jharkhand opportunities for harnessing rural...

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Jharkhand Opportunities for Harnessing Rural Growth Project – Impact Evaluation Baseline Report

Jharkhand Opportunites for Harnessing Rural Growth Project – Impact Evaluation Baseline Report

Jharkhand State Livelihood 3rd Floor, Shantideep Tower Tel +91 (0)651 2360053 Promotion Society Radium Road Email [email protected] Ranchi 834001 Website www.jslps.org Jharkhand, India Oxford Policy Management India 4/6 First Floor Tel + 91 (0)11 4808 1111 Siri Fort Email [email protected] Institutional Area Website www.opml.co.uk New Delhi 110049 India

Jharkhand State Livelihood Promotion Society

May 2020


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Acknowledgements

This baseline report is possible due to the efforts and vision of the Jharkhand State Livelihood Promotion

Society (JSLPS), Rural Development Department, Government of Jharkhand and the World Bank to

integrate an impact evaluation within the design of the Jharkhand Opportunities for Harnessing Rural

Growth (JOHAR) project that will allow for scientific measurement of impact and generate evidence on

leveraging community based institutions for investments in creating sustainable livelihoods in rural

communities in Jharkhand and India.

For commissioning, providing leadership and guidance, and for supporting this work, special thanks to Mr.

Avinash Kumar, I.A.S., Principal Secretary, Rural Development Department, Government of Jharkhand,

Mr. Paritosh Upadhyay, I.F.S., former Special Secretary, Rural Development Department, Government of

Jharkhand cum Chief Executive Officer, JSLPS, Mr. Rajiw Kumar, I.A.S., Special Secretary, Rural

Development Department, Government of Jharkhand cum Chief Executive Officer, JSLPS, Mr. Bipin

Bihari, Project Director (JOHAR), JSLPS, Ms. Priti Kumar, Task Team Leader and Senior Agriculture

Specialist, World Bank, and Mr. Abhishek Gupta, Co-Task Team Leader and Rural Development

Specialist, World Bank.

The report, which describes the technical design of the JOHAR impact evaluation, establishes the baseline

for measurement of impact and provides analytics for programme design has been developed, in

consultation with stakeholders, for JSLPS by the Oxford Policy Management Limited (OPML) team, which

functions as a technical support agency (TSA) for monitoring and evaluation (M&E) and is embedded

within the State Project Management Unit (SPMU) for JOHAR at the JSLPS in Ranchi, Jharkhand.

The OPML team for JOHAR M&E are Mr. Tom Newton-Lewis, Mr. Anand Kothari, Mr. Vinod Hariharan,

and Ms. Jasmeet Khanuja, and additionally supported by Ms. Madhumitha Hebbar, Mr. Udit Ranjan, and

Ms. Rituparna Sanyal on design, analytical, and quality assurance functions on this report. Dr. Nayan

Kumar and Mr. Navin Kumar supported with training and field quality assurance.

The development of this report and the underlying evaluation design has involved multiple rounds of

consultation and discussion with stakeholders and partners. We are grateful for the comments,

suggestions, and advice received through this process, which has helped in the design of this impact

evaluation, development of survey instruments, fieldwork for data collection, and analysis for and writing

of this report. Both the quality and timeliness of the feedback, suggestions, and support have been helpful

to complete this exercise and produce this report. We would like to thank Ms. Rosa Abraham, Post Doctoral

Reserch Fellow at Azim Premji University as well, for advising on the calculation of employment rate

indicators.

The baseline survey was conducted for JSLPS by Kantar Public, who were hired to provide end to end

services for the survey. Their work and contribution is appreciated, specifically in ensuring data quality and

adhering to timelines. Special thanks to the Kantar Public team, which includes Mr. Nitin Sharma, Mr.

Borris Jude-Chapman, Mr. Anadi Mishra, Mr. Prabhat Shekhar, and their field team.

An initial draft of this report was reviewed by the JSLPS M&E team, which includes Mr. Deepak Upadhyay,

State Programme Manager – Monitoring and Evaluation, Mr. Gurpreet Singh, Project Coordinator –

Monitoring and Evaluation, and Mr. Anil Kumar, MIS Coordinator and Mr. Abhishek Gupta from the World

Bank. Comments and feedback received from this review were instrumental in shaping the report. The

report underwent another systematic review by the World Bank’s task team for JOHAR as well and

comments received were valuable in structuring analysis from the baseline survey data to feed in to


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programme design discussions. Thanks to the entire World Bank task team for the thorough review and

feedback that have improved the report and pushed us to think through specific details.

Lastly, we are grateful to and would like to specially acknowledge the efforts and time of all those who

have helped with supervision, coordination, logistics, and on-ground support during the data collection

round, which includes the field quality assurance team, community cadres, block and district staff, and

specifically for the time and courtesy provided by all the respondents to the baseline survey.


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Executive summary

This document provides analysis of the quantitative baseline survey data collected for the impact

evaluation of the Jharkhand Opportunities for Harnessing Rural Growth (JOHAR) project,

implemented by the Jharkhand State Livelihood Promotion Society (JSLPS) under the aegis of the

Rural Development Department (RDD), Government of Jharkhand (GoJ).

About JOHAR

JOHAR aims to support targeted rural producer households to enhance and diversify their household

income, and achieve a 50% increase in real income by 2023. These households are predominantly

small and marginal farmers, practicing rain fed, single crop subsistence farming, with limited access

to irrigation, markets, credit and skill development, and with persistent gender gaps. These

households face high levels of poverty, despite Jharkhand’s recent impressive economic

performance.

JOHAR works through mobilising and collectivising rural producers into Producer Groups (PGs),

further federated into Producer Organisations (POs), which will support households to diversify and/or

intensify their current production, and improve their participation higher up the value chain. This builds

upon the platform of Self-Help Groups (SHGs) established under the National Rural Livelihoods

Mission (NRLM).

JOHAR focuses on High Value Agriculture (HVA), irrigation, livestock, fishery and Non-Timber Forest

Produce (NTFP). It will also work to strengthen the competitive advantage of target rural producers

by transfer of climate resilient production techniques, enhanced opportunities for value addition and

effective market linkages; improve access to financing, including innovative financial products,

through the community institutions platform and formal financial institutions; establish partnerships

with the private sector, including rural entrepreneurs, for effective forward and backward linkages with

producers; and support skill development and financial modalities to facilitate jobs and

entrepreneurship, with a focus on the value chain and agribusiness. It will also have a focus on

women’s economic engagement in production, processing and marketing, especially women from

socially excluded groups.

JOHAR project interventions will be implemented in a phased manner across 68 blocks spread across

17 districts of the state, which have been identified based on geographical spread, incidence of

poverty, and marginalised groups (Scheduled Tribes). The project’s interventions will reach about

200,800 households, with the HVA and irrigation interventions reaching about 150,900 households,

livestock interventions reaching about 51,000 households, the fisheries interventions reaching about

34,500 households, and the NTFP interventions reaching about 29,200 households. Member

producer households that join PGs will have the option of engaging in more than one production

activity, thus causing some overlap in the target numbers.

The total cost budgeted for the project is US$ 142.81 million, which is being financed by a loan of

US$ 100.02 million from the International Bank for Reconstruction and Development and a budget

allocation of US$ 42.79 million from the Government of Jharkhand.

The Theory of Change (ToC) of JOHAR is presented in the following figure:


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Figure 1 JOHAR theory of change

About the Impact Evaluation

The impact evaluation is part of an overall monitoring, learning and evaluation (MLE) system, set out

in a separate MLE Framework, whose objective will be to reinforce a results-based management

culture and provide the basis for evidence-based decision making processes at all levels.

The impact evaluation will involve a panel of households surveyed in three rounds; a baseline in 20181, a midline currently scheduled at the end of project year three in 2020 and an endline currently scheduled for 2023. The impact evaluation in restricted to 14 JOHAR project blocks where the HVA and irrigation interventions are to be rolled out in the first two project years (as this covers 75% of project beneficiaries and 80% of the project budget).

The impact evaluation uses two primary designs:

A cluster randomised control trial, whereby Gram Panchayats (GPs) in the project blocks are

randomly assigned to either receive the JOHAR interventions or act as “internal control” groups

and not be part of JOHAR (and hence act as a counterfactual to the areas that do)

A quasi-experiment using “external” controls, whereby project blocks are matched with control

blocks outside the project blocks and these act as a counterfactual.

The logic for using two separate evaluation approaches is that either may not be sustained throughout

the project duration. There is a chance that the project is scaled across other blocks in Jharkhand,

contaminating the external controls. There is also a chance that the internal controls cannot be

maintained for practical or political purposes and become contaminated.

1 The JOHAR project began in 2017


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The primary effect of interest in the Average Treatment Effect on the Treated (ATT) estimate which

would estimate the average effect only on those who actually received the intervention (i.e. became

a PG member). It is also expected that the project will have spillover benefits to households who do

not join Producer Groups (for example, through access to improved inputs). The evaluation will also

seek to assess these spillover benefits to non-participating households through a secondary sample.

Households with more than one acre of land are included in the primary sample, as this is expected

to be a proxy for a household’s ability to partake in JOHAR’s HVA activities. The evaluation is powered

to detect a 16 percentage points increase in total household annual income.

The baseline survey was conducted by Kantar Public between August and November 2018, with

quality assurance by Oxford Policy Management and JSLPS. A total sample of 5,982 households was

covered. A household questionnaire, administered to adult male and female respondents, was

supplemented by a village questionnaire and an SHG questionnaire. Equivalence tests on the

baseline data validates the evaluation designs and the survey administration.

Data presented in the findings chapters of this report only cover “treatment” clusters as this will be the most relevant to the implementation teams. Only the primary sample findings are discussed in the text, as these are the households that the project is targeting.

Findings: Demographics, socio-economic status, and village infrastructure and services

Of target households, Hinduism is the most prevalent religion (58%) and Scheduled Tribes (ST) is the

most prevalent social category (46%). 3% of the ST households belong to Particularly Vulnerable

Tribal Groups (PVTG). The household head of almost all households (96%) are registered on the

voter list and have a voter identity card. Few households (4%) have all members being illiterate and

50% have household heads who have completed secondary school or higher. About a fourth (24%)

of households have at least one family member who has migrated. 7% of households have a

differently abled member, 100% of households have at least one member with an Unique

Identification Authority of India (UIDAI) identity number, 98% of households have at least one member

with a bank account, and 90% have at least one female member of the household with a bank

account.

Labour Force Participation Rate (LFPR), female LFPR, and Working Participation Rate (WPR) is

50.39, 40.70, and 54.43 respectively which are significantly higher than the rates reported by the

National Sample Survey (NSS) and Census of India for Jharkhand.

Of all members in the target households, 49% are women, 47% of these women in the age group of

5 to 35 years age are currently enrolled in formal education and 69% of all women have a bank

account. 15% of women are employed and 45% are engaged in household work as their primary

activity over the last 12 months. Of women who work, the highest proportion of them (57%) are

engaged in self-employed farming as a primary activity and 55% as their main subsidiary activity.

For target households, handmade tiles are the most common roof material (47%) and mud or unburnt

bricks is the most common wall material (63%). 55% of households have a separate kitchen and 76%

of households have an electricity connection. 58% of households have a latrine. The most commonly

owned asset is a mobile phone (87%) followed by a bicycle (80%). Swachh Bharat Mission – Grameen

is the most widely accessed social security scheme, with 32% of households having received benefits

from the scheme in the last 12 months.


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Of all target villages surveyed (n=100), 89% have a government primary school, 89% have an

aanganwadi centre and 100% have electricity.

Findings: Impact Level Indicators

The average annual household income for JOHAR households is Rs 56,430. This includes the net income (revenue minus the costs of production) plus the value of self-consumption. With an average household size of 5.34, this gives an average individual annual income of Rs 10,567. The project target is for this to increase by 50% in real terms due to JOHAR (Rs 84,645 at current prices).

Nearly all households were involved in agriculture, and over four fifths in livestock. Few households were currently involved in fisheries and NTFP. For the average household, Rs 16,149 comes from agriculture (29%) of which Rs 13,340 comes from non-HVA crops of paddy, maize, ragi and wheat and only Rs 2,809 comes from HVA. The average combined income from livestock, fisheries and NTFP is just Rs 839. This low contribution is likely related to the selection of HVA blocks only for the impact evaluation. This means that the average income from these four livelihood sources is Rs 16,988, or 30.1% of total income.

Wages and enterprise were the most remunerative activities (Rs 57,915 and Rs 60,800 respectively) for the households who were engaged in this. 41.3% of wages come from formal (regular or permanent) labour.

Figure 2 Sources of Average Annual Household Income

The Project Appraisal Document target was for the increase in real annual household income to be driven by a 100% increase in the proportion of income (real) from select livelihoods (defined as self-employed HVA crops, livestock, fisheries, NTFP and non-farm business, and formal labour). The indicator captures the objective of income diversification away from subsistence livelihoods to more productive livelihoods. These livelihoods currently comprise 30% of average annual household income. A doubling of this would mean that, at current prices at endline, 60% of average annual household income (Rs 50,787) would need to be derived from these sources. This would require an increase (at current prices) in the contribution of these sources of Rs 33,818, i.e. a tripling of the current figure.

29%

1%

0%

0%

-1%

9%

36%

1%

24%

Primary Sample

Agriculture Livestock Fisheries NTFP Assets Enterprise Wages NREGA Other Sources


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In terms of dietary diversity, the average household consumed 6.11 out of 12 food groups. Nearly all households consumed cereals in the last normal day, and most households consumed roots and tubers, vegetables, pulses/legumes/nuts, oils/fats and miscellaneous.

Findings: Landholding and land use

Out of all households in the HVA blocks that were part of the baseline listing exercise (n=32,811), 33% were landless. Since cultivator households would require allocating 0.3 acres of their land which is suitable (mid or upland) for HVA, it is assumed (and supported by data from other projects) that households with more than one acre of total land holding are likely to meet the requirement of HVA. Only 28% of households have at least one acre of land (and a further 39% have more than 0.3 acres but less than one acre). This may have equity implications for the benefit incidence of JOHAR, although JOHAR was designed to work with farmers who have or are likely to have marketable surplus which presupposes access to land.

Of households with at least one acre of land, the average landholding is 2.68 acres of which 2.29 acres were cultivable. Two thirds of households cultivate more than one topography (lowland, midland and upland) and land holding is roughly evenly split between the three topographies. The average household cultivated 1.7 acres in Kharif (74% of their cultivatable land) but there was very little cultivation in the Rabi and summer cropping seasons.

Only 6% of households leased out land, and 8.5% of households leased in land. This implies that it will be challenging for households without suitable land to lease in land for JOHAR.

Findings: Agriculture

Out of cultivator households, 99% cultivated in the Kharif season. Only 47% cultivated in Rabi and 14% in the summer season, leading to a low cropping intensity of 110%, suggesting potential for income gains to come from multi-season farming for those who currently only crop in Kharif.

Cultivation in Rabi and summer is largely HVA cultivation. This suggests that increasing the proportion of households who cultivate in Rabi and summer is inherently linked to expanding the proportion of households who undertake HVA. 48% of households currently cultivate HVA crops promoted by JOHAR (primarily potato, tomato and onion), and 67% any HVA crop. JOHAR targeted HVA crops have a significantly (3.3 times) higher average gross sales per acre than non-HVA crops.

However, currently little land is allocated to these HVA crops (only 0.08 acres per household). Access to irrigation is a major predictor of whether households cultivate HVA crops and crop in multiple seasons. The average net income per acre for land that is irrigated is 2.67 times that of land that is not irrigated.

41% of cultivator households had some form of irrigation. Of all cultivated land of all households in the primary sample, 32% was irrigated. For these households, the most frequent source of irrigation was private well (38%) followed by community well (17%) and community pond (16%). 10% or fewer households used other irrigation sources. However, households do not always use irrigation even if they access to it. Between half and two thirds of households who have access to irrigation use it in the Kharif and Rabi seasons (with variation by topography) but this falls to a quarter or fewer during the summer season. The project may want to consider why irrigation is not always used and ensure that investments in irrigation under JOHAR facilitate increased cropping intensity.

There are significant productivity differentials across households suggesting many have the potential to increase productivity through adopting new practices. Market inefficiencies lead to potential gains in income through collective marketing at higher level markets. Input costs eat up a relatively large share of gross income (45%) suggesting that reducing these costs provides scope for increasing household’s net income. Most inputs are provided by dealers. Almost all households used fertiliser,


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three quarters hired female labour and used cow dung, and half hired male labour. One-fifth used pesticides and fungicides. Other inputs were used by very few households, suggesting significant potential for intensification of production.

Findings: Livestock

As the sample only covers HVA target households, some of whom are also engaged in livestock, it is not representative of all households engaged in livestock. Of these households, 86% own at least one animal or bird. Over two thirds of these own cattle and over half own goats and country chickens. Ownership of other livestock is relatively low. In reality, the cattle are probably being used as milch animals or farm labour and not to generate livelihood (though there would be some incidental income from use of the animal as farm labour and manure as an input on the farm).

Half of the households that own any livestock report having earned a cash income from or self-consumed livestock. For households that do own buffalo and cattle, they earned over Rs 10,000 in the last year from sales of produce and animals (Rs 19,530 and Rs 12,461 respectively). Other livestock were substantially less remunerative.

Households sold on average less than a fifth of their livestock in the last year. Most sales were at the farmgate or village market. As noted earlier with HVA produce, there are market inefficiencies but producers are more likely to realise higher prices for their produce in markets farther away from them, such as the block and district market. It is likely that if producers are able to access those markets either individually or collectively their sales revenue from livestock will increase.

Findings: Fishery

As the sample only covers HVA target households, some of whom are also engaged in fishery, it is not representative of all households engaged in fishery. Only 7% of these households engaged in fishery, and 5% earned income from it.

Of the households engaged in fishery, over three quarters produced fish in ponds. Of those earning from fishery, nearly all sell from their household or within their village; there is little marketing outside of the village.

For the households engaged in fishery, they made a negligible income over the last year. Those households who had produced something for sale showed a small annual net income (of Rs 1,618). For HVA households, therefore, fishery is a minority livelihood activity with little income generation. Most of the fish produced is self-consumed or supplied directly to buyers within the village; fisheries as a production activity isn’t leading to a marketable surplus yet in these JOHAR HVA villages. Investment and collective production with access to suitable inputs could lead to a marketable surplus and fisheries being a viable production activity and avenue for diversity among rural producer households.

Findings: NTFP

Of the HVA target households surveyed, 10% are engaged in the collection or processing of NTFP with about half of them earning a cash income. The project may want to reflect on whether there are sufficient households engaged in NTFP for interventions to make a significant impact on household income.

For these households engaged in NTFP, only the collection of Sal leaves (43%), the collection or processing of tamarind (30%) and Mahua flowers (26%) and the collection of mushrooms (20%) were undertaken by more than a fifth of households. These were largely the highest earning products (for those households that engaged in them), with the exception for mushrooms which presumably was


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self-consumed. On average, households who engaged in NTFP earned Rs 1,328 per year. Market development is likely required in order for NTFP to drive income growth.

Findings: Other Livelihood Sources

36% of average annual household income derived from wages and 9% from enterprises. Enterprises were typically owned by men and employed male non-family members. Few enterprises were focused on agriculture or related livelihoods. Only a quarter of households who owned an enterprise had borrowed capital, with the majority relying on their own capital for the enterprise. 59% of households earned wage income but this was predominantly amongst males and involve casual, irregular contracts.

Findings: Other

Some of the other key baseline findings include:

Community Institutions

59% of households are represented in collectives; almost all of whom are in the fold of NRLM supported SHGs. Three-quarters of these SHGs have been formed in the last three years, questioning whether they have sufficient maturity to successfully layer on alternative interventions. Most SHGs have a bank account (88%) and were federated into a Village Organisation (80%). Over three quarters meet regularly but less than a fifth have a balance sheet prepared for the previous financial year and only 38% have prepared a micro-credit plan for the community investment fund. 85% of Village Organisations have a bank account and 69% are federated into a Cluster Level Federation, but only 27% have completed bank linkages, 15% have received the community investment fund, and 9% have a balance sheet prepared for the last financial year, questioning the maturity of these federation level structures.

Fewer than 2% of households are members of a group/co-operative involved in production or marketing of any commodity or engage with them to procure inputs or market produce

Credit

9% of households hold a Kisan Credit Card (KCC). 16% of households have had a loan open in the last year. Households who took out at least one loan, had an average loan amount of Rs 57,788. Half of these households have loans from SHGs and 20% from Commercial banks. Less than 2% borrowed from a money lender and no households took or had loans from a Pawn shop or Cooperative. Loans were mainly taken for farm assets (34% of households that took loans), health reasons (12%) and marriage expenses (13%). 6% of households took loans to buy land for farming. Aside from farm assets and to buy farming land, few households accessed credit for agricultural purposes (e.g. for livestock and fishery). Of the households that take loans, the most prevalent are OBC households and nearly three-fourths of borrower households cultivate HVA crops, and two-thirds use machine labour.

Knowledge and skills

Less than 4% of households had received any skills training in the past three years. For this small sample, under a quarter had received training from JSLPS showing that there is less than one percent contamination at baseline. This validates the inclusion of skills training in the JOHAR theory of change. Fewer than 2% of households had their soil tested in the last three years. Of this small sample, less than a third had received the soil test report. However, 80% of households who had received the report acted upon it, suggesting that this is a potentially efficacious intervention.


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Climate resilience

Just over half of households encountered adverse weather conditions in the last five years. Of these households, the main issues faced were drought (49%) and low rainfall (42%). Heavy rainfall (23%) and plant diseases (15%) were also commonly faced.

Less than one percent of households have received any training on climate related stresses and its impact on livelihoods. Similarly, less than one percent of households have adopted any farm level practices to cope better with a changing climate. Given the level of risks faced, this validates the inclusion of climate resilience as a critical focus of JOHAR.

Between a quarter and a third of households experienced adverse shocks in the last 12 months before the survey. The main shocks faced by these households were medical expenses on treatment of any family member (56%), followed by loss of agricultural crop (28%), and loss of livestock (21%).

Timelines and future activities

Future activities for the JOHAR impact evaluation include a midline and endline survey with reports following the survey rounds. These should be conducted after project year 3 and project completion respectively, with the surveys being fielded in the same months (August to October) as the baseline survey. However, stakeholders would need to deliberate on the timing of a midline survey, given that JOHAR producer households would need time to move along the impact pathways before another round of data collection is conducted.

In addition to this evaluation that focuses on the HVA sub-component, thematic evaluation(s) to generate evidence on income earned from the livestock, fisheries, NTFP and enterprise interventions of the JOHAR project are planned.


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Table of contents

Acknowledgements ......................................................................................................................... 1

Executive summary ........................................................................................................................ 3

About JOHAR ................................................................................................................................. 3

About the Impact Evaluation ........................................................................................................... 4

Findings: Demographics, socio-economic status, and village infrastructure and services ............... 5

Findings: Impact Level Indicators .................................................................................................... 6

Findings: Landholding and land use ................................................................................................ 7

Findings: Agriculture ....................................................................................................................... 7

Findings: Livestock ......................................................................................................................... 8

Findings: Fishery ............................................................................................................................ 8

Findings: NTFP ............................................................................................................................... 8

Findings: Other Livelihood Sources ................................................................................................ 9

Findings: Other ............................................................................................................................... 9

Timelines and future activities ....................................................................................................... 10

List of tables and figures ............................................................................................................... 15

List of abbreviations ...................................................................................................................... 20

1 Introduction ....................................................................................................................... 22

1.1 Purpose and structure of this document ............................................................................ 22

1.2 Rural livelihoods in Jharkhand ........................................................................................... 23

1.3 Development objectives of the JOHAR Project .................................................................. 23

1.4 About the JOHAR Project .................................................................................................. 23

1.5 Description of the JOHAR project intervention components............................................... 25

1.6 The role of the impact evaluation ....................................................................................... 27

2 Impact evaluation design ................................................................................................... 29

2.1 Scope of the impact evaluation .......................................................................................... 29

2.2 Impact evaluation approaches ........................................................................................... 30

2.2.1 Evaluation design options .................................................................................................. 30

2.2.2 What is the effect of interest? ............................................................................................ 31

2.3 Evaluation model 1: Cluster randomised control trial using “internal” controls ................... 33

2.3.1 Design ............................................................................................................................... 33

2.3.2 Sample Size Calculations .................................................................................................. 34

2.3.3 Oversampling .................................................................................................................... 35

2.4 Evaluation model 2: “External” controls ............................................................................. 39

2.4.1 Design and limitations ....................................................................................................... 39


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2.5 Evaluation model 3: Theory based statistical evaluation .................................................... 43

2.6 About the baseline ............................................................................................................. 44

2.7 Implications of the baseline process for the impact evaluation design ............................... 45

2.7.1 Testing baseline equivalence for key indicators between the treatment and two control groups .............................................................................................................................. 45

2.7.2 Implications of the baseline data and dropping clusters on the evaluation power .............. 48

2.8 How to interpret the baseline survey data .......................................................................... 48

3 Demographics, socio-economic status, and village infrastructure and services ................. 49

3.1 Introduction ....................................................................................................................... 49

3.2 Demographics ................................................................................................................... 49

3.3 Gender .............................................................................................................................. 54

3.4 Access and socio-economic status of households ............................................................. 59

3.5 Village infrastructure and services ..................................................................................... 63

4 Impact Level Indicators ...................................................................................................... 64

4.1 Introduction ....................................................................................................................... 64

4.2 Average Annual Real Household Income .......................................................................... 64

4.3 Percent increase in proportion of real income from select livelihoods sources ................... 66

4.4 Dietary diversity ................................................................................................................. 66

4.5 Consumption Expenditure ................................................................................................. 68

5 Landholding and use patterns ........................................................................................... 70

5.1 Introduction ....................................................................................................................... 70

5.2 Landholding patterns from the listing data ......................................................................... 70

5.3 Average landholding .......................................................................................................... 71

5.4 Topography ....................................................................................................................... 72

5.5 Average landholding cultivated .......................................................................................... 72

5.6 Leasing in and out patterns ............................................................................................... 73

5.7 Conclusions ....................................................................................................................... 73

6 Agriculture ......................................................................................................................... 74

6.1 Introduction ....................................................................................................................... 74

6.2 Cultivation and cropping intensity ...................................................................................... 74

6.3 HVA ................................................................................................................................... 76

6.4 Irrigation ............................................................................................................................ 79

6.4.1 Productivity ........................................................................................................................ 81

6.5 Markets ............................................................................................................................. 82

6.6 Inputs ................................................................................................................................ 83

6.7 Conclusions ....................................................................................................................... 84

7 Livestock ........................................................................................................................... 86


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7.1 Introduction ....................................................................................................................... 86

7.2 Livestock rearing ............................................................................................................... 86

7.2.1 Markets and sales ............................................................................................................. 86

7.2.2 Income .............................................................................................................................. 90

7.2.3 Expenditure ....................................................................................................................... 90

7.3 Conclusions ....................................................................................................................... 91

8 Fishery .............................................................................................................................. 92

8.1 Introduction ....................................................................................................................... 92

8.2 Findings............................................................................................................................. 92

8.3 Conclusions ....................................................................................................................... 93

