determinants of rural–urban migration -...
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CHAPTER-5
Determinants of rural–urban migration
Migration of people from rural to urban areas has various socio-economic,
political, demographic, ecological and environmental implications. Earlier
development economists, such as, Lewis41 (1954) and Ranis & Fie (1961)
regarded it an important factor in the economic development of developing
countries. Rural-urban migration is considered as a balancing factor in the
dualistic developing economy as it helps in transferring manpower from low
income activities of rural sector to higher ones of urban sector and thus,
narrows down the rural-urban gap. However, Lewis and Ranis & Fie
development models have failed to explain the phenomenon of coexistence of
surplus labour in urban sector with substantial and steady influx of rural
population in the urban areas. The experiences of developing countries reveal
that the modern sector, with emphasis on highly capital intensive techniques, is
not capable enough to absorb the natural growth of urban workforce. Rural-
urban migration in these countries neither results in rapid economic growth in
urban areas nor brings about fundamental transformations in rural areas (Smit42
1998). Therefore, rural-urban migration is now seen as major contributing
factors to increase urban unemployment rate and affect the carrying capacity of
41 Lewis, W. A. 1954. ‘Economic Development with Unlimited Supplies of Labor’, The Manchester School of Economic and Social Studies 22: 139-191.
42 Smit, W. (1998),’The Rural Linkages of Urban Households in Durban, South Africa’, Environment and Urbanization, Vol.10 (1), 77-87
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urban infrastructure. Todaro43 (1969) explains this paradoxical relationship of
accelerated rural-urban migration in the context of rising urban unemployment
in developing countries postulating that ‘migration proceeds in response to
urban-rural differences in expected rather than actual earnings’. It is,
therefore, necessary to identify the key factors that are responsible for
migration of people from rural areas to urban areas. People migrate from rural
to urban areas due to various factors. These factors are generally classified as
‘Push’ and ‘pull’ factors. Push factors are those factors which force the people
to leave their places. High intensity of poverty & unemployment, in rural areas,
lack of basic amenities, displacement due to development projects, natural
calamities, social and religious conflicts may be the main push factors.
Similarly, better income & employment opportunities, better health &
education facilities, better infrastructure and amenities in the urban areas, are
the key pull factors in the rural-urban migration. This chapter we discuss the
main determinants of rural and urban migration. First we examine various
factors and then we conduct the regression analysis to identify the key
determinants. In order to study the impact of various determinants, rural-urban
migration, is classified into two categories—total rural-urban migration
(RUMT) and rural-urban migration of workers (RUMW).
5.1 ECONOMIC FACTORS
One of the most important factors in the mobility of workforce from one region
to other region or from one location to other location is economic. Since rural
43 Todaro, M. (1969) ‘A Model of Labor Migration and Urban Unemployment in Less Developed Countries’, American Economic Review Vol.59, 138-148.
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people lack better employment opportunities in the villages, they migrate to
urban areas where they expect to get productive employment. Those who have
better education and skill have the high probability to get employment in the
urban organized sector, while those who do not have basic education and skills
get opportunity in the expanded informal sector, such as domestic help, hotels
and dhabas, rickshaw pulling, construction activities, etc. empirical studies
show that most of the migrants, except for forced migrants, move to the urban
areas in search of better economic opportunities. Migration is normally viewed
as economic phenomenon(Mitcheel44, 1959). Most important economic factors
in rural-urban migration are discussed here briefly.
5.1.1 Land Scarcity and Population Pressure
Land is one of the most important assets in the rural area. A good quality
of cultivated land is necessary to support the livelihood of rural people.
The probability of movement of a person is relatively high from a
household who does not have access to land and other productive assets.
In Uttar Pradesh, more than 70 percent of rural people directly depend
on agriculture. Size of operational holding, especially in the Eastern
Region is quite low, while quality of land is relatively poor in
Bundelkhand and Central regions. The high people-land ratio and low
productivity of land tend to drive a large number of rural people to urban
areas in search of better livelihood options. A number of studies have
44 Mitchell J.C (1959), ‘The Causes of Labour Migration’, Bulletin of the Inter-African Labour Institute, Vol.6, 12-47.
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shown an inverse relationship between per capita availability of land and
rural to urban migration (Singh & Agrawal45, 1998, Stiglitz46, 1973,
Shaw47, 1974). Stiglitz (1973) finds that the landless peasants are more
likely to migrate than landed peasants. The increasing pressure of
population on land has led to division and fragmentation of operational
holdings.
5.1.2 Wage and Income Differentials
Another economic factor in the rural –urban migration is considered a
high wage and income difference between rural and urban labour
markets. A number of studies have highlighted this aspect. An ILO
study (ILO, 1966) concludes that the main push factor in the rural to
urban migration is low income from agriculture. In India, the income
inequality between rural and urban areas is quite high and it has further
accentuated during the last two decades of economic reforms. As a
consequence of the neo-liberal policies, there are serious income
disparities, agrarian distress, inadequate employment generation, vast
growth of informal economy and the resultant migration from rural areas
to urban areas. Agriculture which supports about 55 percent of total
population of the country, contributes only about 15 percent to the GDP.
45 Singh, S.P. and R.K. Agarwal, (1998), "Rural-Urban Migration: the Role of Push and Pull Factor Revisited" The Indian Journal of Labour Economics., Vol. 41 (4), pp. 653-68. 46 Stiglitz, J.E. (1973), ‘Alternative Theories of Wage Determination and Unemployment in LDCs’, IDS Discussion Paper, No.125, Narobi. 47 Shaw R.P. (1974), ‘ Land Tenure and the Rural Exodus in Latin America’, Economic Development and Cultural Change, Vol. 23(1), 123-132
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Per worker output is about 3.5 times higher in non-agricultural activities
than the agricultural activities (Singh, 2008). In the Harris-Todaro
model, labour migration is modeled in the context of inter-sectoral
(rural-urban) wage inequality. Migration decisions are made by rational
self-interested individuals looking for higher paid work in urban areas
and migration occurs if the economic benefits in terms of expected
wages at urban destination – accounting for risk of initial spell of
unemployment – exceed economic costs of moving and of foregone
wages at rural origin (Lucas48, 1997).
5.1.3 Differences in Employment Opportunities
The expanded urban sector has created more employment opportunities
for both skilled and unskilled workers. Rural workers move to the urban
areas to get these opportunities, As compared to the rural areas, which
are thinly and sparsely populated, cities are densely populated and
achieve economies of scale. Sinha49 (1983) observes that the
employment opportunities generated in the manufacturing sector is one
of the significant factors in the rural-urban migration. However, in the
recent years, employment in manufacturing has not been increasing in
commensurate with the investment in fixed assets because of more
sophisticated labour displacing technologies being used by the 48 Lucas, R. E. (1997.) ‘Internal Migration in Developing Countries’, in M. Rosenzweig and O. Stark (eds.), ‘Handbook of Population and Family Economics, vol. 1B', Amsterdam: Elsevier Science Publishing
49 Sinha, D.N (1983), ‘Rural-Urban Migration in India, The Indian Jpournal of Economics, Vol. 63 (251), 495-501.
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industries. Nevertheless, the employment has been expanded in the
urban informal economy where most of the rural migrants seek
employment opportunities. It may also be relevant to note that rural to
urban migration continues to grow even in presence of high
unemployment rate in cities.
5.1.4 Inequalities in Access to Resources
Inequalities in the distribution of economic resources across regions, and
social groups also act as a factor in the mobility of people from rural to
urban areas. If Land and physical resources are concentrated only in few
hands, other people would not be able to get their livelihood in the rural
areas and would be forced to move in search of better livelihood options.
High concentration of resources coupled with new technology used in
the farm sector likely to reduce the labour absorption in the farm sector.
Labour is the only of landless workers and if their labour is not gainfully
employed in the rural sector, they would like to migrate to the urban
areas. It may also be argued that the extreme poor people may not be
able to migrate to distanced urban centres due to lack of resources.
However, they may be seasonally migrated to the short distance places.
5.1.5 Technological Advancement and Farm Mechanization
Technological advancement and mechanization of agriculture is also
said to be one of the factors in rural to urban migration. The Green
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revolution technology used in India, initiated in late 60s, is more
external input-intensive and requires relatively more capital than labour.
Penetration of capital intensive methods of production into agricultural
sector, the substitution of factory made tools and other articles for those
produced by the rural artisans and mechanization of certain processes,
reduce labour requirement in rural areas. In a country like India, where
unemployment is widespread, it is economically more desirable to raise
output by increasing employment rather than increasing the per worker
output by reducing the labour. Technological change has two effects,
namely the resource substitution effects and the scale effects.
