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An Integrated Analysis of Migration and Remittances: Modeling Migration as a Mechanism for Selection

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Prior work has modeled individuals’ migration and remittance behavior separately, and reported mixed empirical support for various remittance motivations. This study offers an integrated approach, and considers migration as a mechanism for selection in a censored probit model of remittance behavior. This approach leads to different conclusions about the determinants of remittance behavior in the Thai internal migration setting. To the extent that these determinants capture different remittance motivations, as prior research has presumed, the analysis also provides varying support for these motivations. These results suggest that migration and remittance behavior are interrelated, and it is crucial for an analysis of remittance behavior to control for the selectivity of migration.

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Fig. 1


  1. 1.

     The inconsistencies in findings can also be attributed to differences in context or data. Carling (2008) notes that differences in the migration context, family structure and societal norms may account for the variance in remittance motives across settings. Similarly, Rapoport and Docquier (2006) argue that the nature of data (e.g., cross-sectional versus longitudinal) can determine the extent of evidence for various remittance motives.

  2. 2.

     Many of the key studies on remittance motivations relied on data from internal (often urban-to-rural) remittances. Lucas and Stark (1985) tested altruistic and self-interested motives to remit with data from internal migrants in Bostwana. Hoddinott (1994) found evidence of investment and inheritance-seeking hypotheses among urban migrants in western Kenya. VanWey (2004) compared the prevalence of altruistic and contractual remittance behavior among men and women in a sample of internal migrants in Thailand. Indeed, as Carling (2008, p. 581) noted in his recent review, “Research on the determinants of remittances continues to be heavily influenced by a few studies of internal migration in developing countries in the 1980s.” The underlying assumption in the literature, then, is that similar behavioral models govern internal and international remittance flows.

  3. 3.

     These predictions relate various factors to the amount of remittances. Due to limitations of the Thai data (described in foot note 8), in this study, I focus on whether migrants remit or not, rather than how much they remit. To model the remittance amount, one can apply the standard Heckman procedure: a probit model of migration followed by an OLS model of the remittance amount (assigning zero remittances to nonremitting migrants).

  4. 4.

     This table is based on Rapoport and Docquier’s (2006) Table 2 (p. 39), but differs in three respects. First, I do not report the predictions related to the strategic motive of remittances. This motive suggests that migrants’ remittances are targeted at positively selecting future migrants. The specific empirical predictions of this model, for example, that remittances will “bribe” unskilled migrants in origin, are not testable with the Thai data. Second, I do not report the predictions for the inheritance motive for remittances. This motive implies that migrants remit to assure future inheritances from the household in origin. The predictions of this model are a subset of those of the exchange model, therefore, I include the inheritance-seeking motive within this more general model. Third and finally, I do not indicate relationships that are ambiguous or non-existent.

  5. 5.

     The predictions follow from Funkhouser’s (1995) formal model, which set up a migrant’s utility to include the utility of the origin household. In the empirical application of this model, Funkhouser (1995) argued that a migrant who is farther, and has been away longer, would value his or her own utility more than that of the remaining household members, and reduce the frequency or amount of remittances. Although several studies report a decline in remittances with increased distance to origin and duration in destination (e.g., Durand et al. 1996a; Lowell and de la Garza 2002), they typically attribute these patterns to migrants’ reduced intentions to return, rather than their diminished altruistic inclinations. The former explanation is considered in the exchange motive, where a migrant who is farther away is presumed less likely to return, and less likely to send remittances to bargain for inheritances.

  6. 6.

     The Nang Rong surveys are conducted by the University of North Carolina and Mahidol University in Thailand. The data and information about the surveys are available at http://www.cpc.unc.edu/projects/nangrong/.

  7. 7.

     I considered the potential bias due to individuals lost to follow-up. Using an alternative sample from household rosters, which contained information on all migrants based on remaining household members’ reports, I re-estimated the three migration-remittance models in Table 4. All conclusions remained identical except that the exchange model received less support in one indicator (land ownership) in the Heckman estimation with the expanded sample.

  8. 8.

     The Nang Rong data also record the amount of remittances, however, this information may be unreliable. I conducted fieldwork in the region in 2005 and observed that most migrants were reluctant to reveal how much remittances they sent. Talking to village leaders, I learned that individuals had strong incentives to hide their income in order to qualify for need-based loans from the Million Baht fund (a discretionary fund of approximately 25,000 US$ given to each village by the government). Therefore, in this study, I take a conservative approach and focus on the remittance decision rather than the amount.

  9. 9.

     The data put students and unemployed into the same occupational category, which, combined with farmers, constitute the reference group in our analysis. Students and unemployed individuals are included in the sample as a considerable share of them migrated (18 %) and sent remittances (9 %). A number of scholars suggested that remittance theories are applicable to this group. VanWey (2004) compared the altruistic and contractual models of remittances in the Nang Rong data and included the students and unemployed in her sample. Piotrowski (2006) studied the effect of social networks on remittances with the Nang Rong data and also included the students and unemployed in his analysis. Similarly, Adams (1989) analyzed the impact of remittances on inequality in Egypt, and relied on a sample containing student migrants. The literature thus justifies the inclusion of the student and unemployed category in analysis, but our key results are also robust to its exclusion (results available upon request).