9 NTFP ................................................................................................................................. 94

9.1 Introduction ....................................................................................................................... 94

9.2 Collection and processing of non-timber forest produce .................................................... 94

9.2.1 NTFP sales ....................................................................................................................... 94

9.2.2 Income .............................................................................................................................. 95

9.3 Conclusions ....................................................................................................................... 96

10 Social mobilisation and community institutions .................................................................. 97

10.1 Introduction ....................................................................................................................... 97

10.2 Social mobilisation ............................................................................................................. 97

10.3 Women’s self-help groups ................................................................................................. 98

10.4 Village organisations ....................................................................................................... 100

10.5 Membership in rural producer collectives ......................................................................... 100

10.6 Engagement with rural producer collectives .................................................................... 101

10.7 Conclusions ..................................................................................................................... 102

11 Other livelihood sources .................................................................................................. 103

11.1 Introduction ..................................................................................................................... 103

11.2 Enterprises ...................................................................................................................... 103

11.3 Wages ............................................................................................................................. 105

11.4 Conclusions ..................................................................................................................... 106

12 Debt, Savings and Credit ................................................................................................. 107

12.1 Introduction ..................................................................................................................... 107

12.2 Sources of credit ............................................................................................................. 107

12.3 Conclusions ..................................................................................................................... 110

13 Knowledge and skills ....................................................................................................... 111

13.1 Introduction ..................................................................................................................... 111

13.2 Skills ................................................................................................................................ 111


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13.3 Soil testing ....................................................................................................................... 111

13.4 Conclusions ..................................................................................................................... 112

14 Climate resilience and shocks ......................................................................................... 113

14.1 Introduction ..................................................................................................................... 113

14.2 Climate resilience ............................................................................................................ 113

14.3 Adverse shocks to households ........................................................................................ 114

14.4 Conclusions ..................................................................................................................... 116

15 Timelines and future activities ......................................................................................... 117

Annex A Snapshot of Project Indicators ................................................................................... 118

Annex B References ................................................................................................................ 120


15

List of tables and figures

Table 1 Evaluation blocks ............................................................................................................... 30

Table 2 Baseline data assumptions ................................................................................................ 35

Table 3 Minimum detectable effect (ATT) and number of clusters .................................................. 35

Table 4 Variables for matching ....................................................................................................... 41

Table 5 Matched control blocks ...................................................................................................... 42

Table 6 PS test ............................................................................................................................... 42

Table 7: Baseline Equivalence Tests .............................................................................................. 46

Table 8 Religion of household head (primary sample) .................................................................... 49

Table 9 Religion of household head (secondary sample) ................................................................ 49

Table 10 Social category of household head (primary sample) ....................................................... 49

Table 11 Social category of household head (secondary sample) .................................................. 50

Table 12 Comparison of SC/ST population proportion with Census data ........................................ 50

Table 13 PVTG among ST households ........................................................................................... 51

Table 14 Identification and registry (primary sample) ...................................................................... 51

Table 15 Identification and registry (secondary sample) ................................................................. 51

Table 16 Education level (primary sample) ..................................................................................... 51

Table 17 Education level (secondary sample) ................................................................................. 52

Table 18 Migration, ability, and access (primary sample) ................................................................ 52

Table 19 Migration, ability, and access (secondary sample) ........................................................... 52

Table 20 Labour force and working participation rates (primary sample) (for treatment) ................. 53

Table 21 Labour force and working participation rates (secondary sample) (for treatment) ............. 53

Table 22 NSS and Census reported labour force and working participation rates ........................... 53

Table 23 Literacy and financial inclusion (primary sample) ............................................................. 54

Table 24 Literacy and financial inclusion (secondary sample) ......................................................... 54

Table 25 Primary activity status (primary sample) ........................................................................... 55

Table 26 Primary activity status (secondary sample) ...................................................................... 55

Table 27 Main subsidiary activity status (primary sample) .............................................................. 56

Table 28 Main subsidiary activity status (secondary sample) .......................................................... 56

Table 29 Working participation ........................................................................................................ 56

Table 30 Primary economic activities (primary sample) .................................................................. 57

Table 31 Primary economic activities (secondary sample) .............................................................. 57

Table 32 Main subsidiary economic activities (primary sample) ...................................................... 58

Table 33 Main subsidiary economic activities (secondary sample).................................................. 58

Table 34 Wages and NREGA (primary sample) .............................................................................. 58

Table 35 Wages and NREGA (secondary sample) ......................................................................... 59


16

Table 36 Roof material (primary sample) ........................................................................................ 59

Table 37 Roof material (secondary sample) .................................................................................... 60

Table 38 Wall material (primary sample) ......................................................................................... 60

Table 39 Wall material (secondary sample) .................................................................................... 60

Table 40 Status and household infrastructure (primary sample) ..................................................... 60

Table 41 Status and household infrastructure (secondary sample) ................................................. 61

Table 42 Asset ownership ............................................................................................................... 61

Table 43 Social security schemes availed (primary sample) ........................................................... 62

Table 44 Social security schemes availed (secondary sample) ....................................................... 62

Table 45 Village infrastructure and services .................................................................................... 63

Table 46 Average annual household income (primary sample) ....................................................... 64

Table 47 Average annual household income (secondary sample) .................................................. 65

Table 48 Income from select livelihoods ......................................................................................... 66

Table 49 Household consumption of food groups ........................................................................... 67

Table 50 Variations in diet diversity (primary sample) ..................................................................... 67

Table 51 Dietary diversity by social status ...................................................................................... 67

Table 52 Consumption expenditure (primary sample) ..................................................................... 69

Table 53 Consumption expenditure (secondary sample) ................................................................ 69

Table 54 Households listed in 13 JOHAR HVA (non-partnership) first- and second-year blocks ..... 70

Table 55 JOHAR 1st and 2nd year HVA blocks (non-partnership) .................................................... 70

Table 56 Landholding patterns ........................................................................................................ 71

Table 57 Average landholding by social category (primary sample) ................................................ 71

Table 58 Average landholding by social category (secondary sample) ........................................... 71

Table 59 Cultivation of different topographies of landholding .......................................................... 72

Table 60 Average landholding cultivated......................................................................................... 72

Table 61 Proportion of cultivable land cultivated ............................................................................. 72

Table 62 Leasing-in and leasing-out patterns ................................................................................. 73

Table 63 Leasing in modalities and costs in Kharif .......................................................................... 73

Table 64 Cropping intensity ............................................................................................................ 74

Table 65 Average income by season (primary sample) ................................................................... 75

Table 66 Average income by season (secondary sample) .............................................................. 75

Table 67 HVA cultivation by season ................................................................................................ 75

Table 68 HVA production ................................................................................................................ 76

Table 69 Determinants of HVA production (primary sample)........................................................... 76

Table 70 Cultivation of different crops (primary sample) ................................................................. 77

Table 71 Average gross sales per acre ........................................................................................... 78

Table 72 Average household revenue for households who also grow HVA..................................... 78


17

Table 73 Cultivation of annual/perennial crops ............................................................................... 78

Table 74 Proportion of cultivated land irrigated ............................................................................... 79

Table 75 Average gross sales per acre for irrigated and non-irrigated land ..................................... 79

Table 76 Sources of irrigation ......................................................................................................... 79

Table 77 Irrigation rates by topography (primary sample) ............................................................... 80

Table 78 Irrigation rates by topography (secondary sample) ........................................................... 80

Table 79 Use of irrigation by topography and season (primary sample) .......................................... 81

Table 80 Impact of irrigation on cultivation by season (primary sample) ......................................... 81

Table 81 Market prices ................................................................................................................... 82

Table 82 Gross and Net income ..................................................................................................... 83

Table 83 Use of inputs .................................................................................................................... 83

Table 84 Season wise input expenses ............................................................................................ 84

Table 85 Ownership and revenue from livestock (primary sample) ................................................. 86

Table 86 Ownership and revenue from livestock (secondary sample) ............................................. 86

Table 87 Sales revenue (primary sample)....................................................................................... 87

Table 88 Sales revenue (secondary sample) .................................................................................. 87

Table 89 Units marketed and price realised (primary sample) ......................................................... 87

Table 90 Units marketed and price realised (secondary sample) .................................................... 88

Table 91 Markets where livestock is sold (primary sample) ............................................................ 88

Table 92 Markets where livestock is purchased (primary sample) .................................................. 89

Table 93 Income from livestock rearing (primary sample) ............................................................... 90

Table 94 Income from livestock rearing (secondary sample)........................................................... 90

Table 95 Expenses incurred in livestock rearing (primary sample) .................................................. 91

Table 96 Expenses incurred in livestock rearing (secondary sample) ............................................. 91

Table 97 Households engaged in fishery ........................................................................................ 92

Table 98 Distribution by type of fish production for households engaged in fisheries ...................... 92

Table 99 Fish markets (primary sample) ......................................................................................... 92

Table 100 Income from fisheries ..................................................................................................... 93

Table 101 Proportion of households that purchase fish seed from different sources (primary sample) ........................................................................................................................................... 93

Table 102 NTFP collection/processing and earning revenue (primary sample) ............................... 94

Table 103 NTFP collection/processing and earning revenue (secondary sample) .......................... 94

Table 104 Sales revenue (primary sample) ..................................................................................... 94

Table 105 Quantity marketed and price realised (primary sample) ................................................. 95

Table 106 Net income from collection or processing of NTFP (primary sample) ............................. 95

Table 107 Net income from collection or processing of NTFP (secondary sample) ......................... 95

Table 108 Representation in collectives (primary sample) .............................................................. 97

Table 109 Representation in collectives (secondary sample) .......................................................... 97


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Table 110 Representation in women’s’ self-help groups (primary sample) ...................................... 97

Table 111 Representation in women’s’ self-help groups (secondary sample) ................................. 97

Table 112 Year of formation of NRLM SHGs .................................................................................. 97

Table 113 Households with SHG membership (primary sample) .................................................... 98

Table 114 Self-help group households (primary sample) ................................................................ 98

Table 115 Self-help group households (secondary sample) ............................................................ 99

Table 116 Self-help group key features .......................................................................................... 99

Table 117 Village organisation key features .................................................................................. 100

Table 118 Production or marketing group membership (primary sample) ..................................... 100

Table 119 Production or marketing group membership (secondary sample) ................................. 100

Table 120 Membership in groups and institutions (primary sample) .............................................. 100

Table 121 Membership in groups and institutions (secondary sample) ......................................... 100

Table 122 Purchasing and procurement from producer groups (primary sample) ......................... 101

Table 123 Purchasing and procurement from producer groups (secondary sample) ..................... 101

Table 124 Sale of production through a producer group (primary sample) .................................... 101

Table 125 Sale of production through a producer group (secondary sample) ............................... 101

Table 126 Household enterprise ownership .................................................................................. 103

Table 127 Types of enterprise ...................................................................................................... 103

Table 128 Sources of capital......................................................................................................... 104

Table 129 Enterprise employment ................................................................................................ 104

Table 130 Household wage earnings ............................................................................................ 105

Table 131 Average annual income from different wage sources ................................................... 105

Table 132 Industries for wage/salary (primary sample) ................................................................. 105

Table 133 Industries for wage/salary (secondary sample) ............................................................ 106

Table 134 Kisan Credit Card coverage ......................................................................................... 107

Table 135 Loans taken ................................................................................................................. 107

Table 136 Sources of loans .......................................................................................................... 107

Table 137 Purpose of loans .......................................................................................................... 108

Table 138 Characteristics of households with loans (primary sample) .......................................... 109

Table 139 Receipt of skills training................................................................................................ 111

Table 140 Source of skills training for households who received any training ............................... 111

Table 141 Soil testing ................................................................................................................... 111

Table 142 Households encountering adverse weather conditions (primary sample) ..................... 113

Table 143 Households encountering adverse weather conditions (secondary sample) ................. 113

Table 144 Types of adverse weather conditions (primary sample) ................................................ 113

Table 145 Types of adverse weather conditions (secondary sample) ........................................... 113

Table 146 Climate training and coping strategies (primary sample) .............................................. 114


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Table 147 Climate training and coping strategies (secondary sample) .......................................... 114

Table 148 Households experiencing shocks (primary sample) ...................................................... 114

Table 149 Households experiencing shocks (secondary sample) ................................................. 114

Table 150 Types of shocks (primary sample) ................................................................................ 115

Table 151 Types of shocks (secondary sample) ........................................................................... 115

Table 152 Outcome indicators (combined sample) (for treatment and control) ............................. 118

Figure 1 JOHAR theory of change .................................................................................................... 4

Figure 2 Sources of Average Annual Household Income .................................................................. 6

Figure 3 JOHAR theory of change .................................................................................................. 27

Figure 4 Cluster randomised control trial design summary .............................................................. 38

Figure 5 Quasi experimental evaluation design summary ............................................................... 40

Figure 6 Sources of average annual household income .................................................................. 65

Figure 7 Proportion of households cultivating in different seasons .................................................. 75

Figure 8 Variations in productivity ................................................................................................... 81


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List of abbreviations

ATT Average Treatment Effect on the Treated

BMMU Block Mission Management Unit

BPM Block Project Manager

CAPI Computer Assisted Personal Interviewing

CEO Chief Executive Officer

CRCT Cluster Randomised Control Trial

DID Difference-in-Difference

DMMU District Mission Management Unit

DPM District Project Manager

FANTA Food and Nutrition Technical Assistance

FISC Farmer Producer Organisation Incubation Support Cell

GIS Geographic Information System

GoJ Government of Jharkhand

GP Gram Panchayat

HDDS Household Dietary Diversity Score

HH HVA

Household High Value Agriculture

IAS Indian Administrative Service

IFS Indian Forest Service

ITT Intent to Treat

JICA Japan International Cooperation Agency

JOHAR Jharkhand Opportunities for Harnessing Rural Growth Project

JSLPS Jharkhand State Livelihood Promotion Society

KCC Kisan Credit Card

LFPR Labour Force Participation Rate

M&E Monitoring and Evaluation

MLE Monitoring, Learning and Evaluation

MDE Minimum Detectable Effect

MIS Management Information System

MKSP Mahila Kisaan Sashaktikaran Pariyojna

MLE Monitoring Learning and Evaluation

NABARD National Bank for Agriculture and Rural Development

NREGA National Rural Employment Guarantee Act

NRLM National Rural Livelihood Mission

NRLP National Rural Livelihoods Project

NSS National Sample Survey

NTFP Non-Timber Forest Produce

OPML Oxford Policy Management Limited

PAD Project Appraisal Document

PDO Project Development Objective

PG Producer Group

PO Producer Organization

PRADAN Professional Assistance for Development Action


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PS Propensity Score

PSM Propensity Score Matching

PSU Primary Sampling Unit

RBMF Results Based Monitoring Framework

RCT Randomised Control Trial

RDD Regression Discontinuity Design

RDD Rural Development Department

SC Scheduled Caste

SHG Self-Help Group

SMMU State Mission Management Unit

SPMU State Project Management Unit

SPV Special Purpose Vehicle

ST Scheduled Tribe

ToC Theory of Change

ToR Terms of Reference

TSA Technical Support Agency

UIDAI Unique Identification Authority of India

VO Village Organisation

WPR Working Participation Rate


22

1 Introduction

1.1 Purpose and structure of this document

This document provides analysis of the quantitative baseline survey data collected for the impact

evaluation of the JOHAR project, implemented by the JSLPS.

This report has three purposes:

1) To test the validity of the impact evaluation design and update it as necessary

2) To generate information and insights for the implementing teams to consider in the design and delivery of the project

3) To set baseline values of key indicators that can be used to generate project targets

The report is organised as follows:

The rest of this chapter provides a brief background and the rationale for the JOHAR project, its objectives, and the intervention design

The second chapter describes the technical design of the impact evaluation, the baseline survey process, and uses the baseline data to test the evaluation design and outlines modifications to the design and its statistical power

The third chapter presents demographic and socio-economic data on the sample

The fourth chapter analyses impact level indicators

The fifth chapter analyses the landholding and use patterns of households covered in the JOHAR implementation areas

Chapters six to fourteen present analysis of the current situation of households in terms of the project sub-components, including:

o Chapter six: HVA

o Chapter seven: Livestock

o Chapter eight: Fisheries

o Chapter nine: NTFP

o Chapter ten: Social mobilisation and community institutions

o Chapter eleven: Other livelihood sources

o Chapter twelve: Debt, savings and credit

o Chapter thirteen: Knowledge and skills

o Chapter fourteen: Climate resilience and shocks


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Chapter fifteen provides a timeframe for the future activities planned under the impact evaluation

Annex A provides a snapshot summary of key programme indicators

1.2 Rural livelihoods in Jharkhand

Amongst states in India, Jharkhand has the second to lowest income level in the country2. This is in

spite of having the largest share of mineral resources3 and impressive economic performance during

the 12th five-year plan from 2012 to 2017. More than half of Jharkhand’s labour force is dependent on

agriculture and allied sectors, and a large portion of the farming community is comprised of small and

marginal farmers, 16% and 68 % respectively4, who practice rain-fed single crop subsistence farming.

Access to irrigation is critical and its absence leads to lower income levels since it limits crop choice,

yield, and cropping intensity. Further, poor market access and an underdeveloped financial sector

limit the options and incomes of small producers; persistent gender gaps in agriculture limit the access

and control of women; and skill development in the agriculture and allied sectors lags behind the

growing demand for agriculture production and enterprise.

1.3 Development objectives of the JOHAR Project

The World Bank-supported National Rural Livelihoods Project (NRLP), which is a part of the National

Rural Livelihoods Mission (NRLM) has been implemented in Jharkhand over the past several years,

and has built a robust platform of community institutions that include women’s SHGs and their

federations5.

JOHAR’s objective is to use this base of women’s SHGs and their federations to enhance and

diversify household income in select farm and non-farm sectors for targeted beneficiaries in project

areas. The key indicators to assess the impact of JOHAR on its development objective are:

1. Percent increase in real average annual household income of target households

2. Percent increase in proportion of real income from select livelihoods sources

3. Number of project beneficiaries that are scheduled caste or scheduled tribe

a. Per-cent of female beneficiaries

4. Number of farmers reached with agricultural assets or services

a. Number of female farmers

1.4 About the JOHAR Project

The JOHAR project plans to achieve its development objective by:

2 Anonymous “All India Rural Financial Inclusion Survey 2016-17”, NABARD (2018): 38 3 Nathan, D & Dayal, H. “Resource Curse and Jharkhand.” Economic and Political Weekly; Vol 44, No. 51 (2009): 16 4 Anonymous “Agriculture Census 2010-11: All India Report on Number and Area of Operational Holdings” Agriculture Census Division, Ministry of Agriculture, GoI (2014): 16-18 5 In Jharkhand, partner organisations such as PRADAN and the Tata Trusts have supported the establishment of community institutions.


24

1. Mobilising and aggregating rural producers including women and men from Scheduled Caste,

Scheduled Tribe, and smallholder households into PGs and POs, with focus on diversification

and/or intensification of their current production system, and improving their participation

higher up in the value chain.

2. Strengthening the competitive advantage of target rural producers by transfer of climate-

resilient production techniques, enhanced opportunities for value addition and effective market

linkages.

3. Improving access to financing, including innovative financial products, through the community

institutions platform and formal financial institutions.

4. Establishing partnerships with the private sector, including rural entrepreneurs, for effective

forward and backward linkages with producers.

5. Supporting skill development and financing modalities to facilitate jobs and entrepreneurship

with a focus on the value chain and agribusiness.

The total cost budgeted for the project is US$ 142.81 million, which is being financed by a loan of

US$ 100.02 million from the International Bank for Reconstruction and Development and a budget

allocation of US$ 42.79 million from the Government of Jharkhand.

Project beneficiaries will be from rural households, the majority of whom will be women SHG members

(including Scheduled Caste, Scheduled Tribe, smallholder, and landless households) in selected

blocks across rural Jharkhand. Households with an actual or potential ability to generate a marketable

surplus production will be selected.

JOHAR project interventions will be implemented in 68 blocks spread across 17 districts of the state,

which have been identified based on geographical spread, incidence of poverty, and marginalised

groups (Scheduled Tribes). Some of these are early stage NRLM blocks, where the community

institutions platform is yet to attain the level of maturity required for layering of livelihoods

interventions. Since the project will be implemented in a phased manner, these blocks will receive the

programme interventions in project year three. The project’s interventions will reach about 200,800

households in total, with the HVA and irrigation interventions reaching about 150,900 households,

livestock interventions reaching about 51,000 households, the fisheries interventions reaching about

34,500 households, and the NTFP interventions reaching about 29,200 households. Member

producer households that join PGs will have the option of engaging in more than one production

activity, thus causing some overlap in the target numbers.

The implementing agency for JOHAR is the JSLPS, which is a registered autonomous society, under

the aegis of the RDD, GoJ, for implementation of poverty reduction programmes in the state. JSLPS

is designated as the special purpose vehicle (SPV) for implementation of the JOHAR project. JSLPS

has a governing council with the Minister for Rural Development, GoJ being its chairperson and the

Principal Secretary, RDD, GoJ being its President, and with participation from line departments and

the National Bank for Agriculture and Rural Development (NABARD). JSLPS will be responsible for

the overall outputs and outcomes of the JOHAR project, as well as for mobilizing co-financing through

convergence and for sourcing required technical support through partnerships. For the JOHAR

project, the JSLPS will partner with the Department of Agriculture, which encompasses the

directorates of horticulture, animal husbandry, fisheries and soil conservation, and the Department of

Forest, Environment, and Climate Change.


25

The institutional arrangements, from the community institutions level to the state level, for

implementation of the JOHAR project are briefly described below.

At the community institutions level, the JOHAR project will work with community institutions supported

by the NRLM, which includes women’s SHGs and their federations (Cluster Level Federations and

Block Level Federations). In addition, small producers will be aggregated around key sub-sectors to

form PGs and larger POs (companies or co-operatives). About 3,500 PGs and 25 POs will be formed

or supported across the various sub-sectors. At the community institutions level, there are various

community cadres who have specific functions for project implementation.

At the Block level, JSLPS’ Block Mission Management Unit (BMMU) will manage and oversee the

project with the Block Project Manager (BPM) responsible for outcomes and outputs in that block.

The BPM will be assisted by other block level officers, field thematic coordinators and community

coordinators.

At the District level, JSLPS’ District Mission Management Unit (DMMU) will manage and oversee the

project with the District Project Manager (DPM) responsible for outcomes and outputs in that district.

The DPM will be assisted by other district level officers including thematic officers.

At the State level the project will be steered by a high-level steering committee headed by the Chief

Secretary and comprising of the Principal Secretaries / Secretaries of relevant Departments. JSLPS’

State Mission Management Unit (SMMU) will guide and provide support to the project, and a specific

JOHAR State Project Management Unit (JOHAR – SPMU) will be responsible for planning,

implementation, and review of the JOHAR project. The JOHAR SPMU will also house state level

thematic officers who will lead respective thematic areas.

In addition to these arrangements, various partnerships will be leveraged for project implementation,

such as working in close coordination with the NRLM, entering in to partnerships with relevant national

level missions, research institutes, Non-Government Organisations, and financial institutions.

1.5 Description of the JOHAR project intervention components

The project is structured in to three primary components, as under;

1. Diversified and resilient production and value addition

a. Rural producer collectives

b. HVA development

c. Livestock development

d. Fisheries development

e. NTFP development

f. Irrigation system development (implemented along with the HVA sub-component)

2. Promoting market access, skill development and pro-poor finance systems

a. Market access and private sector participation


26

b. Skill, jobs and enterprise development

c. Pro-poor agricultural finance systems

3. Project and knowledge management

Whilst the JOHAR project has complex impact pathways with multiple intervention components, an

overall theory of change to describe the project and movement along impact pathways can be

simplified into the following framework, structured as a results chain of inputs, outputs, initial

outcomes, intermediate outcomes, final outcome, and impact:

1. The programme activities can be divided into five types of interventions, which are the inputs

in the results chain:

a. Institutional interventions include the collectivisation of producers into producer groups

and producer organisations, which provide the base for the delivery of other

interventions.

b. Input interventions such as making higher quality seeds or hatchlings available and

provision or establishment of infrastructure such as irrigation systems or animal sheds.

c. Market interventions such as supporting farmers to move up the value chain and

undertake additional activity before they sell their produce.

d. Credit or finance interventions that improve the availability of finance to producers by

providing access to credit from community institutions as well as financial institutions.

e. Skills and knowledge interventions such as PoP training, capacity building, and the

provision of climate information.

2. These inputs are expected to lead to four related outputs:

a. Increased and improved availability of inputs, as well as lower cost of inputs.

b. Improved market access.

c. Improved access and increased availability of finance.

d. Improved knowledge and increase in skills.

3. It is then expected that these outputs will enable producers to change their production

decisions (initial outcome) e.g. through diversification or increased use of inputs; and this will

in turn lead to increased production and / or productivity (intermediate outcome) and increased

sales revenue (final outcome).

4. This, in turn, is expected to lead to an impact in terms of meeting programme targets of

increased household income of producer households, who are the target beneficiaries of the

JOHAR Project.

Illustrated below is an overall theory of change for the JOHAR project, showing the impact pathways.


27

Figure 3 JOHAR theory of change

1.6 The role of the impact evaluation

The impact evaluation is part of a broader overall monitoring, learning and evaluation system, whose

objective will be to reinforce a results-based management culture and provide the basis for evidence-

based decision making processes at all levels from the community institutions level to the state level

for strategic decisions as well as operational decisions and processes.

The monitoring, learning and evaluation system is described in the separate Monitoring, Learning and

Evaluation (MLE) Framework which provides a structure for all MLE activities to be undertaken for

JOHAR. It has been developed based on two primary factors: an assessment of the needs of various

stakeholders – what information is required for whom, for what purpose, and when; and the emergent

Theory of Change for the programme as a whole.

The primary stakeholders and their needs have been identified as:

Rural Development Department, Department of Agriculture, Animal Husbandry and Co-operative,

Forest, Environment, and Climate Change Department, and Water Resources Department,

Government of Jharkhand – estimates of impact of the programme to inform decisions to scale-

up across the State; programmatic lessons that can be applied to JSLPS

JSLPS management – programmatic lessons that can be applied to JSLPS

JOHAR management – actionable management information throughout the life of the programme

that can lead to course corrections as required

Intervention teams in JOHAR – intervention specific information on the fidelity of implementation,

the functionality of the interventions, and the downstream effects on targeted households


28

World Bank – monitoring of results framework for accountability purposes; lessons that can be

applied to other programmes in India and elsewhere

NRLM and Ministry of Rural Development, Government of India – programmatic lessons that can

be applied to other States

Broader academic, research and professional community – programmatic lessons and evidence

on impact that can be applied to other contexts

Based on this, four primary MLE questions that track the theory of change and answer the information

needs of the key stakeholders have been identified. These questions are:

Have the programmatic interventions (inputs) been delivered with fidelity?

Have the programmatic interventions been sufficient to achieve the expected outputs (change in

farmers’ knowledge, availability of inputs, and market access)?

Have the programmatic interventions made sufficient difference to targeted households such that

they change their production decisions (intermediate outcome)?

Have changes in production decisions translated into increased production and/or productivity,

increased sales revenue, and increased household income in line with programme targets?