Technological change in agriculture is not resource-neutral; rather it
alters the relative productivities of various resources and consequently
causes change in the composition of resources. Technological
advancement in agriculture shifts the composition of resources in favour
of capital and thereby reducing the labour requirements. Technological
change also generates scale effects which tend to increase the demand
for farm labour. Biological, chemical and mechanical innovations are
basically output-augmenting. Output-augmenting effects of technology
reduce the marginal cost of production. Forward and backward linkages
of modern technology are greater than the traditional technology. The
new technology creates more opportunities in villages. For instance, a
study by Tyagi50 (1994) shows that per hectare employment on tractor-
50 Tyagi B.P. (1994), Agricultural Economics and Rural Development, Jai Prakash Nath & Co., Meerut
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operated farms is higher than on the bullock-operated farms in Gujarat.
New technology also creates employment opportunities in rural non-
farm activities. It has been observed that a large number of rural workers
from Bihar, Eastern Uttar Pradesh and some other agriculturally
backward regions migrate to Punjab, Western Uttar Pradesh and
Haryana to work on farms. There is not any conclusive evidence that
farm mechanization reduces the labour requirement. However, it reduces
the labour requirement per unit of land but the total volume of
employment in the rural areas does not seem to reduce due to the
technological advancement in agriculture.
5.1.6 Land Reform
Uneven distribution of land among the rural people acts as a determining
factor in the rural-urban migration. If land is concentrated in a few
hands, more people would not be able to do intensive cultivation.
Uneven distribution of land also affect the cropping pattern and
cropping intensity and thus reduces the labour absorption in agriculture,
For instance, absentee land lords may not do the intensive cultivation or
they may do agro-forestry, requiring less labour. On the contrary, if land
is distributed evenly among the people, more intensive cultivation can
be done. Land reform programmes are likely to reduce migration among
families whose land holdings are increased to a viable size. However, if
size of land holding is economically unviable, all working members of
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household may not get gainful employment throughout the year in
agriculture and therefore, some of them may migrate to urban areas in
search of better livelihood. In general, an effective land reform
programme tends to reduce the rural to urban migration, especially from
peasant households.
5.2 SOCIAL FACTORS
Various social factors also work in the rural to urban migration. In this sub-
section, some of the key factors are discussed.
5.2.1 Family Structure
Size and composition of family affects the rural to urban migration.
Larger the family size, greater is the probability to migrate. In a joint
family system, male member can migrate leaving his children and wife
at home as the other members of the family can take care of theme,
whereas, in a nuclear family, such support system is not available and
therefore, the probability of migration is quite low. Extended families
are better able to promote migration than the nuclear families. The broad
structure of such families allows and encourages the migration of its
members as a means to create investment opportunities for the family.
Probably, more kin contacts in cities are available to the extended
families, with their wider kinship network that would facilitate
migration. The desire to be close to kin may promote a chain migration.
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5.2.2 Family Conflicts
Family conflicts also lead to migration of people. In bigger families,
occurrence of conflicts among family members is higher than the small
families, which sometimes results in breaking of families or sometime
migration of some family members to avoid day-today altercation. The
quest of young persons for independence from traditional authority and
discipline motivate them to migrate to the urban areas.
5.2.3 Social Status
The society is divided into various social and ethnic groups. Social
pressure in terms of discrimination against a cultural or racial or ethnic
group certainly would have a considerable impact on the rural-urban
migration. The socially backward communities that have suffered social
exclusion for generations in the rural areas quite often look for
opportunities to move to the cities which, in addition to better
employment opportunities and better amenities, have some anonymity
so that social prejudices are of lesser consequence. In India, caste system
is very strong in rural areas. The socially and economically backward
communities do not enjoy the same status as their counterparts enjoy in
villages. Even after the decades of affirmative actions and policies
adopted by the government to empower the weaker sections of societies,
social discrimination still persists in many parts of rural India. On the
other hand, in urban areas, people are not generally aware of people’
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community or caste and therefore, the people coming from the lower
social strata are not discriminated at the same extent as they are
discriminated in the rural areas. Therefore, other things remain the same,
the probability to migrate will be higher among SC, ST and other
socially backward communities.
5.2.4 Social Services and Amenities
Better social services and amenities in the urban areas also attract rural
people to urban areas. As compared to rural areas, the cities have better
health, education, sanitation, physical security and better infrastructure
in terms of roads, electricity, sport facilities, communicant and financial
services. In short, rural population may be attracted towards the urban
areas by ‘bright lights’ of the city. Relatively better off rural people tend
to migrate to the cities more than poor people due to better social
services and amenities in the urban center. In has also been observed
that rich farmers construct their houses in the nearby towns or cities and
some of their family members reside their providing better education
facilities to their children. Moreover, many parents would like to get
their daughters married in those families who have their houses in towns
or cities.
5.3 DEMOGRAPHIC FACTORS
There are several demographic and educational factors that determine the rural-
urban migration. Age, sex, family size, population growth, education, etc are
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the determined factors in the rural-urban migration. Some of these factors, we
have already discussed in patterns and dimensions of migration in the state. For
example, we have discussed rural-urban migration by gender, rural-urban
migration by educational level and also rural-urban migration by age. We have
also examined the reasons for migration in the study area.
Age is considered one of the significant factors in the migration. Most
studies on migration reveal that rural to urban migration in dominated by the
young people. The young have a higher probability to move because the returns
on human capital decline with the increase in age after a point. Moreover,
marriage is also one of the contributing factors to migration and marriages are
held in the young age. Further, after a certain age, people would like to settle
at one place. They may have attachment to the place either because they have
contracted they own houses or they have built up a network of friends and
relatively.
The rural-urban migration also varies across gender. If we exclude the
migration of females due to marriages, the probability of migration of males
would be relatively high. However, the pattern may vary across regions and
social groups. Another important factor in migration is size of household.
Mehta51 (1991) finds a positive relationship between size of family and rural to
urban migration. Big families make possible the diversification of occupation
and thus minimize the risk that may arise due to more people engaged in risky
51 Mehta G.S., (1991), Socio-Economic Aspects of Migration, Deep and Deep Publications, New Delhi.
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agricultural activities. Another demographic factor in the migration is rate of
population growth across regions. The reduction in the mortality rate and slow
decline in the fertility rate increase the population growth which, in turn,
would push more people from rural areas to urban areas. The varying degree of
population pressure and availability of resources causes the movement of
people from high population pressure areas to low pressure areas. Large scale
out-migration from rural areas of Bihar, UP and some other backward regions
to the urban areas of Maharashtra , Gujarat, Delhi, Punjab, Haryana, etc is the
result of high population-resource ratio in these areas.
5.4 EDUCATIONAL FACTORS
Education is one of the most significant factors affecting the rural to urban
migration. Education affects the rural to urban migration in two ways. First is
migration for education and second is education fro migration. Rural areas
most have primary and secondary educational facilities and that too of
relatively poor quality. In order to acquire higher professional education,
resourceful parents of rural areas send their children to urban areas for higher
education. Moreover, resource-poor households also aspire to send their
children to better educational institutions located in the urban areas.
Affirmative actions of the government also help the poor families in their
endeavour. The subsidize education for the SC/ST and other weaker sections of
rural societies and availability of scholarship to the students of these
communities also attract more students from rural areas to urban centres of
higher education.
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Educated and skilled workers have more probability to migrate from
rural to urban areas than the uneducated and unskilled ones. The current
education system does not much relate to the rural life and activities. For
instance, in most of the cases, the rural students do not get education and skills
at middle and secondary levels in rural schools that are required for agriculture
and other rural activities. While in urban areas, expending formal and informal
sectors provided relatively more employment opportunities to the educated and
skilled workers. Therefore, the educated and skilled workers tend to move
from rural areas to urban areas more than their uneducated and unskilled
counterparts.
5.5 NATURAL AND CLIMATIC FACTORS
Natural and climatic factors also affect the migration of people. The
environmental and climatic factors such as, temperature, rainfall, quality of
soil, availability of natural resources, natural disaster like foods, droughts,
cyclones, storms, earth quakes, famine, etc, also explain the rural to urban
migration. As water is essential for human life, scarcity of water compels the
farmers to leave their places for long periods to get alternative livelihood
options. The increase in number of frequent droughts is also one of the key
push factors in the rural to urban migration. Flood and other natural disaster
also displace the people in large number. Floods wash away many villages and
destroy crops and leave the rural people jobless and homeless who are forced to
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migrate to other places, especially in urban areas. Connell52 et.al. (1976) finds
that due to 1974 flood in Bangladesh, population of Dacca increased by 20% as
a result of migration of people from rural areas.
