1. Migration, Risk-Sharing and Subjective Well-being Some evidence from India 1975-2005 Stefan Dercon, University of Oxford Pramila Krishnan, Cambridge University Sonya Krutikova, Oxford University

2. ICRISAT, India 6 villages, semi-arid tropics in Maharasthra and Andhra Pradesh (3 districts: Mahbubnagar, Sholapur and Akola) Villages extensively studied, longitudinal data 1975-84 2005/6 and 2006/07 resurvey of all households in village plus migrants 2005/06

3. Purpose Briefly report on changes within villages 1975- 2005 Focus on migration from villages

4. The data…. Status by 2005 (1) Full sample of individuals included in 1975-1984 (2) Of which: Included in 2005 surveys (3) Population composition 2005 (based on (1)) (4) Attrition (by percentage points in total) 2005 NumbersShares (%) Dead in 2005 449NA21% Temporarily migrated 2005 111405%3% Permanently migrated 2005 72445434%13% In village in 2005 823 39%0% No information in 2005 23NA1% Total 2,1301,317100%38%

5. Overview of Changes in Villages All deflated by rural CPIAL Quick overview of – Land and assets – Consumption – Income sources All suggesting considerable growth VLS1 to 2005

6. Per capita levels in 1975 prices Implements (Rps) Durables (Rps) Land Area Owned (ha) vls120051975- 7920051975-832005 Full sample148754905550.630.51 Growth (annualized) %6.57.2 Large initial landholding27415431228151.200.76 Medium initial landholding185526995840.620.59 Small initial landholding52304713540.360.32 Initial status: labourers14389463570.070.23 Number of Observations772

7. Other changes Substantial income and consumption growth per capita (4% per capita annualised for consumption) More than doubling in consumption per capita, with larger growth in non-food Food share down, cereal and pulses share down (69 to 43%), animal protein up (12 to 23%) Growth across land distribution groups Poverty down from 78% to 18%; landless labourers down to 28%

8. Structure of incomes AGGREGATELARGEMEDIUM SMALLLABOUR vls10104vls10104vls10104vls10104vls10104 On farm income (crop and livestock) 0.640.290.880.330.600.340.480.200.310.27 Labour income 0.290.190.100.110.300.110.400.290.680.40 Transfer 0.020.050.010.050.010.060.03 0.04 Trade and Business 0.050.470.020.510.080.490.100.480.010.30 Shares of Mean Income per Capita

9. 200515 years ago30 years ago Very Rich (%)000 Rich (%)212 Comfortable (%)352315 Manage to get by (%)453424 Never enough (%)153640 Poor (%)2616 Very Poor (%)004 Self-Assessed Welfare Positions (2005)

10. Conclusion Considerable changes in village living standards and assets Consumption poverty and self-assessed poverty down Big changes in income sources

11. Conclusion (2) Regression consumption growth (recall, doubled = increased by 100%+ on average) Strong correlates (with economic significant size) those from literate households 30% more growth Those educated themselves up to end high school +17% High dependence on crop income in VLS1, doing worse Lower caste groups (SC/ST/some BC) -10 to -20%

12. So what about Migrants? Development correlated with internal migration – Out of agriculture – Out of rural areas “physical mobility, economic mobility, social mobility all related” Scale required is massive: – E.g. China: last 20 years, from 80% to 55% in agriculture, much of it involving local or long- distance migration

13. The data…. Status by 2005 (1) Full sample of individuals included in 1975-1984 (2) Of which: Included in 2005 surveys (3) Population composition 2005 (based on (1)) (4) Attrition (by percentage points in total) 2005 NumbersShares (%) Dead in 2005 449NA21% Temporarily migrated 2005 111405%3% Permanently migrated 2005 72445434%13% In village in 2005 823 39%0% No information in 2005 23NA1% Total 2,1301,317100%38%

14. Destinations of migration Permanent migrants MaleFemale Location Nearby village21%10%27% Other village (this district)16%11%19% Other rural areas22%16%26% Urban areas39%61%27% Don’t know/missing2%

15. Reasons for migration Permanent migrantsMale Female Work19%49%3% Looking for work8%19%1% School/college2%4%1% Following family17%18%17% Marriage49%4%74% Other2%0%2% Don’t know missing4%7%2%

16. Views on migration and inequality On evidence Perception of slum living, low wages, high unemployment paints bleak picture of urban living Evidence from poverty measurement suggests much higher rural than urban poverty

17. Views on migration and inequality On theory: (a) Labour market theories Inequality ‘drives’ migration but outcome is equilibrium – so why higher rural poverty? Inequality drives migration without resolving it (HT) (b) Household models Migration is strategic family decision (NEM) with risk-sharing and remittances as one of its reflections – so strong prediction on intra-household inequality (not growing) (RS)

18. The questions (1)Is there a migration premium? (2)Is it consistent with standard theory models? From long-term longitudinal data tracking all within families, data of up 30 years... Evidence: – of relatively large migration, large “returns” to migration, including for female migrants – with a twist on the theory ( or  )