To answer these four questions, a Results Based Monitoring Framework (RBMF) has been developed. This is a set of indicators that relate to every stage of the aggregate project theory of change and the individual theories of change for the main project components, as well as the assumptions that underpin the cause-effect relationships between inputs, outputs, outcomes and impact.

Data sources – primarily the impact evaluation and the project Management Information System (MIS) - have been identified to generate the information required for each indicator within the RBMF.

Additional MLE activities answer a set of secondary questions outlined in the MLE framework. These activities include process monitoring, community-based monitoring, periodic tracking surveys, thematic evaluations and on-demand studies and analysis.

Therefore, the impact evaluation functions both as a theory-based evaluation against the JOHAR theory of change, as well as one mechanism for generating data to answer a more comprehensive set of questions identified in the MLE Framework.


29

2 Impact evaluation design

2.1 Scope of the impact evaluation

As outlined in the introduction chapter, the impact evaluation is one part of a broader MLE framework.

It primarily aims to generate evidence on the effect of JOHAR on target households (“outcomes” and

“impact” in the theory of change). In particular, it covers the headline impact indicators:

The increase in average annual real household income of targeted households

The proportion of real income that comes from select livelihood sources prioritised by JOHAR

The impact evaluation will also generate information (particularly at midline) on implementation

(“inputs”, “processes” and “outputs” in the theory of change) where indicators are amenable to

measurement through household surveys. This will complement data collected from other sources,

such as the MIS.

The impact evaluation will involve three survey rounds; the baseline conducted in 2018, a midline scheduled to be conducted in 2020, and an endline scheduled to be conducted in 2023.

During the inception phase, it was decided by key stakeholders of the evaluation6 that the focus should be on the JOHAR project blocks where interventions will start in project year one and two. This was to allow for the midline to generate meaningful results that can feed back into implementation for the remaining duration of the project. For this reason, stakeholders would need to deliberate on the timing of a midline survey.

It was also decided to only focus on the project blocks where the High Value Agriculture (HVA) intervention is being delivered. This is because these blocks cover 75% of the project beneficiaries and over 80% of the project budget. It was agreed that it would be an inefficient use of evaluation resources to cover other blocks where a very small proportion of households would receive project interventions. The other interventions (i.e. fisheries, livestock and NTFP) are still delivered in different combinations in the HVA intervention blocks. Therefore, they will not be excluded from the impact evaluation. However, the exclusion of blocks without HVA remains an evaluation limitation and means that the evaluation measures the effectiveness of a subset of JOHAR implementation (in those areas where it is delivered with highest effective coverage) and is not therefore fully representative of the intervention as a whole, limiting external validity. Additional thematic studies of the non-HVA interventions will be undertaken to learn about their functioning when they are implemented independently of HVA.

Based on this, 14 blocks were selected to act as evaluation blocks. These were all the blocks that were to receive the HVA interventions in project year one and two, excluding blocks which have implementation partnerships with external organisations such as PRADAN and the Tata Trusts. These partnership blocks have been excluded since these programs have been implementing livelihoods activities for several years now and it would not be possible to disaggregate the relative effects of these partnership programmes and JOHAR. Table 1 below lists the 14 selected evaluation blocks with the sub-components for implementation in each block, and their phasing.

6 Senior technical staff and management from the JSLPS and World Bank’s task team for JOHAR with advice from OPML’s technical team for the evaluation.


30

Table 1 Evaluation blocks

JOHAR Impact Evaluation Blocks

Block District HVA Livestock Fishery NTFP

Chandankiyari Bokaro Y2 Y3 Y2 -

Purbi Tundi Dhanbad Y2 - Y2 -

Ghatshila East Singhbhum

Y1 - Y2 Y3

Potka East Singhbhum

Y2 Y2 Y2 Y2

Patamda East Singhbhum

Y1 Y3 Y1 -

Sisai Gumla Y2 - Y2 -

Khunti Khunti Y1 - Y1 -

Bhandra Lohardaga Y1 - Y2 -

Mandu Ramgarh Y2 Y2 Y2 -

Patratu Ramgarh Y2 - Y2 -

Chanho Ranchi Y2 Y3 Y1 -

Angara Ranchi Y2 Y4 Y1 Y1

Bero Ranchi Y2 Y3 Y1 Y1

Kanke Ranchi Y2 - Y2 -

Source: Government of Jharkhand’s Project Implementation Plan for JOHAR Project

2.2 Impact evaluation approaches

2.2.1 Evaluation design options

The following potential evaluation design options were identified during the inception period:

A randomised control trial (RCT), whereby individuals are randomly assigned to become part of

JOHAR. This wasn’t feasible as the intervention design required all eligible households who want

to partake in the intervention to be able to do so in villages where the intervention is occurring;

otherwise there would not be sufficient numbers to make the Producer Groups viable.

A cluster randomised control trial (CRCT), whereby some areas within the project blocks are

randomly assigned to act as control groups and will not receive the project activities (and hence

act as a counterfactual to the areas that do. This was identified as a feasible evaluation strategy.

A regression discontinuity design (RDD), whereby households who are just below the eligibility

cut off (ownership of at least 0.3 acres of land for HVA) become a control group for households

just above the eligibility cut off. This wasn’t feasible because the eligibility criteria wasn’t likely to

have been applied strictly during the intervention.

A stepped-wedge design, whereby blocks are randomly allocated to phases of intervention and

later blocks act as a counterfactual for blocks that receive the intervention early on. This wasn’t

feasible because the allocation of blocks to intervention years has already been made,

purposively.


31

A quasi-experiment using “external” controls, whereby project blocks are matched with control

blocks outside the project blocks and these act as a counterfactual. This was identified as a

feasible evaluation strategy.

A theory based, statistical evaluation whereby regression analysis is used to estimate the impact

of treatment across all surveyed households. This could also be used to estimate the effects of

different intervention arms. This was identified as a feasible evaluation strategy, as well.

The cluster randomised control trial and the quasi-experimental method (using external controls) are

the primary evaluation methods. The theory based, statistical evaluation will also be used as a

secondary evaluation method. Each is outlined in detail in this chapter.

The logic for using three separate evaluation approaches is that the two primary approaches may not

be sustained throughout the project duration. There is a chance that the project is scaled across other

blocks in Jharkhand, rendering the external control contaminated. There is also a chance that the

internal control within the cluster randomised control trial cannot continue to be maintained for

practical or political purposes, or through spill-over effects, rendering the internal control

contaminated. Using both approaches mitigates these risks. The midline and endline surveys will

measure contamination/spillovers to assess the continued validity of the evaluation design. The

secondary evaluation method (the theory based, statistical evaluation) provides not just

supplementary analysis, making the evaluation richer, but also a “back-up” method in case both the

internal controls based cluster randomised control trial and external controls based quasi-

experimental evaluation become invalid due to scale-up and contamination.

The second reason for using three evaluation strategies is that none are perfect. The cluster

randomised control trial is the most robust evaluation strategy out of the three because the

randomisation of treatment ensures that (if done properly), there are no systematic differences

between treatment and control groups and the control groups are therefore a good counterfactual for

what would have happened to the treatment groups without the intervention. However, the internal

controls option requires compliance in implementation and is vulnerable to contamination and

spillovers (e.g. where farmers in control areas are able to buy improved inputs from PGs in nearby

treatment areas). The external control approach is less robust as matching is not a perfect substitute

for random assignment. However, it is less vulnerable to spillovers and has no implications for

implementation.

It is possible that the internal control is only maintained until the midline and then implementation in

the control areas within the project blocks goes ahead in the second half of the project. This would

mean that the cluster randomised approach is dropped from the evaluation at the endline. This can

be assessed at the time of the midline.

The theory based statistical evaluation allows for contamination, but is limited by the data availability

to overcome the self-selection bias that arises from the non-random take up of the intervention at the

household level.

2.2.2 What is the effect of interest?

The JOHAR project is targeted at households who have the potential to benefit from the interventions.

For HVA, this requires them to be willing to set aside at least 0.3 acres of land for the intervention.

This is unlikely to be a hard criterion, as households are able to lease additional land to take part in

the intervention, and this criterion isn’t strictly applied by implementing teams on the ground.


32

If the intervention is available to everyone with at least 0.3 acres of land in theory, but not all take

part, then there is a choice of impact effect that the evaluation can detect – either using an Intention

to Treat (ITT) estimator or an Average Treatment Effect of the Treated (ATT) estimator.

Firstly, the evaluation can detect the average effect across all households for whom the intervention

was made available, irrespective of whether they choose to take part. This enables analysis of

whether the intervention is sufficient to make a difference at the population level. This is done using

the ITT estimator. Alternatively, the ATT estimator estimates the average effect on everyone who

actually received the intervention. The analysis is undertaken to understand what difference the

intervention makes to the individuals that receive it. As these households are not likely to be

representative of all households, due to a self-selection bias determining whether households choose

to receive the intervention, the ATT estimate does not provide an unbiased estimate of the impact of

the programme on an average household.

The ATT estimate will be used as the primary evaluation specification as the intervention is targeted

at specific households and the main evaluation question of interest is the effect the intervention has

on those households.

It is also expected that the project will have spillover benefits to households who do not join Producer

Groups (for example, through access to improved inputs). The evaluation seeks to assess these

spillover benefits to a secondary sample of non-participating households through replicating the

analysis outlined below on non-participating households in treatment areas compared to control

areas.

The evaluation will use a panel of households that are revisited at midline and endline. Both the ITT

and the ATT effects will be estimated but the ATT effects are the primary estimates.

Both primary evaluation models are expected to use a difference-in-differences estimator applied to

the panel of households selected at the baseline; whereby changes in average levels of indicators

over time (between baseline and midline/endline) in “treatment” households (where the project is

operating) is compared with changes in “control” households (in the internal and external control

areas). The difference-in-differences estimator removes the biasing effect of any factors that do not

change over time but cannot be observed. The precise evaluation model will be decided at endline

based on how the project rolls out.

The use of the differences-in-differences estimator, if that is the model that is chosen, and ITT and

ATT specifications, are common to both primary evaluation models (the cluster randomised trial using

internal controls and the quasi-experimental approach using external controls).

For the ITT estimates, the estimate of programme impact is achieved using the following Difference-

in-Differences specification:

�� = � + �� + �� + �� ∗ �� + ��′�� + �� (1)

�� is the outcome of interest for household h in cluster v at time t

�� is a dummy variable taking value 1 if the cluster was assigned JOHAR and if not, 0

�� is a time dummy taking value 1 at endline (post-JOHAR period) and 0 at baseline (pre-JOHAR period)

�′�� refer to cluster and household-level controls


33

�� is the error term

For the ATT estimates, we need to drop from the sample the households in treated villages who decided not to take up any of the JOHAR programmes and run the following regression.

�� = � + � � �� + �� + �� ∗ �� + ��′�� + �� (2)

where, � � �� is a dummy variable taking value 1 if the household is a JOHAR beneficiary and 0 for control households

As ATT uses a subset of the sample in the treatment areas (i.e. only those who actually receive

project interventions), this sample is no longer representative of the whole sample and is therefore no

longer well matched to the internal and external control areas. This is because of the self-selection

bias that occurs from households who actually receive the intervention likely differing systematically

from households who do not receive the intervention (e.g. due to higher levels of motivation).

We need to limit the control households to households that are comparable with JOHAR beneficiaries in the treatment group, i.e. households which have an equal probability of participating in JOHAR had they been offered the programme and who are similar in terms of pre-treatment dependent variables.

As it is not known which households in the control areas would have received the intervention, this is

done through matching households between treatment and control areas. There are a variety of

econometric techniques to matching (most often using Propensity Score Matching (PSM)). An optimal

matching algorithm that achieves the most balance will be determined before the endline.

Under PSM, in the treatment areas, the observable characteristics of households are used in a

regression to form a predictor equation of which households take up treatment or not, which generates

a propensity score. This predictor equation is also applied to the control areas to estimate the

probability of households in the control areas receiving the intervention, if they were in the treatment

areas, and households are matched between treatment and control areas based on how close their

propensity score is. Households in the control areas that are not matched to households that actually

receive the intervention are dropped from the sample, as are households who receive the intervention

that cannot find a match in the control areas. Therefore, only households in this area of “common

support” are retained.

The limitation of the PSM approach is that it relies on the “conditional independence assumption”.

This requires that there are no unobserved covariates that both affect the probability of receiving

treatment in the treatment areas and affect outcomes that are not included in the PSM regression.

This is mitigated through use of panel data which enables fixed effects models to remove the bias

induced by any unobserved covariates (e.g. motivation) that do not vary over time. The impact of time

variant unobserved covariates remains a limitation.

2.3 Evaluation model 1: Cluster randomised control trial using “internal” controls

2.3.1 Design

This evaluation technique uses “internal” controls, whereby some parts of the project blocks are

randomly assigned to act as control groups and have not received the project activities (and hence

act as a counterfactual to the areas that do).


34

This model is robust because the randomisation of treatment – if done properly – ensures that there

are no systematic differences between treatment and control groups and the control groups are

therefore a good counterfactual for what would have happened to the treatment groups without the

intervention.

However, internal validity will not be maintained if there is imperfect implementation compliance

leading to contamination or spillovers into the control areas (e.g. where farmers in control areas are

able to buy improved inputs from PGs in nearby treatment areas). This will be measured at the midline

to assess whether the evaluation model is still valid.

External validity (the extent to which the evaluation results would be replicated if the project was

scaled up elsewhere) is compromised by the fact that the treatment blocks have been purposively

selected by the programme. This remains an evaluation limitation.

2.3.2 Sample Size Calculations

As not all sampled households in the treatment areas actually receive the intervention, the effective

sample for the ATT estimates will be smaller than the effective sample for the ITT estimates (which

will use the full sample).

As the primary evaluation specification is the ATT estimate, the starting point is calculating the

required sample for this, and then working backwards to calculate the larger ITT sample

The target for the ATT estimate agreed to with stakeholders during the inception phase is a Minimum

Detectable Effect (MDE, the smallest observed effect that can be said with adequate statistical

confidence to be significant from zero) of a 15 percentage points increase in the primary impact

indicator, total annual household income. The overall project target is for a 50 percentage points

increase. The 15 percentage points target has been calculated to be “better” than the mid-term target

of the project to achieve a 20 percentage points improvement in annual income by the third year of

the project. The midline of the evaluation needs to be adequately powered to assess this.

This would require a sample of 420 households per evaluation arm if divided across 28 clusters per

arm; 15 per arm7.

A cluster for this purpose is defined as a Gram Panchayat (GP), the lowest level of elected

government in India that covers five to six revenue villages on average. For practical reasons, the

allocation of treatment and control areas within the 14 intervention blocks selected for the evaluation

would need to occur at the GP level, not the village level. This is because the interventions are

delivered to a group of contiguous villages roughly analogous to a GP, and it wasn’t practically feasible

to withhold treatment to individual villages within a GP (and the spillover effects would have been very

high if attempted) but it is feasible to withhold treatment to entire GPs. Therefore, the evaluation needs

to identify treatment and control GPs, rather than treatment and control villages. The GP therefore

becomes the cluster in the sampling calculations.

This random assignment at the cluster level rather than the village level impacts on the required

sample to achieve a given MDE due to increasing the effect of intra-cluster correlation which reduces

the statistical efficiency of a sample and its power. There are also trade-offs between having a higher

7 This is based on the following assumptions: a baseline starting value of Rs 59,491; and an intracluster correlation of 0.06. Sampling parameters have been taken from secondary datasets (e.g. the NRLM baseline).


35

number of control clusters (and reducing the impact of intra-cluster correlation, giving more power)

and the practical problems this would cause the implementing teams through having more areas that

they cannot implement in. 28 clusters and 15 observations per cluster was believed to be a good

balance between these two competing objectives.

The underlying power calculations can be found below.

As indicated in the Terms of Reference (ToR), two illustrative indicators are used for sample size

estimations: total annual income and total expenditure in the last one month8, with the former being

the primary indicator of interest. We drew on the NRLM baseline data as shown in Table 2 to estimate

MDEs that can be achieved under different sample sizes. We assume that the standard deviation

remains the same across baseline and endline.

Table 2 Baseline data assumptions

Indicator

Baseline assumptions based on NRLM baseline survey for Jharkhand data (2014)

Mean Standard deviation

Total annual income (Rs) 59491 46582

Total expenditure in the last one month (Rs)

3602 2204

Note: Outliers (top 1 percentile) are dropped to ensure that sample size estimation is not skewed.

In addition, an intra-cluster correlation of 0.06 and a baseline correlation of 0.7 was assumed. As is

standard, the statistical power of the impact evaluation was set at 80%. The cluster size required

for the expected MDE was inflated in the oversampling column to 489.

Table 3 shows MDEs for the ATT for alternative sample sizes.

Table 3 Minimum detectable effect (ATT) and number of clusters

MDE Cluster

size

Cluster size with

oversampling

Total annual income Total expenditure last

month

# of clusters per arm

Total sample size per am

# of clusters per arm

Total sample size per am

10% 15 48 62 2976 38 1824

15% 15 48 28 1344 18 864

20% 15 48 17 816 11 528

Note: Estimations done using clustersampsi in Stata 14.

2.3.3 Oversampling

The next stage of the sampling calculations requires choosing a sampling rule for how households

are selected at the village level. The JOHAR implementation design documents show that, in the

8 The ToR suggests average per capita monthly expenditure; however, this is not available in NRLM data and therefore, total expenditure in the last month is used as a proxy. 9 This oversampling is explained in the next section and accounts for the following: 10% for attrition by endline, 33% for sample loss during matching (as not all households will find adequate matches and will need to be dropped) and 48% for sample loss due to lack of take-up.


36

evaluation blocks, only 22.6% of households would receive JOHAR interventions. If households were

randomly sampled, then 66 households would need to be sampled to end up with 15 households who

would end up receiving interventions. This is extremely inefficient and would have led to a big “wasted

sample”. Therefore, an alternative means of identifying households who are likely to receive the

intervention is required so that a smaller proportion of the sample is wasted. This method of targeting

households needs to not exclude large numbers of households that would have ended up receiving

the intervention, as this would undermine the representativeness and internal validity of the

evaluation.

Three options for an improved method of targeting were considered:

Only selecting households with at least 0.3 acres of land (the minimum required to participate in

the intervention). This was rejected as having a low targeting accuracy as 65% of households

have at least 0.3 acres of land10 and only a third of these would end up receiving JOHAR

interventions. In practice, households devote 0.3 acres of land for HVA if they have substantially

greater landholdings.

Only selecting households with at least 0.3 acres of land and who are SHG members. This was

rejected because SHG membership is fungible; households are likely to become SHG members

to access the programme, so excluding non-SHG members at baseline would have excluded a

large proportion of households who would end up receiving the intervention, and make the sample

non-representative.

Only selecting households with at least 1.0 acres of land. This was the preferred option. It does

not exclude large numbers of households who will end up receiving the intervention (JSLPS’

administrative data shows that 80.7% of households who participate in HVA in other programmes

have at least 1.0 acres of land) and improves the targeting accuracy. Based on programme

targets, 48% of households who have at least 1.0 acres of land will end up receiving a JOHAR

intervention.

As the third option was agreed with stakeholders, the predicted programme participation rate is 48%.

This means that, to ensure that we have 15 households in a cluster who will end up receiving the

intervention, we will need to sample 32 households.

Households were randomly sampled from a list of all households in a cluster with more than 1 acre

of land, determined by a household listing exercise. This sample needed to be further inflated to cover

attrition in the panel between the baseline and endline (assumed at 10%) and to account for some

households not being in the area of common support with the control areas during the PSM exercise

(assumed at 33%). This gave a required sample of 47 per cluster.

In addition to this, a secondary sample of 12 households per cluster was taken – a random sample of

households with less than 1 acre of land. This will be used to calculate spillovers when compared with

similar households in the control groups, with an MDE of 17 percentage points.

In clusters (GPs) of more than four revenue villages, the sample was selected from four randomly

sampled villages within the cluster, rather than all villages. This is to limit the scope of the listing

10 Anonymous “Baseline Survey Report on Livelihood in Jharkhand.” JSLPS (2017)


37

exercise and control costs. In this scenario, 12 households with more than one acre were sampled

per village (total 48 per cluster) and 3 with less than one acre per village (total 12 per cluster).

To mitigate the risks of the assumptions being optimistic, the number of clusters sampled in the

treatment arm were increased by a third (to 41 clusters) which gave the ability to drop entire clusters

if no implementation occurred.

This did not need to be repeated in the internal control areas; although during stratified random

sampling of clusters at the block level (to ensure broad representation of the fourteen evaluation

treatment blocks), rounding meant that 31 clusters were selected as internal controls rather than the

minimum 28 needed for the evaluation.

In summary, in the fourteen evaluation blocks, 41 clusters (GPs) were selected through stratified

random sampling as treatment clusters, and 31 clusters in the control blocks. 48 households per

cluster with more than one acre of land were randomly selected (divided between up to 4 villages per

cluster). From the total sample of 1,968, it was expected that 615 households would end up receiving

the intervention. In control areas, of the total sample of 1,488, it was expected that 465 would be

matched to the households in the treatment areas that end up receiving the intervention.

This gives an MDE for the ATT estimates of better than 15 percentage points. In addition, 12

households per cluster with less than one acre of land were randomly selected from all clusters, and

will be used as the basis to calculate spillover effects. This gives an MDE of better than 17 percentage

points.

The evaluation design is summarised in Figure 4 in the following page.


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Figure 4 Cluster randomised control trial design summary

Treatment areas

14 evaluation blocks from within the JOHAR programme

Random sample of 41 GPs (oversampled by a third)

Random sample of 4 villages per GP

Listing of eligible households (>1 acre)

Random sample of 12 eligible households per village

Total sample 1,968 for ITT estimates

Expected sample of 615 for ATT estimates

Secondary sample of 3 households per village with

less than 1 acre

Total sample for measuring spillovers of 492

Internal control areas


Random sample of 31 GPs




Total sample of 1,488 for ITT estimates



less than 1 acre



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2.4 Evaluation model 2: “External” controls

2.4.1 Design and limitations

This quasi-experimental evaluation technique uses “external” controls, whereby the 14 evaluation

blocks are matched with 14 blocks outside of the JOHAR intervention area, which act as a

counterfactual.

The external controls approach is less robust than the internal controls approach that deployed cluster

randomisation as matching is not a perfect substitute for random assignment. This remains a

limitation.

Furthermore, the evaluation method will not be valid if implementation is scaled up outside the JOHAR

intervention area or if other interventions affecting income are implemented in the control areas but

not the treatment areas. For example, other JSLPS initiatives such as Sanjivani and Initiative for

Horticulture Intervention by Micro Drip Irrigation supported by Japan International Cooperation

Agency (JICA) have elements that overlap with JOHAR.

External validity is also compromised by the fact that the treatment blocks have been purposively

selected by the programme. This, too, remains an evaluation limitation.

2.4.1.1 Sampling

The same sampling calculations for the internal control, cluster randomised models are valid for the

external controls model, with the same MDE sizes.

In summary, in the 14 evaluation blocks, 41 clusters (GPs) have been selected through stratified

random sampling as treatment clusters. 48 households per cluster with more than one acre of land

were randomly selected (divided between up to 4 villages per cluster). From the total sample of 1,968,

it is expected that 615 households would end up receiving the intervention given the assumptions

used, but this will be verified at the endline.

In fourteen matched control blocks, 28 clusters (GPs) have been selected through stratified random

sampling as control clusters. 48 households per cluster with more than one acre of land were

randomly sampled (divided between up to four villages per cluster). From the total sample of 1,344,

it is expected that 420 will be matched to the households in the treatment areas that end up receiving

the intervention given the assumptions used, but this will be verified at the endline.

This would give an MDE for the ATT estimates of better than 15 percentage points for primary

outcome indicators.

In addition, 12 households per cluster with less than one acre of land were randomly selected from

all clusters, and used as the basis to calculate spillover effects. This will have an MDE size of 17

percentage points.

The evaluation design is summarised in Figure 5 in the following page.


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Figure 5 Quasi experimental evaluation design summary

Treatment areas


Random sample of 41 GPs (oversampled by a third)




Total sample 1,968 for ITT estimates



less than 1 acre


External control areas

14 blocks outside the JOHAR programme area, matched to

the evaluation blocks

Random sample of 28 GPs




Total sample of 1,344 for ITT estimates



less than 1 acre



41

2.4.1.2 Selection of external control blocks

A total of 32 control blocks were potentially available for the evaluation11. As the treatment blocks were predetermined, we used matching methods to sample 14 control blocks (from the 32 blocks) that closely match the 14 treatment blocks. By ensuring that treatment blocks are similar to control blocks on observable characteristics, matching methods mitigate the bias arising from non-random selection of treatment blocks. We followed Stuart et al (2010)12 on the implementation of matching methods. The first step was to choose variables on which treatment and control blocks would be matched. In this context, we included variables that influenced the selection of treatment blocks into JOHAR, and the variables that were expected to effect the key outcome, i.e. the income. Given that matching methods were used for the design of the study, when no primary data had been collected, we relied on secondary data sources shown in Table 4 below13.

Table 4 Variables for matching

Data source Variables

NRLM Management Information Systems

(MIS) Proportion of households covered by NRLM (in terms of

total rural households as per the Census 2011)

District Statistical Handbook, Department

of Planning and Development, Government

of Jharkhand

Total block area (in sq. metres)

Census 2011

Proportion of SC and ST population

Proportion of population engaged in cultivation or

agriculture as the main occupation

Net area sown as proportion of total area

Area irrigated by source as proportion of total area

The next step was deciding the metric which was used to define the similarity between two blocks. There are several ways to measure distance between two units14, and their optimal performance is determined by different conditions. For instance, Mahalanobis metric matching and exact matching do not work well when X is high dimensional, i.e. when eight or more covariates are used. Further, these methods impose that two units should match in terms of all covariates, which is a hard condition to fulfil. Therefore, we used PSM, which is an alternative method widely used in the literature under these conditions15.

PSM is a two-stage analytical approach that employs a propensity score as a ‘comparator metric’ that

summarises the information of the set of relevant characteristics, i.e. the ones that drive selection

bias. This propensity score can also be interpreted as an estimation of the hypothetical probability of

11 There are 263 blocks in Jharkhand, of which 68 blocks are selected for JOHAR, leaving 195 blocks as potential controls. From these 195 blocks, we selected blocks where the NRLM programme has been rolled out or identified for roll out in FY 2017-18, from which we further filtered out blocks that fall within the Ganga basin (which the JOHAR project is not operating in) and blocks for which JSLPS has a partnership with other organisations for program implementation, and we are left with 47 blocks. Of the 47 blocks that remain, 15 are further eliminated because they have an NRLM exposure of less than 12 months as of January 2018. 12 Stuart, Elizabeth A. "Matching methods for causal inference: A review and a look forward." Statistical science: a review journal of the Institute of Mathematical Statistics 25.1 (2010): 1. 13 Additional data on area under irrigation, rainfed agriculture and vegetable cultivation from Geographical Information Systems (GIS) data of the JSLPS has not been used at this stage due to unresolved consistency issues. 14 Exact matching, Mahalanobis metric matching, and propensity score matching (ibid.) 15 Mahalanobis matching was attempted as an alternative, but enough control units were not found using this algorithm due to stricter conditions imposed by it.