5.6 OTHER FACTORS
Rural to urban migration is a complex phenomenon. It can not be fully captured
by some factors. A number of explored and unexplored factors explain the
variation in the rural to urban migration. Apart from the above mentioned
factors. It is also influenced by political factors, such as political conflicts,
wars, insurgency, etc. For example, due the prolong conflicts and terrorist
activities in Jammu and Kashmir, a large number of Kashmiri families have
migrated to the other part of the countries, especially in cities, like Delhi.
Government policies related to urban and rural development also work
as a factor in the rural to urban migration. For example, Government of India
recently launched National Rural Employment Guarantee Scheme throughout
the country. This scheme provides guarantee of 100 days of unskilled
employment to each will rural household. The Government has been spending
over Rs.40000 crores on the scheme. Since the workers ensure 100 days of
employment in their village itself, they would be less inclined to move out the
village in research of employment. It has been observed that rural to urban
migration has been declined to some extent in those places where the scheme is 52 Connell, J, B. Dasgupta, R. Laishley, M. Lipton (1976), Migration from Rural Areas: The Evidence from Village Studies, Oxford University Press Delhi.
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being implemented effectively. Similarly, government policies to develop the
rural non-farm sector may likely to reduce the rural to urban migration. On the
other hand, new economic policies being initiated by the Government of India
since 1991 have encourage Foreign Direct Investment (FDI) and domestic
investment in emerging sectors which have created more employment
opportunities in formal and informal urban economy. This facilitated
movement of workforce from rural to urban areas. Moreover, during neo-
liberal policy regime, the development role of the government has weakened. It
may be pointed out that during this period public investment in agriculture has
remained stagnated or declined. During this period, due to policy neglect,
Indian agriculture has been going through the severe crisis. Farmers have
committed suicides in several part of the country, including Bundelkhand
region of Uttar Pradesh. Productivity and profitability in the farm sector
substantially declined (Singh, 2008). This reduces the demand for labour in
agriculture and thereby increased out-flow of rural workforce to urban areas.
In addition to the above, migration also affected by distance, cost of
migration, access to information, social capital of the potential migrants, etc.
Distance is inversely related with the migration, while access to information
affects the migration positively. Chatterjee53 and Kundu (1998) argue that cost
is a vital factor in migration. The cost of migration has two components. First
are the money costs which comprise expenditure on transport, food, shelter,
53 Chaterjee, B. and A. Kundu (1998), ‘Cost of Migration and Savings of Rural Labour in a Developing Economy’, The Indian Journal of Labour Economics, Vol. 41 (4), 784-94.
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cost of the inputs into the job search and search for accommodation. Second are
non-money costs which comprise psychic costs associated with personal and
family dislocation and disruption and opportunity costs which include the
earning forgone while traveling, search for and learning a new job. Further, the
influence of information on the migration decision is also relevant in rural to
urban migration. It is found that the migrants are more like to move to areas
about which they have better information.
5.7 RESULTS OF REGRESSION ANALYSIS
In order to study the impact of various determinates of rural-urban, regression
analysis is conducted. Initially, we identified 16 variables for the regression
analysis; however, some of the variables had to drop either because they did
not explain the dependent variable or they had the problem of multi-
collinearity. The functional form of the model and the number of variable are
given in chapter3. A poled regression analysis is conducted by pooling the
district-wise data on two data points (1991 and 2001). Thus, our analysis is
based on unbalance panel data collected from all districts of Uttar Pradesh.
Census 1991 consists of 54 districts, while Census 2001 comprises 70 districts.
D-series of population census provides detailed data on migrant people
and workers. These data are classified according to last residence as last
residence outside India, last residence elsewhere India, last residence within
the state of enumeration but outside the place of Enumeration, last residence
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elsewhere in the district of enumeration, last residence in other districts of the
state of enumeration and last residence in states of India beyond the state of
enumeration. In this study, we consider two categories of rural to urban
migration, namely last residence elsewhere in India and last residences
elsewhere in the district of enumeration. Last residence elsewhere in India
refers to the flow of rural people to urban area of district of enumeration from
any region of the country, including the district of enumeration while last
residence elsewhere in the district of enumeration includes migration of rural
people to urban areas of the district of enumeration only. Since, data on
independent variables are collected district-wise, it would be logical to consider
rural-urban migration rate based on the last residence elsewhere in the district
of enumeration for the regression analysis. For example, workers migrated
from rural area of Jhansi district to urban area of Ghaziabad district would not
affected by the values of independent variables of Ghaziabad district. The
following independent variables are finally identified as determinants of rural
urban migration.
1. Rural Literacy (RLIT): It is expected that a high literacy rate in the rural
areas would encourage people to migrant to the urban areas for getting better
employment opportunities. Literate people have more tendency to migrate to
urban areas not only for better livelihood but also to get higher education as
tertiary education facilities are not generally available in the rural areas. It is
therefore, hypothesize that literacy rate is one of the pull factors in the rural
urban migration of people and workers both.
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2. Length of Pucca Road per Lakh Population (PUCCA_R): Better road
infrastructure is expected to have a positive impact on the rural-urban
migration. It is one of important indicators of mobility of people from one
place to other. Therefore, we hypothesize that road density is positively
associated with rural-urban migration.
3. Net Sown Area per Rural Worker (NSA_RW): Rural livelihoods, among
others, depend on the availability of cultivated land. It is, therefore, expected
that if other things remain same, a decline in the net sown area per rural worker
would increase the migration of rural workforce to the urban area. We
hypothesize an inverse relationship between NSA_RW and rural –urban
migration.
4. Net Irrigated Area as percentage of Net Sown Area (NIA): Irrigation
facilities play significant role in creating additional employment opportunity in
agriculture. An increase in the net irrigated area raises the on-farm employment
via raising agricultural productivity, changing cropping pattern and increasing
cropping intensity. An expansion of irrigation facilities is likely to increase
off-farm employment also. Thus, this variable is expected to reduce the rural-
urban migration.
5. Cropping Intensity (CI): Cropping intensity is likely to have a negative
impact of the rural-urban migration. If other things remain the same, an
increase in cropping intensity would increase the labour absorption in
agriculture and consequently reduce the rural-urban migration, especially of
distress nature.
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6. Percentage of Rural Workforce in Household Industry (R_HHI):
Expansion of rural household manufacturing is likely to reduce migration of
rural workforce to the urban area as workers get employment opportunities in
the rural area itself.
7. Urbanization (URB): Urbanization may have both positive as well as
negative impact on rural-urban migration. Expansion of urban area creates
employment opportunities for the rural educated, skilled and semi-skilled
workers and thus can increase the migration of rural people to urban area.
However, urbanization may also reduce migration of rural workers to the urban
areas by two ways. First, it can create employment opportunities in rural non-
farm and farm activities through generating demand for rural products,
including agriculture. Second, as the urbanization increases, it increases the
cost of living and put more pressure on the carrying capacity of urban basic
infrastructure and amenities and thus discourages the rural to urban migration.
8. Dummy for Central and Bundelkhand Regions (D1= 1 for CR and BK, 0
otherwise): Uttar Pradesh is divided in four regions, as stated earlier. In our
regression analysis, we take dummy variable for Central and Bundelkhand
regions and expect that rural to urban migration rate is higher in these two
regions than the rest of the state.
The detail of variables is given in Appendix 5.A1. Before fitting the
regression model, a correlation matrix of dependent and independents variables
was prepared to analyse the extent of correlation between different variables
and to know the problem of multi-collinearity.
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5.7.1 Total Rural-Urban Migration Rate (RUMT)
In this, section, we conduct three regression analyses, taking total rural-
urban migration rate as a dependent variable and all the above stated
factors as independent variables (separately for person, male and
female). The results are sown in Table 5.1.