19. Empirical challenge Wages for urban and rural hard to compare (differentiated labour markets in skills, tasks, etc) We need to ensure we have counterfactual: living standards if migrant had not migrated – Migrants could be from better families – M could be those with higher earnings potential Setting up via ‘family (risk) sharing model’ as it offers means of both exploiting data and theory predictions Focusing on consumption and subjective well being (“net of remittances”)

20. Model Suppose we have an extended family group that is in involved in perfect (risk) sharing. Let us characterize the outcome and then use this as a basis for testing deviations from this. Let there be (different) (risky) income streams y i for each household i in a group. (Suppose there is no savings.) Suppose now that these households contract with each other to get optimal (risk) sharing, and assuming that the contract is enforceable (binding sharing rule).

21. model (2)

24. “Overidentification” by location: if sharing, location should not matter, or β=0

26. Taking to data... Model can be used for risk-sharing, but test nests more general ‘premium’ test β=0 tests sharing, irrespective of location But also test for presence of migrant premium, ceteris paribus, as if in a difference-in-difference framework

27. Empirical application? Following Beegle, Dercon, De Weerdt, RESTAT 2011 on Tanzania – Initial household fixed effects estimator – With further IV for time varying individual heterogeneity

28. Assessing the impact of migration m Changes in consumption, not levels (in real terms) = control for time-invariant factors that determine levels (diff-in-diff) Initial household fixed effects, to compare the impact of migration between family members initially living together (γ j ) = control for all factors that determine changes common to all those initially living together (“triple difference”)

29. Specification - Individual baseline characteristics (X t-1 ) = control for all observable individual (time-varying and time-invariant) factors that determine changes =individual baseline characteristics: age, sex, education baseline, caste, family educational and wealth background, family composition at baseline, nutrition at baseline. One step further: individual level IV = control for unobservables at individual level determining changes

30. Returns to migration…. (1) OLS all (2) IHHFE all (3) OLS men (4) IHHFE men (5) OLS Women (6) IHHFE Women Moved outside community 0.2170.2050.2680.2870.1830.164 Always significant at 1% Controls for sex, caste, age, schooling, shocks 1984-2005, living conditions at baseline

31. Specification IV -Instruments = control for unobservables at individual level determining changes = predictors of migration, not directly determining ‘incomes’ = predictors explaining why member x went and not member y = relational variables (birth order) plus push factor interacted with age window at baseline: rainfall at the age of 16 First stage, strongly significant, Cragg-Donald 9.42 Results: 0.67 for men, 0.65 for women (sign 1%)

32. Answers Is there a premium to migration? (HT): YES Is this premium fully exploited? NO Are families smoothing over space? (RS): NO But not a simple story of educational investment (life-cycle), sectoral, urban-rural shift... Intra-Family Inequality after migration High premium ‘unexploited’ So Why Undermigration? Theory just wrong?

33. Are we getting the point? They are not ‘sharing’ in space? But what if ‘location’ matters per se? Location as a taste shifter?

34. Are we getting the point? For example: “urban needs” As in “keeping up with the Jones’ consumption ” Are they ‘sharing’ in this space? If θ(location), then finding migration effect could be consistent with risk-sharing Can we test? – Do we have data closer to b ist c ist γ, and not just c ist ? – Possibly via subjective wellbeing data! – We would expect that this ‘controls’ for taste shifter better, so no more migration effect.

35. Assessing the impact of migration m we have data on changes in perceived wealth we also have data on levels of happiness, life evaluation, etc.

36. Subjective assessment of wealth

38. Nostalgia bias? Results may be affected by recall. Can we use cross-section? Needs strong assumption on observability of pareto weight

39. Nostalgia bias? Alternatively: when living together, no compensation for subjective well-being. We treat is as if we were all in initial household at similar subjective wellbeing (and so in fixed effect)

40. Perceived wealth and happiness IHHFE (1)(2)(3)(4)(5) Consum- ption growth Levels of perceived wealth Changes in perceived wealth Happiness Ladder of life Migrant IHHFE0.205***0.078-0.188-0.051-0.235 Migrant IHHFE With IV 0.785** 0.012-0.059 -0.222-0.295

41. Interpretation OVERALL consistent with sharing!!! Migration lowers subjective well being (how one assess own wealth) =Consistent with subjective well-being =relative concept =Could reflect more difficult conditions (being outsider,...) =could reflect ‘relative’ comparison but also huge nostalgia effect As a migrant, your initial family ‘allows’ you to have a huge consumption premium, to compensate you for your miserable existence (taste shifter) Consistent with literature on subjective wellbeing as relative experience

42. Overall conclusion Families may allow inequality to emerge as part of ‘sharing’ strategy HERE: with higher material wellbeing to compensate for otherwise lower overall or subjective wellbeing Still: UNDERmigration in terms of material wellbeing (given seemingly high returns) Policy?