42

any individual being in the treatment group, given its characteristics. We employ nearest first

neighbour matching (without replacement) as the idea is to find for each treatment block, a

comparable control block. Additionally, a caliper of 0.20 was imposed to ensure that the difference in

propensity scores between any two matches did not exceed a set threshold. The table below shows

the treatment blocks and matching external control blocks.

Table 5 Matched control blocks

Treatment Matched Control

District Name Block Name Propensity

Score District Name Block Name

Propensity Score

Lohardaga Bhandra 0.137 Giridih Dumri 0.129

Purbi Singhbhum

Patamda 0.258 Deoghar Palojori 0.249

Gumla Sisai 0.266 Simdega Pakar Tanr 0.268

Khunti Khunti 0.273 Pashchimi Singhbhum

Jagannathpur 0.285

Ranchi Chanho 0.293 Gumla Verno 0.295

Ranchi Bero 0.325 Simdega Thethaitangar 0.326

Bokaro Chandankiyari 0.329 Hazaribagh Dadi 0.354

Ranchi Angara 0.336 Bokaro Nawadih 0.356

Purbi Singhbhum

Ghatshila 0.361 Ramgarh Chitarpur 0.370

Ramgarh Mandu 0.401 Bokaro Gumia 0.396

Purbi Singhbhum

Potka 0.457 Ranchi Namkum 0.457

Ranchi Kanke 0.641 Ranchi Itki 0.488

Ramgarh Patratu 0.564 Gumla Dumri 0.488

Dhanbad Purbi Tundi 0.535 Pashchimi Singhbhum

Noamundi 0.545

Table 6 shows the results of the PS (Propensity Score) test. The pseudo R-squared is 0.036, the

Rubin’s B test is 43.80, and the Rubin’s R is 0.57 (within the recommended range of 0.5 and 2). The

relatively low explanatory power and degree of imbalance is expected with so few observations and

covariates.

Table 6 PS test

Treatment Control % bias t p>|t| V[T]/V[C]

% of households covered by NRLM 52.47 54.12 -8.7 -0.23 0.823 1.01

Proportion of total area sown 0.35 0.35 0.5 0.01 0.99 1.25

Area irrigated 2359.60 1526.90 1.5 0.94 0.355 0.47

Proportion of SC population 0.07 0.07 4.1 0.13 0.901 2.69

Proportion of ST population 0.49 0.50 -3.9 -0.1 0.918 0.43

Proportion of population engaged in agriculture 0.60 0.61 -6.3 -0.16 0.876 1.02


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2.5 Evaluation model 3: Theory based statistical evaluation

A theory based, statistical evaluation16, will use regressions across the whole sample of households

to estimate the impact of receiving the intervention (defined as being a member of a Producer Group).

This technique will also attempt to estimate the relative impact of the different types of interventions

(HVA, livestock, fisheries, NTFP etc.). It will also be used to assess the impact of receiving different

levels of “dose” of JOHAR – with households being part of Producer Groups and also receiving credit

and training being deemed as receiving a full “dose”, households who are just part of a Producer

Group as a partial dose, and so on. Spillover households in treatment areas can be defined as

receiving a small dose.

This is a secondary evaluation technique that will be used to add richness to the primary evaluation

techniques. It will also be used as a back-up primary technique in case spillovers are detected in both

the internal controls and external controls (through project scale-up or confounding factors).

We propose to execute this method using a production function approach. This method essentially uses regression analysis to estimate the impact of the different sub-interventions on our outcome variables of interest. These regression specifications take advantage of the fact that different PSUs and different households will receive different intervention mixes. At the very basic level, this would involve measuring all possible “inputs” (including the component parts or doses of JOHAR) that would determine outcomes and running multivariate regressions that measure the size of the coefficients on the different inputs. Assuming that the regression model is correctly specified in a way that includes all necessary inputs and controls, and deal with potential endogeneities and biases, then this would be a credible way to isolate the impact of the JOHAR project (and its components) – which are identified as different inputs - on outcomes.

This production function approach will be combined with the Differences in Differences approach, essentially using changes in inputs in the production function, rather than levels. This is powerful because it removes time-invariant unobservables across locations and solves some of the selection bias that arises from the non-random implementation.

The self-selection bias that determines treatment remains an issue in this model. We will deal with this by:

Measuring as many control factors as possible, and including those in the regression, to minimise the degree of unobservable factors

Estimate a propensity score of receiving an intervention (at the village or household level) and include that as an instrument (rather than the actual receipt of the intervention)

Constructing a panel of individuals so that we measure differences rather than levels; which addresses the problem of time invariant unobservables (i.e. through household fixed effects) such as household preferences, attitudes to risk, women’s empowerment, etc.

The creation of a panel of households, rather than repeated cross sections, is especially important in ensuring this kind of evaluation is valid because we are able to deal with time invariant unobservables in a panel.

The value-added based production function model can then be elucidated by the following regression:

16 This is a standard technique to evaluate the impact of programmes (see e.g. “The Production of Cognitive Achievement in Children: Home, School and Racial Test Score Gaps”, PIER Working Paper, Todd and Wolpin; and The Long-Term Impacts Of Teachers: Teacher Value-Added And Student Outcomes In Adulthood, Friedman et al, NBER 2011) and essentially mirrors a production function specification in econometrics.


44

∆ ��! = �∆� + ∆"#$ + %∆&�' ��( + )∆*�+� + �∆+��ℎ �� + - (3)

where we are interested in the coefficients , %, ), �

- is the random error term

X includes all the controls

∆ ��! refers to change in household income

∆"#$ refers to the propensity for a household to receive HVA

The analysis will be applied to the existing panel of households collected for the quasi-experimental evaluation specifications.

2.6 About the baseline

The baseline survey involved development of survey instruments and data collection, followed by data cleaning and analysis culminating in this report.

Survey instruments were developed initially as paper-based tools which were field tested and refined based on their pre-tests and pilot as well as a few rounds of detailed stakeholder discussions and feedback. Thereafter the instruments were programmed in nField, a Computer Assisted Personal Interviewing (CAPI) application, and further piloted to test the electronic data entry and data flow process. The survey instruments underwent a rigorous and lengthy review and refinement process, which involved consultations between and feedback from the JSLPS, World Bank, Kantar Public and Oxford Policy Management teams.

The survey instruments administered, include;

A household questionnaire administered to adult male and female respondents of sampled households

A village questionnaire administered to a key informant of sampled villages (Primary Sampling Units)

A VO questionnaire administered to VO office bearers of sampled villages

A SHG questionnaire to SHG office bearer of sampled SHGs

Of these, the household survey instrument is the primary one, which fielded the following modules to generate the required evidence;

Survey information, informed consent, and household identification

Household roster with questions on membership and association, migration, and activity and occupation of all household members

Socio-economic status of households

Livelihood and income generating activities, which included farming, livestock rearing, fish farming, collection and processing of non-timber forest produce, business and enterprise, wages and salaries, transfers, benefits from social security programmes, and other incomes

Climate resilience

Debt, savings, and loans

Asset ownership


45

Consumption expenditure

Dietary diversity

Awareness, trainings, and services received

Shocks and coping strategies

Interviewer observations

The instruments were used to collect data from 5,982 sampled households in the 89 assigned Gram Panchayats (that were defined as the clusters) across the three arms of the evaluation (deviations from the planned sample are described in section 2.7.2).

Fieldwork for the data collection was conducted from August - November 2018 and during which respondents from sampled households were interviewed, in most cases, in one sitting to collect data on their livelihood activities, savings and loans, asset ownership, shocks, trainings and services received, and migration and occupation of household members over the 12 months gone by prior to the date of interview. Some data on consumption expenditure was collected for the 30 days gone by prior to the date of the interview and dietary diversity for the day prior to the date of the interview.

The entire data collection process was supervised, and stringent quality assurance and quality control protocols were implemented, this included back checks conducted on a random sample by a backcheck team from Kantar Public and additional back checks on a random sample as well as targeted back checks conducted by JSLPS’ field quality control team. In addition to this regular field visits and spot checks by team members from JSLPS, Kantar Public and Oxford Policy Management were conducted. Survey data was uploaded to a dedicated server daily and concurrent assessments of the data were conducted with issues raised periodically for action from the survey team.

2.7 Implications of the baseline process for the impact evaluation design

2.7.1 Testing baseline equivalence for key indicators between the treatment and two control groups

As outlined in chapter two, the JOHAR impact evaluation comprises two primary evaluation models. The first is a cluster randomised control trial using “internal” controls, whereby GPs in project blocks were randomly assigned to either receive the project interventions (“treatment”) or to act as control areas and receive no intervention. The second is a quasi-experimental model that uses “external” controls to match project blocks with blocks outside the JOHAR intervention area, which act as a counterfactual.

As part of the baseline data analysis, equivalence tests have been undertaken to ascertain whether there are systematic differences between the average levels of indicators in the treatment areas and the “internal” and “external” control areas. Table 7 presents the average indicator values for various key indicators, and the difference in these indicators between treatment and internal control areas, and treatment and external control areas. These differences are tested for statistical significance, with ** and *** denoting that the differences are statistically significant (with more than 95% and 99% confidence, in line with standard tests). * denotes that that the differences are only statistically significant with 90% confidence which is not normally deemed to be a sufficient level of confidence.

The table shows that there are no significant differences in the average indicator values between the treatment areas and the internal control areas. This implies that the randomisation has been applied successfully and that the evaluation model remains robust.


46

The table also shows that, as expected, there are significant differences in some indicators between the treatment areas and the external control areas. Most of these differences are fairly small, and the key project indicators show no significant difference between treatment and external control areas. Therefore, the evaluation model remains valid. The mitigation strategies outlined in the evaluation design (the use of difference-in-differences analysis which removes the biasing effect of any factors that do not change over time but cannot be observed) will remain important.

Overall, the equivalence analysis validates the evaluation designs and the survey administration.

Table 7: Baseline Equivalence Tests

Treatment Internal control

value

Difference (IC-T)

p value External

control value

Difference (EC-T)

p value

N 1568 1245 1332

Total cultivated area, in acres

2.29 1.96 -0.33 0.17 2.18 -0.11 0.64

Area of land owned, in acres

2.68 2.4 -0.28 0.26 2.69 0.01 0.98

HHs engaged in agriculture

0.96 0.94 -0.02 0.16 0.94 -0.02 0.33

Income from agriculture (including self-consumption)

16149.31 13660.3 -2489.01 0.33 15681.65 -467.66 0.84

HHs engaged in livestock

0.81 0.82 0.01 0.81 0.84 0.03 0.25

Net income from livestock (including self-consumption)

699.86 915.51 215.65 0.73 -588.03 -1287.89*** 0.01

HHs engaged in fisheries

0.08 0.07 -0.01 0.58 0.05 -0.03* 0.06

Net income from fisheries

0.13 144.3 144.17 0.38 210.29 210.16 0.19

HHs engaged in NTFP

0.1 0.09 -0.01 0.67 0.19 0.09*** 0.01

Net income from NTFP with SC

138.7 255.28 116.58 0.2 840.13 701.43*** 0

OBC HH 0.42 0.37 -0.05 0.42 0.4 -0.02 0.69

SC HH 0.08 0.12 0.04* 0.06 0.07 -0.01 0.62

ST HH 0.46 0.48 0.02 0.81 0.5 0.04 0.53

PVTG HH 0.01 0.01 0 0.71 0 -0.01 0.15

HH has a BPL ration card

0.66 0.64 -0.02 0.56 0.72 0.06* 0.06

HH has a NREGA job card

0.57 0.56 -0.01 0.91 0.6 0.03 0.38

Net income from assets

-481.07 -370.84 110.23 0.75 -297.77 183.3 0.52

HH uses latrine 0.48 0.51 0.03 0.54 0.46 -0.02 0.68

Members in the household

5.35 5.6 0.25* 0.1 5.57 0.22 0.13

HH is female headed

0.08 0.06 -0.02 0.27 0.08 0 0.83

HH head is literate

0.69 0.67 -0.02 0.56 0.66 -0.03 0.4


47

Treatment Internal control

value

Difference (IC-T)

p value External

control value

Difference (EC-T)

p value

HH head is at least primary educated

0.66 0.62 -0.04 0.26 0.62 -0.04 0.16

Any member of HH has migrated

0.24 0.25 0.01 0.65 0.29 0.05* 0.08

Any member of HH is a part of SHG

0.56 0.54 -0.02 0.66 0.58 0.02 0.51

Any member of HH engaged in self-employed farming

0.65 0.69 0.04 0.2 0.65 0 0.9

Any member of HH engaged in self-employed animal husbandry

0.09 0.1 0.01 0.49 0.09 0 0.67

Any member of HH engaged in self-employed non-farming

0.03 0.02 -0.01 0.34 0.02 -0.01 0.28

Any member of HH engaged in fisheries

0 0 0 0.98 0 0 0.31

Any member of HH engaged in enterprise

0.09 0.07 -0.02 0.25 0.06 -0.03* 0.1

Any member of HH whose primary activity earns wage

0.4 0.41 0.01 0.73 0.43 0.03 0.42

HHs engaged in enterprise

0.08 0.06 -0.02 0.14 0.07 -0.01 0.59

Income from wage

20570.7 19112.3 -1458.4 0.74 23296.8 2726.1 0.45

HH that earned under NREGA in last 12 months

0.14 0.15 0.01 0.72 0.17 0.03 0.19

Income from NREGA

845.55 1478.61 633.06* 0.06 2251.96 1406.41*** 0

Net income from other sources

13674.13 14150.17 476.04 0.82 21285.95 7611.82*** 0

Net overall income

56430.21 51143.86 -5286.35 0.3 64154.92 7724.71 0.13

HH owns a tractor

0.02 0.02 0 0.56 0.01 -0.01 0.27

HH owns a cultivator

0.01 0.01 0 0.82 0.01 0 0.99

HH owns an irrigation pump

0.1 0.09 -0.01 0.53 0.1 0 0.98

HH owns a bullock cart

0.03 0.01 -0.02* 0.08 0.01 -0.02* 0.06

HH owns a paddy harvester

0 0 0 0.28 0.02 0.02 0.19

HH owns a refrigerator

0.09 0.07 -0.02 0.38 0.06 -0.03 0.14

HH owns a two-wheeler

0.4 0.39 -0.01 0.89 0.32 -0.08** 0.01


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2.7.2 Implications of the baseline data and dropping clusters on the evaluation power

When this evaluation was designed, annual household income and consumption expenditure were considered as the indicators of interest and baseline values from the 2014 Jharkhand NRLM baseline data were used for the statistical power calculations. For the ATT estimates, the calculations gave us a requirement of 48 households (with oversampling) per cluster in 28 clusters (GPs), and this was later increased to 60 households per cluster to accommodate the additional secondary sample of 12 households per cluster with less than one-acre landholding. Therefore, a total of 60 households in each cluster were to be surveyed for MDE of 15 agreed to with stakeholders.

However, prior to the survey beginning, five clusters were dropped in Patratu and Mandu blocks of Ramgarh district, and then a further three clusters in Khunti block of Khunti district were dropped as well. This was due to security concerns. Two clusters in Jagganathpur block (matching block for Khunti) of West Singhbhum district were dropped also as a result, reducing the total sample to 90 clusters. During the survey, due to local disturbance, one more cluster in Noamundi block of West Singhbhum district was dropped as well, leaving us with 89 Gram Panchayats as the clusters for the evaluation.

These 89 clusters are distributed as 36 in the treatment arm, 28 in the internal control arm, and 25 in the external control arm.

This has an implication on the MDE, since 28 clusters per arm were required. To overcome this, and since we knew there was a loss in clusters prior to the survey’s data collection fieldwork beginning, the requirement of households sampled per cluster was increased from 60 to 66 households.

The original estimates sourced sampling parameters from the NRLM baseline, including a mean average annual household income of Rs 59,491, and an ICC of 0.06. The JOHAR baseline gives actual values for these figures of Rs 57,083 and an ICC of 0.057.

Using these achieved parameters, and the updated sample size (25 clusters, 10% more households per cluster), the MDE increases from 15 to 16 percentage points. This is still well within the 20 percentage points increase that the project is targeting by midline. The MDE for the ITT analysis will be 7.3 percentage points.

2.8 How to interpret the baseline survey data

One purpose of the baseline is to test the validity of the evaluation model (by comparing baseline equivalence of treatment and control groups, which is done in chapter 2.7). The second purpose is to provide insights for the intervention teams about the existing livelihoods and lives of the households they are targeting. For this reason, the data presented in the findings chapters only cover the households in the “treatment” clusters and not the households in the control areas.

As described above, the sample is split into a primary sample (households with more than one acre of land who are expected to receive the JOHAR interventions) and a secondary sample (households with less than one acre of land who are not expected to directly receive the JOHAR interventions but benefit through spillovers).

For all tables, where relevant and appropriate, both the estimates for the primary and secondary sample are presented, but only the primary sample findings are discussed in the text, as these are the households that the project is targeting. Outlier values (top and bottom .5% of households) have been removed. For the primary sample, the sample size is 1,568. Some tables cover only households that cultivate (1,471) where this is more appropriate for the indicator of interest. This is marked in the tables. Where the ‘n’ is not marked, it is the whole sample of 1,568. The secondary sample has a sample size of 824.


49

3 Demographics, socio-economic status, and village infrastructure and services

3.1 Introduction

In this chapter, the demographic, gender, socio-economic status, and village infrastructure and services findings are presented. In the demographics sub-section, the findings on religion, social category, comparison of the proportion of SC and ST population with census 2011 data, household identification, education, migration, labour force and working participation rates are listed. Following this, the access and socio-economic status sub-section lists the findings on household infrastructure, asset ownership, and social security schemes availed by households. The final sub-section lists the services and infrastructure of villages.

3.2 Demographics

Of all target households (n=1568), 58% households’ heads are Hindus, 34% are others, and the rest are Christians, Muslims, Parsis, and Jains. The social category of the majority of household heads is scheduled tribes (46%) and other backward classes (42%), with scheduled castes and others making up the rest. Of the scheduled tribe households (n=615), 3% belong to Particularly Vulnerable Tribal Groups (PVTG). Table 8 to Table 13 show the religion and social category of household heads.

Table 8 Religion of household head (primary sample)

Proportion (%) Standard error n

Hinduism 57.91 3.96 1568

Christianity 2.17 0.81 1568

Islam 6.12 1.84 1568

Jainism 0.07 0.07 1568

Parsi 0.14 0.11 1568

Other 33.58 3.48 1568

Table 9 Religion of household head (secondary sample)

Table 10 Social category of household head (primary sample)


Other backward class 41.97 4 1568

Scheduled caste 7.82 1.5 1568

Scheduled tribe 45.97 3.84 1568

Other 4.24 1.04 1568


Hinduism 72.58 3.36 824

Christianity 0.63 0.35 824

Islam 10.5 2.47 824

Other 16.29 2.46 824


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Table 11 Social category of household head (secondary sample)


Other backward class 48.91 4.22 824

Scheduled caste 14.03 3.26 824

Scheduled tribe 29.7 3.54 824

Other 7.36 1.69 824

Table 12 Comparison of SC/ST population proportion with Census data

District Block N SC population (%) (baseline)

ST population (%) (baseline)

SC population (%) (Census)

ST population (%) (Census)

Giridih Dumri 1,665 4.68 13.63 10.37 11.19

Deogarh Palojori 1,031 7.18 48.69 7.30 28.02

Dhanbad Purbi Tundi 1,185 6.67 52.41 10.83 44.58

Bokaro Nawadih 747 4.42 0.00 13.34 12.96

Bokaro Gumia 1,405 13.95 32.88 9.06 29.80

Bokaro Chandaki-

yari 2,851 13.92 9.47 25.27 8.84

Lohardaga Bhandra 735 14.01 54.42 1.71 63.93

East Singhbhum

Patamda 1,259 3.65 26.13 5.85 39.94

East Singhbhum

Ghatshila 1,507 7.90 42.34 4.35 56.44

East Singhbhum

Potka 2,266 13.64 42.89 3.73 55.04

Hazaribagh Dadi 687 10.48 5.68 12.17 31.81

Ramgarh Patratu 1,842 6.46 25.30 9.03 45.32

Ramgarh Mandu 2,796 17.35 12.91 14.20 17.72

Ramgarh Chitarpur 433 6.70 6.24 5.61 14.86

Ranchi Kanke 2,216 15.93 34.48 3.75 34.73

Ranchi Angara 1,341 17.15 52.57 7.85 55.05

Ranchi Namkum 690 6.96 56.52 5.51 68.51

Ranchi Chanho 1,480 18.38 53.85 2.02 53.59

Ranchi Bero 1,558 21.18 57.00 1.97 61.86

Ranchi Itki 369 14.91 43.63 1.36 48.43

Gumla Sisai 1,586 2.96 76.61 1.04 64.37

Gumla Bharno 435 6.90 66.90 1.53 73.83

Gumla Dumri 321 0.00 91.28 2.11 82.20

Simdega Pakar Tanr 331 27.79 64.65 8.76 68.29

Simdega Thethaita-

nagar 693 3.03 85.86 6.06 80.53

West Singhbhum

Noamundi 329 7.29 73.86 2.90 80.67

Total 31578 11.46 37.37


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Table 13 PVTG among ST households


HHs with hh head belonging to PVTG (primary sample) 2.56 0.86 615

HHs with hh head belonging to PVTG (secondary sample) 2.7 1.93 236

Table 14 below shows that most households (96%) have a voter card (household head), 85% have a ration card and 58% have a National Rural Employment Guarantee Act (NREGA) card.

Table 14 Identification and registry (primary sample)

Proportion

(%) Standard

error n

Households with a NREGA job card 57.71 2.12 1551

Households with a ration card 84.94 1.54 1561

Households with head having an election commission voter identity card

96.37 0.62 1551

Table 15 Identification and registry (secondary sample)

Proportion

(%) Standard

error n

Households with a NREGA job card 45.09 3.08 817

Households with a ration card 80.51 2.44 819

Households with head having an election commission voter identity card

94.77 0.86 819

Table 16 shows that for highest educational level attained by the highest-level attaining member of the household, majority of households 34% and 25% of households fall under the category of completed secondary school and completed higher secondary school respectively. Illiterate, literate without formal schooling, less than primary level, primary, diploma, and post-graduate are the levels attained by the highest level attaining member of the household for the lowest proportions (less than 5% each) of households.

Table 16 Education level (primary sample)

Proportion

(%) Standard

error n

All members of household are illiterate 3.76 0.5 1568

Highest educated member is literate without formal schooling 0.1 0.1 1568

Highest educated member has not completed primary school 0.22 0.1 1568

Highest educated member has completed primary school 6.33 0.68 1568

Highest educated member has completed middle school 13.27 1.07 1568

Highest educated member has completed secondary school 33.68 1.45 1568

Highest educated member has completed higher secondary school

24.82 1.34 1568

Highest educated member has completed diploma 1.56 0.41 1568

Highest educated member has completed graduation 12.9 1.12 1568

Highest educated member has completed post-graduation 3.36 0.57 1568


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Table 17 Education level (secondary sample)

Proportion

(%) Standard

error n

All members of household are illiterate 4.94 1.02 824

Highest educated member is literate without formal schooling 0.94 0.44 824

Highest educated member has not completed primary school 0.57 0.26 824

Highest educated member has completed primary school 11.18 1.57 824

Highest educated member has completed middle school 14.87 1.63 824

Highest educated member has completed secondary school 33.93 2.1 824

Highest educated member has completed higher secondary school

20.77 1.79 824

Highest educated member has completed diploma 1.23 0.51 824

Highest educated member has completed graduation 9.43 1.52 824

Highest educated member has completed post-graduation 2.15 0.59 824

Table 18 shows that of all target households (n=1568), 24% households have at least one household

member who has migrated (been away from the house for a continuous period of 30 days or more)

and 9% households have more than one household member who have migrated in the last 12 months,

7% households have a differently abled member, 100% households have at least one member with

UIDAI (Unique Identification Authority of India) identity number, and 98% and 90% households have

at least one member and one female member respectively with a bank account.

Table 18 Migration, ability, and access (primary sample)

Proportion (%) Standard

error n

HHs with at least one migrant member 23.79 1.97 1568

HHs with a differently abled member 6.62 0.81 1568

HHs with at least one member with UIDAI number 99.82 0.12 1568

HHs with at least one member with bank account 98.21 0.47 1568

HHs with at least one female member with a bank account 90.04 1.05 1568

HHs with more than one HH member having migrated in the past 12 months

8.55 1.13 1568

Table 19 Migration, ability, and access (secondary sample)

Secondary Proportion

(%) Standard

error n

HHs with at least one migrant member 22.04 1.8 824

Highest education of the family 5.93 0.11 824

HHs with a differently abled member 7.18 1.3 824

HHs with at least one member with UIDAI number 99.69 0.24 824

HHs with at least one member with Bank account 97.24 0.9 824

HHs with at least one female member with a bank account 89.66 1.48 824

HHs with more than one HH member having migrated in the past 12 months

6.34 1.28 824


53

Table 20 shows that LFPR17 (Labour Force Participation Rate), female LFPR, and WPR18 (Working

Participation Rate) for the population from target households is 50.39, 40.70, and 54.43 respectively.

These indicators measure the employment and participation rate in the economy. Female LFPR is an

important indicator of success for rural livelihoods programmes that focus on the mobilisation of

women to create sustainable livelihoods. These are much higher than the values reported by the

National Sample Survey (NSS) and the Census as seen in Table 22, and is likely due to the abridged

version of questions administered in the JOHAR survey to collect data and replicate findings reported

by the NSS and Census which administer a longer set of questions in more detail. Some difference

may be explained by the NSS and Census findings being based on all of Jharkhand, whereas findings

reported here are specifically for areas where the NRLM has been implemented for some years and

NRLM supported community institutions have attained some maturity.

Table 20 Labour force and working participation rates (primary sample) (for treatment)

Table 21 Labour force and working participation rates (secondary sample) (for treatment)

Rate n

Labour force participation rate 44.87 4123

Female labour force participation rate 34.27 2034

Working participation rate 47.13 4123

Table 22 NSS and Census reported labour force and working participation rates

NSS/Census reported indicator Reported rate for Jharkhand

Labour Force Participation Rate (PLFS (rural) 2017-1819) 31.5

Female Labour Force Participation Rate (PLFS (rural) 2017-1820) 10.9

Working participation rate (Census(rural) 201121) 43

17 LFPR is a rate that denotes the number of persons in the labour force by the total population. The labour force is calculated by summing the number of persons who were employed and unemployed (seeking or available for employment) for the major part of a year (six months or more) and the number of persons not employed or unemployed for the major part of a year, but employed for a minor part of the year (at minimum onemonth). A reference period of one year has been used for LFPR and female LFPR here. 18 WPR is a rate that denotes the total number of persons working by the total population. The total workers are calculated by summing the number of persons working for any amount of time in the reference period. A reference period of one year has been used for calculation of WPR here. 19 Anonymous “Periodic Labour Force Survey (PLFS) 2017-18” National Statistical Office, Ministry of Statistics and Programme Implementation, GoI (2019) http://mospi.nic.in/sites/default/files/publication_reports/Annual%20Report%2C%20PLFS%202017-18_31052019.pdf 20 ibid 21 Anonymous “Women and Men in India (A statistical compilation of Gender related Indicators in India)” Social Statistics Division, Central Statistics Office Ministry of Statistics and Programme Implementation, GoI (2018) http://mospi.nic.in/sites/default/files/publication_reports/Women%20and%20Men%20%20in%20India%202018.pdf

Rate n

Labour force participation rate 50.39 8426

Female labour force participation rate 40.70 4140

Working participation rate 54.43 8426


54

3.3 Gender

The analysis in this sub-section is at the individual level and unweighted. Out of 8,426 members of treatment households, 49% are women (this is for all ages).

