year 2010, the rural households were
compelled to collect more common from property resources as compared to year 2009
and 2008.
TABLE 5.6
Agricultural Production
(in Rs)
CPR Collection
(in Rs)
Percentage of CPR collection
with Agricultural Production
Agricultural Production
(in Rs)
CPR Collection
(in Rs)
Percentage of CPR collection
with Agricultural Production
Agricultural Production
(in Rs)
CPR Collection
(in Rs)
Percentage of CPR collection
with Agricultural Production
Source: Field Survey, 2011
353733 31
62
316259
264000
693720
500178 143366
86274 62194
904407 332823 37
240000
688800869169 387962 45
109549
85022 52682
167733 47437156
177720
57310
74
537005 78
82
274400
39
2009
1829400
1327320
2516456 80
1395265 76
1121191 84
31567201944138 46
126354 40
178090 65
142693 33
139877
1815071
45
42
1706696 897347
1046791
53
537240 237781 44
4198997
783788 43
Ambari 44
Bankura Total 150
571231 207437
2631296 928278 35 2492301
4446367 1712066
36
150
Purulia Santuri
Ramjibanpur 20
Dulaltora 18
Name of village
No. of Households
Panjhoria 26
Grand Total 300
Purulia Total
Marbediya 25
SaltoraSeolibona 54
Agricultural Production and CPR collection
292200380092 162673 43
2010
83
District Block
241967
2008
350800 147165 42
Bankura
77
72
29482370
358137
29
70000Baldanga 7
613601 88
87
314170 76412120
221480
Tantirdanga 25
Jiyathole 81
193420
861488 520611 60926560 454379 49
317280 121972
203173
307968 13895538
Agricultural Risk and Common Property Resources
113
The pictorial representation of agricultural production and CPR collection in the study
villages of Bankura and Purulia district is shown in Figure 5.2 & 5.3 respectively
below:
FIGURE 5.2
FIGURE 5.3
Agricultural Risk and Common Property Resources
114
In order to establish the critical role of CPR during agricultural crisis, we measured
the agricultural shortfall for the year 2008, 2009 and 2010. Agricultural shortfall is the
measure from the actual production (Rs) in terms of rice and the mean agricultural
production (Rs) in one normal year. The level of agricultural shortfall and collection
of CPR in the study area over the three years period is shown in Table 5.7 below:
It is evident from the Table 5.7 that there was no agricultural shortfall in both districts
in the year 2008, since 2008 has been considered as the normal year. However, in the
year 2009 and 2010 the agricultural shortfall has shown an increasing trend. In the
year 2010, the agricultural production variability resulted in agricultural shortfall of
Rs 209032 in Bankura and Rs 164391 in Purulia as compared to the normal year
(2008). The Table shows a positive relationship between agricultural shortfall and
CPR collection in our surveyed villages over the three years period. It is further
observed that even during the period of no agricultural shortfall, household extract
CPRs in order to generate surplus income to mitigate future agricultural risk.
TABLE 5.7
Agricultural Shortfall
(in Rs)
CPR Collection
(in Rs)
Agricultural Shortfall
(in Rs)
CPR Collection
(in Rs)
Agricultural Shortfall
(in Rs)
CPR Collection
(in Rs)
Source: Field Survey, 2011
241967
Bankura Saltora
Panjhoria 26 147165(7395)
Agricultural Shortfall and CPR collection
District Block Name of village
No. of Households
2008 2009 2010
177720
Seolibona 54 330 332823 387962
Ramjibanpur 20 (615) 109549 126354
57310
Dulaltora 18 143366
537005
Baldanga 7 (1050) 52682 62194
89910
22119
5314
314170
193420
Ambari 44 207437 237781
Marbediya 25 121972 138955 13246
4507210442
Jiyathole 81 454379
167733 203173
139877 178090
Tantirdanga 25 142693
106073520611
2516456
897347 1121191
Grand Total 300 1712068
1395265
Purulia Total 150 783789
209032
164391
373423
90795
84321
175116
Bankura Total 150 928279 1046791
1944138
Purulia Santuri
162673
613601
Note: The figures in bracket indicate negative shortfall which implies that agricultural production is more than mean.
26009
25129
40549
(2880)
(17925)
909
5619
56351
1515
12415
13986
74071
(191)
(3885)
(2430)
2205
(3870)
(1215)
(15045)
Agricultural Risk and Common Property Resources
115
The pictorial representation of agricultural shortfall and CPR collection in the study
villages of Bankura and Purulia district is shown in Figure 5.4 & 5.5 respectively
below:
FIGURE 5.4
FIGURE 5.5
Agricultural Risk and Common Property Resources
116
5.3.5 Association between CPR extraction and
Agricultural Risk: Count Data Regression Model We have assumed that forest collection labour is determined not only by socio-
economic, demographic variable but also by agricultural shock and agricultural risk.
We have explained the determinants of forest collection labour through econometric
analysis to give an understanding of the impact of agricultural production risk on the
extraction of forest products following Pattanayak and Sills (2001). The regression
models outlined earlier have been tested using household level data collected through
field survey in Bankura and Purulia district, West Bengal. We have collected several
information including the variables relating to the determinants of forest labour
collection. We have applied Count Data Model using Stata Computer package to
determine the frequency of forest collection trips. We have considered Poisson,
Negative Binomial and Zero-Inflated Negative Binomial Regression Models to
analyse our surveyed data.
The result for the determinants of forest collection labour is given in the following
Tables 5.8 & 5.9 below:
TABLE 5.8
No. of Obs.=115 No. of Obs.=115 No. of Obs.=115LR chi2(9) = 1794.06 LR chi2(9) = 33.41 Inflamation model=logit Prob > chi2 = 0.0000 Prob > chi2 = 0.0001 LR chi2(9 = 59.27 Prob > chi2 = 0.0000Log likelihood = -2265.9415 Pseudo R2= 0.2836 Log likelihood = -693.68771 Pseudo R2= 0.0235 Log likelihood = -612.0182
VARIABLECoeffi cient
Std. Error Z P>|Z| Coeffi cient
Std. Error Z P>|Z| Coeffi cient
Std. Error Z P>|Z|
AGEHEAD .0004 .0027 0.16 0.876 -.0060 .0220 -0.27 0.786 .0006 .0122 0.05** 0.032SQAGEH -.0000 .0000 -1.64*** 0.101 .0000 .0002 0.03 0.979 -.0000 .0001 -0.44*** 0.104FAMSIZE -.0415 .0058 -7.12* 0.000 -.0542 .0493 -1.10 0.272 .0051 .0279 0.18 0.856AVRSCH -.0340 .0040 -8.38* 0.000 -.0335 .0344 -0.97 0.331 -.0146 .0197 -0.74** 0.016LIVESTOCK .0231 .0057 4.00* 0.000 -.0423 .0473 0.89 0.371 .0133 .0269 0.49 0.622FORESTDIST -.0849 .0089 -9.53* 0.000 -.0731 .0702 -1.04 0.298 -.0203 .0407 -0.50 0.617AGRSHTFALL .0013 .0001 13.81* 0.000 .0016 .0010 1.68*** 0.093 .0026 .0006 4.64* 0.000AGRIRISK .0106 .0008 13.24* 0.000 .0298 .0087 3.48* 0.001 .0070 .0053 1.33** 0.012WAGE -.0000 .0000 -3.55* 0.000 -.0000 .0000 -0.34 0.737 -.0000 .0000 -2.05** 0.041Constant 5.2530 .0757 69.40* 0.000 4.6134 .6477 7.12* 0.000 5.1012 .3689 13.83* 0.000
Likelihood-ratio test of alpha=0: Vuong test of zinb vs. standard negative binomial:
chibar2(01) = 3144.51 Prob>=chibar2 = 0.000 z = 2.28 Pr>z = 0.00112
*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent levelSource: Estimated by Stata 8 Computer Software using Field Survey Data of 2011
Forest Collection as a Function of Agricultural Risk(Bankura District)
POISSON REGRESSION NEGATIVE BINOMIAL ZERO INFLATED NEGATIVE BINOMIAL
Agricultural Risk and Common Property Resources
117
From the above Tables 5.8 and 5.9, we have observed that the results are consistent
for most of the variables in both the districts. However our choice of the best model is
Zero Inflated Negative Binomial model (ZINB). To compare the negative binomial
and ZINB model, we apply the Vuong statistic. The Vuong test compares the ZINB
model with a standard Negative Binomial model. A significant Z test indicates that
the Zero Inflated Negative Binomial model is better. Hence we have turned to the
estimated results of ZINB model. We have detected that the association between
forest collection trips and age of the household ( AGEHEAD ) is positive and square
age of the household head ( SQAGEH ) is negative. In our analysis of both the
districts, we have observed significant result. The coefficients on age and the square
of age imply that households with older heads normally take more trips on forest
collection except the oldest household. Household’s accumulated knowledge about
the local forest make it easier for them to take more trips and collect huge amount of
forest products. In fact, younger generation are more comfortable with commercial
substitutes of the traditional forest product. Almost all the household members in the
study area collect CPRs. Hence the larger the family size ( FAMSIZE ) the more is the
forest trip for the collection of CPR products. The result is significant in Purulia
district, but insignificant in Bankura district. Education ( AVGSCH ) i.e. the average
TABLE 5.9
No. of Obs.=123 No. of Obs.=123 No. of Obs.=123LR chi2(9) = 5537.03 LR chi2(9) = 103.55 Inflamation model=logit Prob > chi2 = 0.0000 Prob > chi2 = 0.0000 LR chi2(9) = 54.42 Prob > chi2 = 0.0000Log likelihood = -1949.7648 Pseudo R2= 0.5868 Log likelihood = -724.67487 Pseudo R2= 0.0667 Log likelihood = -605.0434
VARIABLECoeffi cient
Std. Error Z P>|Z| Coeffi cient
Std. Error Z P>|Z| Coeffi cient
Std. Error Z P>|Z|
AGEHEAD 0.0071 0.0026 2.71* 0.007 0.0086 0.0219 0.39 0.695 0.0029 0.0105 0.28*** 0.082SQAGEH -0.0001 0.0000 -3.78* 0.000 -0.0002 0.0002 -0.67 0.505 -0.0001 0.0001 -0.44*** 0.059FAMSIZE 0.0362 0.0035 10.44* 0.000 0.0940 0.0346 2.72* 0.007 0.0276 0.0155 1.78*** 0.075AVRSCH -0.0294 0.0042 -7.06* 0.000 -0.0695 0.0345 -2.02** 0.044 -0.0133 0.0174 -0.76 0.446LIVESTOCK -0.0059 0.0035 -1.66* 0.098 -0.0372 0.0333 -1.12 0.265 0.0004 0.0158 0.02 0.981FORESTDIST -0.7177 0.0163 -44.12* 0.000 -1.1101 0.1268 -8.75* 0.000 -0.4932 0.0688 -7.17* 0.000AGRSHTFALL 0.0000 0.0000 0.22 0.825 0.0002 0.0004 0.40 0.690 0.0001 0.0002 0.74*** 0.058AGRIRISK 0.0128 0.0007 17.96* 0.000 0.0360 0.0088 4.08* 0.000 0.0094 0.0036 2.60* 0.009WAGE -0.0000 0.0000 -4.73* 0.000 -0.0000 0.0000 -2.24** 0.025 -0.0000 0.0000 -0.65 0.515Constant 5.9812 0.0633 94.53* 0.000 6.1501 0.5241 11.73* 0.000 5.7847 0.2535 22.82* 0.000
Likelihood-ratio test of alpha=0: Vuong test of zinb vs. standard negative binomial:
chibar2(01) = 2450.18 Prob>=chibar2 = 0.000 z = 3.21 Pr>z = 0.0007
*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent levelSource: Estimated by Stata 8 Computer Software using Field Survey Data of 2011
Forest Collection as a Function of Agricultural Risk(Purulia District)
POISSON REGRESSION NEGATIVE BINOMIAL ZERO INFLATED NEGATIVE BINOMIAL
Agricultural Risk and Common Property Resources
118
years of schooling of the household has a negative impact on forest collection trips in
both the districts which indicated that households who are better educated get better
job opportunities and therefore are less interested in collecting CPR during
agricultural crisis. The result is significant only in Bankura district.
We have predicted that both wage income and the number of livestock have a
negative impact on forest collection trip to extract NTFPs. In line with our
expectation, wage income (WAGE ) is negatively related with forest collection labour.
The result is statistically significant in Bankura district, but insignificant in Purulia
district. In fact households who have a sufficient wage income are less interested in
forest collection trip. Thus creation of job opportunities in non-farm sectors is
expected to have a significant role in CPR extractions. However, the coefficient of
livestock ( LIVESTOCK ) is observed to be positive, but insignificant. The positive
relation indicates that the household with larger size of livestock take more trips to the
forest in order to gather fodder to feed their farm animals. We have further observed
that forest distance ( FORESTDIST ) i.e. the distance between the residence and the
common forest area have a negative relationship with major forest collection trips.
From our study area, we infer that household who live nearer to the common forest
area extract more CPR and hence generate more income from it and thus help to
mitigate agricultural crisis. Household who live farther away from the forest area are
unable to smooth their income and consumption by collecting CPR products during
agricultural shock. However, the result is significant only in Purulia district.
The key findings of our regression results indicate that the coefficients on agricultural
risk parameters ( AGRIRISK ) and shock parameter ( AGRSHTFALL ) are positive and
significant in both Bankura and Purulia district, which suggest that household with
greater agricultural shortfall and risk are likely to take more forest collection trips.
This result supports our hypothesis that CPR product is used by rural households as a
safety net during the time of agricultural crisis. Thus CPRs help to mitigate
agricultural risk by smoothening the income and consumption of the rural poor.
Agricultural Risk and Common Property Resources
119
5.4 Conclusion
In this study we investigated the impact of agricultural risk on the collection of
common forest products based on our surveyed villages of Bankura and Purulia. Most
of the households in the surveyed area are very poor. Agriculture is their main
occupation and therefore they depend on nature for any agricultural activities. There
are several agricultural risks associated such as adverse weather, seasonal flooding,
unpredictable soil quality, crop diseases, price shocks, etc. The rural poor have limited
credit and insurance facility and therefore they extract forest products not only to
reduce their agricultural risk but also help to smooth their income. As per the
surveyed data, the percentage of CPR collection with respect to agricultural
production is very high in the year 2010 due to lower agricultural production as
compared to the previous years 2008 and 2009.
Since agricultural practice in the study area is backward in nature and subjected to
weather risk in the form of agricultural shock in times of production shortfall, the
farmers fall back upon CPRs for their survival and also addresses significantly their
agricultural risk. As established from our field data in the year 2010, the agricultural
production variability resulted in agricultural shortfall of Rs 209032 in Bankura and
Rs 164391 in Purulia as compared to the normal year (2008). Interestingly, it is
observed that during this period the extraction of CPR is also high compared to the
normal year. To capture the impact of agricultural risk on CPR collection, we have
considered the Count Data model using STATA computer software package. The key
findings of our regression results indicate that the coefficients of agricultural risk
parameters ( AGRIRISK ) and shock parameters ( AGRSHTFALL ) are positive and
significant in both Bankura and Purulia districts, which suggest that household with
greater agricultural shortfall and risk are likely to take more forest collection trips, i.e.
the result goes with the hypothesis..
Hence we can conclude that CPRs supplement the rural livelihood and act as safety
net for the poor seasonally or during the agricultural crisis.
Common Forest and Participatory Management
120
CHAPTER 6
COMMON FOREST AND PARTICIPATORY MANAGEMENT
6.1 Introduction
A majority of the rural poor in India are largely dependent on common property
resources such as forest resources. Forests contribute extensively to the social and
economic well-being of the rural poor. One fifth of the land area of India is covered
by forest. As per World Bank Report (2006), an estimated 275 million people in rural
areas of India depend largely on forests. According to the report, about half of India’s
89 million tribal people, the most disadvantaged section of the Indian society, live in
forest fringe areas, and they tend to have close cultural and economic links with the
forest. Forest products, including the non-timber forest products (NTFPs) like food,
fruits, flowers, medicines etc., provide the means of subsistence for the rural poor.
Forest dwellers, which also include a high proportion of tribal, are among the poorest
and most vulnerable groups in society.
The incentive involved in common property resource management was first
established by Gordon (1954) and later by Hardin (1968). Hardin formulated that over
exploitation of the common property resources led to depletion of shared limited
resources as several individuals acted independently in their own self-interest. The
concept of decentralized collective management of the common property resources
was postulated by Berkes (1989) and Ostrom (1990). Ostrom (1990) was of the view
that the CPR management would be successful if there were defined boundaries, an
efficient and effective conflict-resolution and monitoring mechanism. Gibbs and
Bromley (1989) have advocated that institutions, through rights and rules, provide
incentives for the group members to take certain actions to achieve a desired outcome.
Common property rights and Hardin’s theory of ‘The Tragedy of the Commons’ has
been discussed in Appendix-V.
Several empirical researches dealing with the dependence of common property
resources of the rural poor were conducted in different regions in India. Noteworthy,
Common Forest and Participatory Management
121
amongst them are the Jodha (1986), Iyengar (1997), Beck and Ghosh (2000), whose
theories highlighted the danger of depletion of the common property resources due to
pressure from privatization. The importance of participatory management in resolving
the crisis of CPRs in India was postulated by Chopra et al. (1989). On the study of
historical perspective of Joint Forest Management, Sarker and Das (2006) observed
that resistance movement of the forest communities in Midnapore in West Bengal was
the key to the success of the Joint Forest Management programme. The authors were
also of the view that the immediate survival needs, generating mainly subsistence and
income from non-timber forest products (NTFPs) for the forest protection committee
members were the key to the long term sustainability of Joint Forest Management
system. Balloni (2002), in the study of participatory forest management in India
stresses on the need for equity in the participation and representation of the
marginalised classes (poor and women) with equal benefit between the forest
department and the forest communities. The author also suggests the need for
formulation of new and effective silvicultural practices in order to increase the
productivity of Non-Timber Forest Products (NTFPs).
Highlighting the extent of forest cover and the practice of JFM across the states of
India in general and West Bengal in particular, this chapter assess the nature of
participation in forest management and examines the relationship between forest
dependency and participation in forest management in our study area. The chapter
also focuses the relationship between the intensity of management practice and the
degradation of forest resources.
6.2 Data and Methodology
In our study area we have captured demographic and socio-economic characteristics
of rural households, their dependence on forest products and their participation and
involvement in Joint Forest Management. On the basis of this information, we have
analysed how the socio-economic factors and dependency on forest products affect
the level of participation and collective action in Joint Forest Management. We have
used a tabular method to quantify the Forest Management and enforcement of forest
protection scheme by comparing JFM in 9 villages of these two districts. We have
Common Forest and Participatory Management
122
used both statistical and econometric techniques (Censored Tobit Model) to analyse
the determinants of collective action in JFM and to test several hypotheses. Logit
Regression Method was used to analyse the nexus between active forest management
and forest degradation.
In this chapter we have tested the following hypothesis:
H1: There is a strong relationship between forest dependence and active
participation in JFM
H2: Active participation in forest management plays a positive role in
alleviating the environmental degradation.
6.3 Forest Cover and its Management in India: An Interstate
Analysis
6.3.1 Forest Cover in India
The forest resources play an important role in the environmental and ecological
security of India. Indiscriminate and massive (approximately 4.3 million hectare)
diversion of forest land during 1950-1980 for non-forestry purposes, necessitated the
need for conservation and development of the forest resources. This led to the
enactment of the Forest Conservation Act, 1980 whose primary objective was to
provide a higher level of protection to the forests and to regulate diversion of the
forest land in India for non-forestry activities. However there is depletion of the forest
resources in view of increasing population pressure and development activities.
The forest cover as per Forest Survey of India (FSI) denotes all lands which have a
tree canopy density of more than ten percent when projected vertically on the
horizontal ground, with a minimum areal extent of one hectare. The assessment of
forest cover of the entire country is carried out by FSI at an interval of two years by
interpretation of satellite data. The classification scheme adopted in the assessment is
shown in Table 6.1 below:
TABLE 6.1
Very Dense Forest All lands with tree canopy density of 70% and aboveModerately Dense Forest All lands with tree canopy density between 40% and 70% Open Forest All lands with tree canopy density between 10% and 40% Scrub Degraded forest lands with canopy density less than 10%Non-Forest Area not included in any of the aboveSource: ISFR 2011
Forest Density Classification
Common Forest and Participatory Management
123
As per the India State Forest Report 2011 (ISFR 2011), the forest cover assessment is
based on the IRS P6 LISS-III satellite data in digital form corresponding to the period
October 2008 to March 2009 procured from National Remote Sensing Centre (NRSC)
at Hyderabad.
The Forest Cover Map of India depicting the forest cover in all the states and Union
Territories is shown below:
MAP 6.1
Forest Cover Map of India
Source: India Forest Report 2011
Common Forest and Participatory Management
124
As per the assessment of 2011 (ISFR 2011), the forest cover is shown in Table 6.2
below:
As per the assessment of 2011, the total forest cover is 692,027 km2 which is 21.05
percent of the total geographical area of the country. Based on the density classes, the
area covered by Very Dense Forest is 83,471 km2 (2.54 percent), Moderately Dense
Forest is 320,736 km2 (9.76 percent) and Open forest is 287,820 km2 (8.75 percent).
The forest cover in the different Indian states and Union territories of India is shown
in the Table 6.3 below. As per the FSI assessment of 2011, in terms of area-wise,
Madhya Pradesh has the largest forest cover (77,700 km2) followed by Arunachal
Pradesh (67,410 km2), Chhattisgarh (55,674 km2), Maharashtra (50,646 km2) and
Orissa (48,903 km2). However, in terms of percentage of forest cover to the total
geographical area, the state of Mizoram is the highest with 90.68 percent, followed by
Lakshadweep (84.56 percent), Andaman & Nicobar Islands (81.51 percent),
Arunachal Pradesh (80.50 percent), Nagaland (80.33 percent), Meghalaya (77.02
percent) and Tripura (76.07 percent).
The change in the forest cover state wise of the assessment of 2011 as compared to
the assessment of 2009, indicates that there is positive changes in the states of Punjab
(100 km2), Jharkhand (83 km2), Tamil Nadu (74 km2), Andaman & Nicobar (62 km2),
Rajasthan (51 km2) and Orissa(48 km2). However, it is also observed that has been
negative changes in the states of Andhra Pradesh (281 km2), Manipur (190 km2),
Nagaland (146 km2), Arunachal Pradesh (74 km2), Mizoram (66 km2) and Meghalaya
(46 km2).
