Socioeconomic effects of remittances and migration
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Socioeconomic Effects of Remittances and Migration . David McKenzie World Bank. Growing importance of remittances worldwide. Developing countries recorded remittances have surged in recent years: 73% increase between 2001 and 2005 More than five times as large as in 1990 Reflects:

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Growing importance of remittances worldwide l.jpg
Growing importance of remittances worldwide

  • Developing countries recorded remittances have surged in recent years:

    • 73% increase between 2001 and 2005

    • More than five times as large as in 1990

  • Reflects:

    • Better measurement, shift from informal to formal channels

    • Less regulations and falling costs

    • Some growth in migration

  • This has led to a surge in interest in the impact of remittances on development outcomes


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Traditional approaches to finding the impact of remittances

  • Ask migrants what remittances are for, or families what remittances are spent on.

    E.g. Mexican Migration Project


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result

  • Early studies came to largely pessimistic conclusions about potential of remittances to promote economic growth

    => View remittances as leading to a “cycle of dependency”, money as being wasted on food, drinks, fiestas and conspicuous consumption.


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Newer studies

  • Realize money is fungible, so even if spend remittances on parties, this allows you to spend some of your other income on other uses, including productive investments.

  • Use regression approach, e.g.

    Outcome = a + b*Remittances + c’X + e


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How do we know what the impact of remittances really is?

Approach 1: Treat remittances as manna from heaven…

- assume some households just happen to receive remittances, see what they spend the additional income on

- Problem: suppose I only send remittances to my family when someone is sick – then I would see households which receive remittances have worse health!


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Are remittances any different from other income?

  • We know that more income lowers poverty, improves HD outcomes

  • So should we expect anything different from remittances?


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Are remittances any different from other income?

  • One reason remittances might be spent differently is that they are only sent for specific events, or conditional on certain actions occurring:

    • 66% of remittances received in Tonga were for a special purpose.

    • Main purposes are: misinale (33%), payment of school fees (28%), funeral expenses (14%)

    • Money is fungible, so earmarking only changes consumption if conditions are binding, or if families receiving remittances face different prices.


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Why else might remittances be spent differently?

  • Households may view remittances as being more temporary in nature

  • Permanent income theory suggests households will save a larger fraction of temporary income.

  • But cross-sectional surveys provide very little information on sustainability.

    => PINZMS asks migrants and their families expectations for remittances.


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The expected chance of remittances decays

  • Expectation of receiving remittances declines over time, and declines for almost every single family:


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Implications

  • The chance of receiving remittances is expected to decay over time for both migrants and their families

    • Should expect to see receiving households save or invest more of it than they would for wage income.

    • Overall amount of remittances received in Tonga may not decay if more established migrants start sending larger amounts when they do send (or if sufficient new migrants each year)


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Approach Two: Look at the overall effect of migration

  • Remittances aren’t just manna from heaven, they are accompanied by other events – many of which are tied to the migration decision.

  • Need to think of reasons why one household may have a migrant and another similar household would not (instrument).


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Example 1: Impact on Schooling

  • Remittance effect:

    • Alleviates credit constraints – buy more schooling if you are constrained

    • Income is a normal good – buy more of everything including schooling

  • But what else is going on?


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Example 1: Impact on Schooling

  • But what else is going on?

    Absent parents – may require kids to do things for parents

    Incentives to migrate – seeing parents do well abroad with low schooling lowers incentives to get education.


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How important are these other effects?

  • Survey of Zacatecas students – found those with migrant parents less likely to want to continue their studies.

  • McKenzie and Rapoport (2006):


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Overall effect

  • After controlling for other factors, find children in migrant households attain less schooling than children in non-migrant households

    • Boys migrate instead of continuing in school

    • Girls do housework

  • This is the opposite from what we would predict just looking at remittances!


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Our identification strategy

  • Use historic state-level migration rates as instruments for current migration

  • Rates are for 1924, and reflect pattern of arrival of the railroad into Mexico

  • Initial networks lower cost of subsequent migration, resulting in self-reinforcing process

    • A household living in a community with high levels of early 20th century migration therefore more likely to have a migrant member than an otherwise identical household in a community with low historic migration


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Example 2: Impact on child health outcomes

Hildebrandt and McKenzie (2006):

  • Remittance effect: more income means households spend more on health inputs, improving health outcomes.