Table 23 shows that approximately 47% of the women in the age group of 5 to 35 years, are enrolled in an institution for formal education.

69% of women have a bank account and 40% of women above the age of 18 years are members of a community or non-community institution, amongst these 98% are members of SHGs.

Table 23 Literacy and financial inclusion (primary sample)

Proportion

(%) Standard

error n

Proportion of women in the sample 49.13 0.00 8426

Women enrolled in formal education (age >5, <35) 47.28 1.01 2445

Women with a bank account 68.77 0.72 4140

Women as members of community/non community institutions, age>18 years

39.95 0.96 2626

Women as members of SHGs from women who have membership in community/non-community institutions, age>18

97.71 0.46 1049

Women as cadre of SHGs from those who are members of SHGs, age>18 years

5.66 0.72 1024

Women as office bearers of SHGs from those who are members of SHGs, age>18 year

15.23 1.12 1024

Table 24 Literacy and financial inclusion (secondary sample)

Proportion

(%) Standard

error n

Proportion of females in the sample 49.33 0.00 4123

Women enrolled in formal education (age >5, <35) 45.09 1.41 1253

Women with a bank account 67.8 1.04 2034

Women as members of community/non community institutions, age>18 years

34.32 1.34 1250

Women as members of SHGs from women who have membership in community/non community institutions, age>18

96.97 0.83 429

Women as cadre of SHGs from those who are members of SHGs, age>18 years

6.25 1.19 416

Women as office bearers of SHGs from those who are members of SHGs, age>18 year

13.94 1.7 416

Table 25 and Table 27 present the different primary and main subsidiary activities women engage in.

15% of women are employed as a self-employed, regular salaried, casual wage labour or unpaid family labour as their primary activity. Negligible number of women are unemployed, i.e. who do not work but are seeking or available for work.

45% women are engaged in household work as their primary activity while one-fourth of the women are students. Approximately 2% women are not in the labour force (not working and not seeking any work).


55

Table 25 Primary activity status (primary sample)

Primary activity: for all females Proportion

(%) Standard

error n

Employed 14.52 0.55 4140

Did not work but was seeking and/or available for work 0.07 0.04 4140

Senior citizen 2.32 0.23 4140

Child 0.34 0.09 4140

Household work 45.48 0.77 4140

Student 25 0.67 4140

Beggar/charity/alms collection 0 0 4140

Foraging/rag picking 0.02 0.02 4140

Common property resources collection 0.02 0.02 4140

Not working and not seeking any work 2.49 0.24 4140

Government contractual - Para teacher, AWW, AWH, MDM, Sahiya, and Jal Sahiya

0.05 0.03 4140

Not applicable (age<5 years) 9.69 0.46 4140

Other 0 0 4140

Table 26 Primary activity status (secondary sample)

Primary activity: for all females Proportion

(%) Standard

error n

Employed 12.73 0.74 2034



Child 0.34 0.13 2034


Student 25.07 0.96 2034

Beggar/charity/alms collection 0.05 0.05 2034



Not working and not seeking any work 2.41 0.34 2034

Government contractual - Para teacher, AWW, AWH, MDM, Sahiya, and Jal Sahiya

0 0 2034


Other 0 0 2034

While a small proportion of women (15%) have employment as their primary activity, a higher proportion have (35%) employment as their main subsidiary activity. This highlights the primary role of women in household work. Table 27 also shows that 28% women do not engage in any subsidiary activity.


56

Table 27 Main subsidiary activity status (primary sample)

Secondary activity: for all females Proportion (%) Standard error n

Employed 35.07 0.74 4140



Child 2.34 0.24 4140


Student 1.11 0.16 4140




Government contractual 0.02 0.02 4140


Not engaged in any subsidiary activity in the last 12 months 28.07 0.7 4140

Other 0.07 0.04 4140

Table 28 Main subsidiary activity status (secondary sample)

Secondary activity: for all females Proportion (%) Standard error n

Employed 27.63 0.99 2034



Child 2.9 0.37 2034


Student 1.13 0.23 2034




Government contractual 0 0 2034


Not engaged in any subsidiary activity in the last 12 months 34.76 1.06 2034

Other 0 0 2034

Table 29 shows that the working participation of women is 45% which shows the proportion of women who are workers engaged in any economic activity in either primary or main subsidiary activity.

Table 29 Working participation

For all females Proportion (%) Standard error n

Women who are workers in either primary or secondary activity (Primary sample)

45.46 0.77 4140

Women who are workers in either primary or secondary activity (Secondary sample) 37.02 1.07 2034

Table 30 shows the distribution of employed women across different economic activities. From the 14.52% women who have employment as their primary activity, majority (57%) are self-employed


57

farmers. Approximately 13% of women who are employed are engaged in salaried work while 11% are engaged in unpaid family labour. Only 2.5% women are enterprise or business owners.

Table 30 Primary economic activities (primary sample)

Of women whose primary activity is working Proportion

(%) Standard

error n

Self-employed farming (cultivation) 57.07 2.02 601

Self-employed animal husbandry (owned or leased-in animals) 6.99 1.04 601

Self-employed non-farming 0.17 0.17 601

Fisheries 0.17 0.17 601

Enterprise/business 2.5 0.64 601

Salaried government 7.65 1.09 601

Salaried public sector 1 0.41 601

Salaried private 4.66 0.86 601

Agri wage labour 2.33 0.62 601

Animal husbandry (wage labour) 0 0 601

Non-agri wage labour 6.49 1.01 601

Agricultural family workers (unpaid) 9.48 1.2 601

Non-agricultural family workers (unpaid) 1.5 0.5 601

Table 31 Primary economic activities (secondary sample)

Of women whose primary activity is working Proportion

(%) Standard

error n




Fisheries 0 0 259



Salaried public sector 1.93 0.86 259



Animal husbandry (wage labour) 0.39 0.39 259




From 35% women who have employment as their main subsidiary activity, 55% are in self-employed farming, followed by 11% in self-employed animal husbandry. A small percentage of women are engaged in salaried employment as their main subsidiary activity. Approximately 21% women are engaged in unpaid family labour.


58

Table 32 Main subsidiary economic activities (primary sample)

Of women whose secondary activity is working Proportion

(%) Standard

error n




Fisheries 0 0 1452










Table 33 Main subsidiary economic activities (secondary sample)

Of women whose secondary activity is working Proportion (%) Standard error n




Fisheries 0 0 562










Table 34 shows the distribution of women across different industries for wage or salary. Of all the women, 9% are into salaried or wage employment while 3% are engaged in NREGA.

Table 34 Wages and NREGA (primary sample)


Females who were eligible for wages (category 6-11) 9.3 0.45 4140

Agriculture, hunting, forestry and fishing 10.91 1.59 385

Mining and quarrying 1.3 0.58 385

Manufacturing 0.26 0.26 385

Electricity, gas, and water 0 0 385


59


Construction 11.69 1.64 385

Wholesale and retail trade; restaurants and hotels 0.78 0.45 385

Transport, storage, and communication 0 0 385

Financing, insurance, real estate, and business services 4.94 1.11 385

Community, social, and personal services 13.25 1.73 385

Other 54.55 2.54 385

Don't know 2.86 0.85 385

Females who participated in NREGA 2.95 0.26 4140

Table 35 Wages and NREGA (secondary sample)


Females who were eligible for wages (category 6-11) 12.19 0.73 2034

Agriculture, hunting, forestry and fishing 11.29 2.01 248



Electricity, gas, and water 0 0 248



Transport, storage, and communication 1.21 0.7 248



Other 52.02 3.18 248

Don't know 4.84 1.37 248

Females who participated in NREGA 2.61 0.35 2034

3.4 Access and socio-economic status of households

Of all target households (n=1568), the highest proportion of households (47%) have roofs made of

handmade tiles, followed by concrete (20%) and less than 1% have roofs made of plastic or polythene,

slate, and stone. Similarly, the highest proportion of households (63%) have walls made of mud or

unburnt bricks, followed by burnt brick (24%) and less than 1% have walls made of grass, thatch,

bamboo, or mud, stone not packed with mortar, galvanised iron, metal or asbestos sheets, and wood.

Table 36 Roof material (primary sample)


Grass/thatch/bamboo/wood/mud 6.74 1.11 1568

Plastic/polythene 0.16 0.11 1568

Handmade tiles 47.36 2.36 1568

Machine made tiles 5.78 1.31 1568

Burnt brick 3.62 1.49 1568

Stone 0.04 0.03 1568

Slate 0.1 0.1 1568

GI/metal/asbestos sheets 16.04 1.22 1568


60


Concrete 20.16 2.41 1568

Table 37 Roof material (secondary sample)


Grass/thatch/bamboo/wood/mud 5.35 1.1 824

Plastic/polythene 0.16 0.13 824

Handmade tiles 40.53 2.86 824

Machine made tiles 5.52 1.21 824

Burnt brick 2.71 0.98 824

Stone 0 824

Slate 0.07 0.07 824

GI/metal/asbestos sheets 20.71 2.43 824

Concrete 24.95 3.67 824

Table 38 Wall material (primary sample)


Grass/thatch/bamboo/mud 0.33 0.15 1568

Mud/unburnt bricks 62.66 2.78 1568

Wood 0.04 0.04 1568

Stone not packed with mortar 0.2 0.14 1568

Stone packed with mortar 1.42 0.64 1568

Galvanised iron/metal/asbestos sheets 0.19 0.1 1568

Burnt brick 24.38 2.79 1568

Concrete 10.78 1.32 1568

Table 39 Wall material (secondary sample)


Grass/thatch/bamboo/mud 0.06 0.06 824

Mud/unburnt bricks 52.96 3.11 824

Wood 0.09 0.09 824

Stone not packed with mortar 0.21 0.21 824

Stone packed with mortar 2.13 0.9 824

Galvanised iron/metal/asbestos sheets 0.2 0.15 824

Burnt brick 27.12 2.92 824

Concrete 17.24 2.4 824

Table 40 below shows that more half of the target households have an electricity connection (76%), latrine (58%), and separate kitchen (55%). 6% have members paying a direct tax and less than 1% have a legally released bonded labour or manual scavenger.

Table 40 Status and household infrastructure (primary sample)


HH has a separate kitchen 54.94 1.75 1567


61


HH has an electricity connection 76.14 2.17 1567

HH where a member is a legally released bonded labour 0.1 0.1 1558

HH where a member is a manual scavenger 0.08 0.06 1557

HH where any member pays income or professional tax 5.98 0.82 1555

HHs that have latrine 58.18 3.07 1567

Table 41 Status and household infrastructure (secondary sample)


HH has a separate kitchen 45.39 2.96 824

HH has an electricity connection 74.74 2.43 824

HH where any member pays income or professional tax 2.52 0.59 810

HHs that have latrine 54.94 3.64 824

Table 42 shows that mobile phones, bicycles, and electric fans are the most commonly owned assets,

with 87%, 80%, and 60% of target households respectively owning these assets. Motorised 3-

wheeler, manual rickshaw, landline telephone, and air-conditioner were the least commonly owned

assets with less than 1% of target households owning these assets.

Table 42 Asset ownership

Primary Sample


error n

Secondary Sample

Proportion (%)

Standard error

n

Sewing machine 11.54 1.54 1566 11.35 1.6 824

Refrigerator 8.98 1.34 1565 9.4 1.57 823

Almirah 27.15 2.54 1560 21.68 2.66 818

Kerosene stove 6.27 1.04 1559 8.58 1.8 818

Cook stove 9.26 1.59 1561 10.66 2.47 821

Bicycle 79.79 1.87 1568 71.76 3.09 823

Manual rickshaw 0.24 0.16 1562 0.07 0.07 821

Motorised Two-wheeler 39.82 2.24 1563 33.44 2.45 821

Car/jeep/tempo/mini-truck/truck

2.91 0.46 1561 1.47 0.53 821

Motorised 3-wheeler 0.28 0.14 1561 0.12 0.12 822

Landline telephone 0.2 0.2 1562 0.59 0.49 821

Mobile phone 87.06 1.14 1562 88.03 1.72 821

Television 35.26 2.44 1562 41.41 3.62 821

VCR/CD/DVD player/Set-top Box

12.16 1.58 1563 9.72 2.07 822

Electric fan 59.57 2.71 1566 66.33 3.01 824

Computer/laptop 1.61 0.32 1561 1.25 0.41 818

Pressure cooker 21.95 1.93 1564 23.16 3.32 821

Cooler 3.51 0.73 1565 3.3 0.87 822

AC 0.05 0.05 1560 0.11 0.08 820

Radio/Transistor 1.4 0.48 1561 0.53 0.26 821


62

Primary Sample


error n

Secondary Sample

Proportion (%)

Standard error

n

LPG Cylinder 39.09 2.33 1565 39.59 2.66 820

TATA sky/satellite disc/Cable TV

17.98 1.44 1562 22.89 2.24 823

Gold/silver 53.11 2.7 1451 50.2 3.79 759

Other 1.33 0.72 1465 0.31 0.19 755

Of target households (n=1568), Table 43 below shows that Swachh Bharat Mission-Grameen is the scheme availed by the highest proportion of households (32%), followed by Pradhan Mantri Ujjwala Yojana (14%) and Indira Gandhi National Old Age Pension Scheme (11%). Rest of the schemes were availed by less than 10% of households, many of which were availed by less than 1% of households.

Table 43 Social security schemes availed (primary sample)


Ambedkar Awaas Yojana 0.54 0.27 1568

Janani Suraksha Yojana 0.5 0.19 1568

MGNREGA22 4 0.7 1568

Mukhyamantri Ladli Laxmi Yojana 0.33 0.17 1568

National Family Benefit Scheme 0.25 0.16 1568

NSAP-Indira Gandhi National Disability Pension Scheme 1.39 0.47 1568

NSAP-Indira Gandhi National Old Age Pension Scheme 10.51 1.18 1568

NSAP-Indira Gandhi National Widow Pension Scheme 5.28 1.09 1568

Particularly Vulnerable Tribal Group Pension Scheme 0.3 0.21 1568

Pradhan Mantri Awaas Yojana Grameen 5.32 0.93 1568

Pradhan Mantri Ujjwala Yojana 13.7 1.79 1568

Rajya Vidhwa Samman Pension Yojana 0.54 0.25 1568

Swachh Bharat Mission-Grameen 32.08 2.77 1568

Schemes from Department of Fisheries 0.17 0.12 1568

Schemes from Department of Animal Husbandry 0.2 0.12 1568

Pradhan Mantri Krishi Sinachai Yojana 0.07 0.05 1568

Sub-Mission on agricultural mechanisation 0.27 0.16 1568

Other 0.77 0.25 1568

Table 44 Social security schemes availed (secondary sample)


Ambedkar Awaas Yojna 0.32 0.19 824

Janani Suraksha Yojana 0.25 0.13 824

MGNREGA 2.5 0.74 824

National Family Benefit Scheme 0.18 0.15 824

NSAP-Indira Gandhi National Disability Pension Scheme 2.07 0.98 824

22 The MGNREGA assistance in this table includes assistance received for asset creation and not wages.


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NSAP-Indira Gandhi National Old Age Pension Scheme 6.93 1.07 824

NSAP-Indira Gandhi National Widow Pension Scheme 5.6 1.12 824

Pradhan Mantri Awaas Yojana Grameen 4.4 0.88 824

Pradhan Mantri Ujjwala Yojana 12.24 1.48 824

Rajya Vidhwa Samman Pension Yojana 0.67 0.27 824

Swachh Bharat Mission-Grameen 27.94 2.86 824

Sub-Mission on agricultural mechanisation 0.07 0.07 824

Other 0.2 0.15 824

3.5 Village infrastructure and services

Table 45 below shows that in blocks surveyed, 100% of villages have electricity supply, 89% have a

government primary school, 89% have an Anganwadi centre, 67% have a paved road, 44% have a

government middle school, 29% have a health sub-centre, and 28% have a private primary school.

Less than 20% of villages have other infrastructure and services such as a government secondary or

higher secondary school, private middle, secondary, or higher secondary schools, and colleges

(either government or private).

Table 45 Village infrastructure and services


Village has a government primary school 89.14 3.04 100

Village has a government middle school 43.64 5.91 100

Village has a government secondary school 17.44 3.62 100

Village has a government higher secondary school 4.84 2 100

Village has a government college 0.64 0.64 100

Village has a private primary school 28.45 5.33 100

Village has a private middle school 17.21 3.62 99

Village has a private secondary school 13.56 3.01 98

Village has a private higher secondary school 5.56 2.18 96

Village has a private college 7.61 4.44 91

Village has an Anganwadi centre 88.62 4.82 100

Village has a health sub centre 29.15 3 100

Village has paved road 66.64 5.87 100

Village has electricity 100 100


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4 Impact Level Indicators

4.1 Introduction

This chapter presents the baseline levels for the two key project impact indicators:

1. Percent increase in real average annual household income of target households

2. Percent increase in proportion of real income from select livelihoods sources

Further, the baseline values of dietary diversity are presented as this is a Project Development Objective (PDO) indicator, with the expectation that improved income and diversified production will filter into better household diets. Consumption expenditure is also presented.

4.2 Average Annual Real Household Income

Table 46 shows that the average annual household income for JOHAR households is Rs 56,430. This includes the net income (revenue minus the costs of production) plus the value of self-consumption. With an average household size of 5.34, this gives an average individual annual income of Rs 10,567.

For the average household, Rs 16,149 comes from agriculture (29%) of which Rs 13,340 comes from non-HVA crops of paddy, maize, ragi and wheat and only Rs 2,809 comes from HVA. The average combined income from livestock, fisheries and NTFP is just Rs 839. This low contribution is likely related to the selection of HVA blocks only for the impact evaluation. This means that the average income from these four livelihood sources is Rs 16,988, or 30.1% of total income.

Nearly all households were involved in agriculture, and over four fifths in livestock. Few households were currently involved in fisheries and NTFP.

Wages and enterprise were the most remunerative activities (Rs 57,915 and Rs 60,800 respectively) for the households who were engaged in this. 41.3% of wages come from formal (regular or permanent) labour. Other sources of income include transfer incomes (e.g. migrant remittances) and social security income. This is shown in more detail in section 11.3.

Table 46 Average annual household income (primary sample)

Source of income

Average income across all hhs (Rs)

Proportion of hhs who earn from this source (%)

Average income for hhs who earn from this source (Rs)

Agriculture 16149.31 95.86 16846.63

Livestock 699.86 81.18 862.08

Fisheries 0.13 7.52 1.69

NTFP 138.7 10.45 1327.88

Assets -481.07 24.03 -2002.23

Enterprise 4832.9 7.95 60799.58

Wages 20570.7 35.52 57915.72

NREGA 845.55 13.64 6198.06

Other Sources

13674.13

75.69 18064.97

Total 56430.21


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Table 47 Average annual household income (secondary sample)

Source of income

Average income across all hhs (Rs)

Proportion of hhs who earn from this source (%)

Average income for hhs who earn from this source (Rs)

Agriculture 4473.74 74.66 5992.34

Livestock 328.02 59.88 547.83

Fisheries -112.77 1.72 -6538.32

NTFP 30.61 7.71 397.28

Assets -425.34 10.84 -3924.3

Enterprise 2527.93 11.25 22477.44

Wages 28153.43 46.28 60833.65

NREGA 1097.44 10.13 10832.73

Other Sources 12271.23 72.18 17000.4

Total 48344.31

Figure 6 Sources of average annual household income

29%

1%

0%

0%

-1%

9%36%

1%

24%

Primary Sample


9%

1% 0%0%

-1%

5%

58%

2%

25%

Secondary Sample



66

The JOHAR project targets an increase in real average annual household income of target households of 50 percentage points. As per the Project Appraisal Document, it was assumed that the present average annual household income was Rs 50,000. The above analysis shows that, for blocks covered by the impact evaluation, the baseline average annual household income is Rs 56,430. A 50% increase in real average household income would be Rs 84,645 at current prices.

4.3 Percent increase in proportion of real income from select livelihoods sources

The Project Appraisal Document target was for the increase in real annual household income to be driven by a 100% increase in the proportion of income (real) from select livelihoods (defined as self-employed HVA crops, livestock, fisheries, NTFP and non-farm business, and formal labour). The indicator captures the objective of income diversification away from subsistence livelihoods to more productive livelihoods. Table 48 shows that these livelihoods currently comprise 30% of average annual household income.

A doubling of this would mean that, at current prices at endline, 60% of average annual household income (Rs 50,787) would need to be derived from these sources. This would require an increase (at current prices) in the contribution of these sources of Rs 33,818, i.e. a tripling of the current figure. Whilst this is in line with the PAD assumptions (that agriculture alone will contribute an additional Rs 27,808 to participating HVA households, leaving Rs 6,160 to come from secondary sources, the project may wish to consider whether a 100% increase in the proportion of income from select livelihood sources remains the appropriate target, or whether an absolute increase in income from select livelihood sources would be more appropriate.

Table 48 Income from select livelihoods

Average income across all

households (Rs) Proportion of average annual

household income (%)

HVA crops 2809 5

Livestock 699.9 1

Fisheries 0.1 0

NTFP 138.7 0

Non-farm business 4832.9 9

Formal labour 8488.3 15

Average annual income 56430.2 100

All select livelihoods 16968.9 30

4.4 Dietary diversity

The PDO indicator is: the percent increase in the average dietary diversity score in target households. The dietary diversity score is calculated by adding the number of different food groups consumed by a household from the 12 food groups identified by the Food and Nutrition Technical Assistance guidelines23 for measurement of household food access. Each household score ranges from 0-12 and represents the number of food groups consumed by the household in the last normal day.

23Swindale, Anne, and Paula Bilinsky. "Household dietary diversity score (HDDS) for measurement of household food access: indicator guide (v.2)." Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development (2006). https://www.fantaproject.org/sites/default/files/resources/HDDS_v2_Sep06_0.pdf


67

At baseline, the average household dietary diversity score is 6.11 food groups. Nearly all households consumed cereals in the last normal day, and most households consumed roots and tubers, vegetables, pulses/legumes/nuts, oils/fats and miscellaneous. Table 50 shows that the diet diversity score is only marginally higher for households who engage in HVA as well as paddy compared to households who only engage in paddy. This may show that the current production ecosystem of HVA is insufficiently transformative to household income to impact on consumption.

Table 49 Household consumption of food groups

Food Groups Proportion of households (%) Primary Sample

Standard error

Proportion of households (%)

Secondary Sample

Standard error

Cereals 99.13 0.41 99.51 0.29

Roots and tubers 76.94 1.64 74.16 2.64

Vegetables 85.94 1.49 85.39 2.1

Fruits 20.89 1.51 16.99 2.2

Meat, poultry, offal 7.23 1.07 4.59 0.89

Eggs 8.82 0.86 8.67 1.45

Fish and seafood 12.18 1.12 9.9 2.06

Pulses/legumes/nuts 71.86 1.91 72.73 2.58

Milk and milk products 26.48 1.82 25.39 2.86

Oils/fats 78.23 2.18 75.7 2.78

Sugar/honey 40.91 2.12 42.93 2.81

Miscellaneous 82.6 2.23 83.09 2.15

Household dietary diversity

score (food groups)24 6.11 0.09 5.99 0.13

Table 50 Variations in diet diversity (primary sample)

Household dietary diversity score (food groups) Standard error

All households 6.11 0.09

Households that only cultivate paddy 6.07 0.16

Households that cultivate paddy + HVA 6.11 0.11

Dietary diversity was higher than the average for households from OBC and general social groups,

and lower for SC and ST households.

Table 51 Dietary diversity by social status

Social category Mean score

(primary) Proportion of hhs (%)

Mean score (secondary)

Proportion of hhs (%)

OBC 6.43 41.97 6.22 48.91

SC 5.90 7.82 5.57 14.03

ST 5.75 45.97 5.53 29.7

General 7.35 4.24 7.13 7.36

24 Calculated for two adult members of opposite sex in the household. In cases where no other adult member was reported to have eaten food the last normal day, the score is calculated for one member.


68

4.5 Consumption Expenditure

Consumption expenditure is not a PDO target or included as an alternative measure of wellbeing, but is measured primarily to ascertain the value of self-consumption of agriculture.

Table 52 shows that average monthly per capita consumption expenditure was Rs 1,538. This is

substantially higher than the income figure, which is standard for surveys of agricultural households25 due to issues of consumption recall and the methodological challenges of converting consumption into a monetary value which is done at market prices rather than cost of production.

25 See e.g. NABARD All India Rural Financial Inclusion Survey 2016-17 (2018)

Mean Standard error

Monthly expense on rice, wheat/flour, sugar, kerosene, other cereals 1402.8 37.61

Monthly expense on pulses, meat, chicken, fish, jaggery/other

sweeteners, oil, eggs, milk 999.12 28.66

Monthly expense on milk products, cereals products, vegetables 1093.09 43.75

Monthly expense on items like salt and spices, tea/coffee,

paan/tobacco/intoxicants, fruits/dry fruits, eating out, household

fuel, electricity, entertainment, telephone/mobile/internet charges,

cosmetics, HH items, soap/detergent, transportation, diesel/petrol,

house rent, consumer taxes, services, out-patient medical expenses,

other

2050.31 52.81

Monthly expense including in-patient medical expenses,

school/college fees, private tuition, school books, clothing/bedding,

footwear, crockery/utensils, jewellery/ornaments, personal transport

equipment, therapeutic appliances, personal care and HH items,

other personal goods, repair and maintenance,

vacations/holidays/recreation, and social functions

1713.86 67.93

Per household monthly consumption expenditure 7220.92 150.8

Per capita monthly consumption expenditure 1537.6 37.6


69

Table 52 Consumption expenditure (primary sample)

Table 53 Consumption expenditure (secondary sample)

Mean Standard error

Monthly expense on rice, wheat/flour, sugar, kerosene, Other cereals 1232.14 39.77









other

1997.99 63.08







1621.74 88.51



Mean Standard error

Monthly expense on rice, wheat/flour, sugar, kerosene, other cereals 1402.8 37.61









other

2050.31 52.81







1713.86 67.93




70

5 Landholding and use patterns

5.1 Introduction

In this chapter, the landholding patterns of all households surveyed in the listing data, and then those households surveyed in the baseline are presented. This is followed by data on the use of land for cultivation by season and topography. Finally, leasing in and out patterns are identified.

5.2 Landholding patterns from the listing data

Table 54 shows the landholding patterns for all 32,811 households who were part of the baseline listing exercise in the 13 JOHAR HVA (non-partnership) first- and second-year blocks. 33% of households are landless.

