1. Accounting for Migration and Remittance Effects Susan Pozo Prepared for Conference on Regional Trade Agreements, Migration and Remittances with Special Focus on CAFTA and Latin America Sam Houston State University April 12, 2008

2. Much more attention paid to the migratory process in the past 5 years

3. 1. Is this a research fad? Source: Econ Lit database, 2008

4. 2.Growth in the number of persons affected by the migratory process? Source: U.S. Bureau of the Census, 2008

5. Source: Data from UN (2008)

7. Remittances to Mexico (quarterly frequency, in millions of US dollars) Source: Data from Banco Central de Mexico, 2008

8. Source: World Development Indicators, 2008 Remittances to Mexico (yearly frequency, Percent of GDP)

9. Remittances to Italy as a percent of Italian GDP (1880-1910) Source: Computed by the author with data from Cinel (1991) and from Flandreau & Zumer (2004)

10. 1990 2006 Source: US Census Bureau, http://factfinder.census.gov 3.Increased dispersion of the foreign born?

11. Computed by the author from Census Bureau

14. Increased spread of the foreign-born in 2006 relative to 1990

15. 3. Increased dispersion of the foreign-born? Source: Computed by author from 1990, 2000 Decennial Censuses and 2006 American Community Survey, US Census.

16. Economic Development Effects of the Migratory Process on Labor supply HealthEducation Happiness Poverty levels Business Investments

17. Tend to focus on only one facet of the migratory process… Poverty -- remittances Labor force participation – remittances Education—remittances Business Investment—(return) migration Health – emigration Happiness - migration

18. Migratory Process RemittancesMigration

19. Economic Development Effects of the Migratory Process on Labor supply HealthEducation Happiness Poverty levels Business Investments

20. Migrant HH and Remittance Receipt Source: Amuedo-Dorantes, Georges and Pozo, (2007)

21. Source: Computed by author from LAMP and MMP databases

22. Computed by the author from : Discrimination and Economic Outcomes Survey Database, IADB, 2006 Too large Too small

23. We miss out on the story when we focus on one or the other alone In the modeling of education a typical strategy might be to estimate: Education = βRemit +δX +Є Several problems: i) endogeneity due to reverse causality ii) endogeneity due to omitted variable bias

24. Type of Household All Model Specification Probit VariablesM.E. Remittance Receipt.0067 HH Currently Employed -0.0199 Assets 0.0494*** % dependent age 0.3121 Ed 17+-0.2857* Ed female adult 0.0979 % kids school age -0.3581** Own Child0.1090* Boy-0.0210 Child’s Age0.0075 Firstborn Child -0.0326** Urban -0.1263 No. of Observations327 Wald Chi2-test23.71 Prob>Chi20.0222 Log pseudolikelihood-104.4399 Source: Amuedo-Dorantes, Georges and Pozo, (2007)

25. Typical solution Instrument for remittances: Using migration or variables linked to long- standing migratory patterns, such as the mapping of railroads. Essentially migration networks.

26. Problems with this Approach… 1. An instrument can’t be something that should be in the equation in the first place, i.e. migration and variables proxying for long-standing migratory patterns are likely to impact educational attainment via:  A disruptive effect, in the case of family migration  A network effect, in the case of both family and broadly defined migration networks

27. Education Migratory Process Remittances Migration Migration K/networks Everything else

28. Migration capital/networks Expected value of additional education varies with the probability of future migration EV H = (p H ) R H,H + ( 1 - p H ) R H,US

29. Type of HouseholdAll Model Specification Probit Coefficient Migration networks/capital 0.4827** Household Head Currently Employed0.0037 Current Household Assets0.2743*** Percent of Non-working Age Household Members1.8011 Mean Potential Education of 17 Years +-1.7777** Potential Ed Attainment of Spouse or Head0.3882 Percent of School-age Children in the HH-2.1341*** Own Child0.4865 Boy-0.1973* Child’s Age0.0196 Firstborn Child -0.1239

30. Problems with this Approach… 1. An instrument can’t be something that should be in the equation in the first place, i.e. migration and variables proxying for long-standing migratory patterns are likely to impact educational attainment via:  A disruptive effect, in the case of family migration  A network effect, in the case of both family and broadly defined migration networks 2. We notice significant differences in selectivity with respect to different types of HHs. HHs without migrants receiving remittances are very different from HHs with migrants receiving remittances.

31. Conclusions 1. Redesign of surveys to take into account the diversity in the incidence of migration and remittances. 2. Redesign of econometric methodology to recognize differential “migration,” “remittance” and “migration capital” effects.

32. Type of Household AllNon-migrant Model Specification Probit IV-Probit VariablesM.E. Remittance Receipt.00670.6791*** HH Currently Employed -0.0199-0.2073* Assets 0.0494***0.0213 % dependent age 0.31210.0223 Ed 17+ -0.2857*0.0182 Ed female adult 0.0979-0.2607 % kids school age -0.3581**-0.2329 Own Child 0.1090*0.1594** Boy -0.02100.0214 Child’s Age 0.0075-0.0067 Firstborn Child -0.0326**0.0402 Urban -0.12630.0216 No. of Observations 327258 Wald Chi2-test 23.711181.35 Prob>Chi2 0.02220.0000 Log pseudolikelihood -104.4399-243.2202 IV Exogeneity Test a n.a.0 < = 5.99 Wald Test of Exogeneity n.a.Chi2(1)=19.85 Prob>Chi2=0.0000 Source: Amuedo-Dorantes, Georges and Pozo, (2007)

33. Sources: Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, Trends in Total Migrant Stock: The 2005 Revision http://esa.un.org/migration, Saturday, April 05, 2008; 8:31:39 AM. Marc Flandreau and Frédréric Zumer, The Making of Global Finance, 1880- 1913, OECD 2004. (Italian GDP data) Cinel, Dino, “The national integration of Italian return migration, 1870-1929. Cambridge, Cambridge University Press, 1991.