TABLE 6.2
Class Area (Sq. Km) Percentage of Geographical AreaForest Covera) Very Dense Forest 83,471 2.54b) Moderately Dense Forest 320,736 9.76c) Open Forest 287,820 8.75Total Forest Cover 692,027 21.05Scrub 42,176 1.28Non-Forest 2,553,060 77.67Total Geographical Area 3,287,263 100.00Source: ISFR 2011
Forest Cover in India
Common Forest and Participatory Management
125
On all India level, the very dense and moderately dense forest has shown a net
improvement of 43 km2 and 498 km2 respectively. However, there has been reduction
in the open forest area by 908 km2. The decrease in the forest area in the state of
Andhra Pradesh was mainly due to management interventions like harvesting of short
rotation crops followed by new generation / plantations, forest clearance in some
encroached areas. In Manipur, the decrease in the forest cover was due to shortening
or shifting of the cultivation cycle. The positive change in the state of Punjab can be
attributed to the growth of young plantations carried out under externally aided
project and Agro-forestry activities. In Jharkhand the increase in forest area is
primarily due to effective protection of the forest by the Forest protection committee
and the plantation activities (ISFR 2011).
TABLE 6.3
Forest Cover in States /UT of India (Area in Km2)
State / UT Geogra-
phical Area (GA)
Forest Area as per 2011 assessment
Percentage of Forest
Area to GA
Forest Area as per 2009 assessment
Real Change in Forest
cover from 2009
assessmentAndhra Pradesh 275,069 46,389 16.86 46670 -281Arunchal Pradesh 83,743 67,410 80.50 67484 -74Assam 78,438 27,673 35.28 27692 -19Bihar 94,163 6,845 7.27 6804 41Chhattisgarh 135,191 55,674 41.18 55678 -4Delhi 1,483 176 11.87 177 -1Goa 3,702 2,219 59.94 2212 7Gujrat 196,022 14,619 7.46 14620 -1Haryana 44,212 1,608 3.64 1594 14Himachal Pradesh 55,673 14,679 26.37 14668 11Jammu & Kashmir 222,236 22,539 10.14 22537 2Jharkhand 79,714 22,977 28.82 22894 83Karnataka 191,791 36,194 18.87 36190 4Kerala 38,863 17,300 44.52 17324 -24Madhya Pradesh 308,245 77,700 25.21 77700 0Maharashtra 307,713 50,646 16.46 50650 -4Manipur 22,327 17,090 76.54 17280 -190Meghalaya 22,429 17,275 77.02 17321 -46Mizoram 21,081 19,117 90.68 19183 -66Nagaland 16,579 13,318 80.33 13464 -146Orissa 155,707 48,903 31.41 48855 48Punjab 50,362 1,764 3.50 1664 100Rajasthan 342,239 16,087 4.70 16036 51Sikkim 7,096 3,359 47.34 3359 0Tamil Nadu 130,058 23,625 18.16 23551 74Tripura 10,486 7,977 76.07 7985 -8Uttar Padesh 240,928 14,338 5.95 14341 -3Uttarakhand 53,483 24,496 45.80 24495 1West Bengal 88,752 12,995 14.64 12994 1A&N Islands 8,249 6,724 81.51 6662 62Chandigarh 114 17 14.91 17 0Dadra & Nagar Haveli 491 211 42.97 211 0Daman & Diu 112 6 5.36 6 0Lakshadeep 32 27 84.38 26 1Puducherry 480 50 10.42 50 0Total 3,287,263 692,027 21.05 692,394 -367Source: ISFR 2011
Common Forest and Participatory Management
126
6.3.2 Introduction of Joint Forest Management in India More than 200 million people are partially or fully dependent on the forest resources
for their livelihood in India. The importance of this natural resource was felt even in
the ancient times which can be traced to the ancient text of Atharva Veda. India has a
long history of scientific management of the forest resource. During the British
administration, the National Forest Policy of 1894, the first formal forest policy in
India, was administered which put more emphasis on forest resource as a source of
revenue to the states. After Independence, the first Forest Policy of India, 1952 was
enacted which put more emphasis on plantation of high yield commercial timbers like
teak, eucalyptus, which have relatively low exclusion costs. It recognised the
ecological and environmental aspects of forest management. The JFM program was
an outcome of several struggles and social movements by the local communities who
were dependent on the forest for their subsistence. The ‘Arabari’ experiment in the
early 1980’s in West Bengal was a forerunner of the JFM program. This successful
experiment led to the development of a new forest management strategy which was
later named as ‘Joint Forest Management’ in 1990.
The Indian National forest policy (1988) and the government resolution on
participatory forest management on 1st June 1990, made it possible for the State
Forest Departments to put greater emphasis on the people’s participation in the
management of the forest resources. The Joint Forest Management was an outcome
of the realisation that active and willing participation of the forest communities was
critical for the forest regeneration program to succeed. Further the forest communities
should directly benefit and also have authority. Hence, under the Joint Forest
Management, a village committee popularly known as Forest Protection Committee
(FPC) and the state forest department enter into a JFM agreement. As part of this
agreement, the local villagers assist in safeguarding the forest resources from grazing,
fire and illegal harvesting. In return they receive non-timber forest products and also a
share of the revenue from the sale of timber products. JFM augments the forestry
regime with a process that ensures rapid adaptation to changes in what people need,
want and can do.
Common Forest and Participatory Management
127
The present status on the number of JFM committees, area under JFM, number of
families of Schedule Tribe involved in JFM and the total number of families involved
in JFM is depicted in Table 6.4 as shown below:
As is evident from data in Table 6.4 above, the Joint Forest Management is very
active in the states of Andhra Pradesh, Chhattisgarh, Jharkhand, Madhya Pradesh,
Maharashtra, Orissa and Uttarakhand and it is elaborated in Appendix-VI.
TABLE 6.4
Sl No. State No. of JFM committee
Area under JFM (in ha.)
No. of Families of Scheduled Tribe involved
in JFM
Total no. of families
involved in JFM
1 Andhra Pradesh 8663 2,290,000 480,000 1,590,000 2 Arunachal Prdesh 347 90,000 23,308 23,308 3 Assam 503 80,000 28,459 57,341 4 Bihar 532 370,000 32,303 205,000 5 Chhattisgarh 7050 2,830,000 270,000 6 Goa 26 10,000 336 7 Gujarat 1734 240,000 140,000 200,000 8 Haryana 875 56,000 165,500 9 Himachal Pradesh 1690 420,000 36,000 265,000 10 Jammu & Kashmir 2697 114,100 11 Jharkhand 10903 2,190,000 510,000 1,280,000 12 Karnataka 3887 320,000 24,705 190,000 13 Kerala 327 170,000 12,255 51,300 14 Madhya Pradesh 14173 6,000,000 800,000 1,700,000 15 Maharashtra 10242 2,500,000 500,000 1,800,000 16 Manipur 280 90,000 22,000 26,000 17 Meghalaya 73 4,000 18 Mizoram 270 20,000 40,000 40,000 19 Nagaland 335 20,000 85,000 85,000 20 Orissa 9778 820,000 700,000 1,700,000 21 Punjab 1224 180,000 91,000 22 Rajasthan 4224 580,000 200,000 400,000 23 Sikkim 155 10,000 17,000 46,000 24 Tamil Nadu 1367 480,000 10,000 240,000 25 Tripura 374 100,000 17,000 33,000 26 Uttar Pradesh 1892 80,000 83,000 800,000 27 Uttarakhand 10107 860,000 15,000 500,000 28 West Bengal 4096 630,000 110,000 480,000
TOTAL 97,824 21,554,100 3,886,030 12,238,785 Source: FSI, SFR 2009
Status of JFM in different states of India
Common Forest and Participatory Management
128
6.4 Forest Cover and Joint Forest Management in West Bengal 6.4.1 Forest cover in West Bengal
In the state of West Bengal, the forest cover as per the interpretation of the satellite
data of Nov 2008 – Jan 2009, is 12,995 km2 which is 14.65 percent of the
geographical area of the state (ISFR 2011). West Bengal has 2,984 km2 areas under
dense forests, 4,646 km2 areas under moderately dense forests and 5,365 km2 areas
under open forests. The forest cover of the state is shown in the map below:
MAP 2
Map of Forest Cover in West Bengal
Source: India State Forest Report 2011
Common Forest and Participatory Management
129
The district wise forest cover in the state of West Bengal is shown in the Table 6.5
below:
It is evident from the above Table 6.5 above that the percentage of forest cover to the
total geographical area is highest in the district of Darjeeling, followed by Jalpaiguri,
South 24 Pargana, Mednipur, Bankura and Purulia. We have selected Bankura and
Purulia for our field survey since both of them are covered by vast forest area and the
rural poor are highly dependent on the forest resources for their livelihood. However,
we have noticed that the forest land has been degraded in several districts of West
Bengal.
TABLE 6.5
Very Dense Forest
Mod. Dense forest
Open forest
Bankura 6,882 213 510 333 1,056 15.34 Bardhaman 7,024 44 135 82 261 3.72 Birbhum 4,545 0 42 63 105 2.31 Kolkata 185 0 0 0 0 - Coochbehar 3,387 0 15 79 94 2.78 Dakshin Dinajpur 2,219 0 2 13 15 0.68 Darjeeling 3,149 714 663 912 2,289 72.69 Howrah 1,467 0 53 93 146 9.95 Hoogli 3,149 0 9 52 61 1.94 Jalpaiguri 6,227 681 514 1309 2,504 40.21 Malda 3,733 0 113 51 164 4.39 Mednipur 14,081 253 1,171 1172 2,596 18.44 Murshidabad 5,324 0 63 44 107 2.01 Nadia 3,927 2 74 53 129 3.28 North 24 Pargana 4,094 20 18 51 89 2.17 Purulia 6,259 43 373 381 797 12.73 South 24 Pargana 9,960 1,014 889 503 2406 24.16 Uttar Dinajpur 3,140 0 2 174 176 5.61 Grand Total 88,752 2,984 4,646 5,365 12,995 14.64 Source: ISFR 2011
District wise forest cover in West Bengal (Area in Sq. Km)
District Geographical Area (GA)
Assessment 2011 Total forest cover
Percentage of Forest cover to Geographical
Area
Common Forest and Participatory Management
130
Table 6.6 below shows the degraded notified forest land in West Bengal:
From the Table 6.6 above, we observe that in the surveyed districts of Bankura and
Purulia, the extent of degraded land is very high as compared to other districts of
West Bengal. Encroachment into the forest for agricultural, settlement and other
purpose along with rampant extraction of forest products are responsible for the
degradation.
TABLE 6.6
Bankura 203.64Bardhaman 20.34Birbhum 39.99Kolkata 0Coochbehar 0.15Dakshin Dinajpur 0Darjeeling 21.82Howrah 0Hoogli 0Jalpaiguri 23.32Malda 0Mednipur 110.7Murshidabad 1.57Nadia 0.25North 24 Pargana 0.21Purulia 114.29South 24 Pargana 0.98Uttar Dinajpur 0Grand Total 537.25Source: Wastelands Atlas of India, 2011Published by Ministry of Rural Development , Deptt. of Land ResourcesGovt. of India and NRSA, Deptt. of Space, Govt. of India
Degraded Notified Forest Land in West Bengal (Sq. Km)District Unit (in Sq. Km)
Common Forest and Participatory Management
131
6.4.2 Joint Forest Management in West Bengal West Bengal is the pioneer state for initiating Joint Forest Management in India. The
data on the number of Forest Protection Committees (FPC) and the number of
members participating in them, for different divisions of West Bengal is shown in
Table 6.7 below:
TABLE 6.7
(As on 31.03.2011)
Male Female Total S.C. S.T OthersDarjeeling 73 14,376 3,812 415 4,227 139 1,006 3,082 Kalimpong 64 26,238 3,582 195 3,777 204 875 2,698 Kurseong 46 13,095 2,043 3,068 5,111 431 1,195 3,482 Wildlife I 31 14,905 1,287 1,572 2,859 286 516 1,966 Jalpaiguru 63 20,248 11,431 638 12,069 5,255 3,399 3,415 Baikunthapur 64 12,899 5,978 129 6,107 4,475 628 1,004 Cooch Behar 26 3,904 2,932 209 3,141 1,497 517 491 Wildlife III 26 7,021 4,360 180 4,540 727 2,483 1,330 B.T.R.(E) 17 9,331 3,340 103 3,443 1,548 1,334 461 B.T.R.(W) 33 25,596 4,064 489 4,553 768 2,563 1,173
Raigunj 21 1,163 1,727 74 1,801 864 412 525
Malda 4 210 543 18 561 261 296 4 Midnapur 363 45,956 48,038 2,801 50,839 10,131 9,186 31,522 Jhargram 474 52,179 38,254 2,449 40,703 9,135 14,906 16,662 Kharagpur 254 27,438 18,421 12,281 30,702 6,950 9,504 14,248 Rupnarayan 213 26,398 26,331 1,343 27,674 6,419 7,814 13,441 Bankura (N) 539 43,493 50,437 2,083 52,520 19,819 7,912 24,789 Bankura (S) 620 44,193 53,849 4,422 58,271 13,806 18,427 26,038 Panchet 231 28,383 27,710 1,593 29,303 11,120 4,736 13,447 Purulia 213 30,729 20,741 867 21,608 6,012 6,944 8,652 Kangsabati (N) 244 17,641 23,578 881 24,459 5,677 8,089 10,693 Kangsabati (S) 305 25,169 29,561 569 30,130 4,293 14,944 10,893 Burdwan 73 20,102 16,854 2,928 19,782 7,273 5,771 6,738 Durgapur 23 2,391 1,871 7 1,878 523 699 656 Birbhum 195 10,481 16,332 313 16,645 6,017 5,267 5,401 Howrah 4 479 815 319 1,134 537 238 359 Nadia-Msd. 11 916 957 44 1,001 246 254 603 East Medinipur 19 1,813 4,760 1,097 5,857 1,256 50 4,551 S.T.R 11 12,844 3,958 107 4,065 3,642 254 169 24-Pgs (S) 40 42,534 12,287 12,186 24,473 13,116 517 10,840
4300 582125 439853 53380 493233 142427 130736 219333Source: SDF 2010-11
TOTAL
Forest Protection Committees in West Bengal
Zone Division
Total No. of
FPC
Area Protected
(ha.)
Number of Members
Hilly
Duars- Terai
North Bengal Plains
South Bengal
Estua-rine
Common Forest and Participatory Management
132
As on March 2011, there are 4,300 Forest Protection Committee (FPC) in West
Bengal comprising of 4,93,233 members protecting a forest area of about 5,82,125
hectares. The Total number of Eco-Development Committees (EDCs) in the state is
123 comprising of 22,460 members protecting a protected forest area of 87,537
hectare (SFR 2010-11). From the Table 6.6 above, we have observed that the number
of Forest Protection Committee in the Bankura (N), Bankura (S) and Purulia division
were 539, 620 and 213 respectively (WB SFR 2010-11) which is very high as
compare to other districts of West Bengal. There are 52,520 members involved in
JFM activities in Bankura (N), 53,849 members in Bankura (S) and 30,729 members
in Purulia (WB SFR 2010-11).
The Status of JFM Committees in West Bengal during the period 2008-2011 is shown
in Table 6.8 below:
As is evident from the Table 6.8 above, although there has been an increase in the
number of JFM committees in West Bengal from 4,218 in 2008 to 4,300 in 2011, the
total number of members involved in JFM has decreased from 576,078 to 493,538
during the same period. However, there has been increase in area under JFM from
553,409 ha to 582,161 ha during the period 2008 to 2011.
TABLE 6.8
Male Female
1 31-Mar-2011 4,300 582,161 439,853 53,380 493,538 2 31-Mar-2010 4,271 562,527 428,747 58,032 486,769 3 31-Mar-2009 4,253 557,063 438,269 44,484 482,753 4 31-Mar-2008 4,218 553,409 530,936 47,072 576,078
Source: SFR of WB, Govt of WB
Status of JFM Committees in West Bengal Sl No.
Assessment Year
Number of JFM committee
Area under JFM (in ha.)
Number of members Total Number of members
involved in JFM
Common Forest and Participatory Management
133
6.5 Forest Management in the Study Area
The field survey was undertaken in 6 villages in the district of Bankura and 3 villages
in the district of Purulia in West Bengal. The villages in the study area are situated in
the forest fringes below the hills. They are highly inaccessible and sparsely populated.
Most of the villagers are extremely poor. They are dependent on farming for their
subsistence. However these villages do not have any provision for irrigation and have
to depend entirely on rain for farming. Several households do not own any land and
hence work as hired labourers to meet their livelihood. The villagers are engaged in
agriculture only for about six months. For the rest of the year they are without any
work. So they involve in non-agricultural activities like fishing, hunting and
collection of common forest products. The villagers collect forest products in the form
of twigs, snails, fodder/grasses, fuel wood, cow dung, herbal medicine, bamboo,
timber, fruits, honey, vegetables, fish, birds and broom. As these forest products have
several uses in the socio-economic lives of the rural poor, they are becoming
concerned about protecting these resources.
The villagers of Jiyathole village in Santuri block of Purulia district, Mangal Kora,
Narain Kora and Pradip Mudikora, have commented in the group discussion:
“We have to protect the important plants like sal, kendu, mahua, piyal at any
cost as we use them both for domestic and commercial purposes.” (Dated 20th
December, 2011, medium of language was Bengali and Santali)
In all the surveyed villages it is observed that the villagers are engaged, amongst other
activities, in Joint Forest Management for the protection of their forests. The Joint
Forest Management not only helps to protect the forest against indiscriminate felling
of trees but also provides the survival needs of the villagers. It also plays a critical
role in conservation of the bio diversity through people’s participation. The Joint
Forest Management was initiated in the study villages in 1992 after the JFM
resolution 1990 of Govt. of West Bengal. The Forest Department of the Government
of West Bengal is actively involved in the Joint Forest Management and along with
the villagers are jointly responsible for protection of the forest and wildlife.
Common Forest and Participatory Management
134
A Forest Protection Committee (FPC) in the study areas has been formed whose
prime responsibility is to protect the forest. The FPC was formed after several
discussions of the villagers with the forest staff of the local Beat office. Most of the
village households become member of the Forest Protection Committee. The
household head and spouse of majority of the family becomes member of the General
Body. In the study villages, around 10-15 household members in each of the villages
are part of the Forest Protection Committee. The Forest Protection Committee has an
Executive Committee. As per the by-laws, the FPC is normally headed by a Chairman
of the Panchayat Samiti. The other members include the Gram Panchayat Pradhan,
elected representatives of the villages (maximum 6) and the Beat Officer (as member
secretary). In most of the cases it is observed that the FPC members are persons
normally holding a higher rank among the villages or a leader amongst the villagers.
The Executive Committee plays the critical role of executing the Joint Forest
Management programme. The FPC involves the villagers in plantation of trees and to
take regular care of their plantation. The villagers are entitled to 25 percent of the
usufructs after auctioning of the plantation. They also actively involve the villagers in
protection of the forest resources. The FPC ensures that the villagers are able to meet
their subsistence through collection of forest products. The villagers collect dried
leaves, fire wood, canes, etc. from the forest area. The FPC also involves the local
people in planning, development of the forest and regeneration of the forest through
plantation of trees. The men and women of the villages protect the allotted forest area
through patrolling activities on rotation basis day and night. This has brought down
the illicit felling of trees for timber in the study villages. Every month a General Body
meeting is held by the Forest Protection Committee. The meeting schedule is
announced in every village by beating of drums and all the villagers are requested to
attend the meeting without fail. During the meeting, the villagers also discuss with
the forest officials their need for collection of Non Timber Forest Products from the
forest as they are highly dependent on it.
Common Forest and Participatory Management
135
According to the villagers of Panjhoria village of Bankura district, Ravidas Hembram,
Gurupada Husda and Gopinath Hembram:
“We are twenty six households living in this village. Agriculture is our
primary occupation. Sometimes we also work as wage labourers. Besides this
we collect forest products for our own consumption and at times for sale. We
are members of the forest protection committee. The forest department
provide us job for planting and felling of trees and we are paid for it. We want
to protect our forest as we are allowed by forest department to collect
firewood, fodders, leaves, fruits and vegetables from the forest in a restricted
manner without any cost.” (Dated 28th August, 2011, medium of language was
Bengali.)
The FPC plays a vital role in enforcing the guidelines laid down by the JFM schemes.
In case, anyone breaks the rule enforced by the Forest Protection Committee or
engage in illicit felling of the forest trees, then the villagers reprimand him. In case he
continues to illegally cut the forest trees then, when caught, the Forest Protection
Committee take away his cutting implements and impose a penalty ranging from Rs
100/- to Rs 500/- depending upon the seriousness of the crime. In case the offender
refuses to pay the penalty, he is brought to the Beat Officer who along with the
Executive Committee members decides on the quantum of punishment to be enforced.
It has been observed that imposition of heavier penalties is ineffective, since the
villagers are poor and therefore unable to pay the fine. It is further observed that in
most cases imposition of fine of Rs 100/- along with public denouncement has been a
more effective tool in controlling the offence. The FPC also plays a vital role in
controlling inter-village conflicts.
After the formation of Joint Forest Management in the study villages in 1992, it has
been possible to protect the forest resource to a large extent. It has been possible to
prevent the massive extraction of the forest resources due to felling of the trees. The
villagers have now become aware of the importance of conservation of the forest.
Since the villagers are extremely dependent on the forest resources for their
subsistence, hence they are also responsible to protect the forest.
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136
The statistics on JFM participation for the surveyed villages of Bankura and Purulia is
shown in Table 6.9 below:
As per Table 6.9 above, 75.33 percent of the surveyed household in Bankura district
and 85.33 percent of the surveyed household in Purulia district participate in JFM.