Table : 5.1 Results of Estimated Regression Coefficients for Rural - Urban
Total Migration (Total Migrants) DV: RUMT_P
Independent Variables Un-Standarized
Coefficients (B)
Standard Error (SE)
Standardized Coefficients
(β)
t-statistics
P-value
Intercept 12.124 2.623 4.621 .000 RLIT 0.005 0.020 0.019 0.232 .817 PUCCA_R 0.043* 0.017 0.264 2.606 .010 NSA_RW -2.548 1.810 -0.149 -1.408 .162 NIA 0.021 0.017 0.115 1.221 .225 R-HHI -0.196* 0.063 -0.218 -3.091 .003 CI -0.033** 0.015 -0.177 -2.204 .029 URB -0.145* 0.020 -0.568 -7.373 .000 D1=CRBK 2.593* 0.622 0.317 4.168 .000 R-2 0.546 F-Value 19.502* N 124 Notes: (1) * and ** Significant at 1 and 5 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
In the first regression equation (RUMT_P), dependent variable is
total rural to urban migration rate (person). The value of R-2 given in the
table indicates that 55% variations in rural-urban migration are
explained by the 8 explanatory variables. The F-value is also significant
at one per cent level implying that the systematic variation is
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considerably larger than should be explained by chance. The results
show that out of 8 variables, 5 variables turn out to be statistically
significant in causing variation in RUMT_P. The regression coefficient
for the variable representing length of pucca road per lakh population
(PUCCA_R), as expected, is found positively associated with the rural-
urban migration. The magnitude of coefficient implies that a one unit
increase in the PUCCA_R would increase the RUMT_P by 0.043 units.
The percentage of rural workforce engaged in rural household industries
(R_HHI) does have a negative impact on the rural-urban migration rate
(RUMT_P). Its coefficient is statistically significant at 1 percent level of
significance. The coefficient indicates that if R-HHI increases by one
unit, the RUMT-P would decline by 0.196 units. Cropping intensity is
found inversely associated with rural-urban migration. The magnitude of
coefficient indicates that a one unit increase in the cropping intensity
would reduce 0.033 units in the rural-urban migration. Urbanization also
turns out significant in causing variation in rural-urban migration. The
magnitude of its coefficient is -0.144 which indicates that if the
urbanization increases by one percent point, it would reduce the rural-
urban migration rate by 0.144 percent point.
In order to know whether intensity of rural-urban migration is
higher in CR and BK than the other two regions, we take dummy
variable (D1 = CRBK=1. 0 otherwise). Its value shows that intensity of
rural-urban migration is higher in CK and BK than rest of the State.
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Three variables, namely, RLIT, NIA, and NSA_RW do not have any
significant impact on the rural-urban migration.
Standardized coefficient (β) for each independent variable is also
estimated in order to identify the ranking of the individual variables in
terms of their contribution to causing variation in the dependent
variable. Since, different variables have different unit of measurement,
the magnitudes of un-standardized coefficients can not be considered for
ranking the contribution of the independent variables. In this regards, β-
coefficients are used. It is evident from the values of β-coefficients that
Urbanization explains the largest variation in the dependent variable. It
is followed by D1, PUCCA_R, R_HHI, and CI.
We also conducted regression analysis separately for male and
female rural migrants. The purpose is to know whether there exists any
significant difference in the role of the independent variables in causing
variation in rural-urban migration of male and female population. Table
5.2 shows the results for rural-urban migration of male population
(RUMT_M). The value of adjusted R square indicates that 41 percent
variations in the RUMT_M are explained by the explanatory variables
included in the regression model and rest is explained by the factors not
included in the equation. F-value is also quite high and statistically
significant at one percent level of significance. This shows the
appropriateness of our regression model. Looking at the individual
106
coefficients, we find that out of 8 explanatory variables, only 5 variables
turn out to be significant in explaining the dependent variable.
Comparing the results given in Table 5.2 to that given in Table 5.1, it is
observed that there is no much difference in the findings as far as the
relationship of explanatory variables with the explained variable is
concerned; however, magnitudes of coefficients vary across these two
regression equations, as is obvious from Tables 5.2 and 5.3.
Table : 5.2 Results of Estimated Regression Coefficients for Rural - Urban
Total Migration (Male Migrants) DV: RUMT_M Independent
Variables Un-
standardized Coefficients
(B)
Std. Error (SE)
Standardized Coefficients
(β)
t-statistics
P-value
Intercept 6.989* 2.084 '- 3.354 .001 RLIT -.016 .016 -.093 -.993 .323 PUCCA_R .041* .013 .364 3.152 .002 NSA_RW -1.913 1.437 -.161 -1.331 .186 NIA .019 .013 .154 1.432 .155 R-HHI -.178* .050 -.285 -3.540 .001 CI -.028** .012 -.211 -2.311 .023 URB -.053* .016 -.301 -3.429 .001 D1=CRBK 1.755* .494 .308 3.551 .001 R-2 0.410 F-Value 11.700* N 124 Notes: (1) * and ** Significant at 1 and 5 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
It is significant to know that in case of male migrant workers
(RUMT_M), it is the length of pucca road per lakh population which
explains the largest variation, as is evident from the value of
standardized coefficient given in Table 5.2. Next to PUCCA_R is D1,
followed by URB, R_HHI and CI.
107
Table 5.3 shows the results for rural-urban migration of female
population. It is evident from the Table that the independent variables
explain the rural-urban migration of female population better than that
of the male population. The magnitude of adjusted R square is higher
(0.526) in case of RUMT-F than in case of RUMT-M. As against 41
percent variation explained by the independent variables in RUMT-M,
the corresponding variation explained by the explanatory variables in
RUMT_F is 53 percent. The F-value is also observed much higher in
RUMT_F than in RUMT_M. Similarly, values of individual coefficients
are also found higher for RUMT-F than RUMT_M. Thus, length of
pucca road per lakh population, percentage of rural workforce engaged
in rural household industries, cropping intensity, urbanization, and D1
are the key determinants of migration of rural population to urban areas.
Net irrigated area as percent of new sown area and net sown area per
rural worker do not have any discernible impact on the migration of
rural population to the urban areas.
Values of β-coefficients show that in case of female migrants, it
is urbanization which explains the largest variation in RUMT_F. It is
followed by D1, PUCCA_R, R_HHI and CI. This indicates that the
contribution of various variables to causing variations in the rural
population to urban areas slightly varies across gender.
108
Table : 5.3 Results of Estimated Regression Coefficients for Rural - Urban
Total Migration (Female Migrants) DV: RUMT_F
Independent
Variables
Un-standardized Coefficients
(B)
Std. Error (SE)
Standardized Coefficients
(β)
t-
statistics
P-
value
Intercept 18.069* 4.081 4.427 .000 RLIT 0.025 0.031 0.069 0.822 .413 PUCCA_R 0.045*** 0.026 0.182 1.755 .082 NSA_RW -3.159 2.816 -0.122 -1.122 .264 NIA 0.022 0.026 0.079 .824 .412 R-HHI -0.214*** 0.099 -0.157 -2.174 .032 CI -0.041*** 0.024 -0.141 -1.720 .088 URB -0.246* 0.031 -0.633 -8.051 .000 D1=CRBK 3.547* 0.968 0.285 3.666 .000 R-2 0.526 F-Value 18.060* N 124 Notes: (1) *, ** and *** Significant at 1, 5 and 10 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
5.7.2 Rural-Urban Migration Rate of Workers (RUMW)
In the preceding section, we have examined the impact of explanatory
variables on rural-urban migration rates based on total rural migrants to
urban areas. Total migrants include both workers and non-workers. Non-
workers comprise housewives, children, students and old-aged people.
Some of the independent variables included in the regression equation
may not explain the migration of such people to the urban areas. For
example, variables such as NIA, NSA_RW, CI, may not explain the
mobility of non-workers from rural to urban areas while they could have
significant impact on mobility of rural workforce to urban area. In this
section, we consider only rural migrant workers, excluding the non-
workers. Rural-urban migration rate of workers is estimated by taking
109
rural to urban migrant workers as percentage of total urban workers
(RUMW). Here also, we have conducted three separately regression
analysis for person (RUMW_P), male (RUMW_M) and female
(RUMW_F). The results are shown in Tables 5.4, 5.5 and 5.6.
