Weather Variability, Agriculture and Migration in India K.S. Kavi Kumar Madras School of Economics, Chennai (India) 4 th National Research Conference on Climate Change IIT, Madras 26 2727 October Otb 2013
Structure Context Climate ChangeImpacts and Vulnerability Migration as an adaptation/coping option Triggers of migration Rural population growth trends Climate, Agriculture, Migration Literature Migration Patterns in India Weather Variability and Short term Migration Elasticity of Migration to Weather Variability Summary and Conclusions 2
Climate Change Impacts Agriculture Several strands of literature Agronomic/economic studies Rosenzweig and Parry, 1994; IIASA, 2002; Nelson et al., 2009; Kumar and Parikh, 2001a; Lal et al., 2006; Liu et al., 2001; Wang et al., 2010a; Schlenker and Roberts, 2008; Auffhammer et al., 2012 Ricardian studies Medelsohn et al., 1994; Kumar and Parikh, 2001b; Sanghi and Mendelsohn, 2008; Wang et al., 2010b Ricardian studies with spatial features Polsky, 2004; Schlenker et al., 2005; Kumar, 2011 Other studies: Dechenes and Greenstone, 2007 (explicit focus on weather shocks); Auffhammer et al., 2006 (ABCs and GHGs); Krishnamurthy, 2012; Jacoby and Skoufias, 2011 (distributional issues) Broadly all studies suggest significant welfare implications 3
Triggers of Migration Migration in response to shocks e.g., extreme weather events like hurricane Katrina Migration from rural to urban areas as gradual development process ongoing, albeit at a relatively lower pace in South Asia than rest of the world Migration due to distress in agriculture and rural livelihoods
Annual Growth of Rural Population, 1950 2050: India, China and World (Source: Computed from World Urbanization Prospects 2011, UN Population Division, March 2012)
Annual Growth of Rural Population Chart shows year to year change in rural population growth rate for India, China and the World China s rural population growth rate became negative by mid 1990s India is likely to negative growth rate in rural population by 2030s only Rate of urbanization in India is relatively slow compared to many fast growing developing countries including Latin American countries
Climate Change Migration Literature The literature e on climate atechange/weather eat e variability ab induced migration appears to be distinct across developed and developing countries In developed countries the analysis is happening in a phase where the rural population growth rates are negative Whereas in developing countries the discussion is taking place in a phase where the rural population growth rates are still not negative In case of China, institutional factors may have important role to play
Climate Change Agriculture Migration (Three Way Linkage) Crop productivity weather variability migration Feng et al., (2010); Feng et al. (2012), Bordey et. al (2012) Weather anomalies and migration Marchiori et al. (2012) Migration, agricultural risk and weather variability Dillon et al. (2011) Weather shock and Migration (agent based modelling) Hassani Mahmooei andparris (2012) 8
Thrust of the present study.. Acknowledging that migration can take place due to several reasons, thisstudyfor study Indiafocuses on weather variability induced migration, operating throughthe the channel of agricultural productivity changes. Specific questions addressed include: Does weather variability influence short term and long term migration? Evidence based on NSS data How responsive is inter state long term migration to weather variability? Evidence based on Census data 9
Defining a Migrant India Two sources of data Census (1981 to 2001) and NSS (2007 08) Definition based on Place of Last Residence Is your current place of enumeration different from the place of last residence? Classification of Migrants by Durations of Stay Males and Females Oii Origin and Destination: Rural/Urban; inter state/inter tt/it district/intra district Purpose of migration Marriage/Employment/Family (associated)/studies/others 10
Broad Patterns of Internal Migration in India Significant increase in numbers in postlb liberalizationl period Inter Censal growth rates have not been monotonically positive Marriage is a dominant reason for female migration Official statistics show steady decline in incidence of rural to urban migration Partly because of short term t migration is not properly captured by the Census data 11
Distribution of Migrant Types Non-migrants and Migrant Types Across Migrant Types Migrants are a small fraction of the total population Long-term migrants dominate over other forms of migrants Short-term t migrants are mostly among rural men 12
Migrant Types and Lead/Lag States Rural Urban Rural men from lagging states largely undertake short-termterm migration 13
Migration Trends MPCE Quintiles Short-term migrants are mostly among the poorer segments of the society 14
Weather Variability and Migration Discrete choice model for probability of migration is specified as Y i = Φ(X( i β)+u i Y i = 1 when the individual undertakes short term (or long term) movement out of (into) rural areas Y i = 0 otherwise, which includes non migrants X i = set of independent variables including individual characteristics (like sex, age, employment status, sector of economic activity) household characteristics (like monthly per capita expenditure, household size, religion, caste, landholding class) regional characteristics (like weather variability including district level average of maximum temperature over the past twenty years, district level standard deviation of annual rainfall over the past twenty years; dummy variable representing the lagging states) β = coefficient vector associated with the independent variables 15