The percentage of households participating in JFM meetings in the surveyed villages
of Bankura and Purulia districts are 55.33 percent and 78 percent. As per the survey
report, 82 percent of the surveyed villages in Bankura district are either highly
satisfied or satisfied with the performance of JFM as compared to 94.67 percent in
surveyed villages in Purulia district. The survey result also shows that the percentage
of household in the surveyed villages of Bankura district involved in planning and
TABLE 6.9
Number Percentage Number Percentage Number Percentage113 75.33 128 85.33 241 80.33
83 55.33 117 78.00 200 66.67
=>Highly Satisfied 83 55.33 67 44.67 150 50.00 =>Satisfied 40 26.67 75 50.00 115 38.33 =>Neutral 18 12.00 6 4.00 24 8.00 =>Not Satified 9 6.00 2 1.33 11 3.67
=>Planning & decision making 91 60.67 98 65.33 189 63.00 =>Implementation 19 12.67 24 16.00 43 14.33 =>Benefit Sharing 2 1.33 4 2.67 6 2.00 =>Evaluation 1 0.67 2 1.33 3 1.00 =>Not Involved 37 24.67 22 14.67 59 19.67
=>Strong Participation 83 55.33 117 78.00 200 66.67 =>Occasional Participation 16 10.67 8 5.33 24 8.00 =>Not very Often 8 5.33 3 2.00 11 3.67 =>Hardly ever 6 4.00 0 - 6 2.00 => Not involved 37 24.67 22 14.67 59 19.67
Source: Field Survey, 2011
JFM Participation in the study area of Bankura and Purulia districts
BANKURA PURULIA
No. of Household Participation in JFM monthly meetings
No. of Household participation in JFM
Note: No. of households surveyed: Bankura=150; Purulia=150;
Performance of Community / Forest Management
Participation of house hold members in organizing activities
Rate of family members participation in Community / Forest management
TOTAL
Common Forest and Participatory Management
137
implementation of JFM is 60.67 percent and 12.67 percent respectively as compared
to 65.33 percent and 16 percent in the surveyed villages of Purulia district. Further,
55.33 percent of the surveyed household in the Bankura district strongly participate in
JFM activity as compared to 78 percent in the surveyed villages in Purulia district.
The participation of households and the enforcement of forest protection scheme in
the study villages have been depicted in Table 6.10 below:
From the above Table 6.10, it is observed that the participation of household in Joint
Forest Management activities is low in the villages of Ramjibanpur and Baldanga in
Bankura district. The incidents of cases of violation of rules, number of overuse
caught, warned and freed, and number of cases imposed penalty and refusal of penalty
for the year 2008, 2009 and 2010 have also been shown for the study villages in Table
6.10. From the data, it is observed that the incidents of cases of violation of rules have
shown a declining trend in most of the surveyed villages during the period 2008-2010.
From the above data it is also observed that in the villages of Ramjibanpur, Seolibona
and Baldanga the incidents of cases of rule violation and refusal of penalty imposed
are high as compared to other villages in the study area. It is further observed that in
TABLE 6.10
2008 2009 2010 2008 2009 2010 2008 2009 2010 2008 2009 2010Panjhoria 26 26 18 7 6 4 4 3 2 3 3 2 2 1 1Ramjibanpur 20 10 2 9 7 6 5 4 3 4 3 3 3 3 3Seolibona 54 40 20 13 10 9 8 5 5 5 5 4 3 2 4Baldanga 7 2 0 6 5 5 3 3 2 3 2 3 3 2 3Dulaltora 18 18 15 6 4 3 4 2 2 2 2 1 2 1 0Tantirdanga 25 17 14 8 6 5 5 3 2 3 3 2 2 1 2Jiyathole 81 78 70 7 3 1 3 3 0 4 2 1 1 1 0Marbediya 26 15 8 12 8 6 8 6 3 4 3 3 3 3 2Ambari 43 35 23 9 7 4 5 4 2 4 3 2 2 1 1
300 241 170 77 56 43 45 33 21 32 26 21 21 15 16Source:Survey data 2011
Bankura
Puru
lia
TOTAL
Forest Management and Enforcement of Forest Protection Scheme
District Name of VillageNo. of Households
No. of Households participated
in JFM (2010)
Active JFM participants
(2010)
Incidents of cases of
violation of rules
No. of Overuse caught, warned
and freed
No. of cases imposed penalty
No. of cases refused to pay
penalty
Common Forest and Participatory Management
138
the year 2010, the number of incidents of ‘refused to pay penalty ’ is nil in the villages
of Dulaltora in Bankura district and Jiyathole in Purulia district where forest
degradation is least as compared to other villages (Table 4.12 in Chapter 4). This
implies that forest management is more active in these villages as compared to other
villages in the study area.
The members of Forest Protection Committee in Seolibona village of Bankura district,
Rajesh Mudikora, Dilip Mudikora and Jodeswar Mudikora gave their opinion:
“Involvement of community members need to be organised and the position of
job creation for forest protection need to be made compulsory” (Dated 24th
September, 2011, medium of language was Bengali)
We can therefore infer that with the establishment of Joint Forest Management,
enforcement of forest protection has shown a very positive trend. Further the
households have realised that protection of the common forest is in their own interest
and violating the rules has an adverse effect to them. Forest management thus plays
an instrumental role in forest preservation and conservation. More active the
management greater is the possibility of forest conservation and vice versa.
6.6 Collective Action in Joint Forest Management
Collective action refers to concerted actions of people that share a common interest,
perceive that interest and act to achieve it (World Bank, 1998). It is voluntary or
mandatory depending on the type of action being executed and the institutions within
or through which the action is executed (Gregario, et al., 2004). Our primary focus in
this study is to explore how forest dependency influences household’s active
participation in Joint Forest Management. Local communities, whose income from
agriculture or other sources is uncertain, are very much dependent on forest for their
income and consumption and are thus more interested to conserve forest resources.
Initially, the households who were very much dependent on the forest and whose
income from forest resources was high could not afford the cost of restrained forest
use. These households therefore did not show any interest in the conservation of the
forest resources as they stayed out of the JFM programme and collected forest
Common Forest and Participatory Management
139
products illegally. Later they realised that if they did not conserve the forest resources
then it was not possible to smoothen their income and consumption during the period
of agricultural uncertainty.
The forest scarcity and degradation is the crucial factor to inspire the villagers to join
JFM. The Joint Forest Management becomes effective only if the local rural
community whole heartedly participates in the managing of the common forest
resources. In order to protect the forest from illegal felling of the trees, the forest
patches has to be guarded and monitored day and night by the villagers as per the
guidelines laid down in the JFM. Further, the collection of common forest products
like fuel wood, fodder, fruits, vegetables, etc. is restricted to only certain specific
areas of the forest as per the guidelines of the JFM and thus the local rural villagers
have to travel a longer distance in order to collect the common forest products. The
villagers are to be involved in plantation and regeneration of the high value forest in
order to generate economic gain through the usufructs in the long run. Since a large
part of their participatory labour is to be involved in JFM activity, there is always a
question of trade off with that of agricultural operations. The villagers are involved in
agriculture primarily for their own consumption. The agricultural activity is seasonal.
This is due to the fact that there is lack of proper irrigation facility in the villages and
the villagers have to depend heavily on the rain for their cultivation. Moreover, the
rural households do not have any work during the non-agricultural seasons. Hence the
villagers have to decide about allocating their endowed labours either in agricultural
activities or in forest protection activities that also produces means for their
subsistence. Now, the forest resources that can be managed effectively by the local
rural communities depend on the strength of the collective action i.e. joint action of
the community to conserve forest resources as well as improve rural livelihood.
We have explained the determinants of collective forest management through
econometric analysis to give an understanding of the relationship between forest
dependency and active forest management. Collective action in forest management is
measured by time involvement in the management of forest resources. The 9 villages
in our study area from the two districts of Bankura and Purulia have been considered
separately and their results have been compared. Attempt has been made to capture
Common Forest and Participatory Management
140
the relevant socio-economic variables that influence the strategy of collective action
in forest management in the study area. In this context following Bwalya (2004), we
have specified an econometric model of collective action and discuss the choice and
expected signs of explanatory variables. We estimate the following econometric
model of collective action
12
i
n
M i ij ijj
L a b Y=
= + +∈∑ ………………………………………..….(1)
where 1ia denotes community dummies, ijY is set of explanatory variables including
the index of individual organisational experience, livelihood activities, socio-cultural
heterogeneity, age, gender, household size, wealth, forest condition and ∈ is the error
term. ML is the dependent variable defined as the amount of labour household
contributes to local management and i and j indexes communities and individual
variables respectively. Our intension is to examine the impact of forest dependency on collective action in
JFM. Here the dependent variable is the number of man days per year involves in
JFM activities. Field survey data shows that about 25 percent of the total respondents
in Bankura district and 15 percent in Purulia district allocated zero man days to JFM
activities. Hence we have applied Censored Tobit model as it makes survey data more
convenient to analyse. Since the dependent variable is censored from below, we apply
maximum likelihood estimation to estimate the Censored Tobit model.
Our specified model is
0 1 2 3 4 5iML a b FSIZE b FEMPER b AVRAGE b AVRSCH b PERAGRIN= + + + + +
6 7 8b PERCPRIN b PERCPRCSM b WEALTH+ + + +∈
Here 0a is constant and ( 1,2,...,8)ib i = are the coefficients associated with the
explanatory variables and ∈ is the random disturbance term.
The households devote their time in JFM activities like planning and implementation,
monitoring, silvicultural activities at the cost of other livelihood activities i.e.
agriculture, off farm wage labour, etc. and leisure.
Common Forest and Participatory Management
141
In the following Table 6.11, we present the dependent and explanatory variables and
their description, measurement and expected signs.
TABLE 6.11
Description of Variables in Censored Tobit Model Variables Variable Description Expected Sign
Dependent Variable
Collective Action ( ML )
Number of man days i.e. Labour
contribution in monitoring, planning
& implementation and management of
community forest resource
Independent Variables
1. FSIZE Average number of population of the
household (Size of the family) +
2. AVRAGE Average age of the respondent -/+
3. AVRSCH Average year of schooling of
household +
4. PERAGRIN Percentage of Agricultural income to
Total income -
5. PERCPRIN Percentage of income from common
property resources to Total income +
6. PERCPRCSM Percentage of CPR consumption to
Total consumption +
7. WEALTH Total household assets
WEALTH = 1 for ‘Well to do’
household
WEALTH = 0 for poor household
-
The key independent variables are percentage of Agricultural income to total income
( PERAGRIN ) and CPR income to total income ( PERCPRIN ). It was expected that
households whose agricultural income is steady are less interested in active
participation in JFM. On the other hand household whose percentage of CPR income
to Total income are high i.e. more dependent on forest resource are actively involved
in collective action in Joint Forest Management.
Common Forest and Participatory Management
142
Empirical estimation of model specified in the above equation is described in the
following Tables 6.12 and 6.13:
TABLE 6.12
Determinants of Collective Action (Bankura District) Dependent Variable: LABJFM (Contribution of labour in JFM) Method: ML - Censored Normal (TOBIT) (Quadratic hill climbing) Sample: 1 150 Included observations: 121 Left censoring (value) at zero Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C -8.939720 11.55629 -0.773580 0.4392
FSIZE 1.763608 0.756720 2.330597** 0.0198 AVRAGE 0.280050 0.192963 1.451314 0.1467 AVRSCH -0.009263 0.775809 -0.011940 0.9905 PERAGRIN -0.595780 0.184329 -3.232150* 0.0012 PERCPRIN 0.540002 0.233852 2.309159** 0.0209 PERCPRCSM 0.083001 0.365277 0.227227 0.8202 WEALTH -4.858511 3.303721 -1.470618 0.1414
Error Distribution SCALE:C(9) 13.76905 1.044966 13.17655 0.0000 Mean dependent var 15.67769 S.D. dependent var 12.84926
S.E. of regression 11.75636 Akaike info criterion 6.971670 Sum squared resid 15617.94 Schwarz criterion 7.179622 Log likelihood -412.7860 Hannan-Quinn criter. 7.056127 Avg. log likelihood -3.411455
Left censored obs 25 Right censored obs 0
Uncensored obs 96 Total obs 121 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level
Source: Estimated by EViews7 computer software using field survey data of 2011
Common Forest and Participatory Management
143
TABLE 6.13 Determinants of Collective Action (Purulia District) Dependent Variable: LABJFM (Contribution of labour in JFM) Method: ML - Censored Normal (TOBIT) (Quadratic hill climbing) Sample: 1 150 Included observations: 121 Left censoring (value) at zero Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C 4.171649 7.946446 0.524970 0.5996
FSIZE 1.553762 0.523497 2.968042* 0.0030 AVRAGE 0.109737 0.132708 0.826905 0.4083 AVRSCH -0.070155 0.533623 -0.131468 0.8954 PERAGRIN -0.910248 0.123300 -7.382397* 0.0000 PERCPRIN 0.550850 0.160520 3.431650* 0.0006 PERCPRCSM 0.212440 0.254061 0.836177 0.4031 WEALTH -7.024702 2.260913 -3.107020* 0.0019
Error Distribution SCALE:C(9) 9.637295 0.656865 14.67165 0.0000 Mean dependent var 18.14876 S.D. dependent var 12.78584
S.E. of regression 9.156199 Akaike info criterion 7.152541 Sum squared resid 9473.466 Schwarz criterion 7.360492 Log likelihood -423.7287 Hannan-Quinn criter. 7.236998 Avg. log likelihood -3.501890
Left censored obs 9 Right censored obs 0 Uncensored obs 112 Total obs 121
*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level Source: Estimated by EViews 7 computer software using field survey data of 2011
We have observed from the determinants of Collective Action in JFM that most of the
parameter estimates for household variables have expected sign and are statistically
significant. Results show that family size ( FSIZE ) and average age ( AVRAGE ) have
a positive effect on collective action in JFM in the surveyed villages of both Bankura
and Purulia district. Family size ( FSIZE ) is positive and significant suggesting that a
larger family size collects more CPR product and thus increases the scarcity of the
Common Forest and Participatory Management
144
forest resources. So they are bound to actively participate in the JMF in order to
conserve the forest resources. Positive effect of average age of the respondent
( AVRAGE ) implies that aged people, due to their experience, are more interested to
allocate labour in Joint Forest Management. In the cases of AVRAGE , the result is not
significant. In both the districts we have observed that education ( AVRSCH ) has a
negative effect on collective action in JFM. This implies that educated people are
mostly involved in the service sector or off-farm wage labour and hence they get less
time to devote in monitoring or silvicultural activities in the JFM. However, this result
is insignificant in both the districts. Actually in some cases educated household
understand the importance of preservation of forest resources and are thus more likely
to actively participate in the JFM activity and also motivate other villagers to
participate as well.
It is evident from the analysis that collective action in JFM is negatively related to
percentage of agricultural income to total income of the household ( PERAGRIN ) in
both the district of Purulia and Bankura. In Bankura district, it is significant at 1
percent level of significance and in Purulia district it is highly significant. This
implies that household who has steady income from agriculture does not bother much
about forest resources and are therefore less interested in active participation in JFM.
On the other hand, household who depend on CPR products because of uncertain
agricultural income are more interested in conserving forest resources and give more
labour time in different activities of JFM. In line with our expectation, we observe a
positive relation between percentage of CPR income to total income ( PERCPRIN )
and collective action in JFM in both the districts and the result is significant with
Bankura at 5 percent level and Purulia at 1 percent level. This indicates that the
households participate in the JFM, primarily to gain access to the forest outputs from
the forest resources, so as to overcome the uncertainty and insecurity in their
livelihood.
We have hypothesised a positive relation between percentage of CPR consumption to
total consumption ( PERCPRCSM ) and active participation in JFM. In fact the
households who depend more on CPR for their consumption purposes are very much
concerned about forest resource conservation and hence actively participate in JFM.
Common Forest and Participatory Management
145
However, the coefficient is positive in this case in both the districts although the result
is insignificant. The coefficient of wealth (WEALTH ) have the expected negative
signs on collective action management in the two surveyed districts and significant at
1 percent level in Purulia and 10 percent in Bankura. This signifies that wealth
reduces the incentive among the households to actively participate in the JFM. In few
cases ‘well to do’ households participate in the JFM only for social capital i.e.
personal interest, self-esteem, respect, etc. or for their compulsion of strong ties with
the Government officials. The above result indicates a strong relationship between
forest dependence and active participation in JFM.
6.7 Collective Action and Forest Conservation
Forest management has its inherent impact on the conservation of forests. More active
the management practice is, greater is the probability of efficient use of forest
resources. Forest management lowers the degree of over exploitation and hence the
degradation of forest resources. We now examine the role of forest management to
reduce forest degradation by using logit regression model. A range of socioeconomic
and environmental variable has been considered.
The logit-regression model has been fitted as follows:
1 2 3 4log( )1
i
i
P a b AVRAGE b AVRSCH b FSIZE b OWNLANDP
= + + + +−
5 6 7b LIVESTOCK b POVR b FMACT+ + +
Here the dependent variable is forest degradation ( FDGR ) which is a dummy
variable equal to ‘1’ if there is ‘more degradation’ and equal to ‘0’ if there is ‘less
degradation’.
Forest Degradation is measured on the basis of the data collected in the village survey
from three different variables - Extent of forest damage visually seen (FD), Condition
of the forest informed by the respondents as compared to that of earlier times (FC)
and Forest use penetration i.e. the depth into the forest from the village boundary
where use pressure was evident (FP). All these three variables (FD, FC, FP) are coded
Common Forest and Participatory Management
146
(using four point scale) so that increasing values shows more forest degradation
(Detail in Chapter 4 in Section 4.6.1).
In the following Table 6.14 we have explained the explanatory variables, their
descriptions and expected signs:
TABLE 6.14
Description and Hypothesis in Logit Regression Model Variables Variable Description Expected
Sign
Dependent Variable
1. FDGR
Forest Degradation
FDGR =1, if there is more degradation
FDGR =0, if there is less degradation
Explanatory Variables
1. FSIZE Average number of population of the
household (Size of the family) +
2. AVRAGE Average age of the respondent _
3. AVRSCH Average year of schooling of household +
4. OWNLAND Total land owned by the household _
5. LIVESTOCK Total number of livestock condensed
into animal units +
6. POVR Poverty of the household
POVR =1; if household belongs to BPL
POVR =0; if household belongs to APL
+
7. FMACT Active Forest Management
FMACT =1 in case of active forest
management
FMACT =0 in case of inactive forest
management
_
Common Forest and Participatory Management
147
The logit regression model has been tested using the household level data through
field survey of Bankura and Purulia districts. The result is given in Table 6.15 below:
TABLE 6.15
Determinants of Forest Degradation (Bankura District) Dependent Variable: FRDGR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 ; Included observations: 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C -4.808023 3.657601 -1.314529 0.1887
AVRAGE 0.067295 0.054709 1.230044 0.2187 AVRSCH -0.963454 0.373627 -2.578651* 0.0099 FSIZE 0.259937 0.384891 0.675353 0.4995 OWNLAND -0.497346 0.395422 -1.257761 0.2085 LIVESTOCK 2.019892 0.943984 2.139753** 0.0324 POVR 6.757563 3.032961 2.228042** 0.0259 FMACT -6.082197 2.162583 -2.812468* 0.0049
McFadden R-squared 0.849928 Mean dependent var 0.713333
S.D. dependent var 0.453719 S.E. of regression 0.170349 Akaike info criterion 0.286494 Sum squared resid 4.120680 Schwarz criterion 0.447062 Log likelihood -13.48708 Hannan-Quinn criter. 0.351728 Deviance 26.97415 Restr. deviance 179.7420 Restr. log likelihood -89.87100 LR statistic 152.7679 Avg. log likelihood -0.089914 Prob(LR statistic) 0.000000
Obs with Dep=0 43 Total obs 150
Obs with Dep=1 107 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level
Source: Estimated by EViews 7 computer software using field survey data of 2011
Common Forest and Participatory Management
148
TABLE 6.16 Determinants of Forest Degradation (Purulia District) Dependent Variable: FRDGR Method: ML - Binary Logit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C -0.061882 3.770841 -0.016411 0.9869
AVRAGE 0.025670 0.060074 0.427301 0.6692 AVRSCH -0.401253 0.360988 -1.111541 0.2663
FSIZE 1.240305 0.551595 2.248578** 0.0245 OWNLAND -0.375603 0.263661 -1.424570 0.1543 LIVESTOCK 0.627338 0.511499 1.226470 0.2200
POVR 3.689960 2.208836 1.670545*** 0.0948 FMACT -5.095440 2.001949 -2.545240** 0.0109
McFadden R-squared 0.897336 Mean dependent var 0.673333 S.D. dependent var 0.470565 S.E. of regression 0.139214 Akaike info criterion 0.236391 Sum squared resid 2.752054 Schwarz criterion 0.396958 Log likelihood -9.729344 Hannan-Quinn criter. 0.301625 Deviance 19.45869 Restr. deviance 189.5379 Restr. log likelihood -94.76893 LR statistic 170.0792 Avg. log likelihood -0.064862 Prob(LR statistic) 0.000000
Obs with Dep=0 49 Total obs 150 Obs with Dep=1 101
*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level Source: Estimated by EViews 7 computer software using field survey data of 2011
From the analysis it can be observed that forest degradation ( FDGR ) is positively
related to average age ( AVRAGE ), family size ( FSIZE ), number of livestock
( LIVESTOCK ) and poverty ( POVR ) whereas negatively related to education
( AVRSCH ), total land owned ( OWNLAND ) and Forest Management activity
( FMACT )in both the districts. In fact every family member of the household engages
themselves in collecting forest products and thus degrades the environment to a large
Common Forest and Participatory Management
149
extent. Hence in the case of households with large family size ( FSIZE ), the
probability of the incidence of forest degradation is high. However, the result is
significant only in Purulia district. The positive relation between ( AVRAGE ) and
( FDGR ) suggest that experienced elder members degrade the forest resources
critically by collecting more CPR products from forest. However, the result is
insignificant in both the districts. Again, households with large number of livestock
( LIVESTOCK ) damage the environment badly by accumulating fodder to feed
animals. Hence larger the number of livestock more is the probability of incidence of
forest degradation. The coefficient of poverty ( POVR ) is positive and significant in
both the districts which suggest a strong positive relationship between poverty and
forest degradation.
As expected education ( AVRSCH ) is negatively related to forest degradation.
Educated people have many alternative income opportunities and hence less interested
in collecting forest products. Thus educated people help to reduce the incidence of
forest degradation. However, the result is significant in Bankura district only. In the
case of ( OWNLAND ), the negative relationship indicates that the household who own
large agricultural land get less time to collect CPRs from forest and hence causes less
forest damage. Here the impact is insignificant in both the districts.
The most important finding of this study is the role of active Joint Forest Management
(JFM) in reducing the forest degradation. In line with our hypothesis, Forest
Management activity ( FMACT ) has the expected negative sign i.e. active forest
management is associated with a smaller extent of forest degradation.
