Anne-Sophie Robilliard

IRD, DIAL, Paris

Are There Returns to Migration Experience?

An Empirical Analysis using Data on Return Migrants and Non-Migrants in West Africa

Workshop on “Migration in West Africa. Governance and Links to Labour Markets”

Dakar, 25 March 2010

Flore Gubert

IRD, DIAL, Paris

Philippe De Vreyer

University Paris Dauphine

and DIAL

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Motivations

• Determinants and impact of return migration = under-researched topic

• Migration from West African countries known to be essentially temporary

• Return migration seen as (assumed to be) one of the channels through which migration positively impacts on development. What is the evidence?

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ObjectiveShed light on the following questions:

• How do return migrants compare with non-migrants in the home country?

• Are financial capital and new skills acquired abroad, if ever, used productively back home?

• Are return migrants rather “successes” or “failures”?

=> To this end, we focus on the urban labour market performance of return migrants. We investigate whether, upon returning, migrants’ experience abroad provide an earnings premium for wage earners and/or a productivity advantage for entrepreneurs

« Intuitions »What should be expected from experience abroad?

On the positive side:

-well… additional experience (of course) and new skills, that may result in a wage premium upon return, except if the migrants only get low qualified/unskilled jobs in destination countries or are unable to apply their new skills back home;

- Savings to invest in businesses;

On the negative side:

- Disruption of social networks that have been found to facilitate access to jobs

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Data

• We use 1-2-3 Surveys conducted in 7 major WAEMU cities (Abidjan, Bamako, Cotonou, Dakar, Lomé, Niamey and Ouagadougou) from 2001 to 2002.

• Phase 1 (labour survey) : all individuals 15 years and older (n = 58,459)

• Phase 2 (informal sector) : subsample of small informal microenterprises whose owners were surveyed during phase 1 (n=6,619)

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Definitions:

Return migrant: individual who was born in the country of current residence (or who is a citizen of this country), who lived abroad for some time and then came back.

Non-migrant: individual who never left the country where he was born and interviewed

Limitations of the data: Because the surveys were not designed to investigate migration issues, very limited information on the returnees’ migration experience (no information on year of departure, place of residence at the time of migration, migration duration, etc.)

Data (2)

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Data (3)

Non-migrants Return migrants Immigrants Total WAEMU Emigrants

WAEMU OECD Other All Cotonou 6,623 409 84 193 686 330 7,639 266 Ouagadougou 7,653 752 21 56 829 64 8,546 518 Abidjan 6,083 84 44 36 164 1,265 7,512 41 Bamako 6,878 325 127 77 529 122 7,529 421 Niamey 7,675 161 14 98 273 369 8,317 210 Dakar 12,091 79 45 120 244 163 12,498 93 Lomé 5,264 352 55 462 869 285 6,418 270 Total 52,267 2,162 390 1,042 3,594 2,598 58,459 1,819 Source: 1-2-3 surveys, Phase 1. Note: Individuals aged 15 years and older; “Emigrants” are nationals who are currently living in one of the WAEMU cities of the PARSTAT sample.

Phase 1: Samples’ composition

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Are return migrants different from non-migrants in terms of their individual characteristics?

• On average, compared to non-migrants, return migrants appear older, with a higher share of males, more often married than not, and more educated;

• Compared to return migrants from non-OECD countries, return migrants from OECD countries are on average 5 years older, much more educated (more than 11 years vs. 5.6 years) and with a much higher proportion of men (62.0% vs. 49.3%).

Data (4)

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Are they different in terms of their employment situation?

• Return migrants are not systematically more active than non-migrants except for return migrants from outside the WAEMU;

• Concerning sectors of employment the difference does not appear significant on average between active non-migrants and active return migrants;

• However, because migrants returning from OECD countries are much more educated on average, their rate of public (resp. formal) employment is 18.1% (resp.16.9%), when that of non-migrants is only 5.4% (resp.7.8%)

Data (5)

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• Return migrants from OECD countries have a higher probability of being business owners even once a number of their individual characteristics have been controlled for.

Data (6)

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Non-migrants Return migrants Immigrants Total WAEMU Emigrants

WAEMU OECD Other All Cotonou 40.3 50.9 197.0 73.4 68.4 38.6 42.4 51.0 Ouagadougou 42.7 44.2 312.5 36.2 50.5 43.5 43.6 43.9 Abidjan 71.6 87.3 311.4 48.7 140.9 58.4 71.0 110.8 Bamako 55.9 49.5 117.9 40.3 63.9 63.3 56.5 46.7 Niamey 50.9 79.0 183.0 79.4 84.7 40.1 51.6 49.4 Dakar 58.6 53.3 187.5 60.4 81.5 90.1 59.5 93.4 Lomé 27.5 45.0 172.6 30.4 45.3 52.7 30.8 35.0 Total 55.9 54.7 227.1 46.0 73.4 57.6 56.9 48.4 Source: 1-2-3 surveys, Phase 1. Note: 1000 FCFA PPP; Active individuals age 15 years and older; “Emigrants” are nationals who are currently living in one of the sample cities.

