Migration, Risk-Sharing and Subjective Well-being

Some evidence from India 1975-2005

Stefan Dercon, University of OxfordPramila 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

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

Numbers Shares (%)

Dead in 2005 449 NA 21% 21%Temporarily migrated 2005 111 40 5% 3%Permanently migrated 2005 724 454 34% 13%In village in 2005 823 823 39% 0%No information in 2005 23 NA 1% 1%Total 2,130 1,317 100% 38%

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

Per capita levels

in 1975 prices

Implements (Rps)

Durables (Rps)

Land Area Owned (ha)

vls1 2005 1975- 79 2005 1975-83 2005

Full sample 148 754 90 555 0.63 0.51

Growth (annualized) % 6.5 7.2 -1.0

Large initial landholding 274 1543 122 815 1.20 0.76

Medium initial landholding 185 526 99 584 0.62 0.59

Small initial landholding 52 304 71 354 0.36 0.32

Initial status: labourers 14 389 46 357 0.07 0.23

Number of Observations 772 772 772 772 772 772

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

AGGREGATE LARGE MEDIUM SMALL LABOUR  

vls1 0104 vls1 0104 vls1 0104 vls1 0104 vls1 0104

On farm income (crop and livestock)

0.64 0.29 0.88 0.33 0.60 0.34 0.48 0.20 0.31 0.27

Labour income

0.29 0.19 0.10 0.11 0.30 0.11 0.40 0.29 0.68 0.40

Transfer0.02 0.05 0.01 0.05 0.01 0.06 0.03 0.03 0.03 0.04

Trade and Business

0.05 0.47 0.02 0.51 0.08 0.49 0.10 0.48 0.01 0.30

Shares of Mean Income per Capita

2005 15 years ago 30 years ago

Very Rich (%) 0 0 0

Rich (%) 2 1 2

Comfortable (%) 35 23 15

Manage to get by (%) 45 34 24

Never enough (%) 15 36 40

Poor (%) 2 6 16

Very Poor (%) 0 0 4

Self-Assessed Welfare Positions (2005)

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

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

Numbers Shares (%)

Dead in 2005 449 NA 21% 21%Temporarily migrated 2005 111 40 5% 3%Permanently migrated 2005 724 454 34% 13%In village in 2005 823 823 39% 0%No information in 2005 23 NA 1% 1%Total 2,130 1,317 100% 38%

Destinations of migration

Permanent migrants

Male Female

Location

Nearby village 21% 10% 27%

Other village (this district) 16% 11% 19%

Other rural areas 22% 16% 26%

Urban areas 39% 61% 27%

Don’t know/missing 2% 2% 2%

Reasons for migration

Permanent migrants Male Female

Work 19% 49% 3%

Looking for work 8% 19% 1%

School/college 2% 4% 1%

Following family 17% 18% 17%

Marriage 49% 4% 74%

Other 2% 0% 2%

Don’t know missing 4% 7% 2%

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 inequalityOn 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(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 )

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).

model (2)

“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”)

iJtJ1ititiJt X.m.c

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, 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

Returns to migration….(1)OLSall

(2)IHHFEall

(3)OLSmen

(4)IHHFEmen

(5)OLSWomen

(6)IHHFEWomen

Moved outside community

0.217 0.205 0.268 0.287 0.183 0.164

Always significant at 1%Controls for sex, caste, age, schooling, shocks 1984-2005, living conditions at baseline

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 migrationHigh 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 cist γ, 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.

Subjective assessment of wealth Household living in now

(2005) Household lived in 30

years ago

All Non-migrants

Migrants All Non-migrants

Migrants

Destitute/Poor/Never enough

19.9 51.5

Can manage to get by 41.0 25.3

Comfortable 35.4 18.5

Rich/very rich 3.7 4.8

100 100

Total Observations 1,158 1,158

Subjective assessment of wealth(percentages) Household living in now

(2005) Household lived in 30

years ago

All Non-migrants

Migrants All Non-migrants

Migrants

Destitute/Poor/ never enough

19.9 17.2 24.9 51.5 58.7 37.5

Can manage to get by 41.0 46.1 31.2 25.3 24.3 27.2

Comfortable 35.4 35.0 36.3 18.5 15.1 24.9

Rich/very rich 3.7 1.7 7.6 4.8 1.8 10.3

100 100 100 100 100 100

Total Observations 1,158 761 397 1,158 761 397

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)

Perceived wealth and happiness IHHFE

(1) (2) (3) (4) (5)

Consum-ption

growth

Levels of perceived

wealth

Changes in perceived

wealthHappiness

Ladder of life

Migrant IHHFE 0.205*** 0.078 -0.188 -0.051 -0.235Migrant IHHFEWith IV 0.785** 0.012 -0.059 -0.222 -0.295

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?