This result demonstrates that improved management has a positive role to check
environmental degradation. Again it is observed that the association between Forest
Management activity ( FMACT ) and forest degradation ( FDGR ) is negative and
significant in both the districts. It is observed that most of the rural poor depend on
common property resources for their livelihood. Hence the CPRs should be properly
managed so that the rural poor can get maximum benefit but not at the cost of nature.
Common Forest and Participatory Management
150
6.8 Conclusion
The empirical evidence based on the survey of 6 villages in Bankura and 3 villages in
Purulia suggest that the rural households are highly dependent on Common Property
Resources for their subsistence and are therefore very much concerned about the
regular depletion of the forest resources. In order to ensure the availability of the
forest resources in the long run, the rural households have actively participated in the
Joint forest Management activity in the study area. Empirical evidence suggests that
75.33 percent of the surveyed household in Bankura district and 85.33 percent of the
surveyed household in Purulia district participate in JFM. We have explained the
determinants of collective forest management through censored Tobit model to give
an understanding of the relationship between forest dependency and active forest
management. It is evident from the analysis that income from agriculture plays an
important role in the participation of JFM. In those households where the percentage
of the agricultural income to the total income is very low, the participation in JFM is
very active. We further observed that collective action in JFM is positively related to
percentage of CPR income to total income and the impact is significant in both the
districts. Therefore the result is consistent with our hypothesis. In fact, the households
participate in the JFM activity to gain access to the forest resources and thus mitigate
any uncertainty and insecurity in their income. We had hypothesised that active
participation in forest management plays a positive role in alleviating environmental
degradation. The logit regression result confirms that Joint Forest Management has a
critical role in reducing forest degradation. Improved forest management plays a
positive role in sustainable forest ecosystem.
Women’s Participation in CPR Management
151
CHAPTER 7
WOMEN’S PARTICIPATION IN CPR MANAGEMENT
7.1 Introduction
Forests are a vital livelihood support system for the rural poor. Rural women are
highly dependent on forest resources as it plays an important role in the viability and
subsistence of the households. Every day early morning, groups of women set out
towards the forest to collect firewood and several non-timber forest products like
fruits, medicines. The extreme heat, lack of water and difficult terrain make this work
of CPR collection extremely difficult. Degradation of forest leads to forest produce
being available further away from the place of dwelling, thereby increasing the
drudgery of the women. However, the rural women continue to do so since it provides
them with firewood, food and income through sale. A study in Uttar Pradesh, India
showed that rural women obtained 33 to 45 percent of their income from forests as
compared with only 13 percent in the case of men (IUCN-factsheet-gender and
forestry, 2010). Empirical evidence from southern districts of Bihar, suggest that rural
women collect mahua flowers (Madhuca indica), kendu leaves (Diospyros
melanoxylon) used in making indigenous cigarettes, mushrooms and mahua seeds,
tamarind (Tamarindus indica) (Rao, 1996). In West Bengal, tribal women gather sal
(Shorea robusta) leaves for six months of the years (Poffenberger, 1993). Based on
the empirical study of tribal women in Jharkhand, Kelkar and Nathan (1991), assert
that "within the family the income from sale of forest produce tends to be counted as
the income of the individual who gathers and sells the produce. Increasing the income
from forestry will thus also help strengthen the position of women within the family."
Rout et al. (2010) opine that collection of Non Timber Forest Product provides
employment for the tribal women and they have larger potential for generating
employment in future.
However, lack of rights and responsibilities to control and use the common property
resources make them highly vulnerable. Rural women consider the forest as their
source of livelihood and hence are very much concerned about the long term
sustainable governance of the forest resources. Indiscriminate collection of forest
Women’s Participation in CPR Management
152
resources leads to degradation of forests and the rural women have to travel longer
distance from their home to collect them. Women consider that the environmental
degradation can lead to their reduced supplementary income and community forest
management can help to reduce it. Thus any environmental policy should not only
take into account the economic impact of the women and but also leverage the role of
women in the protection and management of the forest resources (Agarwal, et al.,
2006). Understanding of women’s relationship with the environment needs to
recognise the "relationships of power and authority, negotiation and bargaining and
the wider social relations in which 'decisions' about land and trees are embedded"
(Leach, 1990). Jamisolamin (2012) believes that women can play a key role in
ensuring environmental protection and conservation provided they are allowed to take
decision in the management process. Thus women can play a critical role in the Joint
Forest Management for long term sustainability of the scarce natural resources.
For a sustainable forest management, women should not only have access to the forest
and accrue the benefits from it but also the right and authority to take decisions (Giri,
2012). Securing tenure and access rights to forest resources is a critical step towards
achieving environmental and social justice (Buchy, 2012). The author opines that
there is widespread discrimination against rural women as they face exclusion on
gender and ethnicity grounds and are denied some of the basic rights due to the
unclear, unsecured and unequal tenure rights. Women normally encounter negative
perception not only from the village men but also from the male officials of the forest
department. In West Bengal, for instance, there were many complaints from the rural
women that the male officials discouraged them from coming to the forest office and
also rebuked them if they came in the evening (Narain, 1994). Further, rural women
were also excluded from several other activities like water users associations, village
councils, etc. (Agarwal, 2001).
Active participation of women in forest management involving local decision-making
can not only have a positive impact on the resource related outcomes but also
significantly increase the effectiveness of the institution involved in forest governance
and protection through greater control of illicit harvesting of forest products and
regeneration in the forest (Agarwal, et al., 2006). Women can also play a greater role
Women’s Participation in CPR Management
153
in co-management of forests by increasing their collective bargaining power (Yadama
et al., 1997). Rural women play an important role in the development and peace
process by mediating during conflict situations. Women’s participation in forest
management through Mahila Mandals has led to active monitoring, protecting and
managing the common forest area (Bingeman, 2003). Women Self-Help Groups
(SGHs) play an important role in maintaining and protecting the common forest
which not only helps to generate income but also to their empowerment (Murugesan
& Namasivayam, 2012).
Participation of the women in forest management differ from one region and culture
to another, by one caste and tribe to another, by class position and by position in the
household and hence the forest department and NGOs should bring forth sensitive and
local specific strategies for them (Patricia Jeffery, et al., 1998). Women tend to avoid
participation in the forest management primarily due to lack of security, lack of
confidence, social and cultural restrictions (Dasgupta, 2006). Low participation of
women in Joint Forest Management programmes can be attributed to social and
cultural constrains (Godbole, 2002). In a study of 20 Community Forest Groups, Sarin
(1998) found that 60 percent had no women, and only 8 percent of the 180 Executive
Committee members were women. According to Dasgupta (2006), women
participation in JFM can be enhanced by imparting a simple and low-cost technology
based training programme.
In Bankura district of West Bengal, the District Forest Officer had issued a circular
stipulating that there should be a minimum of 30 percent women in the general body,
which had resulted in raising the female membership in several villages (Viegas &
Menon, 1993). Das & Sarker (2008) opine that communal solidarity, mutual trust and
coordinated actions exits in JFM villages, thereby increasing the social capital.
According to the authors, those Forest Protection Committees (FPCs) where social
cohesion and community solidarity is weak, effective leadership and local support can
bring about in improving the social capital.
Despite the fact that women are the major users of common property resources, their
involvement in Joint Forest Management is generally marginal in India. To regulate
Women’s Participation in CPR Management
154
illicit grazing and removal of forest products, direct involvement of women in JFM is
essential.
Under this backdrop, based on our primary data, this chapter attempts to explore the
role of women participation in sustainability of the common property resources in the
study area. In specific terms the objective of the study are as follows:
i) To explore the role of women in CPR collection
ii) To examine the performance of women in JFM committees
iii) To investigate empirically the effect of women’s active participation in
JFM on forest resource preservation and conservation.
In order to study the impact of women’s participation on sustainability of the forest
resources, we have planned to estimate a binary probit model.
In this chapter we have tested the following hypothesis:
H1: Women’s participation in forest management improves the
sustainability of the forest resources
7.2 Role of Women in CPR collection in the study area
Forests contribute critically to the survival of the rural poor and the women who own
little private land. The common forest resources in the study areas of Bankura and
Purulia provide the rural poor with food, medicine, fodder, firewood, etc. The women
collect firewood for domestic as well as for commercial purposes. They collect certain
tree barks and medicinal herbs which are used as industrial raw material as well as for
medicinal purposes. Apart from collection of firewood, the rural women are also
involved in the collection of fodder from the common forest area. Sal and Kendu
leaves used as vegetables are also collected by the rural women in the study areas on a
regular basis almost throughout the year. The Sal leaves are stitched by hand to make
Sal plates, which are then sold in the local market. The rural women also sell their
collected products like firewood, Sal stick (used as tooth brush) and wild vegetables at
the local market. However, due to poor transportation system and lack of organised
Women’s Participation in CPR Management
155
market in the study areas, they are forced to sell the forest products to mobile agents
or middlemen, who visit their village quite often, even if they receive a lower price.
Generally the men collect forest products only when they are not engaged in
agricultural or other off-farm activities. Thus CPR collection for men is a secondary
job. However, collection of forest products for rural women living in the forest
fringes is always a primary occupation. Thus forest plays a crucial role in their daily
lives.
The women villagers of Panjhoria village of Saltora block in Bankura district, Bani
Mudikora and Durga Hembram, share their views with us:
“We collect fuel wood, fodder, sal and kendu leaves, mahua fruits and flowers
from our nearby forest for domestic as well as commercial purposes without
harming the whole plant. Except during monsoon, we gather forest products
all the year round. When we need money we have to sell the forest products
immediately. Most of the time we do not get the correct price.” (Dated 4th
October, 2011, medium of language was Bengali)
Forest women collect NTFPs as much as possible for commercial purposes. However,
they also gather the same in a restricted way for their own consumption. Although
women are the major collector of NTFPs and have good knowledge about the forest,
they cannot actively participate in JFM which is in general male dominated in our
surveyed area. During the fieldwork a number of women member highlighted the fact
that their opinion was not considered in JFM meeting. When the Forest Department
officers arranges meeting, it is mainly attended by the male villagers. The Beat
officers also meet with male villagers only.
Women members of JFM in village Ambari of Purulia district, Mongala Kora and
Sabita Hansda, have commented in the group discussion:
“We gather fallen seeds, leaves, grass, fruits and flowers so that plants are not
damaged. We collect only those NTFPs which are permitted by the FPC. We
obey them because if Forest Department take any action against us we will
suffer a lot. We are very poor and solely dependent on forest products. Hence
we have to protect the local forest at any cost for our own endurance.” (Dated
28th November, 2011, medium of language was Santali/ Bengali)
Women’s Participation in CPR Management
156
PHOTO 8 PHOTO 9
Interaction with rural women in Jiyathole Women collecting cow dung in Baldanga
In our study villages in the district of Bankura and Purulia in West Bengal, we have
analysed the average income and average monthly contribution of CPR to household
income for male and female headed households and is shown in Table 7.1 below. As
per the table, the percentage of Female headed households in the villages of Dulaltora
(27.78) in Bankura district and Jiyathole (29.63) and Ambari (25) in Purulia district is
relatively high. We have observed from Table 4.12 in Chapter 4, the extent of
depletion of common property resources during 1990-2010 is less in the above
mentioned 3 villages where percentage of female headed household is high as
compared to the rest of the villages in the study area. Hence, we can infer that female
headed households are more concerned about forest conservation as compared to the
male headed households.
The average income of male headed households and the female headed household
excluding and including CPR income along with their average monthly contribution of
CPR as depicted in Table 7.1 shows a particular trend. In most cases in the female
headed households, the average contribution of CPR to the household income is high as
compared to male headed household.
Women’s Participation in CPR Management
157
In the village of Panjhoria in Bankura district, the average monthly contribution of CPR
in the household income for a female household is as high as 40 percent as compared to
22 percent for a male headed household. Similarly in the village of Jiyathole in Purulia
district, the average monthly contribution of CPR in the household income for a female
and a male headed household is 55 percent and 42 percent respectively. The result from
our surveyed villages in Bankura and Purulia district reveal that the average monthly
contribution of CPR in the household income for a female and a male headed
household is 44 percent and 26 percent respectively. This data implies that the female
headed households depend more on CPR collections as compared to the male headed
households to supplement their household income.
In Table 7.2 below, we depict the time spent by the household members in CPR
collections and the employment days generated thereby.
TABLE 7.1
(Rs) (%) (Rs) (%)
Panjhoria 23 3 11.54 653 246 841 412 188 22 166 40 Ramjibanpur 20 0 - 1,058 - 1,254 - 196 16 - -
Seolibona 45 9 16.67 912 554 1,089 847 177 16 293 35 Baldanga 6 1 14.29 529 490 693 641 164 24 151 24 Dulaltora 13 5 27.78 546 844 751 1,138 205 27 294 26
Tantirdanga 24 1 4.00 606 700 771 899 165 21 199 22 Jiyathole 57 24 29.63 757 646 1,314 1,439 557 42 793 55
Marbediya 21 4 16.00 468 579 605 678 137 23 99 15 Ambari 33 11 25.00 825 381 959 505 134 14 124 25
131 19 12.67 771 540 954 798 183 19 258 32 111 39 26.00 724 555 1,083 1,069 359 33 514 48 242 58 19.33 749 550 1,014 977 265 26 427 44
Source: Field Survey, 2011
Bankura
Purulia
Women Headed Household and CPR collection
Bankura TotalPurulia TotalGrand Total
Percentage of female headed
household to total
household
District Name of village
No. of Male HouseHolds
Head
No. of Female
HouseHolds Head
Average income of Male Head HouseHolds excluding
CPR income (Rs)
Average income of Female
Head HouseHolds excluding
CPR income (Rs)
Average income of Male Head HouseHolds
including CPR income
(Rs)
Average income of Female
Head HouseHolds
including CPR income
(Rs)
Average Monthly Contribution of CPR in HouseHold Income
Male Female
Women’s Participation in CPR Management
158
TABL
E 7.2
Male
Fem
aleTo
tal
Male
Fem
aleTo
tal
Male
Fem
aleTo
tal
Male
Fem
aleTo
tal
Panj
horia
2632
3466
00
016
1040
6956
796
1511
Ram
jiban
pur
2026
2450
00
018
2827
6445
929
1411
Seol
ibon
a54
7484
158
260
2644
1270
8011
492
711
9Ba
ldan
ga7
98
170
00
615
740
1355
912
10Du
lalto
ra18
2038
583
03
1350
2252
3602
87
8Ta
ntird
anga
2531
3566
50
519
1127
7646
878
109
Jiyat
hole
8110
314
624
92
02
5306
1052
115
827
69
8M
arbe
diya
2535
4479
20
216
2025
6541
856
77
Amba
ri44
5872
130
80
820
7043
2063
904
86
150
192
223
415
340
3411
726
1968
131
407
811
915
019
626
245
812
012
8996
1740
626
402
68
730
038
848
587
346
046
2072
237
087
5780
97
108
Sour
ce: F
ield S
urve
y, 20
11
to 8
hour
s job
) and
then
divid
ing it
again
with
the n
umbe
r of h
ouse
hold
mem
bers
Wom
en an
d CP
R co
llect
ion in
last
one
mon
th o
f the
dat
e of s
urve
y
Note:
The A
vera
ge E
mplo
ymen
t man
days
for m
ale an
d fem
ale P
er m
embe
r Per
mon
th is
calcu
lated
by di
viding
the t
ime s
pent
by th
em in
CPR
colle
ction
by 8
(One
man
day i
s equ
ivalen
t
Bank
ura T
otal
Puru
lia To
tal
Gran
d Tot
al
Dist
rict
Nam
e of v
illage
No. o
f Ho
useH
olds
No. o
f Adu
lts in
volve
d in
CPR
Colle
ction
No. o
f Chid
ren i
nvolv
ed in
CPR
Co
llect
ionTim
e inv
loved
in C
PR C
ollec
tion
in las
t 1 m
onth
(Hou
r)
Aver
age E
mplo
ymen
t man
days
Pe
r mem
ber P
er m
onth
in C
PR
colle
ction
Bank
ura
Puru
lia
Women’s Participation in CPR Management
159
From Table 7.2, we observe that in the study villages in Bankura and Purulia districts,
out of total 873 adult members involved in CPR collections in the last 1 month, 485
members are female and 388 are male. This figure implies that the female adult
members consider CPR collection as an important household activity as compared to
the male adults in the study area. The time involved in CPR collection in the last 1
month in hours for the male and female members shown in Table 7.2. The data
reveals that female members of the household spent more time in CPR collections as
compared to the male members in all the villages in the study area. The income
generated by collection of CPRs by the household members supplement the total
household income. In order words, collection of CPRs by the household members is a
means of employment. We have depicted the average employment man days
generated in collection of CPRs for both adult male and female. One man day is
equivalent to 8 hours of work. Thus the average employment man days for male and
female is calculated by dividing the time consumed by them in CPR collection by 8
and then dividing it with the number of household members. From the table, we
observe that the average employment man days in CPR collection for male and
female in the study villages of Bankura district is 8 and 11 man days per month
respectively. Similarly, in the study villages in Purulia district, the average
employment man days in CPR collection for male and female are 6 and 8 man days
per month respectively. From the Table 7.2, we notice that the female adult members
are involved in CPR collections to a much larger extent as compared to the male
members. Further, the female members generate higher employment man days
through CPR collections as compared to the male members in the household.
Hence we can conclude that the women play a much larger role in CPR collections as
compared to the men in the study area. Further, the women are able to supplement
the household income through CPR collection. The CPR collections are not only used
for consumption but also act as an additional source of income through sale.
Women’s Participation in CPR Management
160
7.3 Women’s Participation in Forest Resource Management
Government of India (GoI) had launched the Joint Forest Management programme in
1990 in order to conserve, protect the forest and also rejuvenate the degraded ones.
JFM was initiated for sharing of products, responsibilities, control and decision
making authority over forest lands between the local community and the forest
department. JFM provided the opportunities for the forest dependent people to meet
their subsistence requirement. It provided the users a stake in the forest benefits and a
role in planning and management for sustainable development of the forest. However,
due to social, economic and cultural constrains, participation of women in JFM
program is limited. The lack of participation by women greatly reduced their
opportunity to share information and knowledge. Further it also prevents them to
voice their opinions. Thus several activities could negatively impact on both women
and their use of the forests. Further, women’s concerns weren’t heard at JFM
meetings, mainly due to the fact that men always decided the timing of the meetings.
Even when women were physically present at meetings their views weren’t heard and
only the opinion of the men are taken into account (Patricia, et al., 1998). While, the
women are interested in ensuring increased and sustained availability of NTFPs, the
men are generally interested in maximising monetary returns. The poor involvement
of women also meant that the choice of species for planting in JFM areas was often
decided by men, who chose cash profits over fuel and fodder yields. This reduced
women’s involvement and interest in the JFM program. In several cases information
about the provisions, rules and responsibilities of JFM program are not communicated
to the women folk. The Forest Department staffs also do not make efforts to
understand women’s point of view and push them for active participation in the JFM
program. The views of the women are not considered. For the women, participation in
JFM meeting means loss of wage. No provisions are made for the security of the
women taking active part in the JFM program. Women normally collect firewood
from the forest and the forest department staffs often prohibit such activities in the
protected areas. Since no alternatives are made for women who depend on the
firewood for their income, they easily become resentful of JFM (Pathak, 2000).
In Table 7.3, we have depicted the participation in the Joint Forest Management in the
study villages in 2010.
Women’s Participation in CPR Management
161
TABL
E 7.3
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Male
Fem
ale
Panjh
oria
255
287
3611
155
72
52
20
72
Ramj
ibanp
ur9
010
212
28
20
00
02
02
0Se
olibo
na36
1042
1251
1823
1210
010
44
04
2Ba
ldang
a3
03
04
03
00
00
00
01
0Du
laltor
a16
821
1025
1412
85
37
20
01
1Ta
ntirda
nga
2411
3012
4220
1512
102
84
20
72
Jiyath
ole91
2310
231
128
4777
3020
715
710
06
3Ma
rbediy
a16
218
522
815
42
14
10
01
2Am
bari
3415
4118
5525
2010
124
128
50
63
113
3413
443
170
6576
3932
730
1210
022
714
140
161
5420
580
112
4434
1231
1615
013
825
474
295
9737
514
518
883
6619
6128
250
3515
Sour
ce: F
ield
Sur
vey,
2011
Parti
cpati
on of
Hou
seho
ld me
mber
s of s
tudy
area
in JF
M
No. o
f non
-ac
tive
mem
bers
in JF
M (20
10)
Distr
ictNa
me
of
villa
ge
No. o
f Hou
seho
ld
mem
bers
parti
cipat
ion
in
JFM
(201
0)
No. o
f mem
bers
invo
lved
in
plan
ning
&
decis
ion
mak
ing
(2010
)
No. o
f mem
bers
invo
lved
in
Impl
emen
tatio
n (20
10)
No. o
f Hou
seho
ld
mem
bers
parti
cipat
ion
in
JFM
(200
8)
No. o
f Hou
seho
ld
mem
bers
parti
cipat
ion
in
JFM
(2009
)
Bank
ura T
otal
Puru
lia T
otal
Gran
d Tot
al
No. o
f mem
bers
invo
lved
in
Bene
fit sh
arin
g (20
10)
No. o
f mem
bers
invo
lved
in
Eval
uatio
n (20
10)
Bank
ura
Purul
ia
Women’s Participation in CPR Management
162
In Table 7.3, we have shown the breakup of members (male and female) involved in
the different aspects of the JFM i.e. planning and decision making, implementation,
benefit sharing and evaluation. The Non-active members (male and female) have also
been shown separately.
The female villagers of Ambari in Purulia district, Badani Kishu and Neoti Mudikora,
had shared their view on Joint Forest Management:
“We are the major collectors of forest products in our village and also
members of JFM. The Forest Protection Committee members, most of the
time, do not inform us about the meetings ……even if we are present our
opinions are not considered…. we are forced to stay away from planning,
implementation and benefit sharing activities… We feel neglected” (Dated
19th December, 2011. Medium of language- Santali /Bengali)
From Table 7.3, we observe that the participation of the female members in JFM is 65
in Bankura district and 80 in Purulia district which is very low as compared to the
male members. However the participation of the female members in JFM has
increased during the last 3 years in both the districts. Majority of the household
members actively participating in the JFM are involved in the planning & decision
making stage only. The number of members involved in implementation, benefit
sharing and evaluation is very low. It is also observed that there are a sizeable number
of household members who are not active participants in the JFM and therefore they
are not active members. Participation of the female members is restricted to the
planning and decision making stage only.
