Migration, Risk-Sharing and Subjective Well-beingSome evidence from India 1975-2005 Stefan Dercon, University of Oxford Pramila Krishnan, Cambridge University Sonya Krutikova, Oxford University
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
Purpose • Briefly report on changes within villages 1975-2005 • Focus on migration from villages
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
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%
Structure of incomes Shares of Mean Income per Capita
Conclusion • Considerable changes in village living standards and assets • Consumption poverty and self-assessed poverty down • Big changes in income sources
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%
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
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
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)
The questions • Is there a migration premium? • 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 )
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”)
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 yi 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).
“Overidentification” by location: if sharing, location should not matter, or β=0
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
Empirical application? • Following Beegle, Dercon, De Weerdt, RESTAT 2011 on Tanzania • Initial household fixed effects estimator • With further IV for time varying individual heterogeneity
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 (γj) = control for all factors that determine changes common to all those initially living together (“triple difference”)
Specification • - Individual baseline characteristics (Xt-1 ) = control for all observable individual (time-varying and time-invariant) factors that determine changes =individual baseline characteristics: age, sex, educationbaseline, 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
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%)
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?
Are we getting the point? • They are not ‘sharing’ in space? But what if ‘location’ matters per se? Location as a taste shifter?
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 bist cistγ, and not just cist? • Possibly via subjective wellbeing data! • We would expect that this ‘controls’ for taste shifter better, so no more migration effect.
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.
Nostalgia bias? • Results may be affected by recall. • Can we use cross-section? Needs strong assumption on observability of pareto weight
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)
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
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?