Abstract
Despite high rates of internal migration, India is urbanizing relatively slowly. This paper uses new data from rural north India to study short-term migration to urban areas and its role in rural livelihoods. First, we demonstrate the importance of data collection techniques tailored to understanding short-term migration. Second, we consider how traditional theories of migration apply in this context, where the fixed costs of migration are low, the opportunity costs vary by season, and where migration is negatively selective for education and economic status. We conclude by considering the implications of this migration for theories of development and development policies.
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Notes
Authors’ calculations from NSS Employment and Unemployment Survey, Round 64.
These adults are unlikely to be guests for two reasons. First, the definition of household specifically asked about those who pool economic resources. Second, we found that these adults are highly likely to do agricultural work when they are in the village, something that guests would not be expected to do.
Culturable waste includes lands available for cultivation but not cultivated in the past five years.
In practice, adults from the sample villages did not commute to migrant worksites, as the villages were too far from centers of economic activity to come and go in the same day, or even every couple of days, by public transportation.
For comparison, 43 % of rural households in India’s 2005 Demographic and Health Survey had dirt floors, 56 % had electricity, and 30 % had television sets.
That many households would not know their categories considering the government benefits of group membership, it is important to stress just how excluded and isolated this group is. Many people had no education, and so would not be competing for government jobs or slots at institutions of higher learning.
For most adults, their migration data are taken from the adult survey that they answered. Information on some adults, who were away from the village for work at the time of the survey, was reported by another household member. Similarly, children’s migration was recorded along with the migration record of the adult with whom they traveled.
The inverse relationship between migration and agricultural work is present at the household level as well. In the year before the survey, households spent on average 27 adult work days on cultivation in summer, 98 adult work days on cultivation last winter and 247 adult work days on cultivation last monsoon season.
See Mosse et al. (2002) for more information on worksites.
It is important to note that about half of migrants could not tell us the distance to their place of work. Almost all knew how much they had paid to travel to their destinations, however. Therefore, missing values of distance were imputed using the relationship between transport costs and distance for the values we had collected.
Our results about how migrants find work contrast with previous research on seasonal migration, undertaken in the 1990s. Mosse et al. (2002) and Breman (1996), both anthropologists whose studies of the population strongly influenced our survey design, find a strong presence of labor contractors in arranging jobs for migrants. Labor contractors act as go-betweens for migrants and employers. They may make loans on behalf of employers or take a cut of migrants’ pay in exchange for the service of helping them find employment. Both authors describe migrant-labor contractor relationships as exploitative and impoverishing of migrants. While it is possible that different methods of study led to different results, the importance of labor contractors in arranging migrant work may have diminished in the last 15 years. If so, this would reflect improvements over time in the well-being of migrants.
This was a little over US$2 at market exchange rates at the time, and about four times that at purchasing power parity. Equivalent daily wages were computed for weekly, bi-weekly, and monthly payment schedules, and for payments received at the end of work. Since migrants also reported the number of days they worked in a week, we divided weekly, b-iweekly, monthly, or lump sum payments by the number of days that a migrant worked during the pay period.
This correlation is consistent with the behavior of an optimizing migrant who faces convex costs of time away from the village.
Another Harris–Todaro modeling assumption that probably does not apply in this case is that migration decisions are made by individuals, rather than households. Battacharaya (1985) explains that most decisions about internal migration from rural India are household decisions.
If we instead control for the highest level of adult education in the household, the results and interpretation do not change.
For example, Munshi and Rosenzweig (2009) develop a theory of caste networks to explain low levels of permanent out-migration of males from their home villages.
Due to memory problems and survey fatigue, we believe this is probably an underestimate of repeat migration across years.
Of course, our paper is certainly not the first to point beyond dual models of developing economies (c.f. Ranis 2004).
Some prior research has attempted to explain heterogeneity in who finds non-agricultural work. For example, Lanjouw & Murgai (2008), while studying rural non-agricultural employment generally, rather than migration in particular, document that participation is associated with education levels and social status that are rare among the poor. However, the migrants in our data are poor, and supply unskilled, casual labor. Moreover, Foster & Rosenzweig (2002) anticipate that a thick market for migrant labor could raise wages for poor village residents, even if they did not themselves migrate. Our data suggest that exactly such a large market exists in this society.
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Appendix
Appendix
Village Selection and Household Sampling
As shown in Fig. 1, 100 villages were selected, but due to time and budget constraints, 30 villages were dropped. The villages that were dropped were the 30 villages located furthest to the west, as this made surveying of the remaining villages logistically easier. Voting lists were used to randomly sample ten households in each village.
Voting lists are used by the state and local village governments for elections and are meant to include every adult aged 18 and older in the village. In practice, this may not be true as voting lists are updated irregularly. However, during piloting, complete household listings in three villages were completed to compare with the voting lists, one in each state. In all cases, each household enumerated in the full census had at least one adult member listed in the voting list. During the household listing, we defined a household as a group of persons living under the same roof for at least thirty days in the past year, sharing food from a common source when together, and contributing or sharing in a common resource pool. In some cases, the definition of household in the voter list did not correspond to our definition. In particular, the voter list definition was more expansive, often including for example three brothers who lived separately as one household. During surveying, in cases in which the voter list and our household definitions differed, only one economic household was chosen. We chose the household with the eldest head of household. For this reason, more established, older households are likely to be over-represented in the final sample.
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Coffey, D., Papp, J. & Spears, D. Short-Term Labor Migration from Rural North India: Evidence from New Survey Data. Popul Res Policy Rev 34, 361–380 (2015). https://doi.org/10.1007/s11113-014-9349-2
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DOI: https://doi.org/10.1007/s11113-014-9349-2