Thus we can conclude that participation in JFM is mainly dominated by the male
members and therefore the female members have almost no say in the JFM activities
in the study area.
Women’s Participation in CPR Management
163
7.4 Women’s Participation in JFM and Sustainability in Forest Resources
From our study area, we have observed that women members spent more time in CPR
collection as compared to their male counterpart and female headed household depend
more on CPR collections as compared to the male headed households to supplement
their household income. Hence forest degradation affects the women member badly
and thus they are very much concerned about forest resource sustainability. Therefore
we have assumed that active participation of women member in JFM have a positive
impact on sustainability of forest resources. However, sustainability of forest
resources also depends on some socio-economic variables like sex ratio, female
headed household, female literacy, per capita cattle unit, dependency ratio, market
pressure and imposition of fine on CPR rule breakers.
7.4.1 Empirical Model Specification Rural women are responsible for collecting forest products in order to meet the
subsistence needs of the household. Collection of NTFPs also acts as a supplement to
their income. Women greatly value the forest conservation since degradation of the
forest not only leads to depletion of the forest resources but increases the distance to
be covered in order to collect them. Women who are largely dependent on the forest
for collection of forest resources like firewood and fodder for their subsistence are
also very much aware of the forest restoration program.
As women are major users of forest resources, they should involve themselves in
planning, decision making and implementation of the forest conservation programme.
Participation of women in JFM includes reforestation work and protection of
plantation, freely grazing livestock and illicit removal of forest products. Hence
sustainability of forest resources depends on active participation of women in JFM. In
addition to examining the importance of women’s participation in JFM, we have also
considered some demographic factors such as family size, sex ratio, gender gap in
education, male headed household etc. and market pressures. We have also examined
their influence on forest conservations.
Women’s Participation in CPR Management
164
In this section we have identified these factors within rural household and villages
determining the sustainability of forest resources through econometric analysis to give
an understanding between active participation of women in JFM and sustainability of
forest resources ( )SFOREST which can be written as:
SFOREST = f (Family size, Sex ratio, Active participation of women in JFM,
Gender gap in education, Number of male household head, market pressures)
We have used a Binary Probit regression model to examine the determinants of
sustainability of forest resources.
Our specified model is:
0 1 2 3 4 5SFOREST SEXR FHEAD FLIT DEPR PCATTLEα α α α α α= + + + + +
6 7 8WACTPM DISM PUNSHMα α α+ + + +∈ ….……………..(1)
Here the dependent variable is
SFOREST = Sustainability of forest resources
Sustainability is measured on the basis of the data collected in the village survey from
three different variables:
i) Regulate illicit grazing
ii) Control the extensive removal of forest products
iii) Regenerate the allotted forest
All these three variables is a dichotomous variable where we coded ‘Yes’ response as
‘1’ and a ‘No’ response as ‘0’. To construct the sustainability of forest index, we have
created a dummy variable that takes the value of ‘1’ if all the three variables related to
forest sustainability have value’1’ and otherwise have value ‘0’. Thus if the responses
to all the three variables were ‘Yes’, the value of sustainability of forest index would
be ‘1’. If one of the variables were coded as ‘0’, the value would be ‘0’.
Women’s Participation in CPR Management
165
The Explanatory variables are defined in Table 7.4 below:
TABLE 7.4
Description of Variables in Binary Probit Model Explanatory
variables
Description Expected
Sign
SEXR Sex Ratio i.e. ratio of the female to male in the
household +
FHEAD
Female Headed Household
FHEAD =1, if the household head is female
FHEAD =0, if the household head is male
+
FLIT Female Literacy rate i.e. number of years of schooling
of the female members of the household +
DEPR
Dependency Ratio which indicates the employment
condition of the household i.e. proportion of number of
non-working members to the total number of family
members in the household
_
PCATTLE
Per Capita cattle unit which is the ratio of cattle unit to
the family size i.e. 1 cattle unit= 1 bullock /cow or 4
goats or 4 pigs or 100 chicken /hens/ducks
_
WACTPM
Number of women member actively participating in the
Joint Forest Management. Active participation of
women implies they participate in meetings of the
forest protection committees; they are involved in
planning, decision making and implementation of the
forest conservation programme.
+
DISM Distance to the closest market (in km); i.e. measure of
market pressures by variation in distance from markets +
PUNSHM
Punishment against violation of CPR rules
PUNSHM =1, if the JFM committee impose fines on
members who break CPR rules
PUNSHM =0, if no fine is imposed on members who
violates CPR rules
+
Women’s Participation in CPR Management
166
Here 0α is constant and iα ( i = 1, 2, …,8) are the coefficients associated with the
explanatory variables and ∈ is the random disturbance term.
7.4.2 Results and Discussions We have tested this regression equation using household level data collected through
field survey in Bankura and Purulia districts in West Bengal. We have estimated the
regression equation by binary probit model using EViews 7 economic software. The
results of our analysis are presented in Table 7.5 and Table 7.6.
TABLE 7.5
Determinants of Forest Sustainability (Bankura District) Dependent Variable: SFOREST Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 150; Included observations: 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C -6.426145 2.913384 -2.205732** 0.0274
SEXR 3.186774 1.712963 1.860387*** 0.0628 FHEAD -0.056348 1.350760 -0.041716 0.9667
FLIT 1.043060 0.603126 1.729421*** 0.0837 DEPR -2.701982 2.273579 -1.188427 0.2347
PCATTLE 0.623331 1.037894 0.600572 0.5481 WACTPM 1.062673 0.595308 1.785081*** 0.0742
DISM -0.440801 0.646270 -0.682069 0.4952 PUNSHM 3.724002 1.624603 2.292254** 0.0219
McFadden R-squared 0.922108 Mean dependent var 0.426667
S.D. dependent var 0.496250 S.E. of regression 0.133702 Akaike info criterion 0.226300 Sum squared resid 2.520560 Schwarz criterion 0.406938 Log likelihood -7.972472 Hannan-Quinn criter. 0.299687 Deviance 15.94494 Restr. deviance 204.7058 Restr. log likelihood -102.3529 LR statistic 188.7609 Avg. log likelihood -0.053150 Prob(LR statistic) 0.000000
Obs with Dep=0 86 Total obs 150
Obs with Dep=1 64 *Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level
Source: Estimated by EViews 7 computer software using field survey data of 2011
Women’s Participation in CPR Management
167
TABLE 7.6
Determinants of Forest Sustainability (Purulia District)
*Significant at 1 percent level, ** Significant at 5 percent level, *** Significant at 10 percent level Source: Estimated by EViews 7 computer software using field survey data of 2011
From the above tables 7.5 & 7.6, we observe that in most of the cases, the results are
consistent. However, in some cases we have got contradictory results. As expected,
Sex Ratio ( )SEXR has been found to have a positive influence on Sustainability of
Dependent Variable: SFOREST Method: ML - Binary Probit (Quadratic hill climbing) Sample: 1 150 Included observations: 150 Covariance matrix computed using second derivatives
Variable Coefficient Std. Error z-Statistic Prob. C -0.838492 0.906073 -0.925413 0.3548
SEXR 0.041166 0.275027 0.149681 0.8810 FHEAD 1.092519 0.528422 2.067511** 0.0387 FLIT 0.404143 0.156220 2.587015* 0.0097 DEPR -4.668060 1.366426 -3.416256* 0.0006 PCATTLE 0.075123 0.565857 0.132760 0.8944 WACTPM 1.162318 0.450463 2.580275* 0.0099 DISM -0.026964 0.276332 -0.097577 0.9223 PUNSHM 1.226608 0.482273 2.543390** 0.0110
McFadden R-
squared 0.792005 Mean dependent var 0.486667 S.D. dependent var 0.501497 S.E. of regression 0.202104 Akaike info criterion 0.408195 Sum squared resid 5.759286 Schwarz criterion 0.588833 Log likelihood -21.61462 Hannan-Quinn criter. 0.481582 Deviance 43.22924 Restr. deviance 207.8375 Restr. log likelihood -103.9187 LR statistic 164.6082 Avg. log likelihood -0.144097 riProb(LR statistic) 0.000000
Obs with Dep=0 77 Total obs 150
Obs with Dep=1 73
Women’s Participation in CPR Management
168
forest resources in Bankura and Purulia district. As the number of female members in
the family increases relative to the male members, the Sex Ratio increases. Since the
women are mainly involved in collection of forest products, the forest degradation
affects them badly and they are more conscious about forest conservation. Therefore
we observe that there is a positive relation between Sex Ratio and sustainability of
forest resources. Our result is significant in Bankura district but insignificant in
Purulia district.
In the case of female headed households ( FHEAD ), we noticed positive relation with
sustainability of forest resources as female members are more interested to improve
forest quality. In line with our expectation, we have observed positive and significant
result in Purulia district. However, the impact is negative and insignificant in Bankura
district.
The regression results shows a positive relationship between female literacy ( FLIT )
and sustainability of forest resources. As expected we observe that the coefficient of
female literacy has positive and significant impact on sustainability of forest resources
in both the districts. In fact educated female members understand the importance of
forest conservation. Hence with increase in female literacy the probability of forest
sustainability also increases.
In line with our expectation, Dependency Ratio ( DEPR ) is found to be negative in
case of forest resource conservation. As dependent member increases in the family,
burden on the forest products increases which ultimately decreases the probability of
forest resource sustainability. The result is significant in Purulia district and
insignificant in Bankura district.
From the regression analysis, we observe that there is a positive relationship between
per capita cattle unit ( PCATTLE ) and sustainability of forest resources. In fact,
household with higher per capita cattle unit collect more fodder to feed animals and
require higher quantity of fuel wood to prepare concentrated food for the animals.
Hence there is a negative relation between the two. However, in contrary to our
expectation, we have observed positive but insignificant results in both the districts. It
Women’s Participation in CPR Management
169
may be possible that household having large number of cattle become more conscious
about forest conservation for the survival of their livestock.
In the case of distance to the closest market ( )DISM we assume that when markets
are nearer, the motivation for extraction of forest resources is much greater. Contrary
to our expectation, DISM has a negative impact on sustainability of forest resources
in both the districts suggesting that as distance to markets decreases; control of forest
degradation is more prominent. This can be explained by the fact that as distance to
market decreases, the probability of inspection of Government officials to monitor the
forest protection committee increases.
As expected, punishment against CPR rule breakers ( PUNSHM ) has positive and
significant impact on forest resource sustainability in both the districts. In fact, if
forest protection committee imposes fine on rule breakers, illegal extraction of CPR
products is controlled.
The most important finding of this study is impact of active participation of women
member in JFM (WACTPM ) on sustainability of forest resources and the result has
been found to be positive and significant in both the districts. Greater and effective
involvement of women member in planning, decision making and implementation
activities improve the forest quality. Since the women are involved in collection of
NTFPs like firewood and fodder which is their means of subsistence and also a source
of income, they are more likely to prevent illegal harvesting of the forest products. It
is also observed that men who are engaged in patrolling the common forest find it
difficult to apprehend women who involve in indiscriminate use of the forest
resources. Thus the patrol team should consist of both men and women.
The results advocate that gender equity in participation in JFM enhances sustainable
governance of the forest resources.
Women’s Participation in CPR Management
170
7.5 Conclusion
Our study has investigated the impact of women’s active participation in JFM on
sustainability of forest resources in our surveyed villages of Bankura and Purulia
districts in West Bengal. The result of the survey indicate that female headed
household depend more on CPR collections as compared to the male headed
households to supplement their household income. Again, female members generate
higher employment man days through CPR collection in comparisons with male
members of the household. Hence we can conclude that women play a significant
role in CPR collection. Despite the fact that the women are the major users of the
common property resources, their involvement in Joint Forest Management is
negligible. We have estimated the impact of women’s active participation in JFM on
sustainability of forest resources through binary probit model using EViews 7
economic software. The most important finding of this study is the positive and
significant impact of active participation of women member in JFM on sustainability
of forest resources in both the districts, which is consistent with our hypothesis.
Women greatly value the forest conservation since forest degradation leads to
depletion of forest resources which ultimately increases the distance to be covered in
order to collect forest products. Hence, women are more concerned about the
improvement of regeneration of forest resources. Therefore, women should involve
themselves in planning, decision making and implementation of forest conservation
programme. Further women should participate in patrolling the forest with male
guards and thus prevent illegal extraction of forest resources in a better way.
Therefore we conclude that gender equity in JFM participation improves the
sustainability of forest resources.
Summary, Conclusion and Policy Suggestions
171
CHAPTER 8
SUMMARY, CONCLUSION AND POLICY SUGGESTIONS 8.1 Summary In the foregoing chapters, the thesis dealt with Common Property Resources (CPRs)
collection and its implications to rural Poverty and Environmental Sustainability.
Attempt has also been made to analyse the association between Agricultural risks and
CPR Collection. The issues of Property Rights, Gender and Resource management
have also been covered in the study with firm field data of Bankura and Purulia
districts of West Bengal. The thesis is made up of eight integrated chapters. However,
the core of the thesis which dealt with major research questions of the study consists
of four chapters (Chapter 4, 5, 6 & 7). The thesis starts with a brief introduction which
deals with the identification of research problem. Chapter 2 highlights an exhaustive
review of the existing literature on the research problem. The objectives, research
questions, sample design and methodology including the conceptual frameworks of
the study have been outlined in Chapter 3. The four main chapters that we have dealt
with in the thesis are Common Forest Resources: Contribution and Crisis (Chapter 4),
Agricultural Risk and Common Property Resources (Chapter 5), Common Forest and
Participatory Management (Chapter 6) and Women’s Participation in CPR
management (Chapter 7). A brief description of conceptual framework has been given
at the beginning of four main chapters along with objectives. We have tested these
objectives on the basis of empirical data and intensive observations from these
villages. The Summary, Conclusion and Policy suggestions of the study appear in
Chapter 8 followed by Bibliography and Appendices.
In Chapter 4 we have explained the nature of dependency of the rural poor on
CPRs and its impact on income, employment and rural poverty. Environmental
impact of poverty has also been explained by analysing the relationship between rural
poverty and environmental degradation. This chapter broadly analyses the
contribution and crisis of CPRs in the study area.
Summary, Conclusion and Policy Suggestions
172
The major findings can be summarised below:
i) Common Property Resources and Rural Livelihood Common Property Resources (CPRs) play a vital role in the rural livelihood of
our study villages. Rural poor are very much dependent on Common Property
Resources for their subsistence. They collected CPRs from the common forest
area, rivers, ponds and common grazing lands. A large variety of CPRs are
gathered by the poor and not so poor households for domestic and commercial
purposes. The rural households collect various CPRs like fuel wood, dry leaves,
shrubs, dung cakes, etc. for cooking and heating purposes. Common forest
supply raw materials like bamboo, canes, logs from trees, dry leaves for
constructing their houses. Their cattle graze in the common forests and the
shrubs and grasses are utilised as fodder for the animals. Fruits, vegetables,
fishes, root, meat from hunted birds and animals are procured from CPRs and
are used for consumption as well as for sale. Few plants and roots are used as
medicines to cure their ailments. Therefore, Common Property Resources are
means of subsistence for all the households in the surveyed villages.
ii) CPRs as Life Supporting Resource The field survey data reveals that around 19.04 percent of the household income
in Bankura district and around 18.11 percent in Purulia district is coming from
the CPR based activities. For BPL households, 27.28 percent in Bankura district
and 27.92 percent in Purulia district of the total household income comes from
CPR based activities whereas in case of APL it is 2.31 percent and 7.71 percent
respectively. This implies that BPL households enjoy a greater proportion of
income from CPRs both in relative as well as absolute terms. Further, the
contribution of CPRs to the total monthly consumption expenditure is around
22.29 percent and 20.17 percent in Bankura and Purulia district respectively.
Thus CPRs act as a life supporting resource in our surveyed villages.
Summary, Conclusion and Policy Suggestions
173
iii) CPR and Employment Generation CPRs play an important role in employment generation. In our study area, there
is very little scope of employment in non-agricultural sector due to high level of
poverty, illiteracy and lack of technical skills of the rural households. Hence
most of people engage themselves in CPR-based activities. It is interesting to
note that an average household could generate around 116 and 95 employment
man days annually from CPR based activities in the study villages of Bankura
and Purulia district respectively.
iv) CPR as a Source of Energy
Almost all the surveyed households collect fuel wood and cow dung from the
common forest area for the purpose of cooking. The households use very little
commercial fuels like coal, kerosene, electricity and cooking gas as they are
very costly and beyond the reach of the poor households. On an average 79.35
percent and 76.78 percent of the total monthly household energy consumption
were met by the CPR products collected from the common forest area in
Bankura and Purulia districts respectively. It is also observed that both the BPL
and APL households use fuel wood gathered from CPRs for their household
energy needs.
v) CPR and Animal Grazing Most of the households in our surveyed villages utilise common forest area for
animal grazing. The dependency of the households on CPR in our study area for
animal grazing has been estimated in terms of the creation of animal unit
grazing days in common grazing land. As estimated, CPR supported 88 animal
unit grazing days per household in Bankura district in last one month during
survey. The dependency of households on CPR for animal unit grazing in
Purulia district is much higher; an average household gets support of 114 animal
unit grazing days per month, which confirms the heavy dependence of the rural
poor on the CPRs for animal grazing.
Summary, Conclusion and Policy Suggestions
174
vi) CPRs and Rural Poverty CPR plays a vital role to alleviate rural poverty. With the exclusion of the
contribution of CPRs to the total household income, the extent of poverty
increases around 26 percent in our study area.
vii) Regression Results: Determinants of CPR extraction The CPR product extraction by rural households in our study area depends on a
host of factors relating to household and village characteristics. The family size,
average age, level of education, female percentage, land holding pattern,
livestock unit, the distance between dwelling place and the common forest,
market distance are some of the socio-economic, demographic and geographical
factors, which determine the extraction of CPR products. We have used multiple
regression model to examine the determinants of CPR extraction. It is evident
from the analysis that most of the important variables are significant with the
expected sign. From the regression analysis we observed that the family size
( FSIZE ), size of livestock ( LIVESTOCK ), female percentage in the household
( FEMPER ) have a positive impact on community forest income in both the
districts. Since CPR extraction is a labour intensive technique, larger the family
size higher is the labour time available for the collection of community forest
products. Again households with larger number of livestock collect more fodder
to feed the animals. We further observe that female members are mainly
involved in CPR collection in our surveyed villages in both the districts.
Education ( AVRSCH ) i.e. average years of schooling of the household, forest
distance ( FORESTDIST ), distance to the nearest market ( DISM ) have
negative impact on CPR income in both the districts. Household members who
are better educated get better job opportunities and therefore are less interested
in CPR collection. Household nearest to the forest area extracts more CPRs than
the household far away from the forest area. In the case of distance to the
nearest market, the motivation for extraction of CPR product is greater when
market is nearer due to greater marketability of the CPR products. We observed
that there is a positive and significant relationship between average age of the
household ( AVRAGE ) i.e. experience in collecting CPR products and CPR
income in Purulia district. It is easier for an experienced household member to
Summary, Conclusion and Policy Suggestions
175
collect more CPRs and thus smooth their consumption and livelihood. However,
the result is negative and insignificant in Bankura district. Again, we have
observed that the coefficient of OWNLAND is negative and significant in
Bankura district. However the result is contradictory in Purulia district which
implies that households with large ownership of cultivable land collect more
fuel wood. This is mainly due to the fact that land in Purulia district is mostly
infertile and so mere ownership of land fails to reduce the dependency on CPRs.
Most importantly, CPR acts as a life supporting system to the rural poor. In the
absence of alternate employment opportunities to the rural poor, they are highly
dependent on CPRs. Our regression analysis indicates that there is a positive
relationship between poverty ( POVR ) and the CPR income and the result is
highly significant in both the districts. Hence in the case of the poor, the income
from common forest has a higher percentage as compared to that of the ‘not so
poor’.
viii) Poverty and Environment The environment matters a lot to the rural poor. The wellbeing of the poor is
strongly related to the environment. From our field survey report, we have
observed that the common forest area declined around 12.05 percent in the
study villages during the period 1990-2010. However, the depletion of forest
area is high in Bankura district as compared to Purulia district. To analyse the
relationship between poverty and environmental degradation, we have used
logit regression model. The important finding of our study is that the coefficient
of environmental degradation is positive and significant in both the districts
which imply that with more environmental degradation the probability of the
incidence of poverty increases. In fact, most of the households in our study area
are very poor and are heavily dependent on CPRs for their livelihood. The level
of extraction of CPRs is higher than their regenerating process and thus the
environment gets further degraded. However, poverty cannot be made solely
responsible for environmental degradation. Rise in population, growing
commercialisation of agricultural and CPR products, emergence of market are
other factors responsible for forest degradation.
Summary, Conclusion and Policy Suggestions
176
In Chapter 5, the interrelationship among agricultural risk, non-timber forest
collection and the extent of rural poverty has been analysed.
The major findings are:
i) Nature of Agriculture The economy of Bankura and Purulia districts are primarily based on
agriculture. Paddy is the main crop of both the districts. In addition, wheat,
oilseeds, maize, sugarcane, groundnut and pulses are other important crops
grown in the surveyed villages. Since agriculture is largely dependent on the
vagaries of monsoon and drought, there are several risks associated with it in
our study area such as adverse weather, seasonal flooding, unpredictable soil
quality, crop diseases, price shocks, etc. The high dependence of the rural poor
on the CPRs in the form of Non Timber Forest Products (NTFPs) is primarily
due to uncertainty and low productivity in agriculture.
ii) Agricultural Productivity Most of the farmers in our study area belong to small size category and the
nature of agriculture is backward in nature. The average yield of agricultural
productivity in the surveyed villages of Bankura is 2144 kg/hectare whereas in
Purulia district it is 1594 kg/hectare in the year 2010, which is much lower than
the state average (2708 kg/hectare). Most of the households in the villages are
so poor that they cannot afford to purchase mechanised agricultural implements
like power tiller and pump set and therefore used mainly wooden plough and
bullocks for cultivation. From the surveyed data on agricultural labour in the
surveyed villages of both the districts reveal that men and women equally
participate in agriculture in the process of sowing, weeding and harvesting.
