Rural Migration in India: How Important is Weather Variability?

By Brinda Viswanathan and K.S. Kavi Kumar

Introduction

Response strategies to global climate change crucially depend on potential impacts due to climate change on several climate sensitive sectors. From developing country perspective, in order to design adaptation strategies, it is important to not only know the overall impacts; but also the factors that could, in principle, play a crucial role in minimizing the impacts of climate change. 

Several studies have shown that climate change could have significantly adverse impacts on Indian agriculture. The available evidence shows significant drops in yields of important cereal crops like rice and wheat under various climate change scenarios. While several planned adaptation strategies could work towards ameliorating the adverse impacts of climate change, there is a considerable likelihood of migration associated with agriculture sector. Thus, for a large majority, migration could be an effective adaptation strategy1 . 

While some studies have analyzed the linkages between weather variability (and climate change) and migration per se in the past (see, McLeman and Smit, 2006; Perch-Nielsen et al., 2008, Bardsley and Hugo, 2010), the linkages through the agriculture channel and rural-urban wage differentials have recently been analyzed by researchers such as, Feng et al. (2010, 2012); Barbieri et al. (2010); Dillon et al. (2011); and Marchiori et al. (2012).

Following the methodology used by Feng et al. (2010), a recent study in India, explored the linkages between weather variability, agricultural performance, and migration, using state level Census data over the period 1981 to 2001 and district level Census data covering the period 1992-2001. The weather data is sourced from grid level meteorological data released recently by the India Meteorological Department. The analysis is carried out separately for the two main cereal crops: wheat and rice. 

Such a three way nexus, based on a secondary database, has not been investigated rigorously in the Indian context; and this study fills that gap in the literature. Studies based on migration data in India often focus on the push and pull factors determining migration, and are based on single cross sectional data (e.g., Joe et al., 2009; Mitra and Murayama, 2008; Ozden and Swadeh, 2010). This study is based on several years of Census data and multiple durations of stay reported in each Census, that facilitate rigorous econometric analysis.

Summary of Findings

Based on the state and district-level analyses the estimated semi-elasticity of migration to crop yield change is summarized in the table below. The main findings include:

1Some may argue that migration should be seen as a coping strategy and not as adaptation strategy. While this could be true, for the purpose of the discussion here the distinction is considered irrelevant as the focus is only to assess to what extent migration is influenced by climate change induced agricultural impacts. 

a) In case of inter-state out-migration semi-elasticity is similar, irrespective of the choice of crop. This is also close to the estimated semi-elasticity from the reduced-form single equation using per-capita net state domestic product from agriculture. 

b) The semi-elasticity based on district-level estimations is on average higher than those estimated from the state-level analysis, particularly for wheat (see table below). This, perhaps, reflects higher rates of intra-state movement compared to inter-state movements.

c) From the district-level analysis it can be seen that, (i) if the inter-district movement dominates over the intra-district movement, it can be inferred that there will be less ‘in’ migration to a district that fares badly on the agricultural front; and (ii) if intra-district movement dominates over the inter-district movement, then it can be inferred that there will be more within-district movement of people searching for livelihood, in periods when agricultural performance is not good. 

d) Since the estimated semi-elasticity of migration rate with respect to wheat yield is positive (district-level analysis), it might be inferred that inter-district movements dominate intra-district movements due to the geographically sparser cultivation of wheat. In contrast to this, the migration semi-elasticity for rice is negative, indicating the dominance of intra-district movements over inter-district movements. This is due to the fact that rice is grown in geographically contiguous areas in almost all states. Though the dominance of the inter-district migration rate over the intra-district migration rate (and vice-versa) is not quite evident from the estimated coefficients, two additional factors could be influencing the overall sign of the yield coefficient in the migration equation. These are, (i) the observation that both inter-and intra-district migration rates are positively correlated; and (ii) the specific nature of the crop under consideration, and its relative labour intensity. 

e) In the case of wheat, the estimated semi-elasticity of migration rate is nearly the same for both male and total migrants (district-level analysis). Whereas for rice, the estimated semi-elasticity of migration rate for total migrants, is higher than that of male migrants. This could be because in the case of rice, if intra-district migration dominates, then this could possibly be short-distance (and short-duration) migration, prompting the movement of family members along with the male members, as against movement of male members alone. 