Table 5.4 presents the results related to total rural-urban
migration rate of total workers (RUMW_P). As is evident from the
table, all the 8 variables together explain about 60 percent variation in
RUMW_P. Magnitude of F-value is quite high and significant at one
percent level of significant, thus, indicating to the best-fit of regression
equation. It is relevant to note that all the explanatory variables, except
for NIA, turn out to be statistically significant to cause variation in
RUMW_P. Three variables, namely, RLIT, PUCCA_R and D1 do have
positive impact on the dependent variables. Rural Literacy (as proxy
variable for education), as stated earlier, is one of the pull factors in
rural-urban migration. Literate workers have more probability to get
employment opportunities in the emerging manufacturing and service
sectors in the urban areas. Therefore, if literacy rate among the rural
workforce increases, they would have more tendencies to move out of
the villages to get better employment in the urban areas. The value of
coefficient for RLIT indicates that a one percentage point increase in the
rural literacy would increase 0.057 percent point in rural-urban
migration rate. It may be noted here that RLIT does not have any
significant impact on the total rural migration rate (RUMT_P) but it has
positive impact on the rural-urban migration rate of total workers
110
(RUMW_P). Length of pucca road per lakh population (PUCCA_R) is
also found to have positive impact on the rural-urban migration of
workers. Magnitude of its coefficient shows that a one unit change in
this variable would make a 0.099 unit increase in the dependent
variables. Variable D1, which represents CR and BK, indicates that the
intensity of rural to urban migration of workers is higher in these regions
as, compared to other regions.
Four variables, namely, NSA_RW, URB, CI and R_HHI are
found inversely related to the RUMW_P. Availability of cultivated land
in rural area is one of the significant factors to absorb rural workforce.
In the traditional labour intensive farming system, availability of more
land for cultivation would induce to have greater demand for labour on
farms. This means that as net sown area per rural worker decreases it
would increase the migration of workers from rural to urban areas. Our
result of regression analysis highlights that the coefficient of variable
NSA_RW has significant negative value. The value of its coefficient is -
5.03 which manifests that a one unit increase in this variable tends to
reduce RUMW_P by 5.03 units. Thus, decline in per worker NSA
appears to be the significant ‘push’ factor in rural urban migration of
workers. Expansion of irrigation facilities raises the employment
opportunities in rural area via raising agricultural productivity. The
increased use of complementary inputs such as fertilizer, pesticides and
HYVs further enhances labour requirements on irrigated farms. When a
piece of land is brought under irrigation, both ‘off-farm’ and ‘on-farm’
111
employment increases. Therefore, lack of irrigation facilities seems to be
a ‘push’ factor in rural urban migration. However, value of coefficient
for variable NIA does not confirm this because it is found statistically
insignificant. Cropping intensity turns out to have statistically
significant negative impact on RUMW_P. The magnitude of its
coefficient indicates that a one percentage point increase in the cropping
intensity would tend to reduce RUMW_P by 0.048 percent point.
Table : 5.4 Results of Estimated Regression Coefficients for Rural - Urban of workers
(Total Workers) DV: RUMW_P Independent
Variables
Un-standardized Coefficients
(B)
Std. Error (SE)
Standardized Coefficients
(β)
t-
statistics
P-
value
Intercept 11.214 3.200 - 3.505 .001 RLIT 0.057* 0.024 0.180 2.328 .022 PUCCA_R 0.099* 0.020 0.467 4.903 .000 NSA_RW -5.030** 2.207 -0.228 -2.278 .025 NIA 0.007 0.021 0.032 .362 .718 R-HHI -0.263* 0.077 -0.227 -3.411 .001 CI -0.048** 0.018 -0.196 -2.599 .011 URB -0.129* 0.024 -0.389 -5.377 .000 D1=CRBK 2.825* .759 0.267 3.724 .000 R-2 0.598 F-Value 23.876 N 124 Notes: (1) * and ** Significant at 1 and 5 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
Expansion of rural household industries (R_HHI) also reduces
out-flow of rural workforce to urban area. The value of coefficient for
R-HHI indicates that a one percent point increase in R-HHI would
decline the RUMW_P by 0.263 percent point. Urbanization emerges one
of the most significant factors in discourages the rural to urban
112
migration. Table 5.4 also shows the standardized coefficients for the
independent variables. On the basis of values of these coefficients, we
can rank their contribution to cause variation in RUMW_P. As is
evident from values of standardized coefficients, PUCCA_R has the
largest contribution to causing variation in the dependent variable. It is
followed by urbanization, D1, NSA_RW, R-HHI, CI and RLIT.
Table 5.5 shows the impact of various independent variables on
rural-urban migration rate of male workers (RUMW_M). Value of R-2
indicates that about 48 percent variations in the RUMW_M are
explained by the explanatory variables included in the regression model.
F-value is also statistically significant and implies that the systematic
variation is considerably larger than should be explained by chance. As
far as, the contribution of individual factors in causing variations in the
RUMW_M is concerned, we observe that five out of total eight
variables turn out to be significant in explaining the dependent variables.
Rural literacy, NSA_RW and NIA do not have any perceptible impact
on the RUMW_M. Expansion of Pucca road in the state facilitates the
movement of rural workforce to urban area. Cropping intensity and R-
HHI do have negative impact on the dependent variables, while the
dummy variable (D1), representing CR and BK, has the positive impact.
Values of standardized coefficients indicate that PUCC A_R stands first
by having the largest contribution to the total variation in the
RUMW_M. It is followed by D1, URB, R_HHI and CI.
113
Table : 5.5 Results of Estimated Regression Coefficients for Rural - Urban of workers
(Male Workers) DV: RUMW_M
Independent
Variables
Un-standardized Coefficients
(B)
Std. Error (SE)
Standardized Coefficients
(β)
t-
statistics
P-
value
Intercept 9.282 2.973 - 3.122 .002 RLIT 0.004 0.023 0.014 0.164 .870 PUCCA_R 0.072* 0.019 0.418 3.846 .000 NSA_RW -3.158 2.051 -0.175 -1.539 .126 NIA 0.029 0.019 0.153 1.517 .132 R-HHI -0.264* 0.072 -0.279 -3.676 .000 CI -0.045** 0.017 -0.224 -2.596 .011 URB -0.084* 0.022 -0.312 -3.775 .000 D1=CRBK 2.706* 0.705 0.313 3.838 .000 R-2 0.478 F-Value 15.062* N 124 Notes: (1) * and ** Significant at 1 and 5 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
Table 5.6 presents the results for RUMW_F. It is evident from
the table that 48.3 percent variations in the rural to urban migration of
female workers are explained by the independent variables included in
the regression model. Further, it is also found that F-value, which is
used to make joint hypothesis testing about the model appropriateness,
is quite high and statistically significant. In this model, Rural literacy,
pucca road, NSA_RW, NIA and URB are found to have statistically
significant impact on the rural to urban migration of female workers
whereas, R_HHI, cropping intensity and D1 do not have any significant
impact on the dependent variable. Rural literacy and Pucca road do have
positive impact on RUMW_F, while NSA_RW, NIA and URB are
114
found inversely related with the dependent variable. Magnitudes of β-
coefficients show that urbanization ranks first by explaining largest
variation in the RUMW_F, followed by rural literacy, pucca road,
NSA_RW and NIA.
It is interesting to note that there exist some differences in
explaining the variations in the rural-urban migration rates for male and
female workers. For example, rural literacy does not have any impact on
RUMW_M, while it has significant impact on the RUMW_F. Similarly,
intensity of migration of rural male workers is higher in CR and BK
than the other regions, while in case of female migrant workers, there is
no any perceptible difference across regions. Further, NIA and
NSA_RW do not have any impact on RUMW_M, while these variables
have some impact on the RUMW_F. Cropping intensity turns out to
have significant negative impact on RUMW_M but it does not have any
impact on the RUMW_F. In terms of ranking of contribution of
explanatory variables also, we notice variations across gender. In case of
RUMW-M, Pucca raod occupies the first rank, followed by D1, URB,
R_HHI and CI, while in case of RUMW_F, it is URB which has the first
rank. It is followed by rural literacy, pucca road, NSA_RW and NIA.
115
Table : 5.6
Results of Estimated Regression Coefficients for Rural - Urban of workers (Female workers)
DV: RUMW_F
Independent
Variables
Un-standardized Coefficients
(B)
Std. Error (SE)
Standardized Coefficients
(β)
t-
statistics
P-
value
Intercept 34.537** 15.634 - 2.209 .029 RLIT 0.551* 0.119 0.407 4.648 .000 PUCCA_R 0.265* 0.098 0.291 2.696 .008 NSA_RW -18.275*** 10.786 -0.192 -1.694 .093 NIA -0.169*** 0.101 -0.168 -1.674 .097 R-HHI -0.467 0.377 -0.093 -1.237 .218 CI -0.085 0.090 -0.080 -.939 .350 URB -0.616* 0.117 -0.433 -5.276 .000 D1=CRBK 3.742 3.707 0.082 1.009 .315 R-2 0.483 F-Value 15.365* N 124 Notes: (1) * and ** Significant at 1 and 5 percent level of significance respectively. (2) Figures in parentheses are t-statistics.