Since cultivator households would require allocating 0.3 acres of their land which is suitable (mid or upland) for HVA, it is assumed (and supported by data from other projects) that by and large, households with more than 1 acre of total land holding, are likely to meet the requirement of HVA, which is allocating 0.3 acres of mid or upland. Only 28% of households have at least one acre of land (and a further 39% have more than 0.3 acres but less than one acre). This may have equity implications for the benefit incidence of JOHAR, although JOHAR was designed to work with farmers who have or are likely to have marketable surplus which presupposes access to land.

Table 54 Households listed in 13 JOHAR HVA (non-partnership) first- and second-year blocks

Category No of households Proportion

Landless 10,748 33%

Up to 0.3 acre 5,520 17%

Up to 1 acre 12,959 39%

1 acre or more 9,104 28%

Total 32,811

Table 55 shows how this varies across the 13 blocks, with large variations in the proportion of households who are landless or who hold at least one acre. The project may wish to review its block level targets accordingly.

Table 55 JOHAR 1st and 2nd year HVA blocks (non-partnership)

Name of block Households

listed % landless

% landless (Census)

% holding less than one

acre

% holding one acre or more

Angara 2,285 26.6 25.68 39.8 33.6

Bero 1,847 16.9 25.43 35.2 47.9

Bhandra 1,148 17.1 19.65 49.2 33.7

Chandankiyari 4,594 38.2 27.17 40.6 21.3

Chanho 2,084 22.6 16.31 44.4 33.1

Ghatshila 2,326 32.4 52.97 40.9 26.7

Kanke 3,146 33.9 45.49 39.0 27.1

Mandu 4,122 63.6 53.20 20.8 15.6

Patamda 1,719 17.2 19.49 47.1 35.7


71

Name of block Households

listed % landless

% landless (Census)

% holding less than one

acre

% holding one acre or more

Patratu 2,705 36.7 56.05 42.3 20.9

Potka 3,310 32.1 85.94 44.7 23.3

Purbi Tundi 1,381 30.5 27.91 49.6 19.9

Sisai 2,144 9.0 22.72 41.7 49.3

Total 32,811 32.8 39.5 27.7

5.3 Average landholding

Table 56 shows that the average landholding amongst the target households (n=1,568) is 2.68 acres, of which 2.29 acres were cultivable. Very little owned land is allocated on average to plantation/orchards, pastures/permanent fallows, and forest.

Table 56 Landholding patterns

Mean (acres)

(primary sample)

Standard error

n Mean (acres)

(secondary sample)

Standard error

n

Landholding 2.68 0.22 1568 0.48 0.01 582

Area cultivable 2.29 0.22 1568 0.44 0.01 582

Area under plantation and orchards

0.03 0.01 1568 0 0 582

Area under pastures/ permanent fallows

0.22 0.02 1568 0.03 0.01 582

Area under forest 0.05 0.01 1568 0.01 0 582

Table 57 shows that the average landholding of ST households is higher than the overall average and these make up 46% of the sample. 42% are OBCs and 8% are SCs.

Table 57 Average landholding by social category (primary sample)

Social category Proportion of

households in the social category (%)

Average land holding Standard error n

OBC 41.97 2.29 0.28 1568

SC 7.82 2.18 0.13 1568

ST 45.97 3.16 0.46 1568

Others 4.24 2.19 0.30 1568

Table 58 Average landholding by social category (secondary sample)

Social category Proportion of

households in the social category (%)

Average land holding Standard error n

OBC 48.91 0.45 0.02 824

SC 14.03 0.45 0.04 824

ST 29.7 0.54 0.22 824

Others 7.36 0.53 0.05 824


72

5.4 Topography

Table 59 shows that between a half and two-thirds of households cultivate the three different topographies (lowland, midland and upland) and two-thirds cultivate more than one topography.

Table 59 Cultivation of different topographies of landholding

5.5 Average landholding cultivated

Table 60 shows that the average household cultivated 1.7 acres in Kharif (74% of their cultivatable land) but there was very little cultivation in the Rabi and summer cropping seasons. This is due to the low proportion of households cultivating in these seasons, as outlined in the subsequent chapter. There was a reasonable balance of land cultivated between the three different topographies.

Table 60 Average landholding cultivated

Table 61 Proportion of cultivable land cultivated

Proportion (%)

(primary sample)

Standard error

n Proportion (%)

(secondary sample)

Standard error

n

Proportion of area cultivated in lowland of total cultivable land

35.35 0.03 1566 30.58 0.03 578

Proportion of area cultivated in midland of total cultivable land

34.57 0.02 1566 35.79 0.03 578

Proportion (%)

(primary sample) Standard

error n

Proportion (%) (secondary sample)

Standard error

n

HHs that cultivate lowland

62.79 2.28 1568 43.21 3.45 582

HHs that cultivate midland

62.51 2.34 1568 50.3 3.19 582

HHs that cultivate upland

58.33 2.88 1568 45.3 2.97 582

HHs with more than one topography under cultivation

66.68 2.41 1568 41.81 3.13 582

Mean (acres)

(primary sample) Standard

error n

Mean (acres) (secondary sample)

Standard error

n

Area cultivated in Kharif

1.7 0.23 1568 0.33 0.02 582

Area cultivated in Rabi

0.14 0.01 1568 0.04 0 582

Area cultivated in Summer

0.03 0 1568 0.01 0 582

Area cultivated in Lowland

0.79 0.1 1568 0.14 0.01 582

Area cultivated in Midland

0.79 0.14 1568 0.16 0.01 582

Area cultivated in Upland

0.67 0.14 1568 0.14 0.01 582


73

Proportion (%)

(primary sample)

Standard error

n Proportion (%)

(secondary sample)

Standard error

n

Proportion of area cultivated in upland of total cultivable land

28.47 0.01 1566 31.93 0.02 578

5.6 Leasing in and out patterns

Only 6% of households leased out land, and 8.5% of households leased in land during the Kharif season. Negligible households leased in land during the Rabi and summer seasons. This relative lack of leasing in and leasing out land implies that it may be challenging for households to lease in land for JOHAR (i.e. those households without current landholdings sufficient for JOHAR but would be interested in joining the programme), unless the increased returns from JOHAR made leasing in land more economically viable.

Table 62 Leasing-in and leasing-out patterns

Proportion (%)

(primary sample)

Standard error

n Proportion (%)

(secondary sample)

Standard error

n

Households that own land and lease-out

5.63 0.76 1568 2.06 0.74 582

Households that lease-in 10.77 1.15 1568 16.45 1.82 824

Just over half of households who leased in paid through a share of their crop, and just under half paid through cash. For those who paid in cash, the average cost was between Rs 2281 and Rs 3313 per hectare.

Table 63 Leasing in modalities and costs in Kharif

Topography

Type of rent (lease-in)

Average cost of leasing in per hectare (Rs)

Cash (%)

Share of crop (%)

Cash and share of crop (%)

None (%)

Lowland 46% 54% 0% 0% 2281

Midland 42% 52% 0% 6% 3313

Upland 34% 59% 3% 3% 3002

5.7 Conclusions

The primary sample is drawn from households with at least one acre of land, as it is assumed that these are the households that will be able to participate in HVA cultivation. This covers 28% of households. Few households lease in and/or lease out land, suggesting households with less than one acre of land may find it challenging to lease in land to participate in JOHAR. Most households have multiple topographies and land is equally split between lowland, midland and upland. The low level of cultivation in Rabi and summer seasons compared to Kharif is explored further in the next chapter.


74

6 Agriculture

6.1 Introduction

In this chapter, the baseline findings on agriculture are presented. Firstly, data on cultivation and

cropping intensity is presented. Then, the levels and predictors of HVA production are presented.

Thirdly, the role of irrigation is explored, followed by variations in productivity. Finally, analysis of the

different types of markets that produce is sold into, and the different types of inputs used, is presented.

6.2 Cultivation and cropping intensity

Out of cultivator households (n=1450), 99% cultivated in the Kharif season. Only 47% cultivated in Rabi and 14% in the summer season. The current cropping intensity (gross cropped area/net sown area) is only 110%. 41% of households cultivate the same crop on all of their land.

Table 64 Cropping intensity

Primary (%)

Standard error

n Secondary

(%) Standard

error n

HHs that cultivate in at least one of the three seasons (cultivator HHs)

93.67 0.86 1568 68.68 4.05 824

Of cultivator HHs (%)

HHs cultivating in only one season

52.2 3.64 1450 63.53 2.87 514

HHs cultivating in more than one season

47.8 3.64 1450 36.47 2.87 514

HHs cultivating in all three seasons

11.77 2.34 1450 5.39 1.6 514

HHs cultivating in Kharif 99.07 0.25 1450 97.32 0.82 514

HHs cultivating in Rabi 46.77 3.57 1450 35.5 2.98 514

HHs cultivating in Summer 13.73 2.5 1450 9.03 2.19 514

HHs cultivating more than one crop in any season

59.42 2.76 1450 43.51 3.04 514

HHs cultivating multiple crops in two seasons

20 2.28 1450 7.15 1.48 514

HHs cultivating multiple crops in all seasons

2.76 0.63 1450 1.39 0.59 514


75

Figure 7 Proportion of households cultivating in different seasons

Table 65 shows that for households who cultivated in the Rabi and summer seasons earned on average an additional net income of Rs 7,007 and Rs 6,994 respectively. Those who cultivated annual/perennial crops earned an additional Rs 2,055. Increasing the proportion of households who cultivate in multiple seasons will therefore be an important mechanism to achieve JOHAR targets.

Table 65 Average income by season (primary sample)

Season Average income earned in the season for hhs who cultivate in that season (Rs) n

Kharif 13186.68 1433

Rabi 7006.85 630

Summer 6994.03 179

Annual/ perennial 2054.72 349

Total

Table 66 Average income by season (secondary sample)

Season Average income earned in the season for hhs who cultivate in that season (Rs) n

Kharif 4083.62 497

Rabi 4661.4 215

Summer 491.68 45

Annual/ perennial 1214.78 127

Total

Cultivation in Rabi and summer is largely HVA cultivation. This suggests that increasing the proportion of households who cultivate in Rabi and summer – key to achieving JOHAR targets – is inherently linked to expanding the proportion of households who undertake HVA.

Table 67 HVA cultivation by season

Season Proportion of households who cultivate in that season who undertake HVA (Rs) n

Kharif 20.45% 1433

Rabi 84.22% 630

Summer 88.14% 179

Total

0

20

40

60

80

100

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Proportion of households (%)


76

6.3 HVA

JOHAR focuses on a specific set of HVA crops, including beans, chillies, garlic, coriander, potato, onion, tomato, spinach, cabbage, brinjal, peas, lady’s finger, cauliflower, cucumber, bottle gourd, bitter gourd, cowpea, turnip, amaranths, beans and capsicum.

Table 68 HVA production

Primary sample Standard error Secondary Sample Standard error

HHs that cultivate JOHAR HVA (%) 48.11 3.32 29.18 3.14

HHs that cultivate any HVA (%) 67.41 2.42 48.26 3.56

Whilst 67% of households engage in any HVA production, 48% engage in the production of the JOHAR focus HVA crops.

Table 69 shows the results of regression analysis undertaken to identify predictors of households undertaking HVA production as opposed to just paddy. HVA production is strongly correlated with the possession of midland and upland, and the use of irrigation. It is not correlated with landholding size or other independent variables. This validates the emphasis on providing irrigation to households to facilitate HVA and suggests that the project should focus on upland and midland. This is supported by the fact that 82.9% of households who have access to irrigation undertake HVA cultivation compared to only 56.6% of households without irrigation.

Table 69 Determinants of HVA production (primary sample)

Coefficient Standard error P score

Landholding size in acres -.0008135 .0019841 0.683

Use of irrigation .1937255*** .0342672 0.000

Cultivate lowland -.0023375 .0185997 0.900

Cultivate midland .0642231** .026014 0.016

Cultivate upland .1595239*** .0234123 0.000

Household size .0054211 .0046654 0.249

Household has a loan in the last twelve months .0395622 .0294599 0.183

* Denotes significance at 90% confidence, ** 95% confidence and *** 99% confidence

Table 70 shows the proportion of households who cultivated different crops (HVA and non-HVA), and – for those households who did cultivate – the average quantity cultivated and sales per acre. Net income after expenses is not possible to calculate because some input costs cannot be allocated to specific crops. Crops which were cultivated by less than 1.0% of households were removed from the table26.

For HVA crops, 37% of households cultivated potato, 14% cultivated onion and 13% cultivated tomato. Other HVA crops were cultivated by fewer than 10% of households. There was a wide variation in the average gross sales per acre for different crops (but net income cannot be calculated).

26 This includes, amongst non-HVA crops: barley, small millets, and other cereals. Amongst non-HVA crops, this includes turnip, amaranths, beans, capsicum, green chillies, barley, small millets, other cereals, moong, masur, other pulses, other sugar crops, pepper, ginger, turmeric, other spices, elephant foot yam, other tuber crops, carrot, other gourds, vench, drumstick, sunflower, other food crop, other oilseeds, jute, sunhemp, other fibres, other dyes, other flowers, other aromatic plants and other non-food crops


77

For non-HVA crops, 97% of households cultivated paddy, 20% maize, 17% ragi and 11% wheat. Other crops were cultivated by fewer than 10% of households.

Table 70 Cultivation of different crops (primary sample)

Crop

Proportion households

cultivating (%)

For households cultivating

Total land

allocation (acres)

Total sales (kgs)

Average sales

price per kg (Rs)

Total gross sales

(Rs)

Average gross

sales per acre (Rs)

JOHAR HVA

Beans (pulses) 3.79 0.2 200.59 16.45 3299 16498

Chillies 2.04 0.07 156.96 36.56 5738 81977

Garlic 8.04 0.03 51.61 35.57 1835 61192

Coriander 2.43 0.23 483.11 34.28 16561 72004

Potato 36.73 0.23 712.98 11.57 8249 35865

Onion 13.54 0.07 228.21 12.79 2918 41697

Tomato 12.91 0.26 1435.12 11.41 16374 62979

Spinach 1.62 0.03 137.8 12.43 1712 57095

Brinjal 8.95 0.16 429.87 11.95 5136 32105

Peas (vegetable) (green) 1.93 0.36 392.64 18.56 7287 20242

Lady’s finger (bhindi) 8.55 0.11 238.45 14.1 3362 30564

Cabbage 1.84 0.15 1018.29 17.99 183194 122126

Cauliflower 2.53 0.27 2018.7 15.59 31471 116561

Cucumber 1.73 0.24 4978.32 9.64 47991 199962

Bottle gourd (lauki) 4.54 0.13 428.96 10.99 4714 36263

Bitter gourd 3.61 0.14 554.25 19.83 1099 78505

Cowpea 6.1 0.07 110.64 11.74 1298 18555

Non-JOHAR HVA

Gram 1.38 0.4 86.35 42.85 3700 9250

Tur (arhar) 3.29 0.2 40.22 31.22 1255 6278

Urad 17.84 0.5 46.96 35.02 1644 3289

Horse gram 1.45 0.28 31.35 27.87 873 3120

Peas (pulses) 4.78 0.41 742.58 17.47 12972 31641

Other leafy vegetable 1.1 0.11 239.69 39.8 9539 86724

Colocasia/arum 3.31 0.07 24.48 12.05 294 4214

Radish 5 0.06 139.93 9.93 1389 23158

Pumpkin 1.84 0.34 194.11 14.68 2849 8380

Other vegetables 4.39 0.39 399.66 12.76 5099 13076

Groundnut 7.79 0.19 581.11 29.11 16916 89032

Rapeseed & mustard 4.47 0.17 95.3 26.89 2562 15074

Non-HVA

Paddy 96.91 1.6 970.08 12.58 12203 7627

Maize 20.06 0.28 246.28 10.66 2625 9376


78

Crop

Proportion households

cultivating (%)


Total land

allocation (acres)

Total sales (kgs)

Average sales

price per kg (Rs)

Total gross sales

(Rs)

Average gross

sales per acre (Rs)

Ragi 16.43 0.3 232.7 15.73 3660 12201

Wheat 10.55 0.45 238.52 15.34 3658 8130

Table 71 shows that the average gross sales per acre is 3.3 times higher for JOHAR targeted HVA crops (Rs 24,922 per acre) than for non-HVA crops (Rs 7,831). This is despite the fact that, on average, only 4.7% of cultivated land was allocated to these crops (0.08 acres per household). This validates the project design for impacting household income by enabling cultivator households to cultivate select HVA crops and increase the land size on which select HVA crops are cultivated. This is a contributory factor to the fact shown in Table 72 that the average total revenue from agriculture for households who undertake both paddy and HVA is substantially (85%) higher than for households who just undertake paddy.

Table 71 Average gross sales per acre

Average gross sales per acre (Rs)

JOHAR HVA crops 25922

Non-HVA crops 7831

Table 72 Average household revenue for households who also grow HVA


Households that only grow paddy 20290

Households that grow paddy + HVA 37464

Table 73 shows the proportion of households who cultivated different annual/perennial crops, and – for those households who did cultivate – the average quantity cultivated and gross sales. Net income after expenses is not possible to calculate because some input costs cannot be allocated to specific crops. Crops which were cultivated by less than 1.0% of households were removed from the table27.

Table 73 Cultivation of annual/perennial crops

Crop Proportion

of hhs cultivating


Average area of

cultivation (acres)

Average number of

trees/plants

Average harvest per

acre (kg/acre)

Average sales price per kg (Rs)

Average income per

acre (Rs)

Average income

(Rs)

Mango 63.63 0.31 3.05 3745.47 15.04 5609.4 1990.45

Lemon/lime 11.5 1.41 15.88 568.45

Other citrus fruit 1.15 1.91 60 600

Banana 6.8 6.21 23.47 958.47

Papaya 27.84 2 2.29 7.5 14.17 435.61

Guava 32.75 0.01 1.55 52422.8 14.34 473.04

Jackfruit 33.35 0.02 1.5 83773.4 15.37 727915 439.27

27 This includes: grapes, cashewnuts, lichi and musk melon


79

Crop Proportion

of hhs cultivating


Average area of

cultivation (acres)

Average number of

trees/plants

Average harvest per

acre (kg/acre)

Average sales price per kg (Rs)

Average income per

acre (Rs)

Average income

(Rs)

Watermelon 1.07 0.53 5172.36 8.99 42080.3 15981.4

Ber 12.21 0.01 2.29 5000 3.33 100

Bel 3.79 1.22

Other fruits 5.64 0.01 1.86 1000 27.9 286.3

Perennial crops 4.27 3.95 24.73 1301.26

6.4 Irrigation

41% of cultivator households had some form of irrigation. Of all cultivated land of all households in the primary sample, 32% was irrigated.

Table 74 Proportion of cultivated land irrigated

Proportion (%)

(primary sample)

Standard error

n

Proportion (%)

(secondary sample)

Standard error

n

Proportion of households irrigating

41.41 2.85 1566 31.46 2.73 578

As well as increasing the probability of undertaking HVA, it also increases average cropping intensity to 193% compared to the average of 110%. Therefore, irrigation is key to increasing cropping in multiple seasons which has been shown in section 6.2 to be crucial to achieving JOHAR’s targets. In fact, the average net income per acre for land that is irrigated is 2.67 times that of land that is not irrigated

Table 75 Average gross sales per acre for irrigated and non-irrigated land


Irrigated land 104209.1

Non-irrigated land 28392.7

For these households, the most frequent source of irrigation was private well (38%) followed by community well (17%) and community pond (16%). 10% or fewer households used other irrigation sources.

Table 76 Sources of irrigation

Proportion (%)

(primary sample)

Standard error

n

Proportion (%)

(secondary sample)

Standard error

n

HHs irrigating from Dhoba 8.71 1.24 622 4.9 1.5 438

HHs irrigating from Private pond

10.18 1.87 622 3.41 1.15 438

HHs irrigating from Private well

38.07 4.07 622 17.28 3.35 438

HHs irrigating from Community pond

16.27 1.76 622 8.88 2.7 438


80

Proportion (%)

(primary sample)

Standard error

n

Proportion (%)

(secondary sample)

Standard error

n

HHs irrigating from Community well

16.68 2.19 622 11.93 2.83 438

HHs irrigating from Irrigation schemes by PG

0 622 0 438

HHs irrigating from Irrigation canal

8.42 1.61 622 4.23 1.31 438

HHs irrigating from Lift Irrigation

0.89 0.51 622 0 438

HHs irrigating from Dam 5.22 1.56 622 1.7 0.86 438

HHs irrigating from Natural springs

3.63 0.94 622 2.07 1.41 438

HHs irrigating from Tubewell/borewell

2.56 0.84 622 1.46 0.61 438

Table 77 shows that irrigation rates are higher for upland areas and midland areas than lowland areas. Fewer than half of households with irrigation have fully irrigated land.

Table 77 Irrigation rates by topography (primary sample)

% households use any

irrigation

Average % of land irrigated, if any

irrigation occurs

% of households with land fully irrigated

Lowland 16.22 83.18 7.37

Midland 28.17 82.65 13.37

Upland 35.37 71.40 12.81

Table 78 Irrigation rates by topography (secondary sample)

% households use any

irrigation

Average % of land irrigated, if any

irrigation occurs

% of households with land fully irrigated

Lowland 13.19 95.07 4.55

Midland 23.41 90.68 8.34

Upland 26.67 92.62 10.35

Table 79 shows that households do not always use irrigation even if they have access to it. Between half and two-thirds of households who have access to irrigation use it in the Kharif and Rabi seasons (with variation by topography) but this falls to a quarter or fewer during the summer season. The project may want to consider why irrigation is not always used and ensure that investments in irrigation under JOHAR facilitate increased cropping intensity.

Table 80 shows odds ratios – the probability of a household undertaking cultivation in that season and topography if they have irrigation compared to if they don’t have irrigation. This table shows that households with irrigation are only more likely to cultivate in the summer season (all topographies) and Rabi (only for upland). Irrigation is not correlated with a higher probability of cultivating in Rabi


81

for lowland and midland. This implies that the project should target increasing irrigation access for households with upland who currently do not cultivate in Rabi.

Table 79 Use of irrigation by topography and season (primary sample)

Of those who have access to irrigation, how many use it in

Kharif


Rabi


summer

Lowland 66% 57% 16%

Midland 71% 56% 18%

Upland 53% 74% 26%

Table 80 Impact of irrigation on cultivation by season (primary sample)

Odds ratio - cultivate in Kharif if irrigation

compared to no irrigation

Odds ratio - cultivate in Rabi if irrigation


Odds ratio - cultivate in summer if irrigation


Lowland 99% 82% 127%

Midland 99% 97% 199%

Upland 99% 126% 345%

6.4.1 Productivity

Figure 8 shows – for the example of paddy production in Kharif (cultivated by almost all households) how the quantity of production per acre varies across households. The 30th percentile most productive household generates 2.5 times that per acre than the 30th percentile least productive household.

Figure 8 Variations in productivity

This would imply that changes in production choices can lead to significant changes in productivity. On average, moving households up the productivity rankings by one decile would increase production by 36%. This validates the project theory of change that adopting a package of efficacious practices has the potential to increase productivity and therefore income.

0

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

0 1 2 3 4 5 6 7 8 9

Kg

Decile

Kg/acre by decile


82

6.5 Markets

Table 81 shows the proportion of cultivating households who sell crops at different markets, and the average price received per kg. This is shown only for the Kharif season (with the highest number of cultivators), for a subset of crops that have a substantial number of cultivators and a diversity of markets. Whilst there is a wide variation in the average price received for the same crop in different markets, and this is on average higher in block and district markets than at the farmgate, this is not always true for every crop. This suggests considerable market weakness, and the presence of middlemen. In general, for most crops, households are either selling their produce at the farmgate or in village markets.

Table 81 Market prices

Crop Markets Proportion of HHs selling crop in

the market (%) Average price received for crop

(Rs/kg)

Tomato Farmgate 15.28 15.91

Village 55.14 12.76

Block 32.22 21.56

Brinjal Farmgate 9.2 16.44

Village 69.5 13.45

Block 16.37 12.31

Cowpea Farmgate 10.29 20

Village 66.95 11.79

Peas Village 71.98 16.03

Block 28.02 18.33

Potato Farmgate 5.92 14.48

Village 46.88 20.71

Block 30.13 14.77

District 25.53 11.49

Other 4.6 15.38

Lady's finger

Farmgate 14.46 13.95

Village 65.05 12.4

Block 16.63 13.54

Paddy Farmgate 48.15 12.43

Village 35.12 12.67

Block 12.37 12.93

District 0.56 15.21

Maize Farmgate 27.52 10.44

Village 46.25 10.85

Block 25.7 10.54

Ragi Farmgate 30.92 15.83

Village 29.19 14.62

Peas (pulses)

PG 19.85 30

Village 1 12.48

Block 41 13.61


83

Crop Markets Proportion of HHs selling crop in

the market (%) Average price received for crop

(Rs/kg)

Groundnut Farmgate 37.44 35.44

Village 46.27 27.71

Block 13.73 18.86

Other 2.55 17

6.6 Inputs

Table 82 shows that, on average, gross income (i.e. sales revenue) per cultivator household was Rs 31,631 and net income (i.e. after taking into account cropping expenses per season) was Rs 17,267 across the three seasons. This is slightly higher than the final net income from agriculture figure despite not including revenue from annual crops because there are additional expenses that cannot be attributed to a particular season (including transport, at an average of Rs 910 per household, diesel (Rs 1484) and electricity (Rs 64). In general, therefore, nearly half of sales revenue covers the cost of production expenses, and just over half is retained as net income for the household.

Table 82 Gross and Net income

Average gross

income (Rs) Average net income (Rs)

Expenses (Rs) Expenses/ gross

income (%)

in Kharif 24672.98 13050.73 11622.25 47.1%

in Rabi 5526.53 3262.11 2264.42 41.0%

in Summer 1431.39 984.28 477.11 33.3%

Total 31630.9 17267.12 14363.78 45.4%

Table 83 shows the proportion of cultivator households that used different types of production inputs in different seasons (of those who cultivated in that season). Almost all households used fertiliser, three quarters hired female labour and used cowdung, and half hired male labour. One fifth used pesticides and fungicides. Other inputs were used by very few households.

Table 83 Use of inputs

Proportion of cultivator households (%) Kharif Rabi Summer

Hired male labour 48.05 2.18

Hired female labour 76.44 0.48

Used seed treatment 0.47 0 0.89

Used cowdung 75.54 64.2 62.14

Used vermicompost 3.43 3.4 4.32

Used fertilisers 93.97 86.48 86.7

Used micronutrients 0.81 0.68 0

Used plant growth hormones 6.01 4.55 5.52

Used pesticides and fungicides 15.77 13.32 9.55

Used insect traps 0.26 0.95 0.21

Used no production inputs 1.12 6.03 6.65


84

For most inputs, dealers were the main input source, with over 90% of seed treatment, vermicompost, fertiliser, micronutrients, plant growth hormones, and pesticides and fungicides being sourced from dealers. Only cowdung was mainly sourced from a household’s own livestock (85%).

Only 3% of households had their soil tested in the last three years.

Table 84 shows the average expense incurred by cultivator households on different productive inputs across seasons (of those who incur at least some expense on that input).