Further it is also observed that children are also involved in agricultural labour
in the several surveyed villages.
Summary, Conclusion and Policy Suggestions
177
iii) Agricultural Risk and CPR CPR in our study area mitigates a sizeable proportion of agricultural risk. Since
agricultural practice in the study area is backward in nature and subjected to
weather risk in the form of agricultural shock in times of production shortfall,
the farmers fall back upon CPRs for their survival and also addresses
significantly their agricultural risk. Here CPRs act as a safety net during the
period of agricultural risk. As established from our field data in the year 2010,
the agricultural production variability resulted in agricultural shortfall of Rs
209032 in Bankura and Rs 164391 in Purulia as compared to the normal year
(2008). Interestingly, it is observed that during this period the extraction of CPR
is also high compared to the normal year.
iv) Agricultural Risk and Extraction of Forest Products: Count Data
Regression Model We have explained the determinants of forest collection labour through
econometric analysis to give an understanding of the impact of agricultural
product risk on the extraction of forest products. The forest collection labour is
determined not only by social, economic, demographic variable but also by
agricultural shock and agricultural risk. We have applied Count Data Model
using STATA computer software package. As per the Count Data analysis, the
association between forest collection trips and age of the household head
( AGEHEAD ) is positive and significant whereas square of age of the household
head ( SQAGEH ) is negative and significant which imply that households with
older heads normally take more trip on forest collection except the oldest
household member. As almost all the household members in the study area
collect CPRs, the larger the family size ( FAMSIZE ), the more is the forest trips
to collect NTFPs. As expected education ( AVRSCH ) i.e. the average years of
schooling of the household has a negative impact on forest collection trips in
both the districts. On the basis of our results we can infer that households with
large number of livestock ( LIVESTOCK ) are equally responsible for excessive
extraction of forest products as they take more trips to gather fodder to feed
animals. It was observed that forest distance ( FORESTDIST ) i.e. the distance
Summary, Conclusion and Policy Suggestions
178
between the residence and the common forest has a negative impact on forest
collection trips. The key findings of our regression results indicate that the
coefficients of agricultural risk parameters ( AGRIRISK ) and shock parameters
( AGRSHTFALL ) are positive and significant in both Bankura and Purulia
districts, which suggest that household with greater agricultural shortfall and
risk are likely to take more forest collection trips. Hence we can conclude that
NTFP has a supporting role in the wellbeing of the rural poor in the form of
‘natural insurance’. By collecting NTFP, rural households smooth their income
as well as consumption in the period of agricultural crisis.
We have examined the nature of participation in forest management and its impact
on resource utilisation and conservation in Chapter 6. The chapter also focused on
the relationship between the intensity of management practice and the degradation of
forest resources.
The major findings are:
i) Joint Forest Management (JFM) in the Study Area The surveyed villages in the study area are covered by vast forest area. Most of
the villages are extremely poor and are highly dependent on forest products for
their subsistence. Since the forest communities have more knowledge about the
forest, their active involvement is necessary for the sustainability of the forest
resources. The Joint Forest Management was initiated in the study villages in
1992 after the JFM resolution (1990) of the Govt. of West Bengal. In order to
ensure the availability of the forest resources in the long run, the rural
households have actively participated in Joint Forest Management (JFM). A
Forest Protection Committee (FPC) in the study areas has been formed whose
prime responsibility is to protect the forest. The FPC involves the local people
in planning, development of the forest and regeneration of the forest through
plantation of trees. The FPC plays a vital role in enforcing the guidelines laid
down by the JFM schemes. In case anyone breaks the rule enforced by the
Forest Protection Committee or engage in illicit felling of the forest trees, then
FPC take away his cutting implements and impose a penalty ranging from Rs
100/- to Rs 500/- depending upon the seriousness of the crime.
Summary, Conclusion and Policy Suggestions
179
ii) JFM Participation As per the surveyed data, 75.33 percent of the surveyed households in Bankura
district and 85.33 percent in Purulia district participated in JFM. The survey
result also shows that the percentage of household involved in planning and
implementation of JFM in the surveyed villages of Bankura were 60.67 percent
and 12.67 percent respectively as compared to 65.33 percent and 16 percent in
the surveyed villages of Purulia. The data reveals that the incidents of cases of
violation of rules have shown a declining trend in most of the surveyed villages
during the period 2008-2010. It is further observed that the Joint Forest
Management is active in the villages of Dulaltora in Bankura district and
Jiyathole in Purulia district. From our surveyed data we can infer that with the
establishment of JFM, enforcement of Forest protection has shown a positive
trend. Further, the households have realised that protection of the common
forest is in their own interest and violating the rules has an adverse effect on
them.
iii) Determinants of Collective Action in JFM Collective action refers to concerted actions of people that share a common
interest, perceive that interest and act to achieve it (World Bank, 1998). The
forest resources that can be effectively managed by the rural poor depend on the
strength of the collective action i.e. joint action of the community to conserve
forest resources as well as improve rural livelihood. We have explained the
determinants of collective forest management through censored Tobit Model to
give an understanding of the relationship between forest dependency and active
forest management. We have observed from the determinants of collective
action in JFM that family size ( FSIZE ) and average age ( AVRAGE ) have a
positive effect and education ( AVRSCH ) has a negative effect on collective
action in the surveyed villages of both Bankura and Purulia districts. The
coefficient of agricultural income to total income ( PERAGRIN ) is negatively
related to collective action in JFM which implies that households with steady
income from agriculture are less interested in forest product collection and in
active participation in JFM. Again wealth (WEALTH ) has negative and
significant impact on collective action management in the surveyed villages of
Summary, Conclusion and Policy Suggestions
180
both the districts. It is evident from the analysis that collective action in JFM is
positively related to percentage of CPR income to total income ( PERCPRIN )
and the impact is significant in both the districts. This indicates that households
whose dependence on forest product is high are more interested in active
participation in JFM to overcome the uncertainty and insecurity in their
livelihood. It is further observed that percentage of CPR consumption to total
consumption ( PERCPRCSM ) has a positive impact on active participation in
JFM which implies that household who depend more on forest products for
their consumption purposes are very much concerned about forest conservation
and hence actively participate in JFM.
iv) Collective Action and Forest Conservation Our field survey data reveals that collective action in forest management has its
inherent impact on the conservation of forests. Active forest management
lowers the degree of over exploitation and hence the degradation of forest
resources. We have explained the role of forest management in the reduction of
forest degradation by using logit regression model. The result demonstrates that
improved management has a positive role to alleviate environmental
degradation. It is observed that the association between Forest Management
activity and forest degradation is significant in both the districts. As most of the
rural poor depend on CPR, especially forest products for their livelihood, CPRs
should be properly managed so that the rural poor can get maximum benefit
maintaining the sustainability of forest resources.
In Chapter 7, we have examined the role of women in CPR collection and their
performance in JFM committees. We further investigated empirically the effect of
women’s active participation in JFM on forest resource conservation.
Summary, Conclusion and Policy Suggestions
181
The major findings are:
i) Female Headed Household and CPR collection Forest plays an important role in livelihood support system for the rural poor
women in our surveyed villages. Our field survey data reveals that the average
contribution of CPR to the total household income for a female headed
household is 44 percent as compared to 26 percent for male headed household.
This implies that female headed household are heavily dependent on CPR
collections to supplement their household income. Further, female members
generate higher employment man days through CPR collection as compared to
the male members in the household. Thus, women are able to supplement the
household income through CPR collection which is used for consumption as
well as a source of income through sale.
ii) Women’s Participation in JFM Despite the fact that the women are the major users of CPRs, their
involvement in JFM is negligible. As rural women are highly dependent on
forest resources, they greatly value the forest conservation since degradation of
the forest not only leads to depletion of the forest resources but increase the
distance to be covered in order to collect them. We have observed from our field
survey report that the number of adult female participants in JFM in 2010 is 65
and 80 in Bankura and Purulia district, which is extremely low as compared to
170 and 205 of adult male participants in the said district. We further observed
that the female participation in JFM has increased in the year 2010 as compared
to the previous years. However, male domination in JFM still remained in the
study area of both the districts.
iii) Gender Equity in JFM and Forest Resource Sustainability We have estimated the impact of women’s active participation in JFM on
sustainability of forest resources through binary probit model using EViews 7
economic software. We have considered some other socio-economic variables
like sex-ratio, female headed household, female literacy, per capita cattle unit,
dependency ratio, market pressure and imposition of fine on CPR rule breaker
on which sustainability of forest resources also depend. Our regression result
Summary, Conclusion and Policy Suggestions
182
indicates that sex-ratio, female headed household and female literacy have a
positive influence on sustainability of forest resources. Again, dependency ratio
( DEPR ) is found to be negative in case of forest resource conservation which
implies that as dependent member increases in the family, burden on forest
products increases which ultimately degrades the forest resources. The per
capita cattle unit ( PCATTL ) also has a positive impact on sustainability of
forest resources. Again, punishment ( PUNSHM ) against CPR rule breaker has
positive and significant impact on forest resource sustainability in both the
districts. However, distance to the closest market ( DISM ) has a negative
impact on sustainability of forest resources. The most important finding of this
study is the positive and significant impact of active participation of women
member in JFM (WACTPM ) on sustainability of forest resources in both the
districts. Active involvement of women in planning, decision making and
implementation prevent illegal harvesting of the forest products and thus
improve forest quality. Hence gender equity in JFM participation enhances
sustainability governance of the forest resources.
8.2 Conclusion
The theme of the research is Common Property Resource (CPR) collection and its
impact on Poverty and Environmental Degradation and Conservation. The use of
CPRs mainly forest resources is manifold in the livelihood of forest fringe people.
Systematic and Sustainable development of CPRs can make better the standard of
living of the rural poor and play a crucial role in the conservation of forest resources.
Our field survey has provided various research findings. Firstly, our survey report
indicates a very high dependency of the rural poor on CPRs. The data reveals that
18.67 percent of household income and 21.28 percent of household expenditure is
derived from CPR extraction. Due to enormous demand, collection of Non Timber
Forest Products (NTFPs) is an important occupation for our surveyed households.
Almost all the households collect fuel wood from the common forest area for the
purpose of cooking. Most of the households utilise CPRs for animal grazing. Thus
CPRs have an immense role in rural livelihood. The data shows that with the access to
Summary, Conclusion and Policy Suggestions
183
common property resources the poverty level reduces by around 26 percent, which
signifies that the extent of poverty increases sharply in the absence of CPR income.
However, the high extraction of forest resources along with population growth,
growing commercialisation of agricultural and CPR products and emergence of
market in our surveyed villages have resulted in the decline of forest area of 12.66 sq.
km which further impoverishes the forest livelihood.
In our study we also investigated the impact of agricultural risk on the collection of
common forest products. Agriculture is the main occupation of most of the surveyed
households and therefore they depend on nature for any agricultural activities. During
any natural calamities they have no other alternate livelihood opportunities except to
extract forest products. We have observed from our field data in the year 2010 that
due to high agricultural shortfall, the households have extracted more CPR as
compared to the normal year.
In order to ensure the availability of the forest resources in the long run, rural
households have actively participated in collective forest management. Our field
survey data reveals that active forest management has a crucial role in reducing forest
degradation. The field survey report indicates that, although JFM is still under male
domination, women being the major users of forest resources greatly value the forest
conservation and therefore participate in JFM activities. However, the female
participation in JFM is far from satisfactory.
In view of the present condition of the forest resources and the rural livelihood in our
study area, there is an immediate need to conserve forest resources through collective
action of rural communities and it is expected that gender equity in participatory
management will improve the sustainability of forest ecosystem.
Summary, Conclusion and Policy Suggestions
184
8.3 Suggestions and Policy Implications
On the basis of our findings of the study, we now propose some important policy
suggestions for the betterment of the rural livelihood in the context of our surveyed
villages of Bankura and Purulia districts of West Bengal.
The rural poor in our study area are highly dependent on Common Property
resources especially forest resources due to the lack of alternative livelihood
opportunities. The wellbeing of the forest communities is strongly related to the
sustainability of forest resources. However, during our research study we have
observed many situations where this dependence of forest products leads to forest
degradation. Non-availability of alternate income opportunities compel the village
dwellers to depend on CPRs for their livelihood. Thus without offering alternative
income opportunities to the villagers, it is not possible to control excessive
extraction of forest resources. Hence there is an urgent need to expand economic
opportunities especially through the development of non-farm activities so that the
dependence of rural poor on CPRs is reduced drastically.
Under this backdrop, we have the following policy suggestions:
1) Small scale eco-friendly industries including handicrafts should be encouraged
and incentivised by the government.
2) Commercialisation of the agricultural crops should be promoted.
3) Awareness about the real commercial value of the NTFPs at the actual market.
4) NTFPs based industries should be established for the economic development
of forest community.
5) Infrastructure in the form of roads, electricity, drainage system and medical
facilities for the poor should be ensured.
6) Awareness about the importance and benefit of conservation of the forest
resources should be propagated.
7) Unconventional energy sources like solar energy and bio gas needs to be
adopted in order to reduce the dependence on the forest resources.
Summary, Conclusion and Policy Suggestions
185
8) Educational opportunities need to be improved in the study area, so that they
realise the economic value of NTFPs as well as the importance and benefit of
conservation of forest resources. Further, education gives better job
opportunities and hence reduces dependency on forest resources.
Our field survey report indicates that CPRs supplement the rural livelihood and
act as safety net during agricultural crisis. Since the villagers in our study area are
dependent on nature for any agricultural activities, there are several agricultural
risks such as adverse weather, seasonal flooding, unpredictable soil quality, crop
disease etc. associated with it. Based on the above research findings we have
proposed few policy suggestions which not only encourage agricultural
development but also reduce rampant extraction of valuable forest products at the
time of agricultural shortfall.
1) Agricultural production needs to be increased by the use of modern
fertilisers and tools.
2) Traditional mode of livelihood of the rural poor should change by
involving them in new agro-economic activities like off-season
vegetables, etc.,
3) Irrigation system needs to be drastically improved including the rain
water harvesting system to cater to agricultural shocks due to drought.
4) ‘Dry land’ farming should be encouraged.
5) Government should take initiative in providing easy credit facilities at
the time of agricultural crisis.
Empirical evidence of our study confirms that collective action of forest
communities in forest management has an important role in forest resource
conservation. Hence there is a need to motivate the local villagers to actively
participate in forest management activities. The suggestions below follow our
research findings of related issues.
1) Motivate the rural households to actively participate in the
conservation and regeneration of the common forest areas, so that they
Summary, Conclusion and Policy Suggestions
186
understand the relationship between environment and their
development.
2) JFM should implement such rules so that indiscriminate felling of trees
or over grazing of the cattle should be prohibited.
3) The policies and strategies for the afforestation programmes should be
formulated in such a manner that it leads to high success rate and
encourages whole hearted participation of the rural poor.
4) The forest policies should cater mainly to the overall conservation of
the ecosystem and ensure that the forest degradation is minimised to
the maximum extent.
5) The forest department officials should be properly trained and
groomed to ensure that they mix well with the rural households and
disseminate the information for development of the forest area.
The most important finding of our research is the lack of active participation of
women in JFM despite the fact that they are the prime collectors of forest
products. Since women are greatly concerned about the forest conservation their
active involvement in forest management improves the sustainability of forest
resources. On the basis of our research findings we have suggested few policy
prescriptions:
1) Women’s active involvement in forest management should be ensured
and they should be encouraged to take decisions.
2) Forest management policies should be directed at favouring the women
to avoid discrimination.
3) Women’s organisation in the villages needs to be strengthened, so that
they are not marginalised.
4) Emphasis should be made on female literacy so that they can get
alternative income opportunities.
Summary, Conclusion and Policy Suggestions
187
Since our research has been conducted in the economically highly backward villages
of Bankura and Purulia districts of West Bengal, collection of data and information
was not an easy work at all. Keeping in mind all the limitations of the research study
we can conclude that systematic and sustainable use of forest products, creation of
alternative employment opportunities and extensive afforestation programme in our
surveyed area can only save the forest environment. Government should take proper
initiative to generate more awareness among forest communities for the protection,
regeneration and development of Common Property Resources.
Bibliography
188
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Appendices
205
APPENDIX-I
VILLAGE CHARACTERISTICS OF STUDY AREA
District-Bankura Bankura district is the fourth largest district in the state of West Bengal and is located
in its western part, which is popularly known as ‘Rarh’ from time immemorial. It is
situated between 22° 38’ and 23° 38’ North latitude and between 86° 36’ and 87° 46’
East longitude. The total geographical area is 6,882 km2. Geographically, on the north
and north-east, the district is bounded by Bardhaman district from which it is
separated mostly by the Damodar River. On the south-east it is bounded by Hooghly
district, on the south by West Medinipur and on the west by Purulia district. In shape,
it resembles an isosceles triangle wedged in between Purulia and Bardhaman. It is the
“connecting link between the plains of Bengal on the East and Chota Nagpur plateau
on the West.”
MAP A1.1 MAP A1.2
Map of West Bengal-Bankura District Map of Bankura
Bankura district comprises of three subdivisions: Bankura Sadar, Khatra and
Bishnupur. The Bankura Sadar subdivision consists of Bankura municipality and
eight Community Development (CD) blocks. The Khatra subdivision consists of eight
CD blocks, whereas, Bishnupur subdivision consists of Bishnupur and Sonamukhi
municipality and six CD blocks. In total there are 22 Community Development
Blocks. The field survey has been conducted in Bankura Sardar sub-division under
Saltora CD Block.
Appendices
206
As per Census 2011, the population of Bankura district is 3,192,695 of which male
and female were 1,840,504 and 1,755,788 respectively. There was change of 12.64
percent in the population as compared to population as per Census 2001. The areas to
the east and north-east are low lying alluvial plains, whereas to the west, the surface
gradually rises, giving way to undulating tracks and interspersed with rocky hillocks.
The western part of the district has poor, ferruginous soil and hard beds of laterite
with scrub jungles and sal woods. Three major categories of soil are found in Bankura
district: i) red soil ii) alluvial soil and iii) laterite soil. Red soil is found in southern
parts of the district, predominantly in Bishnupur. Bankura has a rich source of forest
with a recorded forest area covering 1482 km2 which is 21.53 percent of the total
geographical area of the state (SFR 2010-11).
The economy of Bankura district is primarily based on agriculture with nearly 62
percent of the main workers of the district engaged in the agricultural sector. The net
area under cultivation is 356904.4 hectare. Paddy is the main agricultural crop and is
produced in 90 percent of the total cultivable land of the district. The cereals
cultivated are Aus, Aman, Boro, Wheat and Maize. The land is primarily of 3 types-
Sali, Suna and Tara or Danga. Sali is suitable for growing of Aman rice, Suna for
various crops like Aus, kharif, sugarcane, cotton, tobacco. The other crops grown in
the district are potato, sugarcane, groundnut and pulses. Livestock also plays an
important role in the economy of Bankura district.
The field survey was undertaken in 6 villages under Saltora Block in the district of
Bankura in West Bengal, India; viz. Panjhoria, Ramjibanpur (Bandhghat), Seolibona,
Baldanga, Dulaltora and Tantirdanga. The village of Ramjibanpur (Bandhghat) and
Seolibona has a total area of 205 hectares. The community is Kora. Ramjibanpur
(Bandhghat) has 54 households and the population is around 250. Out of the total
number of households, 52 are landless. The number of BPL (Below Poverty Line)
card holders in this village is 47. There is no pacca road and the kachcha road present
that can be driven on by a jeep is around 3 km which was constructed in the year
2009-10. The nearest post office and the bank is about 2.5 km and 4 km away
respectively from the village. The market is about 2.5 km away, while the police
station is 12 km away. The high secondary school is at a distance of 10 km. The
Appendices
207
primary health centre is 4 km away. The cultivated land is around 18 bighas. The
common forest land is about 4 km. The village has an individual pasture of 1 bigha
and a village common pasture of around 2.5 bighas. There are 2 ponds and 2 canals.
Around 290 acres of the forest land is planted and 60 hectares are unplanted.
The village of Dulaltara lies under the CD Block Saltora of Bankura district. The
community is Santal. The total area is 37.6 hectares. There are 18 households with a
population of 77. All the households in the village are BPL card holders. There is
pacca road of 2.5 km and kachcha road of 1 km. The bus stand, post office, bank,
market, primary school and primary health centre is about 2 - 2.5 km away from the
village. The secondary school and police Station is 9 km and 12 km away respectively
from the village. All the households gather common property resources in the form of
firewood and fodder from the forest. Although hunting of animals and birds is
prohibited, the rural poor indulge in hunting to a small extent. Fishing in the ponds
and lake is restricted to once in a week.
The village of Panjhoria and Tantirdanga has 19 and 25 households respectively. In
Panjhoria 17 households are landless; whereas in Tantirdanga 23 households are
landless. There exists the Forest Protection committee which is responsible for
maintaining the community rule for the access and extraction of common property.
The share of the Forest Protection Committee is equally distributed among the
community members.
The villagers collect bamboo, wood, leaves and canes for the construction of their
houses. Fruits, roots, leafy vegetables, mushroom, etc. are collected form the common
forest area. The leafy vegetables mainly collected are Shushni shak and Kulakhara
shak. The common fruits collected by the households from the common land are
mango and jamun. The households also collect several medicinal plants to take care of
the health of the households. The common medicinal plants collected are neem leaves,
basak leaves, kalmegh, etc. The common land is used by the households for grazing
of their livestock. The households also collect fodder from the common forest area for
the livestock. The household hunts animals to some extent in the common forest area
and also fish in the common ponds. The households collect small fishes viz. gorou,
chunamach, etc. from the common property resources like ponds and lakes. The
households also collect wild mushrooms (called karan chatu in local dialect) from the
common forest area.
Appendices
208
District-Purulia Purulia is the westernmost district of the state of West Bengal. It has all-India
significance because of its tropical location, its shape as well as function like a funnel.
It funnels not only the tropical monsoon current from the Bay of Bengal to the
subtropical parts of north-west India, but also acts as a gateway between the
developed industrial belts of West Bengal and the underdeveloped areas in Orissa,
Jharkhand, Madhya Pradesh and Uttar Pradesh (http://purulia.gov.in). Purulia district
lies between 22°60’and 23°50’ north latitudes and 85°75’ and 86° 65’ east longitudes.