Data (7)

• Since return migrants from OECD countries have more favourable characteristics and positions in the labour market, it does not come as a surprise that their earnings are higher than those of non-migrants …

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… Research question: Does this holds true when controlling for individual characteristics and selection biases?

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Empirical strategy

• Using data from phase 1, we first estimate individual earnings functions to measure the impact of return migration on earnings.

• We then investigate whether return migrants are more productive microentrepreneurs using data on the sample of self-employed workers and small firm owners surveyed in phase 1.

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• Test 1: Earnings equations

Empirical strategy (2)

iiii eDXY

where Y is the natural-log of monthly earnings, X is a vector of personal characteristics, and D is a dummy variable indicating whether the individual is a return migrant or not.

Note: Two selections: (1) working selection; (2) migration selection Sol. Estimate a model controlling for a double process of self-selection. BUT very difficult to estimate given the data at hand

So we completed the earnings equation by only equation describing the probability of being a return migrant:

The two equations were then estimated jointly using MLE.

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Empirical strategy (3)

iii vQRM *

Instruments:

- Father’s characteristics when the worker was 15; - Share of return migrants in neighborhood.

Validity of the instruments indirectly tested through the use of two alternative estimators (TWOS and MLE).

Provided the model is correctly specified, these two estimators should give (asymptotically) equivalent results.

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Empirical strategy (4)

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ResultsEarnings equations (pooled sample)

WEAMU OECD Other

MLE TWOS MLE TWOS MLE TWOS

Males Return migrant dummy

coefficient 0.0625 0.185 0.384** 0.396* -0.0088 0.185

(standard error) (0.140) (0.220) (0.193) (0.205) (0.152) (0.220)

Correlation coefficient -0.059

-0.190

-0.0409

(0.107)

(0.155)

(0.106)

Females

Return migrant dummy coefficient 0.703*** 0.244 0.907*** 0.744*** 0.0206 0.0783

(standard error) (0.119) (0.218) (0.175) (0.238) (0.414) (0.396)

Correlation coefficient -0.536***

-0.574***

0.0684

(0.086)

(0.130)

(0.318)

All

Return migrant dummy coefficient 0.292** 0.367** 0.681*** 0.751*** 0.0389 0.128

(standard error) (0.143) (0.163) (0.122) (0.161) (0.150) (0.239)

Correlation coefficient -0.205*

-0.380***

-0.0353

(0.111)

(0.091)

(0.104)

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• Test 2: Production functions

Empirical strategy (5)

),( LKFY

where Y is the value added of the firm, K is the capital stock and L is labour

Migration selection issue: production function and return migration equation jointly estimated by MLE

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Results (2)Production functions on pooled data

WAEMU Other OECD MLE TWOS MLE TWOS MLE TWOS

Log(Capital) 0.172 0.171 0.161 0.161 0.162 0.162 (12.92)*** (12.91)*** (12.04)*** (12.04)*** (12.00)*** (11.98)*** Dummy = 1 if no capital 0.279 0.278 0.259 0.259 0.258 0.257 (3.91)*** (3.90)*** (3.59)*** (3.59)*** (3.55)*** (3.54)*** Log(Labour) 0.407 0.407 0.400 0.400 0.414 0.413 (20.26)*** (20.27)*** (19.88)*** (19.88)*** (20.20)*** (20.20)*** Return migrant 0.136 0.643 0.293 0.277 1.094 2.034 (0.52) (1.10) (0.81) (0.60) (2.32)** (2.22)** % RM in neighborhood Constant 1.496 1.469 1.622 1.622 1.486 1.497 (9.37)*** (9.05)*** (8.88)*** (8.88)*** (9.16)*** (9.20)***

Observations 5,438 5,438 5,323 5,323 5,214 5,214

Conclusion

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• Experience abroad results in a substantial wage premium for migrants returning from an OECD country but not for other return migrants.

• The level of the premium varies between sexes with women enjoying a much higher premium than men.

• Past migration in an OECD country also results in a productive advantage for returnees who became entrepreneurs upon returning.

(Channels?)

• Given the low share of return migrants in the population of WAEMU countries, the effectiveness of return migration as a driver of development is only moderate.