Table 84 Season wise input expenses

Average expenses on: for HHs who make positive expense

Kharif Rabi Summer

Rent 6944.9 5056.31 3604.93

Seed 2586.36 1654.02 892.14

Inputs 2536.52 1202.39 921.55

Diesel 1113.43 1606.27 1450.34

Electricity 858.07 710.07 996.31

Transport 1568.44 1646.96 2043.8

Marketing 353.3 909.76 464.67

Hired Male labour 2387.43 1574.84 1892.48

Hired Female labour 4332.51 1199.23 1706.17

Meals 447.13 155.8 217.05

Animal 1536.56 671.98 501.6

Machine 3263.27 1418.95 1145.07

6.7 Conclusions

The findings support the project theory of change that cultivator households could be supported to increase their income through the interventions that JOHAR is implementing.

There is significant potential income gain that could arise through multi-season cropping as only 47% of cultivator households crop in Rabi and 14% in the summer season, leading to a low cropping intensity of 110%.

Cultivation in Rabi and summer is largely HVA cultivation. This suggests that increasing the proportion of households who cultivate in Rabi and summer is inherently linked to expanding the proportion of households who undertake HVA. 48% of households currently cultivate HVA crops promoted by JOHAR (primarily potato, tomato and onion), and 67% any HVA crop. JOHAR targeted HVA crops have a significantly (3.3 times) higher average gross sales per acre than non-HVA crops.

However, currently little land is allocated to these HVA crops (only 0.08 acres per household). Access to irrigation is a major predictor of whether households cultivate HVA crops and crop in multiple seasons. The average net income per acre for land that is irrigated is 2.67 times that of land that is not irrigated.

41% of cultivator households had some form of irrigation. Of all cultivated land of all households in the primary sample, 32% was irrigated. For these households, the most frequent source of irrigation was private well (38%) followed by community well (17%) and community pond (16%). 10% or fewer households used other irrigation sources. However, households do not always use irrigation even if they access to it. Between half and two thirds of households who have access to irrigation use it in the Kharif and Rabi seasons (with variation by topography) but this falls to a quarter or fewer during


85

the summer season. The project may want to consider why irrigation is not always used and ensure that investments in irrigation under JOHAR facilitate increased cropping intensity.

There are significant productivity differentials across households suggesting many have the potential to increase productivity through adopting new practices. Market inefficiencies lead to potential gains in income through collective marketing at higher level markets. Input costs eat up a relatively large share of gross income (45%) suggesting that reducing these costs provides scope for increasing household’s net income. Most inputs are provided by dealers.

Almost all households used fertiliser, three quarters hired female labour and used cowdung, and half hired male labour. One fifth used pesticides and fungicides. Other inputs were used by very few households, suggesting significant potential for intensification of production.


86

7 Livestock

7.1 Introduction

In this chapter, the baseline findings from livestock rearing are presented. The analysis is limited since the evaluation is focused on the HVA sub-component and the survey instrument was geared to collect detailed data on the agriculture production model, not livestock. The sample is not representative of all livestock farmers as it is focused on areas where HVA is the primary livelihood and on households with sufficient landholding for HVA. The available data is analysed to show ownership patterns, markets where livestock is sold and purchased, sales revenue and per unit prices realised and finally the net income earned from and expenses incurred on account of livestock is presented. A planned thematic evaluation will generate more representative information.

7.2 Livestock rearing

Of all target households (n=1568), 85.59% own at least one animal or bird in the 12 months before the survey, with cattle, goat, and country chicken being the most widely owned livestock. Half of the households that own any livestock report having earned a cash income from or self-consumed livestock.

Table 85 Ownership and revenue from livestock (primary sample)

Proportion (%) Standard Error n

Households with any livestock

85.59 1.32 1568

Households with any livestock and a cash or kind inflow or outflow

81.18 1.41 1568

Households earning a cash income from livestock (incl. own account consumption)

47.62 2.04 1568

Table 86 Ownership and revenue from livestock (secondary sample)

Proportion (%) Standard Error n

Households with any livestock

65.34 3.35 824

Households with any livestock and a cash or kind inflow or outflow

59.88 3.26 824

Households earning a cash income from livestock (incl. own account consumption)

29.58 2.55 824

7.2.1 Markets and sales

Table 87 shows that over two-thirds of households own cattle and over half own goats and country chickens. Ownership of other livestock is relatively low. For households that do own buffalo and cattle, they earned over Rs 10,000 in the last year from sales of produce and animals (Rs 19,530 and Rs 12,461 respectively). Other livestock were less remunerative.


87

Table 87 Sales revenue (primary sample)

Proportion of hhs

Number of livestock

Total sales revenue from produce and animals

Cattle 72.27 2.35 12460.79

Buffalo 10.54 1.94 19530.41

Goat 59.07 4.48 5471.28

Sheep 6.75 6.95 5304.24

Pig 5.97 4.67 6980.52

Broilers 19.03 10.59 2504.29

Layers 0.56 9.28 582.1

Country chicken 54.19 9.58 1284.16

Duck 12.01 11.84 2335.86

Other 3.63 122.4 820.27

Table 88 Sales revenue (secondary sample)

Proportion of

hhs Number of

livestock Total sales revenue from produce and

animals

Cattle 61.19 1.7 11170.38

Buffalo 5.37 1.29 3213.6

Goat 49.5 3.23 5776.89

Sheep 2.62 5.54 8000

Pig 1.15 3.16 8539.72

Broilers 16.18 8.43 963.35

Country chicken

51.01 6.39 1087.45

Duck 8.89 3.83 483.61

Other 2.29 6.75 100

Table 89 shows that households sold on average less than a fifth of their livestock in the last year.

Table 89 Units marketed and price realised (primary sample)

Average number of units sold per household

(livestock)

Average sales as a proportion of units

owned (%)

Average price received per unit (Rs)

Cattle 0.31 14.28 8930.4

Buffalo 0.37 21.84 14969.33

Goat 0.64 14.06 3277.64

Sheep 1.02 17.03 1873.15

Pig 0.76 9.24 3084.96

Broilers 1.68 28.84 347.78

Layers 0.9 8.23 166.67

Desi chicken 1.67 17.82 217.78

Duck 0.64 13.67 390.13

Other 2.25 10.89 150.57


88

Table 90 Units marketed and price realised (secondary sample)

Average number of units sold per

household (livestock)

Average sales as a proportion of units owned

(%)

Average price received per unit (Rs)

Cattle 0.18 14.05 8979.52

Buffalo 0.15 22.74 5063.07

Goat 0.38 14.98 2835.23

Sheep 0.86 6.76 2000

Pig 0.31 27.67 5134.93

Broilers 0.87 7.72 246.86

Desi chicken 0.97 12.76 235.51

Duck 0.13 5.03 283.46

Other 19.99 6.66 100

Table 91 shows that, for most livestock, the majority are sold at the farmgate or village market. As noted earlier with HVA produce, there are market inefficiencies but producers are more likely to realise higher prices for their produce in markets farther away from them, such as the block and district market. It is likely that if producers can access those markets either individually or collectively their sales revenue from livestock will increase.

Table 91 Markets where livestock is sold (primary sample)

Livestock Market Proportion of households

selling in market (%) Standard error n

Cattle Farm gate 61.97 6.15 142

Village market 25.72 6.87 142

Block market 10.76 2.96 142

District market 0.32 0.32 142

PG 2.91 1.55 142

FPO 1.68 1.29 142

Other 0.46 0.46 142

Buffalo Farm gate 71.27 11.19 24



Goat Farm gate 61.75 5.18 168


Block market 9 3.12 168

District market 0.53 0.53 168

Sheep Farm gate 66.62 9.29 23



PG 3.62 3.7 23

Pig Farm gate 45.85 8.57 13


Broilers Farm gate 64.74 10.01 46



89

Livestock Market Proportion of households

selling in market (%) Standard error n


Other 1.61 1.59 46

Layers Village market 1 1

Desi chicken Farm gate 59.11 5.61 118



PG 7.47 2.31 118

Duck Farm gate 61.02 13.61 19


Other Farm gate 67.87 23.36 5


Table 92 below shows that the village and block markets are the ones most used to purchase

livestock, with fellow farmers being a source for purchase of cattle, buffalo, goat, and country chicken.

District markets or collectives aren’t being accessed to purchase livestock.

Table 92 Markets where livestock is purchased (primary sample)

Livestock Market Proportion of households purchasing in market (%)

Standard error n

Cattle Village market 47.58 4.56 146


Kisan mela 5.33 1.81 146

Fellow farmers 12.26 3.69 146

Other 9.97 2.87 146

Buffalo Village market 28.06 11.88 17


Kisan mela 22.42 12.37 17


Other 3.9 4.48 17

Goat Village market 56.21 7.36 95


Kisan mela 2.37 1.49 95


Other 1.71 0.91 95

Sheep Village market 33.56 22.55 3


Pig Village market 48.86 9.57 33



Broilers Village market 82.44 8.03 30




90

Livestock Market Proportion of households purchasing in market (%)

Standard error n

Other 3.45 3.32 29

Layers Village market 50.71 49.99 2

Other 49.29 49.99 2

Country chicken Village market 62.27 6.9 53



Other 1.62 1.65 53

Duck Village market 69.69 8.65 55


Kisan mela 0.78 0.78 55

NGO 0.69 0.7 55


Government department

3.48 3.29 55

Other 4.27 2.66 55

Other Village market 26.42 27.49 2


7.2.2 Income

Table 93 shows that the net average income from livestock for all households is INR 699.86. Keeping in mind that this survey was conducted in HVA blocks, this income level from livestock shouldn’t be considered as a baseline value since the JOHAR project will intervene on livestock development in different areas where the potential for livestock is believed to be better. Sales revenue from sale of livestock and produce from livestock is considerably higher than net income (which is calculated as sales revenue + imputed value of consumption from own production – production costs), suggesting that production costs are high but collectivisation, access to better markets, reduction in input costs, and better health services provided by JOHAR could improve net income earned from livestock rearing, and become a larger component of household income.

Table 93 Income from livestock rearing (primary sample)

Mean (Rs) Standard error n

Cash income from livestock (all households) -426.08 377.33 1568

Net income from livestock (all households) 699.86 374.83 1568

Table 94 Income from livestock rearing (secondary sample)


Cash income from livestock (all households) -500.47 249.39 824

Net income from livestock (all households) 328.02 295.15 824

7.2.3 Expenditure

Table 95 shows that a very low proportion of households incur feed or transportation expense on account of livestock rearing, and these households have spent a little over Rs. 1,000 in the last year


91

on these expenses. A significantly higher proportion of households have incurred expenses on vaccinations for their livestock. This suggests that most households do not purchase feed for their livestock and health care related expenses are the widely incurred expense.

Table 95 Expenses incurred in livestock rearing (primary sample)


Expense on transportation 518.37 116.88 53

Expense on feed 611.6 172.21 30

Expense on vaccination 862.5 69.43 592

Table 96 Expenses incurred in livestock rearing (secondary sample)


Expense on transportation 381.86 100.7 29

Expense on feed 331.84 94.85 15

Expense on vaccination 726.14 89.69 206

7.3 Conclusions

For HVA households, the proportion that own livestock is high, but this is mostly cattle who are probably being used as milch animals or farm labour and not to generate livelihood (though there would be some incidental income from use of the animal as farm labour and manure as an input on the farm). Some revenue is being earned from cattle, goats, pigs, and country chicken though a developed market for these doesn’t exist. In most cases, livestock is sold at the farmgate or village market. Those households, who own and sell livestock and produce from livestock do report a revenue and positive income from sales, and expenses (other than purchase) on account of livestock rearing are low.


92

8 Fishery

8.1 Introduction

In this chapter, the baseline findings on fishery are presented. The analysis is limited since the evaluation is focused on the HVA sub-component and the survey instrument was geared to collect detailed data on the agriculture production model, not fisheries. The sample is not representative of all fishery farmers as it is focused on areas where HVA is the primary livelihood and on households with sufficient landholding. The available data is analysed to show ownership patterns, markets where fish are sold, sales revenue and per unit prices realised and finally the net income earned from fishery. A separate detailed thematic evaluation on fisheries is planned.

8.2 Findings

Table 97 shows that 7% of households engaged in fishery, and 5% earned income from it (as opposed to just self-consumption). Of the households engaged in fishery, over three quarters produced fish in ponds (Table 98). Of those earning from fishery, nearly all sell from their household or within their village; there is little marketing outside of the village (Table 99).

Table 97 Households engaged in fishery

Proportion (%)

Primary Standard

error Proportion (%)

Secondary Standard

error

Households with any fishery activity

6.80 0.84 1.31 0.5

Households with fisheries that gives some revenue

4.56 0.78 1.02 0.38

Table 98 Distribution by type of fish production for households engaged in fisheries

Fishery production system Proportion of hhs

engaging (%) Primary n

Proportion of hhs engaging (%) Secondary

n

Fish production in pond 76.7 102 73.23 18

Fish seed production 2.34 102 3.31 18

Fish culture in dhoba 18.17 102 23.46 18

Other production system 2.8 102 0 18

Table 99 Fish markets (primary sample)

Market Proportion of household selling

fish in market (%) Standard error n

Household level 49.79 6.74 76

Local 11.53 3.69 76

Village 13.71 6.68 76

Block 0.91 0.93 76

PG 1.85 1.81 76

Other 27.78 5.97 76


93

Table 100 shows that, for the households engaged in fishery, they made a negligible income over the last year. This reflects the fact that fishery investment can take some time to lead to production. Those households who had produced something for sale showed a small annual net income (of Rs 1,618).

Table 100 Income from fisheries

Mean (Rs)

Primary n

Mean (Rs) Secondary

n

Net income from fisheries: for all HHs 0.13 1568 -112.77 824

Net income from fisheries: for all HHs engaged in fishery

1.69 121 -6538.32 21

Net income from fisheries: for HHs who earn some revenue

1617.54 80 -2441 10

Table 101 shows that a majority of inputs were purchased from private hatcheries with little role of producer groups.

Table 101 Proportion of households that purchase fish seed from different sources (primary sample)

Source of purchase Proportion of households purchasing

fish seed from source (%) Standard error n


Farmers with hatcheries 9.81 4.25 102

Private hatcheries 62.26 6.93 102

Department of fisheries 6.99 3.01 102

Producer groups 9.06 4.34 102

Others 5.25 1.84 102

8.3 Conclusions

For HVA households, fishery is a minority livelihood activity with low income generation. The small sample of households who reported some production for sale made a small net annual income from fish production; others reported a negligible income. Most of the fish produced is self-consumed or supplied directly to buyers within the village, fisheries as a production activity isn’t leading to a marketable surplus yet in these JOHAR HVA villages. Investment and collective production with access to suitable inputs could lead to a marketable surplus and fisheries being a viable production activity and avenue for diversity among rural producer households.


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9 NTFP

9.1 Introduction

In this chapter, the baseline findings from NTFP collection and/or processing are presented. The analysis is limited since the survey instrument was geared to collect detailed data on the agriculture production model, not NTFP. The available data is analysed to show engagement patterns, expenses, sales revenue and per unit prices realised and finally the net income earned from NTFP.

9.2 Collection and processing of non-timber forest produce

Of all target households (n=1568), slightly over 10% of them are engaged in the collection or processing of NTFP with about half of them earning a cash income. The project may want to reflect on whether there are sufficient households engaged in NTFP for interventions to make a significant impact on household income.

Table 102 NTFP collection/processing and earning revenue (primary sample)

Table 103 NTFP collection/processing and earning revenue (secondary sample)


NTFP households 7.71 1.7 824

Households earning a cash income from NTFP 2.93 0.82 824

9.2.1 NTFP sales

Table 104 shows that, for the small proportion of households engaged in NTFP, only the collection of Sal leaves (43%), the collection or processing of tamarind (30%), collection of Mahua flowers (26%) and the collection of mushrooms (20%) were undertaken by more than a fifth of households. These were largely the highest earning products (for those households that engaged in them, with the exception for mushrooms which presumably was self-consumed).

Table 104 Sales revenue (primary sample)

Product

Proportion of HHs engaged in the

collection or processing of

product (%)

Average total expenses (Rs)

Average total sales (Rs)

Average net cash income (Rs)

Lac on ber 0.69 1275 6000 4725

Lac on kusum 1.36 111.27 993.63 882.36

Moringa leaves 8.69 2.3 200 160

Honey 4.1 0

Sal seed 3.89 20 116.85 96.85

Sal leaf 43.07 3.16 84.5 70.85

Mahua flower 25.54 72.17 2985.65 2855


NTFP households 10.45 1.99 1568

Households earning a cash income from NTFP 5.07 1.06 1568


95

Product

Proportion of HHs engaged in the

collection or processing of

product (%)

Average total expenses (Rs)

Average total sales (Rs)

Average net cash income (Rs)

Mahua seeds 13.85 62.39 1046.45 1046.45

Kendu leaf 16.43 140.61 2810.63 2652.55

Kendu fruit 7.99 0

Chironji fruit 0.43 0 2000 2000

Kusum 1.19 2809.18 30 -4820

Neem 1.45 35 35 0

Mushrooms 19.72 0

Khajur leaf broom grass

15.7 19.37 150 -150

Tamarind 30.42 136.05 1730.69 1420.66

Others 15.3 4.25 4400 4400

Table 105 shows average quantities sold in the market per household that is engaged in the collection or processing of a product and average per kilogram price received for the various products, only for those products where more than 5% of NTFP households are engaged in them and there were some sales.

Table 105 Quantity marketed and price realised (primary sample)

Product

Average quantity (Kgs) per hh sold in the market

Average price received per kg (Rs)

Moringa leaves 10 20

Mahua flower 84.44 28.8

Mahua seeds 36.94 26.38

Kendu leaf 116.62 100.33

Tamarind 81.24 20.71

9.2.2 Income

Table 106 shows that net income from collection or processing of NTFP in Rs. 1,328 for the small proportion of households in the sample who engage in NTFP, which averages to Rs. 139 for all households.

Table 106 Net income from collection or processing of NTFP (primary sample)


Net income from NTFP (HHs who earn a cash income from or self-consume)

1327.88 376.02 123

Net income from NTFP (all HHs) 138.7 45.05 1568

Table 107 Net income from collection or processing of NTFP (secondary sample)


Net income from NTFP (HHs who earn a cash income from or self-consume)

397.28 155.14 40

Net income from NTFP (all HHs) 30.61 11.87 824


96

9.3 Conclusions

Only a tenth of HVA households are engaged in the collection or processing of NTFP, and even fewer of them sell produce in the market. The project may want to reflect on whether there are sufficient households engaged in NTFP for interventions to make a significant impact on household income. Further, NTFP is a low-cost livelihood activity but faces under developed markets. Market development is likely required in order for NTFP to drive income growth.


97

10 Social mobilisation and community institutions

10.1 Introduction

In this chapter the baseline findings on mobilisation, SHGs, VOs, and rural producer collectives are presented, including membership and engagement with production and marketing collectives and SHGs.

10.2 Social mobilisation

Of all target households (n=1568), 59% are represented in collectives or are in the fold of various groups and almost all of these are in the fold of women’s SHGs supported by the NRLM. However, a lot of these SHGs are recently formed, with close to half (45%) formed in 2017 and a little over a fourth (28%) formed in 2016. Their maturity will be instrumental to JOHAR’s success in implementing collective group-based interventions, which implies that continued and focussed support from the NRLM to the community institutions platform is important for the JOHAR project.

Table 108 Representation in collectives (primary sample)

Proportion (%)

Standard error

n

Households with representation in collectives 58.84 1.96 1568

Households with women members representing in collectives 56.96 2.02 1568

Table 109 Representation in collectives (secondary sample)

Proportion (%)

Standard error

n

Households with representation in collectives 47.79 3.06 824

Households with women members representing in collectives 46.63 3.05 824

Table 110 Representation in women’s’ self-help groups (primary sample)

Proportion (%)

Standard error

n

Households with representation in women’s’ SHGs 56.1 2.06 156

8

Table 111 Representation in women’s’ self-help groups (secondary sample)

Proportion (%)

Standard error

n

Households with representation in women’s’ SHGs 45.75 3.13 824

Table 112 Year of formation of NRLM SHGs

Proportion (%) n

2002 0.33 302

2003 0.33 302

2006 0.99 302


98

Proportion (%) n

2007 0.66 302

2008 1.99 302

2009 0.99 302

2010 0.66 302

2011 0.33 302

2012 2.98 302

2013 4.64 302

2014 3.64 302

2015 2.98 302

2016 28.15 302

2017 45.7 302

2018 5.63 302

10.3 Women’s self-help groups

Of target households that have representation in women’s SHGs (n=858), 41% and 48% are OBC and ST households respectively. Almost all of them (96%) have landholding, 46% have access to irrigation, and 73% engage in HVA cultivation (by the wider definition of HVA crops). Less than a fourth (22%) have an open loan or loan closed in the last 12 months (compared to 16% of all target households having an open loan of loan closed in the last 12 months) but a little over half are cultivating JOHAR HVA and close to three-fourths (73%) are cultivating any HVA (compared to 48% and 67% of all target households cultivating JOHAR HVA and any HVA respectively). This implies that SHG households access credit more and are more likely to cultivate HVA, and further that target households with SHG representation aren’t accessing credit for farm production. 77% of these households have an electricity connection and over half (59%) have a latrine (compared to 76% and 58% of all target households having an electricity connection and a latrine respectively).

Table 113 Households with SHG membership (primary sample)


OBC 40.96 0.04 858

SC 8.53 0.02 858

ST 47.74 0.04 858

PVTG 3.28 0.01 347

Landholding 96.4 0.01 858

Irrigation 45.57 0.03 858

Engage in HVA farming 73.48 0.03 858

Engage in JOHAR HVA farming 53.96 0.04 858

Proportion of landless who are in SHGs (not from primary sample)

39.37 0.06 241

Table 114 Self-help group households (primary sample)


Households with representation in women’s SHGs that have an outstanding loan or loan closed in the last 12 months from any source

21.73 1.64 858


99


Households with representation in women’s SHGs that have an electricity connection

76.84 2.28 858

Households with representation in women’s SHGs that have a latrine

58.74 3.75 858

Households with representation in women’s SHGs cultivating HVA (JOHAR definition)

53.96 3.57 858

Households with representation in women’s SHGs cultivating HVA (wider definition)

73.48 2.89 858

Table 115 Self-help group households (secondary sample)

Secondary Proportion (%) Standard error n

Households with representation in women’s SHGs that have an outstanding loan or loan closed in the last 12 months from any source

30.37 2.91 367

Households with representation in women’s SHGs that have an electricity connection

75.21 3.5 367

Households with representation in women’s SHGs that have a latrine

55.07 3.96 367

Households with representation in women’s SHGs cultivating HVA (JOHAR definition)

33.39 3.33 367

Households with representation in women’s SHGs cultivating HVA (wider definition)

55.64 3.51 367

Table 116 shows that most SHGs have a bank account (88%) and are federated into a VO (80%), most have regular meetings with 23% having less than required number of meetings in the past 3 months and 30% having less than full attendance in the last 1 month. 40% SHGs report having transacted outside the SHG meeting, 31% have had members leave since formation, less than a fifth (18%) have a balance sheet prepared for the previous financial year and 38% of SHGs that knew of the process have prepared a micro-credit plan for the community investment fund.

Table 116 Self-help group key features


SHGs with a bank account 88.08 1.87 302

SHGs that are members of a VO 79.47 2.33 302

SHGs with less than 12 meeting in last 90 days 22.85 2.42 302

SHGs that transact outside the SHG meeting 39.74 2.82 302

SHGs that have a penalty system for absenteeism 80.79 2.27 302

SHGs with less than full attendance recorded at SHG meetings in last 30 days

30.46 2.65 302

SHGs with a system of collecting penalties for delayed loan repayment

19.54 2.29 302

SHGs who prepared MCP for CIF (of SHGs who knew of process)

38.25 2.88 285

SHGs with balance sheet prepared for the previous financial year

17.88 2.21 302

SHGs that have had members leave since formation 30.79 2.66 302


100

10.4 Village organisations

Table 117 below shows that 85% of VOs have a bank account, 69% are federated in to a Cluster Level Federation (CLF), 27% have completed bank linkage, 15% have received community investment fund, 11% have received vulnerability reduction fund, and 9% have a balance sheet prepared for the previous financial year. This suggests that there is either a lag in community institutions receiving funds or that a desired level of maturity required to receive these funds hasn’t been attained, which could be checked from administrative data by the NRLM programme.

Table 117 Village organisation key features


VOs with a bank account 85.14 4.16 74

VOs that are members of a CLF 68.92 5.42 74

VOs that have completed bank linkage 27.03 5.2 74

VOs that have received vulnerability reduction fund 10.81 3.63 74

VOs with balance sheet prepared for the previous financial year 9.46 3.43 74

10.5 Membership in rural producer collectives

Of all target households (n=1568), less than 2% of households are members of a group/co-operative involved in production or marketing of any commodity. This shows the lack of collectivisation that JOHAR will address.

Table 118 Production or marketing group membership (primary sample)


Households that are members of a group or co-operative that is involved in production or marketing

1.77 0.49 1568

Table 119 Production or marketing group membership (secondary sample)


Households that are members of a group or co-operative that is involved in production or marketing

1.01 0.5 824

Table 120 shows that over half of all target households are members of a SHG. It is expected that non-SHG members will join one to become part of JOHAR, as this is the community institutions platform that JOHAR builds upon.

Table 120 Membership in groups and institutions (primary sample)

Proportion of households (%) Standard error n

Membership in Self-Help Groups 56.1 2.06 1568

Table 121 Membership in groups and institutions (secondary sample)

Proportion of households (%) Standard error n

Membership in Self-Help Groups 45.75 3.13 824


101

10.6 Engagement with rural producer collectives

Table 122 shows that very few households who cultivate (n=1450), own livestock (n=1311), and have a fish production system (n=102) engage with producer collectives for purchasing or receiving inputs. There are no cultivator households who use a PG managed irrigation scheme and no NTFP collector or processor households that receive inputs from a PG.

Table 122 Purchasing and procurement from producer groups (primary sample)

Proportion

(%) Standard

error n

HHs that procure production inputs from PG: of HHs who do cropping

1.82 0.63 1450

HHs that procure seed from PG nursery: of HHs who do cropping 0.55 0.3 1450

HHs that purchased livestock from a PG: for HHs who own any livestock

0.86 0.38 1311

HHs that procure fish seeds through a PG: for HHs who have fish production system

9.06 4.34 102

Table 123 Purchasing and procurement from producer groups (secondary sample)

Proportion

(%) Standard

error n

HHs that procure production inputs from PG: of HHs who do cropping

1.23 0.63 514

HHs that procure seed from PG nursery: of HHs who do cropping 0.09 0.09 514

HHs that purchased livestock from a PG: for HHs who own any livestock

0.53 0.44 501

HHs that procure fish seeds through a PG: for HHs who have fish production system

3.69 4.4 18

Table 124 shows that a similarly small proportion sell produce through producer collectives.

