Abstract
Given urban areas contribute to more than 65 % of India’s gross domestic product (GDP), the sustainability of the rapid 8–9 % GDP growth India has experienced in the last decade, is dependent to a large extent on urban areas. With migration being one of the important factors contributing to the growth of urban population, we attempt to understand whether it is push (out of the rural area) or pull (toward the urban area due to its perceived benefits) which explains migration in India, taking the case of Bangalore, which has one of the largest proportions of in-migrants to total population. An examination of these factors is done using a primary survey of migrants in Bangalore. Using a probit model, we find that the lower the level of education of the migrant, the greater the importance of the push factors whereas with increasing level of education of the migrant, pull factors become more important in migration. Women are more likely to be “pulled” toward urban areas. We find migrants from within Karnataka are “pushed”. This suggests that nonfarm employment opportunities have to be increased, rural infrastructure improved, and the development of small and medium towns encouraged.
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Notes
The Todaro (1969) model and the Harris and Todaro (1970) models are variants of the same model explaining rural–urban migration. The Todaro model explains rural–urban migration as a function of the income differential adjusted for the probability of finding a job. The Harris–Todaro model attempts to explain the phenomenon of accelerating rural–urban labor migration despite the existence of positive marginal products in agriculture and significant levels of urban unemployment.
Circular migration in an urban context is a form of migration by which migrants move to the city for a few months and then return to the village when they can be most useful there. It is often part of a larger household strategy that seeks to diversify income streams and maximize consumption (http://en.wikipedia.org/wiki/Circular_migration).
There are significant disparities in India across the southern and northern states (see Paul and Sridhar 2009; Sridhar and Venugopala Reddy 2011). For instance, states such as Uttar Pradesh (UP) are significantly poorer than Karnataka as may be seen in the fact that UP’s per capita net state domestic product was only Rs.6,138 when compared with Rs.13,820 for Karnataka (in 1993–1994 prices) as of 2004 (based on data from Economic and Political Weekly Research Foundation).
When we tried an error of only 5 % instead of the 10 % we actually decided to go with, the sample size turned out to be 2,585, which was well beyond our budget.
The primary surveys were conducted by AC Nielsen ORG-MARG Centre for Social Research.
The questionnaires are available upon request.
We tried the criterion of migration into Bangalore during the last 0–5 years, but the hit rate of respondents with this criterion was very low during the pretesting, with the result that we relaxed this criterion to 0–10 years.
We, along with a team from AC Nielsen, conducted a detailed, day-long briefing session for the enumerators so that they understood the objectives of the survey and our intentions in asking specific questions. Mock interviews of the instruments were conducted by the enumerators among themselves during the day-long briefing session.
We provided a list of nationalized public sector banks to the enumerators for this purpose.
We provided enumerators with a list of all district headquarters in all states of the country to enable them to make a decision regarding rural areas.
As per the NSSO, a declared slum is notified by the respective municipality, local body, corporation, or development authority. The undeclared slums were defined as an aerial unit having 25 or more katcha structures mostly of temporary nature, or inhabited by persons with practically no private latrine, or inadequate access to public latrines and water.
We note that a large middle class is emerging with rising incomes in the urban areas and the resultant demand for other services (household/domestic services such as cooking and cleaning) fuels demand for employment in urban areas (which may be classified as pull factors since they refer to creation of job opportunities in the urban informal economy).
A data caveat with the multiple response scheme is that the reasons chosen for migration were not ranked according to their importance in making the decision to migrate. Therefore, we are forced to treat all chosen reasons to be of equal importance.
It should be remembered that Todaro’s model is based on the expected income differential rather than the actual income differential, which we are discussing here.
The Government of India introduced the “growth centres” (GCs) program in June 1988 to give impetus to industrialisation in backward regions (http://dipp.nic.in/growth.htm). According to this program, 71 GCs were set up throughout the country that were to be allotted to the various states on the basis of combined criteria consisting of area, population and the extent of industrial backwardness. These GCs provide basic industrial infrastructure like power, water, telecom, and banking to enable the states to attract industries. See Sridhar (2006) for details of this program.
About Rs 48,000 crore will be made available to gram panchayats or local bodies which is expected to boost rural demand for consumer goods, cement, and steel. Gram panchayats will spend the amount to create productive assets like roads, bridges, and other facilities and give 100 days of work for every rural household whose adult members volunteer to work.
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Acknowledgments
We thank the South Asia Network of Economic Research Institutes (SANEI) and the Global Development Network (GDN) for funding the work. We thank AC Nielsen’s ORG MARG Center for Social Research for conducting the primary surveys. At ORG MARG, we specifically thank Tathagata Dasgupta, Suchandra Nandy, and Maya Kilpadi for their assistance. We appreciate the inputs from Sita Sekhar and Telakula Satyan Prasad at Public Affairs Foundation for their ideas regarding the survey instrument. We thank Lars Forjahn of the University of Heidelberg, Germany, for his assistance with the literature survey, checking of data, and analysis. We thank all the seminar participants at PAC, where this was internally presented, for their comments. Thanks are due to Gopakumar Thampi, then Director of PAC, for facilitating this work by employing AC Nielsen. We also thank the participants of SANEI’s tenth annual conference which was held in Dhaka, where this was presented, for their comments. We thank S.R. Osmani of the University of Ulster especially for his invaluable comments which enabled to improve this paper significantly. Thanks are due to the anonymous reviewers of this manuscript for their comments. We thank Shanthi Shetty at PAC for assistance with the accounting for the project. Any errors remain ours.
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Sridhar, K.S., Reddy, A.V. & Srinath, P. Is it Push or Pull? Recent Evidence from Migration into Bangalore, India. Int. Migration & Integration 14, 287–306 (2013). https://doi.org/10.1007/s12134-012-0241-9
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DOI: https://doi.org/10.1007/s12134-012-0241-9
Keywords
- India
- Migration
- Bangalore
- Cities
- Rural–urban migration
- Skilled and unskilled migrants
- Migrants and non-migrants
- Push versus pull factors in migration