The total geographical area of the district is 6259 km². This district is bordered on the
east by Bankura, Paschim Medinipur districts, on the north by Bardhaman district of
West Bengal state and Dhanbad district of Jharkhand state, on the west by Bokaro and
Ranchi districts of Jharkhand state and on the south by West Singhbhum and East
Singhbhum districts of Jharkhand state.
MAP A1.3 MAP A1.3
Map of West Bengal-Purulia District Map of Purulia
Purulia district consists of three subdivisions: Purulia Sadar East, Purulia Sadar West
and Raghunathpur. Purulia Sadar East comprises of Purulia town and seven
Community development (CD) blocks, Purulia Sadar-West consists of Jhalda
municipality and seven other CD blocks, Raghunathpur subdivision comprises of
Raghunathpur municipality and six community development blocks. Purulia district
has in total 20 Community Development blocks. The field survey has been conducted
in Raghunathpur sub-division under Santuri CD Block.
Appendices
209
The population of Purulia district as per Census 2011 was 2,930,115 of which male
and female were 1,496,996 and 1,433,119 respectively. In 2001 census, Purulia had a
population of 2,536,516 of which males were 1,298,078 and remaining 1,238,438
were females. There was change of 15.52 percent in the population compared to
population as per Census 2001. As per 2011 census, 87.26 percent population of
Purulia districts lives in rural areas of villages. Purulia is one of the drought prone
districts of West Bengal. It has a sub-tropical climate nature and is characterized by
high evaporation and low precipitation. The average annual rainfall varies between
1100 and 1500 mm. The topography of Puruila district is undulated and thus nearly 50
percent of the rainfall flows away as runoff. The district is covered mostly by residual
soil formed by weathering of bed rocks. The district has rich forest cover. The total
forest area in Purulia district is 876 km² comprising of 112 km² of Reserved Forest,
729 km² of protected forest and 35 km² of unclassed state forest. The forest area is
about 14 percent of the total geographical area of the district (SFR- 2010-11). The
forest consists mainly of Sal and few other species.
Purulia is one of the most economically backward districts of West Bengal. It lags
behind in agricultural arena as compared to other districts of West Bengal because of
unpredictable weather conditions, unfavourable soil and poor irrigation system. The
main cultivated crops are paddy, wheat, sugarcane, mustard and potatoes. A large
number of the rural population works as agricultural wage labourers. Purulia is
famous for lac cultivation. The lac industry is being encouraged by the government as
well as other private organizations. There is very high dependence of the rural poor on
the forest for their subsistence.
The field survey was undertaken in 3 villages under Santuri Block in the district of
Purulia in West Bengal, India; viz. Jiyathole, Marbediya and Ambari. The village of
Jiyathole is about 3 km away from the town of Santuri. It has 81 households with a
population of 445, out of which 65 households are BPL card holders. Agriculture and
wage labour are their main occupation. Marbediya village is situated about 3.5 km
away from Santuri. It has 25 households with 138 household members. Here 15
households are poor. Marbediya has 1 primary school and 1 high school in which
students of Jiyathole and Marbediya study. Ambari village is about 4 km from the
town of Santuri comprising of 44 households and a population of 231. It has one
primary school.
Appendices
210
1.1 Characteristics of the population in the study area in Bankura and
Purulia The socio-economic characteristics of the 300 surveyed households in the districts of
Bankura and Purulia during the survey year 2011 is depicted. Here we have analysed
the family size, the literacy rate, caste composition and employment status of the
households.
1.1.1 Family Size and Literacy rate
The village wise analysis of the family size and literacy rate has been shown in Table
A1.1 below:
The surveyed 300 households in Bankura and Purulia districts had a population of
1522 during the survey year 2011. The average family size was 4.72 and 5.43 for the
two districts respectively. From the above table it is evident that the literacy rate in the
study area of Bankura and Purulia was 45.34 and 44.35 respectively, which implies
that more than half the population are illiterate. Ramjibanpur in Bankura district had
the highest literacy rate of 51.61 percent and Tatirdanga has the lowest (32.46
percent). The low level of education in the study area is very alarming.
TABLE A1.1
Male Female Total
Panjhoria 26 66 63 129 4.96 51.16Ramjibanpur 20 51 43 94 4.70 52.13
Seolibona 54 131 131 262 4.85 49.24Baldanga 7 14 16 30 4.29 36.67Dulaltora 18 37 42 79 4.39 36.71
Tantirdanga 25 63 51 114 4.56 32.46Jiyathole 81 222 223 445 5.49 44.94
Marbediya 25 72 66 138 5.52 36.96Ambari 44 110 121 231 5.25 47.62
150 362 346 708 4.72 45.34150 404 410 814 5.43 44.35300 766 756 1522 5.07 44.81
Source: Field Survey, 2011
Purulia TotalGrand Total
Bankura Total
Total populationDistrict Block Name of
villageNo. of
Households
Purulia Santuri
Village wise Characteristics
Bankura Saltora
Average Family
Size
Literacy Rate in
Percentage
Appendices
211
1.1.2 Caste Composition: In our study area, majority of the households belong to schedule tribe and the
community is Santhal. The villagers follow the religion of Sahi Dharam.
1.1.3 Occupation: The occupation of the working population in the study area has been classified into
four categories- i) primary sector comprising of agriculture, cottage and related
industries ii) secondary sector comprising of factory and business related and iii)
Tertiary sector comprising of service and Wage labour. Table A1.2 shows the village
wise occupational composition of the surveyed population.
From Table A1.2 it is evident that in Bankura district, out of 447 total working
population, 281 (62.80 percent) are involved in agriculture and 146 (32.64 percent) in
wage labour. In Purulia district, 353 (69.05 percent) household members are engaged
in agriculture and 147 (28.67 percent) in wage labour, out of total working population
of 512. We can thus infer that agriculture is the main occupation of the surveyed
households, followed by wage labour.
TABLE A1.2
Agriculture Business Service Wage Labour
Panjhoria 26 54 3 6 19 82Ramjibanpur 20 37 0 4 16 57
Seolibona 54 107 0 6 54 167Baldanga 7 9 0 0 6 15Dulaltora 18 24 0 0 22 46
Tantirdanga 25 50 0 1 29 80Jiyathole 81 193 1 2 74 270
Marbediya 25 64 0 2 26 92Ambari 44 96 0 7 47 151
150 281 3 17 146 447150 353 1 11 147 512300 634 4 28 293 959
Source: Field Survey, 2011
Total working
population
Village wise occupation composition of the surveyed population
Purulia TotalGrand Total
Working population engaged inDistrict Block Name of
villageNo. of
Households
Bankura Saltora
Purulia Santuri
Bankura Total
Appendices
212
APPENDIX-II
PRICE LIST OF CPRs The price list of the common property resources in the study area is shown in Table
A2.1 below:
TABLE A2.1
Sl No. Unit Market Price(Rs) GrassShrubDry leaves
2 per kg 2.50 3 per kg 5.00
Kalmegh (leaf) per kg 2.00 Neem (leaf) per kg 15.00 Basak (leaf) per kg 2.00 Bamboo per piece 20.00 Sal (leaf) per 1000 plates 50.00 Kend (leaf) per jhuri 30.00
6 per log 1,200.00 Bel (fruit) per kg 2.50 Amla (fruit) per kg 3.50 Tal (fruit) per piece 3.00 Mango (fruit) per kg 25.00 Kend (fruit) per kg 12.00 Jamun (fruit) per kg 10.00 Mahua (flower) per kg 5.00 Honey per kg 25.00 Meat per kg 80.00 Snail per kg 40.00 Shak (leafy vegetable) per kg 5.00 Kachu per kg 30.00 Oal (Tuber root) per kg 30.00
10 per kg 60.00 11 per piece 5.00
Source : Field Survey 2011Broom
Vegetables9
Fish
Timber
Cow dung (dung cake)
4 Herbal medicine
Fuelwood
5 Bamboo / Sal & other leaves
Hunded birds / animals /snails8
7 Fruits / Flowers / Honey
Price List of Common Property Resources in the Surveyed Villages
Common Property Resource
1 Fodder per kg 2.00
Appendices
213
APPENDIX-III
SUMMARY STATISTICS
Based on our primary data, we present the summary statistics in Table A3.1 & A3.2
below:
TABLE A3.1 Descriptive Statistics of Quantitative and Dummy variables for Bankura District
FSIZ
E FEM PER
AVR AGE
AVR SCH
OWN LAND
LIVE STOCK
FORESTDIST POVR CPRIN DISM
Mean 4.69 48.87 28.79 2.249 3.1246 2.9478 1.67700 0.886 11047 2.130
Median 5.00 50.00 26.98 1.790 2.3000 2.8350 1.00000 1.000 11583 2.000
Maximum 14.0 100.0 75.00 6.670 18.000 7.3100 3.60000 1.000 22915 5.000 Minimum 1.00 0.000 12.67 0.000 0.0000 0.0000 0.50000 0.000 240.0 1.000 Std. Dev. 1.93 18.70 11.37 1.962 2.6352 1.6605 0.98656 0.318 3309 0.924
Skewness 0.85 0.003 1.523 0.472 2.2860 -0.068 0.61780 -2.440 -0.434 0.719
Kurtosis 5.69 3.572 6.158 1.963 9.9368 2.7280 1.69192 6.951 3.939 3.226
Sum 704 7331 4319 337.4 468.70 442.17 251.550 133.0 16570
63. 319.6
Sum Sq. Dev. 555 52122 19286 573.6 1034.7 410.86 145.023 15.07 1.63E
+09 127.2
Observations 150 150 150 150 150 150 150 150 150 150
Source: Estimated by EViews 7 computer software using field survey data of 2011
TABLE A3.2 Descriptive Statistics of Quantitative and Dummy variables for Purulia District
FSIZE FEM PER
AVR AGE
AVR SCH
OWN LAND
LIVE STOCK
FORESTDIST POVR CPRIN DISM
Mean 4.340 45.22 30.93 5.034 4.5776 2.3303 1.96593 0.620 9577 1.813 Median 4.000 48.00 28.16 4.750 4.0000 2.05000 1.75000 1.000 9627 2.000 Maximum 14.00 100.0 80.00 11.00 15.000 10.7600 4.25000 1.000 13950 4.000 Minimum 1.000 0.000 11.41 0.000 0.0000 0.00000 0.45000 0.000 275 1.000 Std. Dev. 2.454 16.62 12.67 2.842 3.1882 1.79158 0.87217 0.487 2130 0.814 Skewness 0.800 0.329 1.219 0.043 0.8079 1.17314 0.47161 -0.494 -0.512 0.802 Kurtosis 4.000 2.987 4.459 1.886 3.0024 5.58171 2.00872 1.244 3.049 3.123
Sum 651.0 6783 4640 750.1 686.65 349.550 294.890 93.00 1436631 272.0
Sum Sq. Dev. 897.6 41181 23947 1195 1514.5 478.256 113.343 35.34 6.76E
+08 98.77
Observations 150 150 150 149 150 150 150 150 150 150
Source: Estimated by EViews 7 computer software using field survey data of 2011
Appendices
214
We have observed that in all the cases in Bankura and Purulia district the mean is
almost equal to the median and the distribution has nearly zero skewness, which
implies normal distribution i.e. data are symmetric about mean. However, as per the
result, Kurtosis of all the variables is not nearly 3, except for female percentage and
CPR income in Bankura district.
Table A3.1 and A3.2 depicts average family size is nearly 4 in both the districts and
percentage of female member in the household is nearly 48 in Bankura district and 45
in Purulia district. The average age of our sample household varies from 12 to 75
years in Bankura district and 11 to 80 years in Purulia district. The Table further
shows that average education is nearly 2 in Bankura district and 5 in Purulia district.
The area of ‘ownland’ varies from 0 to 18 bighas in Bankura district and 0 to 15
bighas in Purulia district. In fact, few of our sample households are landless; some are
small and marginal farmers (less than 2 bighas) whereas some are medium (2-4
bighas) and large (more than 4 bighas) farmers. The average livestock owned by the
household is nearly 2 in both the districts. The forest distance varies from 0.5 to 3.6
km in Bankura district and 0.45 to 4.25 km in Purulia district. The above Tables show
that annual CPR income of our sample household varies from Rs 240 to Rs 22915 in
Bankura district and Rs 2275 to Rs 13950 in Purulia district. The distance to the
closest market varies from 1 km to 5 km in Bankura district whereas 1 km to 4 km in
Purulia district.
Appendices
215
APPENDIX-IV
LAND OWNERSHIP PATTERN Table A4.1 illustrates the land ownership pattern in the surveyed villages in the
district of Bankura and Purulia.
The total area of ‘lease in’ land for the 150 surveyed households of Bankura and
Purulia district is 28.47 hectare and 7.09 hectare respectively (Table A4.1). This is in
sharp contrast to the area of ‘own land’ for the surveyed households of Bankura and
Purulia which is 14.48 hectare and 35.49 hectare respectively. The above data shows
that the households in the surveyed villages of Bankura are poor and therefore they
have very meagre land of their own. Theses households have to ‘lease in’ the land for
cultivation and meet their ends. The households in the surveyed villages of Purulia
seem to be better off as compared to the surveyed villages of Bankura. These
households have comparatively larger ‘own land’ and therefore the area of ‘leased in’
land is less.
The land ownership pattern for the own land in the surveyed villages of Bankura and
Purulia has been illustrated in Table A4.2 below. Here we have considered different
size class based on the area of the own land. The different size class used are i) 0
hectare i.e. landless farmers ii) 0-0.25 hectare iii) 0.25-0.50 hectare iv) 0.50-0.75
hectare v) 0.75-1.0 hectare vi) above 1.0 hectare.
BANKURA PURULIA TOTAL1 No. of Households 150 150 3002 Total area of lease in land (Hectare) 28.47 7.09 35.563 Total area of lease out land (Hectare) 0.28 0.27 0.554 Total area of ownland (Hectare) 14.48 35.49 49.975 Total area of operated land (Hectare) 42.66 42.31 84.98
Source: Field Survey, 2011
TABLE A4.1Land Ownership Pattern
Appendices
216
From Table A4.2 above, we can infer that 44 percent of the surveyed households in
the district of Bankura who have no ‘own land’. It can also be observed that 90.7
percent of the surveyed households in the district of Bankura belong to landless and
marginal size own land holders (0-0.25 hectare) and they cater to 68 percent of the
total own land. It is further observed that 9.4 percent of the surveyed households
belong to small and medium land owners (0.25-0.75 hectare) having 32.1 percent of
the total ‘own land’. There are no large size (>1 hectare) own land holders. This
implies that majority of the households have almost equally very small area of own
land which indicates that households are extremely poor and have fragmented land.
The Gini coefficient which shows the degree of inequality in the distribution of ‘own
land’ holding is 0.52. Thus there is high degree of inequality in the ownership
distribution of land owners.
In the case of the surveyed villages in the district of Purulia, it is observed that 6.7
percent of the households are without any own land. Survey results show that 93.3
percent of the surveyed households in the district of Purulia have ‘own land’ in the
range of 0-0.50 hectare which account for 78 percent of the total own land, whereas
only 1.3 percent of the surveyed households belong to large size group (> 1 hectare)
having 6 percent of the total own land. Here the Gini coefficient is 0.33.
TABLE A4.2
BANKURA
Size Class (in hectare)
No. of holdings
Percentage of holding
Cummulative percentage of holdings
Xt
Area of own land (Hectare)
Pecentage of area
Cummulative percentage of
area of own land Yt
Gini Coefficient
0 66 44.0 44.0 0.00 0 0.00 - 0.25 70 46.7 90.7 9.83 68.0 68.00.25 - 0.50 13 8.7 99.3 4.13 28.6 96.50.50 - 0.75 1 0.7 100 0.50 3.5 1000.75- 1.0 0 0 100 0.00 0 100> 1.0 0 0 100 0.00 0 100Total 150 100 14.46 100
PURULIA
Size Class (in hectare)
No. of holdings
Percentage of holding
Cummulative percentage of holdings
Xt
Area of own land (Hectare)
Pecentage of area
Cummulative percentage of
area of own land Yt
Gini Coefficient
0 10 6.7 6.7 0.00 0.0 0.00 - 0.25 89 59.3 66.0 13.91 39.2 39.20.25 - 0.50 41 27.3 93.3 13.80 38.9 78.00.50 - 0.75 5 3.3 96.7 2.88 8.1 86.10.75- 1.0 3 2.0 98.7 2.80 7.9 94.0> 1.0 2 1.3 100 2.12 6.0 100Total 150 100 35.51 100Source: Field Survey, 2011
Land Ownership Pattern (Own Land)
0.527067773
0.330582934
Appendices
217
The land ownership pattern for the ‘operated land’ in the surveyed villages of
Bankura and Purulia has been illustrated in Table A4.3 below:
From Table A4.3 above, we observed that 9.3 percent of the surveyed households in
the district of Bankura have no operated land. Survey results reveal that 93.3 percent
of the surveyed households in the district of Bankura belong to landless, marginal and
small category of farmers and they cater to 82.4 percent of the total operated land. It is
further noted that only 1.3 percent of the surveyed households are large size operated
land holders (>1 hectare) who have 6.4 percent of the total operated land. This implies
that majority of the households have almost equally small area of operated land. The
Gini coefficient is 0.26, which is much lower than the degree of inequality as in the
case of own land.
From the survey results in the district of Purulia, it is observed that 6 percent of the
households are without any operated land. Further, 86.7 percent of the surveyed
households have operated land in the range of 0-0.50 hectare which account for 63.7
percent of the total operated land, whereas only 2 percent of the surveyed households
belong to large size group (> 1 hectare) having 7.7 percent of the total operated land.
Here the Gini coefficient is 0.36.
TABLE A4.3
BANKURA
Size Class (in hectare)
No. of holdings
Percentage of holding
Cummulative percentage of holdings
Xt
Area of operated
land (Hectare)
Percentage of area
Cummulative percentage of
area of operated land
Yt
Gini Coefficient
0 14 9.3 9.3 0.00 0.0 0.00 - 0.25 42 28.0 37.3 8.04 18.8 18.80.25-0.50 84 56.0 93.3 27.13 63.6 82.40.50 - 0.75 7 4.7 98.0 3.99 9.4 91.80.75-1.00 1 0.7 98.7 0.77 1.8 93.6> 1.00 2 1.3 100 2.74 6.4 100Total 150 100 42.67 100
PURULIA
Size Class (in hectare)
No. of holdings
Percentage of holding
Cummulative percentage of holdings
Xt
Area of operated
land (Hectare)
Percentage of area
Cummulative percentage of
area of operated land
Yt
Gini Coefficient
0 9 6.0 6.0 0.00 0.0 0.00 - 0.25 79 52.7 58.7 12.74 30.6 30.60.25-0.50 42 28.0 86.7 13.79 33.1 63.70.50 - 0.75 11 7.3 94.0 6.42 15.4 79.10.75-1.00 6 4.0 98.0 5.50 13.2 92.3> 1.00 3 2.0 100 3.19 7.7 100Total 150 100 41.64 100Source: Field Survey, 2011
0.362992315
0.260695258
Land Ownership Pattern (Operated Land)
Appendices
218
APPENDIX-V PROPERTY RIGHTS AND ‘THE TRAGEDY OF THE COMMON’ The concept of over exploitation of the natural resources in the ‘Commons’ was first
published by Garett Hardin (1968) in his highly debatable article ‘The Tragedy of the
Commons’. The ‘Tragedy of commons’ is a dilemma arising from the situation in
which several individuals, acting independently and rationally consulting their own
self-interest, will ultimately deplete a shared limited resource, even when they are
fully aware that it is not in anyone’s long-term interest for it to happen. The article
highlighted two important aspects about the natural resource, usually referred to as a
common-pool resource to which a large number of people have full access:
i) There is huge demand on the natural resources due to high growth in
human population and therefore there is increased usage of the resources.
ii) Excessive demand of the natural resources leads to unlimited usage by
many users, resulting in over exploitation.
The concept has been used to explain the reason for overgrazing in pastures,
overexploitation of fisheries, air and water pollution, depletion of fuel wood and
ground water, decline in wild life, etc. (Stevenson, 1991). The article describes the
situation in a common pasture where there is no restriction to entry for the herdsmen.
In primitive times, overpopulation of the pasture by herds did not occur because of
natural attrition. However, in modern times, it is not so and the balance will be tipped
as adding one more animal to the grazing land will cause it to be overpopulated. In
spite of this, each rational herdsman wants to maximize his gains by adding one more
animal to the herd. The marginal utility of adding one more animal to the herd has one
positive and one negative component. The positive component is a function of the
increment of one animal. Now that the herdsman receives all the benefits from the
sale of the additional animal, the positive component can be considered to be almost
equal to +1. The negative component is the function of the additional overgrazing
which resulted due to addition of one more animal to the herd. In this particular case it
may be noted that the effect of over grazing due to addition of one more animal to the
herd by a herdsman is shared equally by all the herdsmen. Hence the negative utility
for a herdsman is a fraction of -1. Thus the rational herdsman concludes that it is
Appendices
219
logical to add one more animal to the herd since it maximizes his gain while the cost
is distributed equally amongst all the herdsmen. Since all the herdsmen reach the
same calculation, overgrazing is inevitable. Each herdsman will continue to impose
costs on all of the others, until the pasture is depleted, which is detrimental to all.
Thus the ‘free riding’ leads to the Tragedy. Hardin had recommended that the tragedy
of the commons could be prevented by strict regulation by the government. He also
felt that privatization of the common property could solve the problem.
The term ‘common property’ as described by Hardin (1968) has been highly debated
as he failed to distinguish between ‘common property’ and ‘open access’ resource.
Common Property represents private property for the group (since all others are
excluded from use and decision making), and that individuals have rights (and duties)
in a common property regime (Ciriacy-Wantrup and Bishop, 1975). Open access is a
situation in which there is no property. Since there is no property rights in an open
access situation, “everyone‘s property is nobody’s property”. It can only be said that
“everybody’s access is nobody’s property” (Bromley, 1999). Thus the critics argue
that Hardin’s article is more apt for open access resource instead of common property
resource. Elinor Ostrom (2000) emphasized that the tragedy of the commons may not
be as prevalent or as difficult to solve as Hardin implies, since locals have often come
up with solutions to the commons problem themselves. Contrary to Hardin’s view on
the Tragedies of the commons, the pastures were well looked after for many centuries
before they depleted for reasons unrelated to any flaw in the commons (Cox, 1985).