Determinants of Short-term and Long-term Migration in India: NSS Data Probability of Inter- Inter-District Inter-State Intra-District Temporary Migration District/State Variable Coeff p- value Coeff p- value Coeff p- value Coeff p- value Average Annual Temperature 0.034 *** 0.000 0.056 *** 0.000-0.002 0.817 0.053 *** 0.000 Std. Dev. of Annual Avg. Temp. -0.798 *** 0.000-0.622 *** 0.003-0.955 *** 0.000 0.668 *** 0.002 Average Monsoon Rainfall -0.0003 *** 0.000-0.0001 *** 0.003-0.0004 *** 0.000-0.0001 ** 0.039 Std. Dev. of Monsoon Rainfall 0.0017 *** 0.000 0.0010 *** 0.000 0.0019 *** 0.000 0.0010 *** 0.000 No. of Observations 122536 116920 118495 115502 Pseudo R 2 0.1604 0.1173 0.1961 0.0722 Probability of Inter-District/State Inter- District/State and Intra-District Permanent Migration i Variable Coeff p-value Coeff p-value Average Annual Temperature 0.005 0.606-0.043 *** 0.000 Std. Dev. of Annual Avg. Temp. -0.256 0.250-0.714 ** 0013 0.013 Average Monsoon Rainfall 0.00005 0.345-0.0001 0.205 Std. Dev. of Monsoon Rainfall -0.00010 0.659 1.05E-08 0.950 No. of Observations 116088 114299 Pseudo R 2 0.1346 0.1548 16
Determinants of Short term and Long term Migration in India: NSS Data Weather variability captured through annual temperature, standard deviation of annual temperature, average monsoon rainfall, and standard deviation of monsoon rainfall over 20 yearperiod hassignificantinfluence onshort term migration Favourable weather leads to lower within district movement Results show that weather variables have insignificant influence on the long term migrationdecisions of households However, the weather variables may influence migration decisions through agriculture channel Non availability of agriculture data at household level doesn t permit further analysis based on NSS data, and hence Census data is utilized 17
Elasticity of Migration to Weather Variability Migration Data Rural inter state out Migration rate using three Censuses: 1981, 1991 and 2001 and two durations of stay 1 to 4 years and 5 to 9 years Overall 6 Time periods: 1972 1976, 1977 1981, 1982 1986, 1987 1991, 1992 1996, 1997 2001 Covering 15 States Excludes marriage and place of birth as reason for migration Sample Size 90 observations 18
Elasticity of Migration to Weather Variability Temperature and Rainfall Data generated dfor the year 1970 to 2001 using gridded data (1 o x1 o lat/lon resolution) mean temperatures t and rainfall are used for the corresponding periods Yield data for two main cereal crops rice and wheat Annual date for 1961 to 2010 Mean values for the periods under consideration 19
Econometric Methodology Two stage model l( (Feng et al., 2010) (1) M it = α + β ln(y it ) + d i + r t + ε it, and (2) ln(y it ) = γ + δt it + p i + c t + ν it M it Migration rate (number of migrants as a proportion of the rural population); ) Y it Agriculture variable T it set of weather variables for region i' at period t d i and p i regional (fixed) effects; r t and c t time (fixed) effects These fixed effects are included to capture the omitted variables that could be correlated with the variables (yield and weather) included in the equations (1) and (2) 20
Estimates based on Crop Yields WHEAT YIELD EQUATION Variable Coefficient p-value June-September Temp. (pre-sowing) 0.328 ** 0.027027 October-November Temp. (growing) -0.169 0.136 Std. Dev. of January-March Rainfall -0.002 ** 0.041 Adjusted R 2 0.936 Joint Significance of Weather Variables F(3,58) = 3.20 ** 0.0299 RICE YIELD EQUATION Variable Coefficient p-value Average Annual Temp. -1.467 0.135 Square of Average Annual Temperature 0.028 0.136 Adjusted R 2 0.932 Joint Significance of Weather Variables F(2,68) = 1.14 0.3249 21
Estimates based on Crop Yields (contd.) Out-migration OLS 2SLS LIML Rate Coeff p-value Coeff p-value Coeff p-value log of Wheat Yield -0.00066 0.348-0.0036 ** 0.054-0.0048 * 0.085 Intercept 0.0017 *** 0.001-0.00036 0.799-0.0012 0.558 Adjusted R 2 0.782 0.747 0.711 Test for χ 2 (1) = 2.75 * p-value = 0.097 Endogeneity Test for Weak Instruments Cragg-Donald Wald F statistic = 3.796 Critical Value = 9.08 Out-migration OLS 2SLS LIML Rate Coeff p-value Coeff p-value Coeff p-value log of Rice Yield 0.0027 *** 0.008-0.0074 * 0.094-0.0076 * 0.096 Intercept 0.0007200072 0.205 0.006006 ** 0011 0.011 0.00610061 ** 0.012012 Adjusted R 2 0.796 0.558 0.545 Test for χ 2 (1) = 5.74 ** p-value = 0.0166 Endogeneity Test for Weak Instruments Cragg-Donald Wald F statistic = 0.964 Critical Value = 19.93 22
Hind Casting: Inter State Out Migration Rates Using the estimated elasticities it is feasible to hind cast migration rates In the period between 1971 to 2001, the average migration rate was 0.4% If the annual temperature were 1 o C more during this period, the migration rate would have been 0.44% operating through decline in rice yields If the October November temperature were 1 o C more during this period, the migration rate would have been 0.46% operating through decline in wheat yields 23
Summary and Conclusions Weather variability ld led agriculture distress could lead to rural out migration Magnitude of the response is relatively low in India compared to those estimated for other countries In the absence of other livelihood opportunities this channel may not have led to significant migration in case of India Thus, low elasticity estimated need not imply insignificant role for migration as adaptation strategy Evidence of significant influence of weather variability on temporary migration in India suggests that short duration migration may serve as an important coping strategy now and in future for households facing adverse weather conditions 24
THANK YOU! kavi@mse.ac.in; kavikumar@gmail.com 25