  • Additional migration effect: mothers in migrant households have better health knowledge, allowing them to get better health outcomes out of the same inputs.

  • Source of exogeneity: historic networks.


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Results

  • Find 3 to 4.5% lower infant mortality rate in migrant households

  • Being in a migrant household raises birthweight by 364 grams, and lowers probability of being underweight by 6.9%

  • Only part of this effect is due to greater wealth/income, some is due to improvements in health knowledge

  • But: some costs – less likely to be breastfed, receive all vaccines on schedule in first year.


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Example 3: Increase in income from Migration

  • McKenzie, Gibson and Stillman (2006).

  • Look at migration from Tonga to New Zealand

  • Have a great way of identifying the effect of migration: New Zealand has a quota to allow a certain number of Tongans in each year, uses a lottery to decide who can come in

  • We surveyed winners and losers in the lottery.

  • We also surveyed people who didn’t apply for the lottery, and use this to calculate non-experimental measures of the income gain


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Monthly income gain from migration

  • Difference in GDP per capita would suggest a gain of NZ$546 per week

  • Comparing winners and losers in the migration lottery gives an experimental estimate of NZ$274 per week

  • How well do different non-experimental estimators do at approaching this?


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Non-experimental approaches

  • If you can, find a good instrument…

    We use distance in Tonga to the NZ embassy

    - this affects likelihood of applying to migrate

    - but shouldn’t affect incomes in NZ

    Get an estimate of NZ $279 (only 1.8% different from experimental estimator)


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Non-experimental estimators

…but a bad instrument can really hurt…

Use migrant network in New Zealand

  • Seems reasonable that you would be more likely to migrate if you have more family there (first stage F-stat is 14)

  • But exclusion restriction violated

  • Estimate is $499 (82% higher than experimental!)


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What if a good instrument is not available

  • OLS regression: 31-40% too high

  • Single difference: 25% too high

  • Difference-in-differences: 20% too high

    • Note both single-difference and diff-in-diff require panel data on retrospective information on outcome of interest, before and after migration


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Matching

  • Results are about 26% too high in a simple match

  • Improvement to 19% too high when using past income in matching, and bias-adjustment

  • Results not that sensitive to trimming or to number of matches here


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Conclusions

  • It is really hard to separate the effect of remittances from the overall effect of migration

    • To do so requires thinking of a exogenous reason why one migrant might send more remittances than another migrants (exchange rate shocks studied by Dean Yang in Philippines come close)

  • In general then, want to look at overall effect of migration, of which remittances is an important part

  • Migration has a number of socioeconomic effects which differ from the pure effect of remittances


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Do the poor benefit from remittances?

  • Typical cross-sectional survey has total income, remittances

  • Questions of interest: are remittances going mainly to poor households? Do remittances lift households out of poverty?

  • Naïve answer 1: look at where households receiving remittances lie in the income distribution.

    Problem: this is AFTER remittances


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Poverty and Remittances

  • Naïve answer 2: subtract remittances from total income, and see where those receiving remittances lie in the distribution

    Problem: treats remittances as manna from heaven

  • “Solution”: try and calculate what household income would have been in absence of remittances


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Considerations

  • Lost income of migrant: some approaches try and calculate this by predicting income for migrant if s/he had stayed

  • Receiving remittances might change labor supply of other household members, might allow them to overcome liquidity constraints on entrepreneurship, etc.

  • Absence of a member might change labor supply, income-earning opportunities in household

    i.e. income of other household members affected by receipt of remittances and by migration

  • Household resources per person is higher due to absence of an eater

    => Need to consider all these factors when attempting to look at poverty impact


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So what should we do…

  • Try and think of possible good instruments

    • E.g. exchange rate shocks (Philippines), labor market conditions in receiving country, historic networks, policy changes, cost of sending money changes,…

  • Use pre- and post-migration data for difference-in-differences or matching

  • Think about possible directions of biases, and whether non-experimental methods are likely to give upper or lower estimate.


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