5.8 SUMMING UP
This chapter examines various factors that affect the rural to urban migration.
We have discussed various socio-economic, demographic, natural and climatic
factors that explain the variation in rural to urban migration. The key
determinants are identified thorough regression analysis. The analysis is based
on data collected from 1991 and 2001 population Censuses, with corresponding
district-wise data from Statistical Abstract of the State Government. All the
districts of the State are covered by the study. The entire state is divided into
four regions, namely, WR, CR, BK and ER. Initially 16 variables were selected
for the regression analysis; however, after doing some statistically exercises for
116
model building, including removal of problem of multi-collinearity in some
variables, we finally select eight explanatory variables for the analysis.
The empirical results indicate that eight variables together explain 41-55
percent variation in total rural-urban migration rates (RUMT_P, RUMT_M and
RUMT_F) and 48-60 percent variation in the rural-urban migration rates of
workers (RUMW_P, RUMW_M and RUMW_F). The estimated F-values are
found significant at 1 per cent level in all the regression models, implying that
the systematic variation is considerably larger than should be explained by
chance. The findings of regression analysis show that in case of total rural to
urban migration rate (RUMT_P) five out of 8 variables turn out to be
statistically significant in causing variation the rural to urban migration of
people. Length of pucca road and D1 are found to have positive impact on
rural-urban migration, while R_HHI, CI and URB do have negative impact on
RUMT_P. Standardized coefficients (βs) show that urbanization explains the
largest variation in the dependent variable, followed by D1, PUCCA_R,
R_HHI, and CI. It is evident from the findings that the explanatory variables
explain the rural-urban migration of female population better than that of the
male population. The magnitude of adjusted R square is found higher (0.526) in
case of RUMT-F than in case of RUMT-M. The F-value is also observed much
higher in RUMT_F than in RUMT_M. Similarly, values of individual
coefficients are also found higher for RUMT-F than RUMT_M.
117
We have also examined the impact of key determinants on the rural-
urban migration of workers (RUMW_P). The empirical results show that
RUMW_P is better explained by the explanatory variables when compared to
RUMT_P. It is evident from the magnitudes of regression coefficients that all
the explanatory variables, except for NIA, turn out to be statistically significant
to causing variation in the RUMW_P. Three variables, namely, RLIT,
PUCCA_R and D1 do have positive impact on the dependent variables, while
four variables, namely NSA_RW, URB, CI and R_HHI are found inversely
related to the RUMW_P. Values of standardized coefficients indicate that
length of pucca road ranks first in terms of its contribution to the RUMW_P,
followed by urbanization, D1, NSA_RW, R-HHI, CI and RLIT. The empirical
results also reveal that the contribution of explanatory variables varies across
gender. For example, RLIT does not have any impact on RUMW_M, while it
has significant impact on the RUMW_F. Similarly, D1 is statistically
significant for RUMW_M, but insignificant for RUMW_F.
118
Appendix 5A
Table 5A.1: Details of Dependent Variables District
RUMT_P
RUMT_M
RUMT_F
RUMW_P
RUMW_P
RUMW_P
1991 census Saharanpur 7.21 5.75 8.88 6.77 6.71 8.14 Muzaffarnagar 10.13 6.49 14.25 10.09 9.17 24.83 Bijnor 6.80 2.10 12.08 3.45 2.86 18.36 Moradabad 2.95 1.51 4.59 2.16 1.96 5.46 Rampur 2.12 1.35 2.98 1.76 1.62 3.97 Meerut 5.77 3.45 8.43 4.88 4.61 9.39 Ghaziabad 3.22 2.48 4.11 2.82 2.80 3.09 Bulandshahr 4.29 2.33 6.52 3.28 3.03 8.48 Aligarh 6.67 3.23 10.64 5.11 4.63 13.20 Agra 2.97 1.68 4.47 2.28 2.16 4.61 Mathura 7.13 3.46 11.40 5.98 5.40 15.61 Firozabad 6.70 5.37 8.24 7.57 7.36 11.99 Etah 9.16 6.13 12.64 8.75 8.33 17.69 Mainpuri 9.70 7.99 11.64 11.02 10.58 22.02 Budaun 8.45 4.32 13.16 5.78 5.38 16.26 Bareilly 4.65 2.49 7.12 3.36 3.26 5.72 Pilibhit 5.22 2.76 8.06 3.81 3.55 9.49 Shahjahanpur 5.71 3.52 8.21 4.05 4.03 4.44 Farrukabad 4.03 1.90 6.46 3.06 2.50 8.29 Etawah 5.56 2.63 8.93 3.91 3.53 11.47 Kheri 4.30 1.01 8.11 1.85 1.56 8.39 Sitapur 8.58 5.14 12.57 6.86 6.54 12.34 Hardoi 8.01 2.64 14.19 4.29 3.70 15.38 Unnao 15.05 10.44 20.28 14.86 14.11 27.11 Lucknow 1.58 0.90 2.36 1.38 1.23 3.03 Raebareli 11.03 7.83 14.67 11.88 10.66 28.02 Kanpur dehat 13.14 8.87 18.10 13.46 12.05 37.10 Kanpur nagar 0.91 0.63 1.25 0.92 0.88 1.63 Fetahpur 19.02 12.03 27.01 18.75 16.14 50.26 Barabanki 6.50 1.13 12.55 3.01 1.65 15.04 Jalaun 9.70 7.02 12.85 8.86 8.75 10.70 Jhansi 5.80 2.89 9.10 5.58 4.14 18.06 Lalitpur 11.27 8.74 14.11 14.14 13.34 20.94 Hamirpur 11.49 5.95 17.99 10.82 8.16 32.93 Banda 12.62 8.36 17.75 13.16 11.06 31.65 Pratapgarh 11.30 5.31 18.10 9.38 7.08 33.62 Allahabad 3.45 1.62 5.67 2.82 2.10 10.56 Faizabad 4.69 1.52 8.40 2.78 2.32 8.67 Sultanpur 10.89 8.72 13.44 11.50 10.63 21.66 Bairaich 4.96 2.60 7.65 3.98 3.48 12.25 Gonda 8.18 4.66 12.25 7.02 6.16 19.62 Siddharhnagar 8.45 3.98 13.53 6.84 5.22 34.21 Maharajganj 9.09 5.14 13.57 7.83 6.42 23.36 Basti 4.95 2.30 8.03 4.04 3.33 11.57 Gorakhpur 4.60 2.61 6.92 4.23 3.86 9.03
119