Estimates of Semi-Elasticity (Elasticity) of Migration Rate to Agricultural Performance– Summary

Notes: (1) In case of out-migration, the total migrant population excludes marriage migration and place-of-birth migrants; (2) District-level in-migration includes all migrants from rural areas as reason for migration is not available from the Census; (3) Values in brackets are elasticity of migration rates to Agricultural Performance; Agricultural Variable caries across different specifications and is indicated in column

Conclusions

The above discussion suggest that while the weather variability led agricultural distress could lead to migration from rural to urban areas in India, the magnitude of the response is relatively small compared to those reported elsewhere in the literature for other countries. Further, it varies across the crop types, and level of (geographical) disaggregation used in the analysis. In the absence of other livelihood opportunities in rural, as well as urban areas, the weather induced migration operating through the agriculture channel may not lead to significant migration. The low semi-elasticity values reported in this study substantiate this observation. Further, the rural to urban migrants have registered larger growth between 1991 and 2001, compared to the previous decade, and this is more so in inter-district and inter-state streams of migration. Thus, it appears that for the current level of development in India, migration is largely explained by the development angle. This could be teased out if there were other variables like education level of the state’s or district’s population, and other infrastructure variables. An even clearer picture could emerge if longitudinal data of individual migrants was available at a regional level.  

From the climate change context, the findings of the study have important policy implications, as migration is often seen as an effective adaptation option. Despite the low magnitudes of the impact of crop yield, and the reported changes in migration rates suggests the presence of linkages between weather variability, agriculture, and migration.  As conclusively established in this study, it suggests that migration could still be an important adaptation option in India. This is more likely to be the case given the long time-lags that are typically associated with the manifestation of climate change impacts, and the likely upward movement of India along the development ladder by that time.

References

Barbieri, A.F., E. Domingues, B. L. Queiroz, R.M. Ruiz, J.I. Rigotti, J.A.M. Carvalho, and M.F. Resende (2010). “Climate Change and Population Migration in Brazil’s Northeast: Scenarios for 2025-2050”, Population and Envronment, 31: 344-370.

Bardsley, D. K., and Hugo, G. J. (2010). “Migration and climate change: examining thresholds of change to guide effective adaptation decision-making”, Population and Environment, 32(2-3), 238-262.

Dillon, A., V. Mueller, and S. Salau (2011). “Migratory Responses to Agricultural Risks in Northern Nigeria”, American Journal of Agricultural Economics, 93: 1048-1061.

Feng, S., Krueger, A. B., and Oppenheimer, M. (2010). “Linkages among climate change, crop yields and Mexico–US cross-border migration”, Proceedings of the National Academy of Science, 107(32), 14257-14262.

Feng, S., M. Oppenheimer, and W. Schlenker (2012). “Cliamte Change, Crop Yields, and Internal Migration in the United Staets”, NBER Working Paper No. 17734, NBER, Cambridge.

Joe, W., P. Samaiyar, U.S. Mishra (2009). “Migration and Urban Poverty in India: Some Preliminary Observations”, Working Paper 414, Centre for Development Studies, Trivrandrum.

Marchiori, L., J-F. Maystadt, and I. Schumacher (2012). “The Impact of Weather Anomalies on Migration in sub-Saharan Africa”, Journal of Environmental Economics and Management, 63:355-374.

McLeman, R., and Smit, B. (2006). “Migration as an Adaptation to Climate Change”, Climatic Change, 76(1-2), 31-53.

Mitra, A. and M. Murayama (2008). Rural to Urban Migration: A District Level Analysis for India, IDE Discussion Paper 137, IDE, JETRO: Chiba.

Ozden, Caglar and Mirvat Swadeh (2010). ‘How Important is Migration’ in Ejaz Ghani (ed.) The Poor Half Billion in South Asia: What is Holding Back Lagging Regions?, OUP: New Delhi (India).

Perch-Nielsen, S., Bättig, M., and Imboden, D. (2008). “Exploring the link between climate change and migration”, Climatic Change, 91(3-4), 375-393.

About the Authors 

Dr Brinda Viswanathan

Dr K.S. Kavi Kumar

                                                                                                                                                                                 

The authors are attached to the Madras School of Economics, Chennai, India.

This article is based on a recently completed project on the topic funded by the South Asian Network for Development and Environmental Economics (SANDEE). The authors can be reached respectively atbrinda@mse.a.cin and kavi@mse.ac.in.