Table 124 Sale of production through a producer group (primary sample)

Proportion

(%) Standard

error n

HHs that market crops through PG: of HHs who do cropping 1.58 0.46 1450

HHs that sold livestock through a PG: for HHs who own any livestock 1.21 0.35 1311

HHs that sold livestock produce through a PG: for HHs who own any livestock

0.02 0.02 1311

HHs that market fish through a PG: for HHs who have fish production system

1.85 1.81 76

Table 125 Sale of production through a producer group (secondary sample)

Proportion

(%) Standard

error n

HHs that market crops through PG: of HHs who do cropping 0.13 0.13 514

HHs that sold livestock through a PG: for HHs who own any livestock

0.56 0.37 501

HHs that sold livestock produce through a PG: for HHs who own any livestock

0 501


102

Proportion

(%) Standard

error n

HHs that market fish through a PG: for HHs who have fish production system

0 8

HHs that market NTFP through a PG: for HHs who do NTFP 0.13 0.13 514

10.7 Conclusions

Very few households at baseline are members of a producer collective or engage with them for the procurement of inputs or the sales of produce. This supports the relevance of the JOHAR collectivisation approach. Only just over half of households are currently members of SHGs, which may prevent JOHAR take-up unless more households join them in order to participate in JOHAR. The project may need to review the maturity and suitability of the community institutions platform that JOHAR will be layered onto. There is close to no difference in the proportion of all target households and all target households that have representation in SHGs with regard to having an electricity connection and a latrine, whereas target households with representation in SHGs are more likely to access credit and cultivate HVA.


103

11 Other livelihood sources

11.1 Introduction

Figure 6 showed that, for the primary sample, 36% of average annual household income derived from wages and 9% from enterprises.

11.2 Enterprises

8% of households owned a business enterprise, although less than 1% of households had an enterprise headed by a female. 26% of households who owned a business enterprise had borrowed capital for that enterprise (on average, Rs 115,660). On average, enterprises used Rs 33,228 of a household’s own capital.

Table 126 Household enterprise ownership

Primary Sample Secondary Sample

Proportion

(%) Standard

error n

Proportion (%)

Standard error

n

HHs that own business enterprise

8.01 1.22 1568 11.25 1.97 824

HHs with an enterprise run by a female

0.91 0.3 1568 2.44 0.76 824

HHs that borrow capital for enterprise

26.02 3.72 144 28.12 5.46 102

Average capital borrowed (Rs)

115659.5 34734.53 36 46903.26 21949.47 28

Own capital used 33227.77 7155.67 144 25414.53 11901.03 102

Of these enterprises, only 5% related to agriculture, hunting, forestry and fishing, with a third relating to wholesale and retail trade, restaurants and hotels, and a fifth related to financing, insurance, real estate and business services.

Table 127 Types of enterprise

Primary sample (n=144) Secondary sample (n=102)


error Proportion

(%) Standard

error

Agriculture, hunting, forestry, and fishing

4.86 2.96 5.73 3.28

Mining and quarrying 1.37 0.96 4.75 3.26

Manufacturing 10.84 2.49 15.28 5.42

Electricity, gas, and water 0.32 0.32 0

Construction 4.53 2.12 0.76 0.73

Wholesale and retail trade; restaurants and hotels

33.22 4.83 39.36 9.72

Transport, storage, and communication 5.81 2.12 0.59 0.53

Financing, insurance, real estate, and business services

20.56 4.45 17.66 6.53


104



error Proportion

(%) Standard

error

Community, social, and personal services

10.93 2.63 6.5 3.83

Others 10.36 3.46 11.96 4.95

Most enterprises who borrowed capital accessed this from banks or friends and relatives.

Table 128 Sources of capital



error Proportion

(%) Standard

error

Loan from SHG 6.54 5.75 4.59 4.82

Loan from other community organisations 0.82 0.84 8.03 6.47

Loan from MFI 0 0

Loan from Relatives/friends 42.76 8.95 24.29 5.07

Private money lenders 1.13 1.13 0

Landlord 3.12 3.19 0

Employer 0 0

Co-operative societies 1.67 1.7 10.78 7.63

Shop keeper 0.68 0.73 6.51 5.26

Agri input traders 0 0

Private banks 0 6.91 4.64

Nationalised banks 35.18 9.4 24.58 7.47

Rural development banks 7.33 5.24 25.59 13.16

Government schemes 0 0

Kisan credit card 1.59 1.59 0

LIC 0 0

Other 0 0

On average, enterprises only employed one family member but 25 non-family members. Enterprises primarily employed males.

Table 129 Enterprise employment


Number Standard

error n Number

Standard error

n

Number of male non-family workers in an enterprise

24.8 4.11 141 26.78 6.06 100

Number of female non-family workers in an enterprise

0.19 0.12 109 8.45 4.04 69

Number of family workers in an enterprise

1.26 0.08 144 1.36 0.07 102


105


Number Standard

error n Number

Standard error

n

Number of non-family workers in an enterprise

24.95 4.12 141 26.84 6.05 100

11.3 Wages

59% of households earned wage income. Of these, just over a third had female wage earners. Only 6% of households who earned wage income were on a regular, permanent or longer term contract. On average, wage earners worked 20 days per month.

Table 130 Household wage earnings


Number Standard

error n Number

Standard error

n

HHs that earn wage income 59.24 2.02 1568 67.21 2.81 824

HHs with female wage earners 37.04 2.16 956 42.08 3.32 546

HHs with casual daily workers 34.95 2.61 956 41.08 3.41 546

HHs with casual piece workers 20.83 2.24 956 24.55 3.12 546

HHs with contractual workers 3.7 1.05 956 3.02 0.9 546

HHs with regular/permanent/longer contract workers

6.09 1.08 956 5.67 1.41 546

Average no. of days worked by a wage earner per HH

19.58 0.45 609 20.38 0.49 385

Table 131 shows that permanent labour was the most remunerative and casual labour the least.

Table 131 Average annual income from different wage sources

Type of labour Average annual HH income from wage and bonus (Rs)

Average monthly HH income from wage and bonus (Rs)

n

Permanent labour 176724.1 14727.01 75

Casual labour (daily or piece)

46,687.61 3890.634

518

Contractual (less than one year)

76241.96 6353.497

49

Table 132 shows how labour was distributed across sectors, with just over half of households who earned wage income working outside traditional sectors and a third in construction.

Table 132 Industries for wage/salary (primary sample)

Proportion (%)

Standard error

n

Agriculture, hunting, forestry, and fishing 3.01 0.72 1568





106

Proportion (%)

Standard error

n






Others 30.27 1.99 1568

Don't know 2.67 0.46 1568

Table 133 Industries for wage/salary (secondary sample)

Proportion

(%)

Standard error

n

Agriculture, hunting, forestry, and fishing 5.09 1.34 824









Others 29.17 2.64 824

Don't know 2.62 0.74 824

11.4 Conclusions

36% of average annual household income derived from wages and 9% from enterprises. Enterprises were typically owned by men and employed male non-family members. Few enterprises were focused on agriculture or related livelihoods. Only a quarter of households who owned an enterprise had borrowed capital, with the majority relying on their own capital for the enterprise. 59% of households earned wage income but this was predominantly amongst males and involve casual, irregular contracts.


107

12 Debt, Savings and Credit

12.1 Introduction

In this chapter, the baseline findings on credit are presented. First the coverage of the KCC and loans

is presented, followed by the distribution for sources of credit, purposes for which loans were taken,

and characteristics of households that borrow.

12.2 Sources of credit

Table 134 shows that 9% of households hold a Kisan Credit Card (KCC). Of these, two-fifths have a credit limit of at least Rs 50,000.

Table 134 Kisan Credit Card coverage



error n Proportion (%)

Standard error

n

Households with a Kisan Credit Card

8.67 1 1568 2.39 0.6 824

HHs that have a KCC with Rs 50,000 or more credit limit

41.54 4.31 136 37.94 10.17 23

Table 135 shows that 16% of all households have taken a loan. This refers to loans that were open or outstanding at the time of the survey, open during the 12 months before the survey or loans closed during the 12 months before the survey, which means that about a sixth of target households had loans payable or outstanding during the past year. Households who took out at least one loan, had an average loan amount of Rs 57,788.

Table 135 Loans taken


Value Standard

error n Value

Standard error

n

Households with loans (%) 16.06 1.1 1568 20.26 1.97 824

Average number of loans (Rs) 1.19 0.03 260 1.24 0.05 167

Average loan amount (Rs) 54788.28 11580.28 260 43245.31 6639.68 167

Table 136 shows the sources of loans for households that have loans. Half of these households have loans from SHGs and one-fifth from Commercial banks. Less than 2% borrowed from a money lender and no households took or had loans from a Pawn shop or Cooperative.

Table 136 Sources of loans


Source of loan Proportion of HHs (%)

Standard error

n Proportion of HHs (%)

Standard error

n

Relative 3.29 0.87 260 6.54 2.36 167

Commercial bank 19.48 3.03 260 23.4 3.45 167

MFI loan to individual 0.46 0.34 260 0.17 0.17 167

SHG 51.01 3.84 260 50.72 5.67 167


108


Source of loan Proportion of HHs (%)

Standard error

n Proportion of HHs (%)

Standard error

n

Another savings group 2.39 1.25 260 7.72 2.49 167

Pawn shop 0

260 0 167

Money lender 1.83 0.86 260 0.21 0.21 167

Friend 5.47 1.51 260 7.6 3 167

Neighbour 3.4 1.34 260 1.28 1.14 167

Shopkeeper 2.15 1.05 260 0 167

Cooperative 0

260 0 167

Chit fund 6.4 2.36 260 2.59 1.66 167

Finance company 2.64 1.5 260 0.33 0.33 167

Provident fund 1.92 0.92 260 4.6 1.84 167

SC/ST/OBC Corporation 0.82 0.57 260 0.33 0.33 167

Employer 0.21 0.21 260 0.35 0.35 167

Others 1.34 1.02 260 0 167

Table 137 shows the purposes for which households who have loans (n=260) took loans for. The three most common purposes for which loans were taken, and which account for more than half the households, are for farm assets (34% of households that took loans), health reasons (12%), and dowry or other marriage expenses (13% of households that took loans). 6% of households took loans to buy land for farming. Aside from farm assets and to buy farming land, few households accessed credit for agricultural purposes (e.g. for livestock and fishery).

Table 137 Purpose of loans


Purpose of loan Proportion of

HHs (%) Standard

error n

Proportion of HHs (%)

Standard error

n

Health 12.33 2.23 260 22.18 4.13 167

Buy food 0.11 0.11 260 1.1 0.95 167

Repay old debt 3 1.3 260 3.38 1.56 167

Dowry/other marriage expense

12.55 2.22 260 11.7 3.14 167

Funeral expenses 0.91 0.49 260 1.13 0.72 167

Other social ceremony 1.17 0.84 260 1.86 0.98 167

Education 7.45 2.32 260 2.75 1.14 167

Regular household expenditure

5.99 1.56 260 11.36 3.37 167

Migration 0

260 0.44 0.45 167

Buy lands for house 0.77 0.64 260 0.15 0.15 167

Building a new house 6.56 1.55 260 5.3 1.54 167

House repairs 4.25 1.54 260 6.15 2.44 167

Buy land for farming 6.19 2.03 260 0.72 0.73 167

Farm assets 34.47 4.53 260 18.21 4.54 167

Starting a new business 6.25 1.82 260 11.59 4.06 167


109


Purpose of loan Proportion of

HHs (%) Standard

error n

Proportion of HHs (%)

Standard error

n

Purchasing business assets

3.29 1.1 260 6.8 2.36 167

Regular investments 2.01 0.98 260 2.8 1.69 167

Livestock health 0

260 0.33 0.33 167

Buy HH durable good 1.2 0.56 260 0.89 0.72 167

Buying jewellery 0

260 0 167

Land improvements 0.28 0.28 260 0 167

Vocational skill training 0

260 0 167

Livestock housing 0.92 0.55 260 1.16 0.83 167

Livestock breeds 2.23 1 260 8.29 2.84 167

Fishery input 0

260 0 167

Cage culture inputs 0.31 0.32 260 0 167

Pen culture inputs 0

260 0 167

Input procurement for NTFP

0.88 0.65 260 0 167

Other 4.32 1.47 260 3.43 1.95 167

Table 138 below shows that of the 16% of households that had open loans in the last 12 months (n=260), OBC households are the highest proportion of borrowers, and three-fourths (76%) of households have SHG membership. Most households (93%) that borrow are households that cultivate, and half of these borrower households (50%) are households with cultivated land being irrigated. 71% of borrower households cultivate HVA crops (wider definition) and 66% use machine labour.

Table 138 Characteristics of households with loans (primary sample)

Proportion

(%) Standard

error n

Proportion (%)

Standard error

n

Households who have taken a loan Households who have not taken a loan

OBC 45.25 5.15 260 41.35 4.2 1308

SC 9.74 3.35 260 7.45 1.39 1308

ST 37.48 5.42 260 47.6 3.94 1308

Households with household head belonging to PVTG

1.88 1.25 76 2.66 1.03 539

Any member of household is a part of SHG

75.88 3.24 260 52.32 2.2 1308

Households that cultivate in at least one of the three seasons

93.03 2.4 260 93.79 0.92 1308

Households with cultivated land being irrigated

50.48 4.13 259 39.67 2.9 1307


110

Proportion

(%) Standard

error n

Proportion (%)

Standard error

n

Households who have taken a loan Households who have not taken a loan

Households that cultivate HVA (Fruits/crops): all except cereals

70.94 3.54 260 66.74 2.51 1308

Households that cultivate JOHAR HVA

55.32 4.44 260 46.73 3.52 1308

Households that use machine labour (in any season)

65.61 4.67 236 53.22 2.58 1214

12.3 Conclusions

About a sixth of JOHAR target households take loans or have outstanding loans in the 12 months before the survey. Half of these households have taken loans from SHGs, showing the reasonable maturity of the community institutions platform. Aside from for farm assets, few households access credit for other agricultural purposes. Of the households that take loans, the most prevalent are OBC households and nearly three-fourths of borrower households cultivate HVA crops, and two-thirds use machine labour.


111

13 Knowledge and skills

13.1 Introduction

In this chapter, the baseline findings on farmer knowledge and skills are presented. First the coverage

of skills training is presented, followed by the source of trainings, and lastly findings on soil testing.

13.2 Skills

Less than 4% of households had received any skills training in the past three years. For this small sample, under a quarter had received training from JSLPS showing that there is less than one percent contamination at baseline.

Table 139 Receipt of skills training


Proportion

(%) Standard

error n

Proportion (%)

Standard error

n

HHs where someone has received skill training in past 3 years

3.89 0.6 1565 3.15 0.71 818

Table 140 Source of skills training for households who received any training


Proportion

(%) Standard

error n

Proportion (%)

Standard error

n

JSLPS 22.46 7.31 66 12.48 6.89 32

DDUGKY 10.16 4.36 66 5.23 3.1 32

Rural self-employment training institutions

14.86 7.11 66 9.11 4.59 32

Other govt training agencies

35.51 7.68 66 48.3 14.2 32

Others 20.42 5.03 66 24.89 14.03 32

13.3 Soil testing

Fewer than 2% of households had their soil tested in the last three years. Of this small sample, less than a third had received the soil test report. However, 80% of households who had received the report acted upon it, suggesting that this is a potentially efficacious intervention.

Table 141 Soil testing


Proportion (%)

Standard error

n Proportion

(%) Standard

error n

HHs whose soil has been tested in last 3 years

1.84 0.45 1450 1.35 0.65 514

Of those tested who received soil test report

30.84 11.3 26 45.93 21.62 6


112


Proportion (%)

Standard error

n Proportion

(%) Standard

error n

Of those who received who followed advice

79.68 14.62 8 13.88 15.51 3

13.4 Conclusions

Very few JOHAR target households have been exposed to skills training or soil testing, validating the focus of these interventions in the project theory of change.


113

14 Climate resilience and shocks

14.1 Introduction

This chapter presents analysis of a set of common objective questions on climate resilience28.

14.2 Climate resilience

Just over half of households encountered adverse weather conditions in the last five years. Of these households, the main issues faced were drought (49%) and low rainfall (42%). Heavy rainfall (23%) and plant diseases (15%) were also commonly faced.

Table 142 Households encountering adverse weather conditions (primary sample)


Households who encountered adverse weather conditions in last 5 years

51.24 3.25 1557

Table 143 Households encountering adverse weather conditions (secondary sample)


Households who encountered adverse weather conditions in last 5 years

49.79 3.57 807

Table 144 Types of adverse weather conditions (primary sample)

Proportion (%) of those households encountering

adverse conditions Standard error n

Delayed weather 13.42 2.23 846

Less rain in season

42.24 3.64 846

Insects pests 2.46 0.66 846

Plant diseases 15.32 2.74 846

Hot weather 12.24 2.28 846

Heavy rains 22.54 2.48 846

Hailstorms 8.22 1.8 846

Drought 49.27 2.73 846

Table 145 Types of adverse weather conditions (secondary sample)



Delayed weather 12.06 3.15 358

Less rain in season

27.79 3.72 358

Insects pests 2.82 1.46 358

28 McCarthy, Nancy. "Understanding agricultural households' adaptation to climate change and implications for mitigation: land management and investment options." Inc., LSMS-ISA, The World Bank (2011).


114



Plant diseases 11.54 3.23 358

Hot weather 22.64 4.7 358

Heavy rains 25.49 3.57 358

Hailstorms 8.19 2.2 358

Drought 48.86 5.18 358

Less than one percent of households have received any training on climate related stresses and its impact on livelihoods. Similarly, less than one percent of households have adopted any farm level practices to cope better with a changing climate.

Table 146 Climate training and coping strategies (primary sample)


HHs received any training on climate related stresses and its impact on livelihoods

0.88 0.26 1560

HHs adopted any farm level practices to cope better with a changing climate

0.84 0.4 1560

Table 147 Climate training and coping strategies (secondary sample)


HHs received any training on climate related stresses and its impact on livelihoods

0.6 0.27 812

HHs adopted any farm level practices to cope better with a changing climate

1 0.51 812

14.3 Adverse shocks to households

Between a fourth and a third of households experienced adverse shocks in the last 12 months before the survey. The main shocks faced by these households were medical expenses on treatment of any family member (56%), followed by loss of agricultural crop (28%), and loss of livestock (21%). Other shocks were experienced by 10% or lesser of these households that experienced any adverse shocks.

Table 148 Households experiencing shocks (primary sample)


Households who experienced adverse shocks in the last 12 months

29.46 3.25 1568

Table 149 Households experiencing shocks (secondary sample)


Households who experienced adverse shocks in the last 12 months

27.09 2.7 824


115

Table 150 Types of shocks (primary sample)

Table 151 Types of shocks (secondary sample)


Difficulty or failures in non-farm business 10.37 3.87 224

Loss of livestock due to death, theft or disease 9.84 3.04 223

Loss of employment 0.96 0.49 224

Loss of agricultural crop 17.86 4.31 224

Substantial fall in crop price 2.5 1.55 224

Medical expenses due to illness or treatment of any family member

62.17 4.84 223

Expenses due to accident of family or any family member 14.63 2.73 224

Physical damage to home or any property/structure 1.8 0.78 224

Theft 0.87 0.53 224

Money lender demands 0.91 0.53 223

Death of main income earner 8.21 2.94 224

Death of other member in family 9.53 2.64 224

Conflict 1.42 0.77 223

Displacement 0 224

Reduction of regular assistance 1.42 1.14 224

Divorce or separation 0.32 0.31 224

Marriage, including giving dowry 4.5 1.76 224

Other shocks 0.74 0.74 224


Difficulty or failures in non-farm business 8.73 2.66 449

Loss of livestock due to death, theft or disease 21.29 2.76 449

Loss of employment 1.9 0.83 449

Loss of agricultural crop 28.5 3.48 449

Substantial fall in crop price 6.59 1.47 449

Medical expenses due to illness or treatment of any family member

56.46 3.45 448

Expenses due to accident of family or any family member 10.94 1.78 449

Physical damage to home or any property/structure 3.73 1.09 449

Theft 2.43 0.88 449

Money lender demands 1.1 0.54 449

Death of main income earner 2.77 0.84 449

Death of other member in family 10.18 1.92 449

Conflict 2.67 0.93 449

Displacement 0.49 0.5 449

Reduction of regular assistance 1.83 0.76 447

Divorce or separation 0.2 0.2 449

Marriage, including giving dowry 7.43 1.49 448

Other shocks 0.62 0.43 446


116

14.4 Conclusions

Whilst many households face climate related risks to their livelihoods, mainly related to rainfall, very few have acted upon this or received training, validating its critical focus in JOHAR. With regard to adverse shocks, a little less than a third of households reported having experienced one in the 12 months before the survey, with most of them facing a shock due to medical treatment expenses, loss of crop or loss of livestock (due to death, theft, or disease). Given the high proportion of losses or shocks to households being from two key intervention sub-components of JOHAR, the project may want to consider crop and livestock insurance interventions for participating producer households.


117

15 Timelines and future activities

Future activities for the JOHAR impact evaluation include a midline and endline survey with reports following the survey rounds. These should be conducted after project year 3 and project completion respectively, with the surveys being fielded in the same months (August to October) as the baseline survey. However, stakeholders would need to deliberate on the timing of a midline survey, given that JOHAR producer households would need time to move along the impact pathways before another round of data collection is conducted. JOHAR’s process monitoring could help inform this decision.


118

Annex A Snapshot of Project Indicators

Table 152 Outcome indicators (combined sample) (for treatment and control)

Indicators Value Standard

error n

Average Number of livelihood sources 1.68 0.04 5923

Proportion of HHs with one livelihood source out of agri, livestock,

fishery, NTFP

21.85 1.2 5923

Proportion of HHs with two livelihood sources out of agri, livestock,

fishery, NTFP

56.93 1.86 5923

Proportion of HHs with three livelihood sources out of agri, livestock,

fishery, NTFP

10.45 1.18 5923

Proportion of HHs with four livelihood sources out of agri, livestock,

fishery, NTFP

0.22 0.07 5923

Area of land owned, in acres 1.07 0.05 5923

Total cultivated area in Kharif, incl leased-in, excl leased out 1.01 0.05 4892

Total cultivated area in Rabi, incl leased-in, excl leased out 0.7 0.46 2200

Total cultivated area in Summer, incl leased-in, excl leased out 0.22 0.04 603

Average Expense on production inputs in kharif 1508.33 73.35 4417

Average Expense on production inputs in rabi 639.7 52.58 1784

Average Expense on production inputs in summer 693.18 134.79 483

Average Number of crops grown in Kharif: for HHs who crop in the

season

1.98 0.04 4892

Average Number of crops grown in Rabi: for HHs who crop in the

season

2.19 0.07 2200

Average Number of crops grown in Summer: for HHs who crop in the

season

1.79 0.1 604

Average Total harvest in Kharif (in kg) 1410.79 106.25 4892

Average Total harvest in Rabi (in kg) 662.19 111.59 2200

Average Total harvest in Summer (in kg) 496.53 79.59 602

Average Quantity sold in Kharif (in kg) 774.68 42.74 1920

Average Quantity sold in Rabi (in kg) 580.18 61.86 897

Average Quantity sold in Summer (in kg) 736.69 183 323

Average Sales revenue in Kharif 11421.15 1111.77 1919

Average Sales revenue in Rabi 9138.88 1038.2 895

Average Sales revenue in Summer 7513.23 1016.76 322

Livestock


119

Indicators Value Standard

error n

Average Expense on productive inputs of livestock 2088.45 173.89 4130

Average number of Cattle sold in last 12 months 1.68 0.07 395

Average number of Buffalo sold in last 12 months 1.69 0.09 60

Average number of Goat sold in last 12 months 1.94 0.12 507

Average number of Sheep sold in last 12 months 2.46 0.35 39

Average number of Pig sold in last 12 months 1.99 0.26 37

Average number of Broilers sold in last 12 months 6.46 0.68 140

Average number of Layers sold in last 12 months 6

1

Average number of Desi chickens sold in last 12 months 5.26 0.46 344

Average number of Duck sold in last 12 months 3.68 0.8 47

Others 6.72 1.68 17

Sales revenue from livestock 8586.89 629.29 1234

Sales revenue from produce 4731.74 1410.25 425

Fisheries

Expense for fish production 3714.03 1190.47 248

Quan of fish sold (in kgs) 70.21 14.24 113

Quan of fish sold (in numbers) 495.41 283.3 4

Sales revenue from fish 8525.59 1572.61 131

Price of fish realised 120.42 5.02 117

NTFP

Expense for production/collection 124.94 72.59 610

Quantity produced of NTFP (in kgs) 7.18 1.14 5923

Quantity produced of NTFP (in numbers) 5.99 1.59 5923

Quantity sold of NTFP (in kgs) 5.48 0.98 5923

Quantity sold of NTFP (in numbers) 2.83 1.05 5923


120

Annex B References

Anonymous “All India Rural Financial Inclusion Survey 2016-17”, NABARD (2018): 38 Anonymous “Agriculture Census 2010-11: All India Report on Number and Area of Operational Holdings” Agriculture Census Division, Ministry of Agriculture, GoI (2014): 16-18 Anonymous “Baseline Survey Report on Livelihood in Jharkhand.” JSLPS (2017) Anonymous “Periodic Labour Force Survey (PLFS) 2017-18” National Statistical Office, Ministry of Statistics and Programme Implementation, GoI (2019) http://mospi.nic.in/sites/default/files/publication_reports/Annual%20Report%2C%20PLFS%202017-18_31052019.pdf Anonymous “Women and Men in India (A statistical compilation of Gender related Indicators in India)” Social Statistics Division, Central Statistics Office Ministry of Statistics and Programme Implementation, GoI (2018) http://mospi.nic.in/sites/default/files/publication_reports/Women%20and%20Men%20%20in%20India%202018.pdf Chetty, Raj, John N. Friedman, and Jonah E. Rockoff. "The Long-Term Impacts of Teachers: Teacher Value-Added and Student Outcomes in Adulthood. NBER Working Paper No. 17699." National Bureau of Economic Research (2011). McCarthy, Nancy. "Understanding agricultural households' adaptation to climate change and implications for mitigation: land management and investment options." Inc., LSMS-ISA, The World Bank (2011). Nathan, Dev, and Harishwar Dayal. "Resource curse and Jharkhand." Economic and Political Weekly (2009): 16-17. Stuart, Elizabeth A. "Matching methods for causal inference: A review and a look forward." Statistical science: a review journal of the Institute of Mathematical Statistics 25, no. 1 (2010): 1. Swindale, Anne, and Paula Bilinsky. "Household dietary diversity score (HDDS) for measurement of household food access: indicator guide (v.2)." Washington, DC: Food and Nutrition Technical Assistance Project, Academy for Educational Development (2006). https://www.fantaproject.org/sites/default/files/resources/HDDS_v2_Sep06_0.pdf Todd, Petra E., and Kenneth I. Wolpin. "The production of cognitive achievement in children: Home, school, and racial test score gaps." Journal of Human capital 1, no. 1 (2007): 91-136.

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Jharkhand Opportunities for Harnessing Rural Growth Project – Impact Evaluation Baseline Report

JOHAR PROJECT Jharkhand State Livelihood Promotion Society

Rural Development Dept., Govt. Of Jharkhand

3rdfloor,LNCorporateTower,KutcheryRoad,Ranchi-834001Ph. : 0651&2360053, 2360391

Web. : www.jslps.org E-mail : [email protected] : facebook.com/onlineJSLPS • Twitter : @onlinejslps

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