To avert the tragedy of the commons, different solutions have been put forth by
several Economists. One view is that the overexploitation and the degradation of the
common property resource can be creating and enforcing private property rights
(Demsetz 1967). Private property helps to incorporate the externalities generated from
over exploitation of the common property resource. Another view to overcome the
overexploitation is to entrust the commons to the state regime which would have full
authority over it (Hardin 1968). Of late, a large number of scholars have advocated
that the best solution to overcome the ‘Tragedy of the commons’ is for the users to
form a decentralized collective management of the CPRs. This theory of the use of
Appendices
220
Collective management of the CPRs has been favoured by Jodha (1986), Chopra
et.al., (1989), Berks 1989, Ostrom (1990).
The influencing article of the ‘Tragedy of the Commons’ by Hardin (1968) could also
be described as a dilemma arising from a situation wherein several users act
independently and rationally in their own self-interest, ultimately depleting the shared
limited resource although they are very well aware that it would lead to long term
adverse situation for all. This theory is explained with the help of Figure A5.11 as
shown below:
FIGURE A5.1
Relationship among effort, cost and revenue
*q 0q
Source: Ostrom, E, et.al.(2002); The Drama of the Commons
As per the illustration in Figure A5.1 above, on the X-axis we have depicted the effort
required in extraction of the common property resource (q) and on the Y- axis we
have depicted the Total Cost (TC) / Total Revenue (TR) associated with the
extraction. The effort in extraction of the resource ranges from *q , the social optimum
achieved under private maximization i.e. MR MC= to 0q , the level of extraction
Appendices
221
under open access with AR AC= and profit ( ) 0π = . In order to achieve *q , there has
to be cooperation amongst all the members of the community and they must all agree
to exercise restraint in their effort to extract from the resource. Here the q is above *q in the case of CPR without cooperation, since each member tries to impose on
others a negative externality in extracting from the resource that he does not take into
account. This in turn leads to depletion of the resources.
The dilemma associated with the herdsmen whether to act independently at one’s
own interest or to cooperate and act collectively looking into the long term interest of
the CPRs can be best compared with the Prisoner’s Dilemma (PD). The Prisoner’s
Dilemma is a problem in game theory that demonstrates as to why two people or
group of people will not cooperate even if it is in both their best interest to do so. The
Prisoner’s Dilemma (PD) is the theoretical foundation to explain the tragedy of the
commons or the non-cooperative use of a CPR (Baland and Platteau, 1996).
A classic example of the prisoner's dilemma (PD) is presented as follows:
In an example of forest management, if the forest department and the village
community agree to participate, then both the forest department and village
community may share the forest produce equally at Rs 3 lakhs each. If the village
community alone participates and not the forest department, then the forest
department may collect all the forest produce and auction it and earn a forest revenue
to a tune of Rs 5 lakh (and not a total of 6 lakh) , but the village community gets
nothing and will be the net loser. If the village community does not participate, it may
indulge in illegal cutting of some timber and get its market price worth Rs 5 lakhs and
the forest department gets nothing and is the net loser. Finally, if both the forest
department and the village community do not participate in the forest management,
then the forest department may collect some timber and other forest produce and the
village community may collect some forest produce illegally, thereby each gaining
revenue equal to just Rs 1 lakh (Table A5.1).
Appendices
222
TABLE A5.1
Prisoner’s Dilemma
(Rs Lakhs)
Village
Community
Forest Department
Participate Do not participate
Participate 3 ; 3 0 ; 5
Do not participate 5 ; 0 1 ; 1
In the classic form of this game, cooperating is strictly dominated by defecting, so that
the only possible equilibrium for the game is for all players to defect. No matter what
the other player does, one player will always gain a greater payoff by playing defect.
Since in any situation playing defect is more beneficial than cooperating, all rational
players will play defect, all things being equal. In the above example, both the village
community and the forest department do not participate in the forest management and
settle for a pay-off of Rs 1 lakh each. However the best set of pay-offs could have
been each getting Rs 3 lakhs.
According to Amartya Sen, all it takes to make cooperation individually optimal is
‘assurance’ that others will cooperate. Hence, this is an assurance problem. (Sen
1967). It may be possible to overcome the prisoner’s dilemma by converting the
dilemma into an assurance problem by changing either incentives or personal
motivations.
Appendices
223
APPENDIX-VI JOINT FOREST MANAGEMENT IN INDIA
The forest cover and the status of Joint Forest Management in different states of India
are discussed in detail below:
Andhra Pradesh is one of the pioneer states to adopt Joint Forest Management in
India. Based on the Satellite (IRS P6 LISS-III 2009) data of Oct 2009 to Mar 2010,
the forest cover in the state is 42,163 km2 which is 15.32 percent of the total
geographical area of the state (AP SFR 2011). In line with the guidelines on JFM
issued by the Government of India in 1990, the Government of Andhra Pradesh issued
formal orders in 1992 for adopting Joint Forest Management (JFM) as a strategy for
rehabilitation of degraded forests. In 1993, detailed guidelines were also issued for
taking up JFM through village level committees called ‘Vana Samrakshana
Samithies’ (VSS).As per the Andhra Pradesh State of Forest Report 2011, there are
7,718 Vana Samrakshana Samities (VSSs) or JFPCs in the State. A forest area
covering 15,199.8 km2 which is 23.8 percent of the total forest area is under the
purview of Community Forest Management (CFM). Total 1,539,000 members are
involved in CFM which includes 465,000 members belonging to Scheduled Tribe
(S.T) and 323,000 members belonging to Scheduled caste (SC).
Arunachal Pradesh is the largest state in the North East region of India. The forest
cover as per satellite data of Nov-Dec 2008 is 67,410 km2, which is 80.50 percent of
the total geographical area of the state (India State Forest Report 2011).
Approximately one third of the state’s land area is managed by the Forest Department;
the remaining two third is largely under the management of the tribal communities. Joint
Forest Management was introduced in the state in 1997. There are 347 JFM
committees which manage about 90,000 hectare of forest area. The total number of
members involved in JFM activity is 23,308 all of which belongs to the Scheduled
Tribes (SFR 2009).
The forest cover in the state of Assam, based on the satellite data of Nov 2008-Jan
2009 is 27,673 km2 which is 35.28 percent of the state’s geographical area (India SFR
2011). With the objective to reduce environmental degradation and rural poverty, the
Govt. of Assam promulgated the 'Assam Joint (people's participation) Forestry
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Management Rules, 1998'. Total 28 Forest Development Agencies (FDAs) were
constituted during 2002-03 with 503 Joint Forest Management Committees (JFMCs)
managing 80,000 hectares of forest area. Government of Assam adopted the 'Assam
Forest Policy, 2004' which emphasized JFM to graduate to Community Forest
Management aiming at sustainable forest management. The total number of families
involved in JFM in the state of Assam is 57,341 including 28,459 families of
Scheduled Tribes (SFR 2009).
The forest cover in the state of Bihar based on the satellite data of Nov 2008-Jan
2009 is 6,845 km2 which is 7.27 percent of the total geographical area of the state
(ISFR 2011). Joint Forest Management was initiated in 1990. There are 532 JFM
committees managing 3.7 lakh hectare of forest area as on March 2005. About 2.05
lakh families are involved in JFM of which 32,303 families belonged to Scheduled
Tribes (SFR 2009).
The State of Chhattisgarh has a forest cover of 55,674 km2 based on the satellite date
of Oct 2008-Jan 2009, which is 41.18 percent of the total geographical area of the
state (India SFR 2011). Joint Forest Management began in the state in 1991.
However, with the creation of the new state a new JFM resolution was issued in 2001.
Out of the total of 19,720 villages in the State, 11,185 villages situated within the 5
km range of the forest areas form the JFM area. There are 7,887 JFM committees in
the state. A total of about 33,190 km2 of forest area is under the purview of JFM,
which is about 55.52 percent of the total forest area of the state. There are more than
2,763,000 families involved in JFM activity consisting of 1,521,000 families of
Scheduled Tribe and 471,000 families of Schedule Caste (Source: Forest Dept., Govt.
of Chhattisgarh).
Goa has a forest cover of 2,219 km2 based on the satellite data of Feb 2009, which is
59.94 percent of the total geographical area of the state (ISFR 2011). Joint Forest
Management was started by the Government of Goa in 2003. Subsequently, three
Forest Development Agencies were constituted, i) North Goa Forest Development
Agency ii) South Goa Forest Development Agency and iii) Forest Development
Agency (Wildlife). This greatly helped in the participation of the local community in
the planning & the implementation of appropriate Afforestation Programme at the
grass root level and help in the decision making process. There are 26 JFM
committees managing 10,000 hectares of forest area with 336 families involved in
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JFM activity. As per JFM notification of Goa, sharing of benefits is not based on
harvested forest produce but on the value addition to the forests being protected by the
JFM committees (Source: Forest Dept., Govt. of Goa and SFR 2009).
On the basis of the satellite data of Oct-Nov 2008, the forest cover in the state of
Gujarat is 14,619 km2, which is 7.46 percent of the total geographical area of the
state (ISFR 2011). Based on the National Forest Policy, 1988 and the guidelines in
June, 1990 regarding the involvement of the local communities and voluntary
agencies in forest protection, management and regeneration to rejuvenate degraded
forest lands, the Joint Forest Management (JFM) programme was launched in Gujarat
vide government resolution in March 1991. As of March, 2012, there are 3,274 JFM
committees managing the forest area of 0.43 million hectare. The working under the
Joint Forest Management has been appreciated by the Government of India as three
of the Van Kalyan Samities namely- Pingot, Bapda, Balethi and Motia have been
awarded Priyadarshani Vriksha Mitra Award in recognition of their services for the
protection and regeneration of the degraded forest (Source: Forest Dept. Govt. of
Gujarat). There are 0.81 million members involved in JFM activities. The total
number of families involved in JFM is 0.2 million, of these 0.14 million families
belong to the Schedule Tribes (SFR 2009).
The state of Haryana is among the pioneers in implementation of JFM which began
in 1976, much before the formulation of the forest policy by the Government of India.
The model developed in Sukho Majri village of Haryana, employing participation of
people in protecting forests who were given rights over water and forest produce in
return, is now world famous. Haryana has a forest cover of 1,608 km2 based on the
satellite data of Oct-Nov 2008, which is 3.64 percent of the total geographical area of
the state (ISFR 2011). Village level forest committees (VFC) have been constituted in
over 817 villages under National Afforestation Programme (NAP). Moreover, 1135
VFCs have also been constituted under Japan International Cooperation Agency
(JICA) funded Integrated Natural Resource Management Project and Haryana
Community Forestry Project (HCFP). The benefits obtained from JFM programme
due to increased production in forest areas as well as water from the water-harvesting
dams are distributed equally among all the households (Source: Dept. of Forest, Govt.
of Haryana). About 165,500 families are involved in JFM covering a forest area of
56,000 hectares (SFR 2009).
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Himachal Pradesh has a forest cover of 14,679 km2 based on the satellite data of
Oct-Dec 2008, which is about 26.37 percent of the total geographical area of the state
(ISFR 2011). The Joint Forest Management was started in Himachal Pradesh in 1993
which has about 11 percent of the forest area under it. Total 1,690 JFM committees
manage a forest area of 0.42 million hectares. Around 2,65,000 families are involved
in JFM activities , of which 36,000 families belong to the Schedule Tribes (SFR
2009).
The forest cover in the state of Jammu and Kashmir based on the satellite data of
Oct-Dec 2008 is 22,539 km2 which is 10.14 percent of the state’s geographical area
(ISFR 2011). JFM started in J&K in 1990 and it has about 11 percent of the forest
area under its purview. There are 2697 JFM committees managing 1,141 km2 of the
forest area. The members are entitled to a share of 25 percent of the net proceeds from
the first major harvest of the plantation and are also entitled to collect grass, fodder,
fallen wood, etc. free of cost (Handbook of Forest Statistics, J&K Forest Department,
2006).
The satellite data of Nov 2008 – January 2009 reveals that Jharkhand has a forest
cover of 22,977 km2 which is 28.82 percent of the state’s geographical area (ISFR
2011). JFM was started in the state in 1990 and it has about 92.80 percent of the forest
area under it. The participatory approach was applied in degraded forests through the
constitution of village forest management and protection committees (VFMPC).
There are 10,903 JFM committees managing 2.19 million hectares of the forest area.
Around 1.28 million families are involved in JFM activities, of which 0.51 million
families belong to Schedule Tribes (SFR 2009).
Karnataka is one of the earliest states to issue a Government order in 1993 on
Participatory Management, by adopting JFPM policy to involve local community in
protection and management of degraded forests having canopy density up to 0.25 and
also provide 50 percent share in forest produce to the VFCs from JFPM areas. As per
the satellite data of Oct 2008-Jan 2009, the state has a forest cover of 36,194 km2
which is 18.87 percent of the total geographical area of the state (ISFR 2011). There
are 5200 VFCs / EDCs managing a forest area of 0.34 million hectares (Source:
Forest Dept., Govt. of Karnataka). In the state of Karnataka, around 0.19 million
families are involved in JFM activity, of which 24,705 families belong to Schedule
Tribes (SFR 2009).
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The forest cover in the state of Kerala based on the interpretation of satellite data of
February 2009 is 17,300 km2, which is 44.52 percent of the total geographical area of
the state (ISFR 2011). In 1998, JFM was started in the state of Kerala, which covers
about 15 percent of the forest area of the state. Several organisations like Vana
Samrakshana Samithi (VSS), Adivasi Vana Samrakshana Samithi (AVSS), Eco-
Development Committee (EDC) are in operation by the state forest department.
There are 327 JFM committees managing about 0.17 million hectare of forest area.
Around 51,300 families are involved in JFM activity of which 12,255 families
belonging to Schedule Tribe (SFR 2009).
Madhya Pradesh is a pioneering state in implementation of JFM programme. The
Government of M.P. issued the first resolution in this regard in 1991. Learning from
experiences, the State Government revised JFM resolution in 1995, 2000 and 2001.
Steps have been taken to integrate the local institutions by involving the Gram Sabha
in the formation and functioning of JFM Committees. The forest cover in the state
based on satellite data of Oct-Dec 2008, is 77,700 km2 which is 25.21 percent of the
total geographical area of the state (ISFR 2011). A total of about 60,000 km2 of forest
area is under JFM, which is about 63 percent of the total forest area of the State. So
far 14,073 JFM committees have been constituted, of which 9035 are VFCs, 4201 are
FPCs and 785 are EDCs, manage 6 million hectare of forest area (Source Forest
Dept., Govt. of Madhya Pradesh). About 1.7 million families are involved in JFM of
which 0.8 million families belong to Schedule Tribes (SFR 2009).
Joint Forest Management started in the state of Maharashtra in 1990. The JFM
programme gathered momentum in the year 1996-97 with the aid of a World Bank
Project. The forest cover, based on the satellite data of Oct-Dec 2008, is 50,646 km2
which is 16.46 percent of the state’s geographical area (ISFR 2011). There are 12,648
JFM committees managing around 2.6 million hectare of forest area which is about 40
percent of the forest area of the state. More than 25 lakh families are involved in JFM,
of which around of which around 6 lakh families belong to the Scheduled Tribes
(Annual Report 2010-11, Forest Dept., Govt. of Maharashtra). Under JFM, the
communities have full entitlement to Non Timber Forest Product (NTFP) species
excluding Tendu and Cashew (SFR 2009).
The State of Manipur has a forest cover of 17,090 km2 based on the satellite data of
Jan-Feb 2009, which is 76.54 percent of the state’s geographical area (ISFR 2011).
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Joint Forest Management started in Manipur in 1990 and has 280 JFM committees
which manages about 90,000 hectare of forest area, which is 5.39 percent of the total
forest area of the state. Around 26,000 families are involved in the JFM programme,
of which 22,000 families belong to the Scheduled Tribes (SFR 2009).
Meghalaya joined the rest of the country in the JFM activity when it notified the
constitution of Forest Development Agency and application of JFM principles on 9th
September, 2003. The Forest & Environment Dept. registered 7 FDAs constituted in 7
(seven) Social Forestry Divisions covering all the districts of the State. The Joint
Forest Management Committees formed by respective FDAs have been registered by
the concerned Conservator of Forests (Social Forestry) (Source: Forest &
Environment Dept., Govt. of Meghalaya). The forest cover in Meghalaya based on
satellite data of Dec 2008-Jan 2009 is 17,275 km2 which is 77.02 percent of the state’s
geographical area (ISFR 2011). There are 73 JFM committees in Meghalaya,
managing around 4000 hectares of forest area. All the families involved in JFM
belong to Schedule Tribes (SFR 2009).
Mizoram has a forest cover of 19,117 km2 based on the satellite data of Jan 2009,
which is 90.68 percent of the total geographical area of the state (ISFR 2011). JFM
was introduced in Mizoram in 1990. Active involvement of the local people through
the mechanism of JFM has significantly helped the Forest Department in its efforts to
enrich and protect the valuable forest wealth of the State. There are 270 JFM
committees managing about 20,000 hectare of forest area. More than 40,000 families
are involved in JFM activity, mainly belonging to the Scheduled Tribes (SFR 2009).
JFM was introduced in the state of Nagaland in 1997. The forest cover of the state as
per the satellite data of Nov 2008 – Feb 2009 is 13,318 km2 which is 80.33 percent of
the total geographical area of the state (ISFR 2011). Nagaland has only 11.7 percent
of Forests under the Government and the remaining 88.3 percent of the Forests belong
to Non-government Communities of the villages. Community forest committees have
been formed in Kohima (8), Mokokchung (33), Tuensang (30), Wokha (34), Doyang
(9), Zunheboto (37), Mon (19), Peren (12), forest divisions and formation of more
number of community forest committees is in progress in different forest divisions
(Source: Forest Dept., Govt. of Nagaland). There are 335 JFM committees managing
20,000 hectares of forest area. Around 85,000 families are involved in JFM, mostly
belonging to the Scheduled Tribes (SFR 2009).
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Orissa is one of the pioneer states to initiate forest management as early as 1950s. It
is the first state to issue the resolution in August 1988, where the villagers were
assigned specific roles in the protection and conservation of Reserve Forests (RF)
adjoining their villages, and in turn, were granted few concessions in meeting their
requirements of fuel wood, fodder, bamboo and small timber. The forest cover based on
the satellite data of Oct 2008-Jan 2009, is 48,903 km2 which is 31.41 percent of the
geographical area of the state (ISFR 2011). There are 9,778 JFM communities managing
0.82 hectare of the forest area, which is about 14 percent of the forest area. About 1.7
million families are involved in JFM, of which around 0.7 million families belong to the
Scheduled Tribes (SFR 2009).
The forest cover in the state of Punjab, based on the satellite data of November 2008
is 1,764 km2, which is 3.50 percent of the geographical area of the state (ISFR 2011).
The JFM programme was started in the state in 1993. There are 1,224 JFM
committees managing about 0.18 million hectares of forest area, which is about 58
percent of the forest area of the state. More than 91,000 families are involved in JFM
activity (SFR 2009).
Rajasthan has a forest cover of 16,087 km2 based on the satellite data of Oct-Dec
2008, which is 4.70 percent of the geographical area of the state (ISFR 2011). The
JFM programme started in the state in the year 1991. There are 4,882 JFM
committees managing about 0.78 hectare of forest area which is about 19 percent of
the forest area of the state (Source: Forest Dept. JFM Report, Govt. of Rajasthan).
More than 0.4 million families are involved in the JFM programme, of which around
half of the families are from Scheduled Tribes (SFR 2009).
The JFM programme in the state of Sikkim started in 1998. Sikkim has a forest cover
of 3,359 km2 based on the satellite data of Dec 2008, which is 47.34 percent of the
total geographical area of the state (ISFR 2011). There are 155 JFM committees
managing a forest area of 10,000 hectares. More than 46,000 families are involved in
the JFM activity, of which 17,000 families belong to Scheduled Tribes (SFR 2009).
The evolution of JFM in Tamil Nadu has been much influenced by the social forestry
movement and practices prevailed in the state. Tamil Nadu has a forest cover of
23,625 km2 based on the satellite data of Oct 2008-May 2009, which is about 18.16
percent of the geographical area of the state (ISFR 2011). JFM started in the state in
1997 which coincided with the launch of Japan Bank for International Cooperation
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(JBIC) project. The JFM concept of Institution building was in the form of
constitution of Village Social Forestry Committees (VSFC) in the Social Forestry
Villages. Out of total 12,612 Village panchayats in Tamil Nadu, Village Social
Forestry Committee (VSFC) have been constituted in 4,343 villages, roughly covering
35 percent of the Village panchayats in the state. More than 0.24 million families are
involved in the JFM programme, of which 10,000 families belong to the Scheduled
Tribes. Water harvesting structure is an important component of the JFM programme
in Tamil Nadu (SFR 2009).
Tripura has a forest cover of 7,977 km2 based on the satellite data of Jan 2009, which
is 76.04 percent of the geographical area of the state (ISFR 2011). There are 472 JFM
committees managing about 0.12 million hectare of forest area which is about 18
percent of the forest area of the state. More than 44,882 families are involved in the
JFM activity, of which 26,891 families belong to Scheduled Tribes (Dept. of Forest,
Govt. of Tripura).
The forest cover in the state of Uttar Pradesh based on the satellite data of Oct 2008-
Jan 2009 is 14,338 km2, which is 5.95 percent of the geographical area of the state
(ISFR 2011). Joint Forest Management started in Uttar Pradesh in 1992. Here VFCs
have been characterized as Forest Officers under the Village Forests, under section 28
of the Indian Forest Act 1927, thus empowering village community with the rights of
the FD over the Reserve Forest. There are 1,892 JFM committees managing about
80,000 hectares of forest area. More than 0.8 million families are involved in JFM
activity, of which 83,000 families belong to Scheduled Tribes (SFR 2011).
Uttarakhand has a forest cover of 24,496 km2 based on the satellite data of Oct-Dec
2008, which is 45.8 percent of the state’s geographical area (ISFR 2011). The JFM
project was implemented in the state of Uttarakhand in 1992, when it was a part of
Uttar Pradesh. Initially, delay in the formation of VFCs and ineffective
implementation of the JFM Project by the Forest Department by not ensuring
adequate and active participation of the village communities especially women
defeated the objective of creating sustainable forest management. There are 10,107
JFM communities managing about 0.86 million hectares of forest area, which is about
25 percent of the forest area of the state. Around 0.5 million families are involved in
JFM activity, of which around 15,000 families belong to the Scheduled Tribes (SFR
2009).