Deoria 12.84 7.79 18.54 13.21 11.30 38.45 Mau 2.89 1.31 4.59 2.49 1.88 4.93 Azamgarh 6.85 2.52 11.60 4.61 3.39 13.43 Jaunpur 5.28 1.30 9.70 2.50 1.75 11.52 Balia 12.72 3.12 23.35 7.58 4.43 40.78 Ghazipur 8.80 2.70 15.62 6.26 4.13 28.85 Varanasi 2.53 0.86 4.48 1.76 1.23 7.60 Mirzapur 7.11 1.95 13.07 4.57 2.80 23.53 Sonbhadra 6.70 6.12 7.48 6.35 6.02 13.71 2001 Census Saharanpur 4.68 2.56 7.12 3.99 3.31 15.51 Muzaffarnagar 8.04 3.84 12.80 8.66 6.01 44.68 Bijnor 6.83 1.97 12.22 4.57 3.03 33.90 Moradabad 3.26 1.61 5.11 3.16 2.45 13.18 Rampur 2.32 1.01 3.77 1.68 1.34 5.20 Jyotiba Phule Nagar 4.77 2.66 7.11 4.42 3.98 7.46 Meerut 3.60 2.23 5.16 4.51 3.79 13.93 Baghpat 10.95 6.38 16.22 13.68 9.58 68.67 Ghaziabad 4.36 3.41 5.46 5.59 5.31 8.91 Gautam Buddha Nagar 3.02 2.46 3.70 2.62 2.57 2.91 Bulandshahr 8.13 3.23 13.66 8.51 4.88 41.73 Aligarh 3.88 2.32 5.66 5.22 4.26 15.98 Hathras 3.42 1.87 5.18 3.54 2.84 12.56 Mathura 5.89 3.38 8.85 7.20 4.79 34.51 Agra 2.09 1.18 3.16 2.62 2.30 6.43 Firozabad 4.89 3.73 6.21 6.55 6.08 11.12 Etah 7.38 4.07 11.11 7.74 6.57 22.52 Mainpuri 9.01 6.66 11.65 11.64 10.74 23.85 Budaun 6.94 2.87 11.51 6.19 4.74 29.01 Bareilly 3.62 1.82 5.65 3.72 2.87 14.32 Pilibhit 5.00 2.65 7.65 6.05 5.27 16.62 Shahjahanpur 3.65 2.00 5.56 3.96 3.47 10.42 Etawah 6.81 5.22 8.62 8.31 7.90 12.84 Auraiya 10.57 6.45 15.22 12.16 9.89 40.85 Farrukhabad 3.29 1.62 5.20 3.50 2.87 9.56 Kannauj 7.23 3.93 10.91 11.96 7.06 49.23 Kheri 8.82 5.37 12.77 9.36 8.73 18.01 Sitapur 8.39 4.60 12.59 9.22 7.11 32.96 Hardoi 8.53 3.69 14.01 7.50 5.36 33.47 Unnao 13.45 8.43 19.05 17.74 14.08 60.50 Lucknow 0.84 0.64 1.08 1.24 1.03 2.91 Rae Bareli 8.04 5.02 11.35 10.99 8.84 30.05 Kanpur Dehat 17.80 12.60 23.79 24.31 21.64 55.96 Kanpur Nagar 0.40 0.32 0.49 0.52 0.48 0.95 Fatehpur 9.94 4.45 16.04 11.70 7.20 54.63 Barabanki 5.90 2.07 10.16 6.15 1.97 34.36 Jalaun 11.75 5.93 18.46 13.29 10.21 46.83 Jhansi 5.26 2.80 8.09 6.82 3.96 27.56 Lalitpur 11.67 7.59 16.21 17.52 13.08 47.41 Hamirpur 12.68 7.56 18.65 19.41 12.31 87.95 Mahoba 7.71 3.81 12.15 11.55 6.12 55.88 Banda 13.90 8.64 20.06 19.13 14.46 57.43
120
Chitrakoot 10.19 5.88 15.12 15.75 11.67 57.85 Pratapgarh 10.11 3.42 17.36 12.26 6.67 55.69 Kaushambi 12.43 2.41 23.55 18.06 4.40 108.57 Allahabad 2.34 1.49 3.37 3.62 2.40 13.01 Faizabad 3.16 0.85 5.88 3.75 1.43 25.16 Ambedkar Nagar 6.88 1.73 12.44 5.03 1.97 38.03 Sultanpur 9.09 5.39 13.24 13.77 9.96 48.42 Bahraich 2.11 0.72 3.67 2.46 1.21 19.01 Shrawasti 7.86 1.98 14.36 4.62 2.56 35.04 Balrampur 6.47 2.30 11.11 7.19 3.92 43.93 Gonda 5.42 3.01 8.26 5.62 4.62 16.24 Siddharthnagar 10.02 3.15 17.58 9.85 5.65 70.99 Basti 6.83 4.07 9.94 7.36 5.78 20.73 Sant Kabir Nagar 8.41 2.50 14.92 8.41 3.44 54.09 Maharajganj 9.95 3.36 17.17 11.19 6.29 61.56 Gorakhpur 4.23 2.24 6.47 4.57 3.42 17.45 Kushinagar 9.96 3.47 17.14 8.13 4.74 54.10 Deoria 11.74 4.27 19.85 10.41 6.08 56.68 Azamgarh 7.50 2.11 13.28 7.40 3.25 31.63 Mau 3.64 1.21 6.24 4.70 1.58 13.30 Ballia 11.47 1.68 22.28 10.91 3.27 75.72 Jaunpur 5.71 1.81 9.98 5.76 2.34 33.18 Ghazipur 8.06 2.14 14.57 7.76 3.40 44.52 Chandauli 9.58 4.39 15.42 11.67 8.11 52.12 Varanasi 1.61 0.83 2.51 1.97 1.31 7.48 Sant Ravidas Nagar Bhadohi 4.23 0.50 8.46 3.33 0.88 30.65 Mirzapur 4.00 0.94 7.49 4.15 1.68 26.64 Sonbhadra 5.59 4.95 6.38 7.99 7.30 19.85
Note: Migration rates are based on last residence elsewhere in the district of enumeration
121
Table A5.2: Details of Independent Variables
District
RLIT
PUCCA_R
NSA _RW
NIA
R-HHI
CI
URB
D1
1991 Census Saharanpur 28.3 48.20 0.54 84.20 1.55 160.00 25.54 0.00 MuzaffarNagar 32.3 45.63 0.49 94.10 1.54 155.60 24.60 0.00 Bijnor 30.24 59.72 0.66 64.60 3.73 128.50 25.07 0.00 Moradabad 18.9 37.92 0.57 62.90 1.74 150.70 27.65 0.00 Rampur 14.76 49.97 0.58 68.90 0.90 167.30 26.14 0.00 Meerut 36.91 34.61 0.49 91.80 1.86 158.40 37.02 0.00 Ghaziabad 38.06 36.58 0.47 98.10 0.84 164.70 46.16 0.00 Bulandshahr 33.72 48.36 0.57 89.90 1.28 175.10 20.80 0.00 Aligarh 32.45 39.95 0.57 95.90 2.85 163.60 25.14 0.00 Agra 31.75 53.58 0.63 72.60 3.29 131.60 40.39 0.00 Mathura 28.65 77.08 0.74 90.50 1.65 138.30 23.57 0.00 Firozabad 33.21 56.33 0.58 85.60 0.48 139.70 26.58 0.00 Etah 29.3 61.58 0.63 85.10 1.62 163.20 16.72 0.00 Mainpuri 38.01 63.23 0.58 94.80 0.34 166.50 13.21 0.00 Budaun 16.36 39.57 0.57 71.80 0.51 149.10 17.61 0.00 Bareilly 19.31 38.08 0.55 62.20 1.51 151.20 32.79 0.00 Pilibhit 21.79 55.50 0.71 75.40 0.36 165.10 18.46 0.00 Shahjahanpur 21.77 44.11 0.69 66.60 0.56 150.20 20.76 0.00 Farrukabad 35.73 45.05 0.58 74.30 1.43 146.10 18.63 0.00 Etawah 41.03 59.22 0.49 74.70 0.49 146.80 15.71 0.00 Kheri 21.28 41.59 0.68 32.40 0.54 137.00 10.66 1.00 Sitapur 22.53 39.02 0.52 34.80 1.62 130.60 12.03 1.00 Hardoi 27.24 43.47 0.51 65.50 1.17 146.80 11.74 1.00 Unnao 28.86 50.02 0.48 65.70 1.60 149.20 13.60 1.00 Lucknow 28.25 41.25 0.46 74.80 0.56 135.50 62.66 1.00 Raebareli 28.41 66.35 0.41 73.20 1.23 149.80 9.04 1.00 Kanpur Dehat 40.38 58.86 0.61 57.20 2.57 135.60 5.71 1.00 Kanpur Nagar 39.83 22.63 0.52 72.80 0.06 144.50 84.24 1.00 Fetahpur 34.32 52.70 0.38 53.00 3.36 134.00 9.90 1.00 Barabanki 23.11 42.46 0.53 70.60 2.88 165.10 9.28 1.00 Jalaun 37.7 84.38 1.20 30.50 0.68 107.00 22.08 1.00 Jhansi 33.04 68.01 1.10 35.40 3.12 115.90 39.61 1.00 Lalitpur 20.65 97.51 1.00 49.10 0.66 125.70 14.03 1.00 Hamirpur 28.68 78.25 1.26 24.30 1.20 105.70 17.36 1.00 Banda 25.34 67.18 0.86 20.00 1.36 117.40 12.86 1.00 Pratapgarh 31 56.69 0.36 68.80 0.87 154.10 5.52 0.00 Allahabad 27.23 51.57 0.37 57.50 5.18 142.20 20.77 0.00 faizabad 29.91 38.54 0.37 70.30 1.36 163.10 11.66 0.00 Sultanpur 20.66 58.82 0.39 60.30 1.49 152.80 4.46 0.00 Bairaich 17.54 37.29 0.54 27.50 0.34 157.80 7.85 0.00 Gonda 19.71 35.47 0.45 40.20 0.84 154.00 7.41 0.00 Siddharhnagar 21.59 40.42 0.46 55.90 0.64 151.70 3.48 0.00 Maharajganj 26.79 41.74 0.37 63.30 0.54 170.90 4.95 0.00 Basti 29.4 36.02 0.42 67.70 2.06 151.70 6.42 0.00 Gorakhpur 27.64 25.76 0.38 71.80 0.76 142.10 18.76 0.00 Deoria 27.64 32.43 0.37 62.10 1.51 155.60 7.35 0.00 Mau 29.5 37.65 0.38 78.00 2.95 171.20 16.88 0.00
