Socioeconomic Effects of Remittances and Migration

David McKenzieWorld 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:– 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

Traditional approaches to finding the impact of remittances

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

E.g. Mexican Migration Project%

Health expenses 57Food and maintenance 15Construction and repair of house 9debt payment 7purchase of consumer goods 5savings 1.5purchase of house or lot 1.1Education expenses <1Start/expand business <1Purchase of tools <1Purchase of agricultural inputs <1

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.

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

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!

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?

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.

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.

The expected chance of remittances decays

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

Mean expected chance of remitting/receiving remittances

RemainingMigrants Household

In one year's time 79.6 78.1In five years' time 63.7 68.3In ten years' time 31.5 36.9

Implications

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

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).

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?

Example 1: Impact on Schooling

• But what else is going on?Absent parents – may require kids to do

things for parentsIncentives to migrate – seeing parents do

well abroad with low schooling lowers incentives to get education.

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):

Migrant Non-migrant Migrant Non-migrantHousehold Household Household Household

Percent attending school 27 32 24 27Percent who have migrated 8 0 3 0Percent housework as main activity 1 1 45 41Percent unpaid family workers 26 21 9 10

Males 16 to 18 Females 16 to 18

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!

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

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.

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.

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

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?

Non-experimental approaches

1) 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)

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!)

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

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

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

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

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

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

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.