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Azamgarh 31.6 42.26 0.40 77.90 5.99 160.90 7.16 0.00 jaunpur 31.83 46.54 0.38 74.50 2.18 158.20 6.89 0.00 Balia 33.43 45.32 0.40 63.80 0.67 153.30 9.91 0.00 Ghazipur 32.53 55.23 0.43 63.50 0.61 149.00 7.38 0.00 Varanasi 32.51 49.18 0.30 77.90 26.66 157.10 27.20 0.00 Mirzapur 28.28 63.58 0.44 55.80 12.02 141.00 13.80 0.00 Sonbhadra 21.7 62.92 0.49 25.80 0.74 141.60 13.40 0.00 2001 Census Saharanpur 58.80 57.03 0.53 90.84 3.74 157.73 25.81 0.00 Muzaffarnagar 58.80 57.32 0.48 98.47 3.07 151.70 25.51 0.00 Bijnor 57.00 67.92 0.63 81.71 5.70 134.87 24.31 0.00 Moradabad 39.20 54.29 0.45 74.29 3.76 167.98 30.54 0.00 Rampur 34.00 55.10 0.56 94.27 2.87 183.69 24.97 0.00 Jyotiba Phule Nagar 47.90 59.97 0.60 98.25 3.29 156.95 24.56 0.00 Meerut 62.70 34.93 0.52 96.04 3.91 156.25 48.44 0.00 Baghpat 63.30 59.45 0.47 97.27 4.27 157.97 19.71 0.00 Ghaziabad 63.10 41.88 0.40 100.00 4.66 159.38 55.20 0.00 Gautam Buddha Nagar 64.90 53.24 0.79 79.29 3.31 119.20 37.39 0.00 Bulandshahr 58.00 52.38 0.46 86.41 3.71 171.31 23.15 0.00 Aligarh 72.20 60.52 0.59 99.33 4.36 169.14 28.90 0.00 Hathras 61.80 86.23 0.61 98.63 4.79 164.44 19.80 0.00 Mathura 57.70 77.95 0.68 98.14 2.91 158.36 28.30 0.00 Agra 57.30 57.45 0.62 80.97 3.85 142.97 43.30 0.00 Firozabad 63.40 72.09 0.56 94.32 4.78 158.57 30.32 0.00 Etah 52.60 69.88 0.58 87.93 2.41 158.27 17.33 0.00 Mainpuri 63.50 85.86 0.59 94.09 2.25 163.84 14.60 0.00 Budaun 34.70 54.44 0.64 84.78 1.85 162.13 18.15 0.00 Bareilly 42.00 51.07 0.56 76.36 2.56 159.69 32.93 0.00 Pilibhit 47.40 68.93 0.75 88.34 2.65 165.17 17.88 0.00 Shahjahanpur 46.60 52.04 0.73 79.12 2.43 162.55 20.63 0.00 Etawah 67.40 96.72 0.64 78.23 2.61 160.21 23.01 0.00 Auraiya 68.50 62.12 0.58 93.10 2.11 159.87 14.32 0.00 Farrukhabad 58.20 49.54 0.55 77.71 3.37 139.61 21.75 0.00 Kannauj 61.00 70.92 0.48 85.11 7.37 158.55 16.70 0.00 Kheri 46.00 56.84 0.64 71.99 2.86 145.93 10.77 1.00 Sitapur 45.70 53.38 0.52 53.60 3.59 144.20 11.95 1.00 Hardoi 49.90 62.47 0.53 79.21 2.56 152.80 11.99 1.00 Unnao 51.90 73.07 0.50 87.96 3.08 146.79 15.24 1.00 Lucknow 53.90 38.79 0.43 87.32 3.78 157.35 63.63 1.00 Rae Bareli 51.70 64.44 0.49 83.90 3.78 143.25 9.54 1.00 Kanpur Dehat 65.80 150.32 0.61 68.84 2.76 134.43 6.89 1.00 Kanpur Nagar 34 65.70 40.31 0.57 73.71 3.53 153.66 67.12 1.00 Fatehpur 54.60 67.58 0.54 63.14 3.13 140.25 10.30 1.00 Barabanki 45.90 78.28 0.38 83.79 5.08 174.29 9.30 1.00 Jalaun 62.20 122.73 1.23 42.90 2.86 112.88 23.41 1.00 Jhansi 57.50 81.84 1.12 54.85 3.41 126.71 40.79 1.00 Lalitpur 44.80 123.86 1.00 69.57 2.38 134.15 14.52 1.00 Hamirpur 54.40 117.85 1.41 26.77 2.65 110.55 16.65 1.00 Mahoba 49.40 165.43 1.31 41.55 2.82 114.39 21.86 1.00 Banda 50.80 81.50 0.95 33.82 2.84 128.58 15.87 1.00 Chitrakoot 63.60 84.18 0.84 24.57 2.25 111.61 9.99 1.00 Pratapgarh 56.60 83.15 0.40 80.09 5.76 153.19 5.29 0.00
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Kaushambi 45.80 88.70 0.41 57.60 4.87 134.66 7.10 0.00 Allahabad 56.00 46.17 0.44 71.27 9.70 157.31 24.45 0.00 Faizabad 53.30 59.07 0.38 84.39 3.36 153.99 13.46 0.00 Ambedkar Nagar 57.00 45.49 0.42 98.21 4.41 165.58 8.93 0.00 Sultanpur 54.60 71.64 0.45 75.35 6.68 53.48 4.74 0.00 Bahraich 31.70 37.97 0.49 31.21 2.04 151.79 10.00 0.00 Shrawasti 33.10 52.96 0.51 58.82 1.98 225.51 2.84 0.00 Balrampur 32.00 53.79 0.43 43.78 1.90 165.45 8.06 0.00 Gonda 40.20 45.34 0.43 67.02 2.11 158.89 7.03 0.00 Siddharthnagar 41.20 50.39 0.52 56.05 2.28 135.42 3.81 0.00 Basti 50.90 43.36 0.45 53.37 3.48 140.42 5.56 0.00 Sant Kabir Nagar 49.70 46.82 0.48 72.31 3.75 150.91 7.08 0.00 Maharajganj 45.20 51.24 0.45 72.55 3.22 175.37 5.09 0.00 Gorakhpur 53.70 51.63 0.48 77.48 3.86 146.98 19.59 0.00 Kushinagar 45.80 44.52 0.41 68.16 3.75 141.90 4.58 0.00 Deoria 56.90 58.61 0.48 78.00 3.92 157.50 9.89 0.00 Azamgarh 55.70 57.36 0.43 89.77 7.51 166.73 7.55 0.00 Mau 60.00 48.92 0.43 89.15 8.82 164.67 19.44 0.00 Ballia 56.70 59.28 0.49 74.09 4.69 158.72 9.77 0.00 Jaunpur 58.70 56.78 0.40 78.35 7.40 153.15 7.40 0.00 Ghazipur 58.30 65.97 0.44 80.61 5.38 153.50 7.68 0.00 Chandauli 57.80 77.04 0.46 87.77 8.57 165.68 10.56 0.00 Varanasi 61.90 43.62 0.26 82.61 19.34 148.98 40.16 0.00 Sant Ravidas Nagar Bhadohi 56.50 66.85 0.30 78.87 27.51 147.73 12.82 0.00 Mirzapur 53.00 71.22 0.50 60.55 11.10 144.76 13.54 0.00 Sonbhadra 40.70 73.79 0.65 26.94 3.11 139.13 18.82 0.00
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Table A5.3 Zero Order Correlation Matrix of Independent Variables
Variables RLIT PUCCA_R NSA_RW NIA R_HHI CI URB D1 RLIT 1.000 PUCCA_R 0.378 1.000 NSA_RW 0.054 0.621 1.000 NIA 0.405 -0.178 -0.427 1.000 R-HHI 0.340 0.055 -0.269 0.180 1.000 CI -0.036 -0.384 -0.513 0.505 0.016 1.000 URB 0.167 -0.173 0.137 0.265 0.024 -0.033 1.000 D1 -0.012 0.357 0.484 -0.427 -0.168 -0.441 0.105 1.000
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