  10. 10.

     According to Thailand National Statistics Office (Labor Force Survey, Table 7), manufacturing occupations paid 5,870 baht/month on average in 2007. The average wages for service workers (employed in private households or hotels/restaurants) and construction workers were 5,012 baht/month and 4,715 baht/month respectively.

  11. 11.

     Non-agricultural occupations include wage positions (e.g., factory or construction worker, teacher, government official) as well as entrepreneurial activities (e.g., shop owner or street vendor), and typically provide higher earnings than agricultural work (e.g., harvesting for pay).

  12. 12.

     Lagging the wealth (or income) indicators does not solve the endogeneity problem if current migration decisions are correlated with past migration, which affect household wealth in the past, or if there are omitted variables related to both wealth and migration. To test if this is the case, I perform a procedure suggested by (Spencer and Berk 1981). I estimate a model of wealth (for each of the land, cattle, household members in a non-agricultural occupation and economic activities indicators) with exogenous regressors (rain shortages, which are likely to affect wealth and income). Then, in the migration equation, I add the residuals from the four wealth equations as extra regressors. The coefficients for the four regressors are jointly insignificant (F-statistic = 1.51, p = 0.20), and the null hypothesis that the wealth indicators are orthogonal to the errors cannot be rejected. I repeat this analysis for the remittance equation, and similarly find that the coefficients for the residuals are jointly insignificant (F-statistic = 0.49, p = 0.75). These results suggest that the lagged wealth (and income) indicators can be treated as exogenous to current migration or remittance decisions. Crucially, this treatment does not preclude an association between wealth and past migration or remittances. But this link does not seem to bias our estimates of the effect of lagged wealth on migration or remittances.

  13. 13.

     In the classical Heckman (1979) example, one observes wages for women who are employed. Employed women constitute a nonrandom sample, if, for example, more productive women self-select into employment based on their higher expectations about wages. In that case, focusing on the censored sample alone leads us to overestimate the wages in the overall population. One can extend this analogy to remittances: more productive individuals may self-select into migration behavior based on their expectations about earnings in destination, and consequently, their potential for sending remittances to origin. But one can also imagine the opposite situation, where less productive individuals self-select into migration due to the lack of opportunities in origin. The first case of positive selection leads us to overestimate remittances in the overall population, and the second case of negative selection leads us to underestimate them. By observing the sign and magnitude of the correlation term (rho), we can assess the plausibility of each scenario.

  14. 14.

     One objection to distance as an instrument is that omitted characteristics may be related to both residential choices and subsequent migration decisions. Distance, in that case, may not be assumed exogenous to migration behavior. Two analyses consider this possibility. The first introduces village-level indicators to the migration model (the proportion of households growing cassava and sugar cane—the crops that create agricultural jobs—and the presence of a nearby factory) capturing the conditions that may have attracted settlers to a village initially, and may have affected their migration decisions later on. The second analysis estimates the migration model on a sample of individuals born in their village of residence (who, as a result, have not chosen that residence area themselves). The estimated effect of distance on migration remains robust in both analyses (results available upon request), which provide no evidence against the assumption that distance is an exogenous determinant of migration.

  15. 15.

     Individuals’ proximity to the final destination (e.g., Bangkok) is a more accurate measure of travel costs. But this measure is only available for migrants, therefore, cannot be used as an instrument for the migration decision.

  16. 16.

     I conducted 24 focus group interviews, with the participation of 158 individuals, in 8 of the 22 Nang Rong survey villages in November 2005. In each village, I consulted with village headmen to identify potential participants for three focus groups: (1) village leaders (village headman and village committee members), (2) migrant sending household members, and (3) return migrants. Focus groups consisted of six to eight participants, typically equal number of men and women, who discussed the motivations for, and the consequences of, migration and remittance behavior.

  17. 17.

     In all models, the standard errors are adjusted for clustering at the household level.


  1. Achen, C. (1986). The statistical analysis of quasi-experiments. University of California Press

  2. Adams, R. (1989). Worker remittances and inequality in rural Egypt. Economic Development and Cultural Change, 38(1), 45–71.

  3. Agarwal, R., & Horowitz, A. W. (2002). Are international remittances altruism or insurance? Evidence from Guyana using multiple-migrant households. World Development, 30(11), 2033–2044.

  4. Ahlburg, D., & Brown, R. (1998). Migrants’ intentions to return home and capital transfers: A study of Tongans and Samoans in Australia. Journal of Development Studies, 35(2), 125–151.

  5. Banerjee, B. (1984). The probability, size and uses of remittances from urban to rural areas in India. Journal of Development Economics, 16(3), 293–311.

  6. Berk, R. (1983). An introduction to sample selection bias in sociological data. American Sociological Review, 48(3), 386–398.

  7. Boyes, W., Hoffman, D., & Low, S. (1989). An econometric analysis of the bank credit scoring problem. Journal of Econometrics, 40(1), 3–14.

  8. Cai, Q. (2003). Migrant remittances and family ties: A case study in China. International Journal of Population Geography, 9(6), 471–483.

  9. Card, D. (1993). Using geographic variation in college proximity to estimate the return to schooling. NBER working paper.

  10. Carling, J. (2008). The determinants of migrant remittances. Oxford Review of Economic Policy, 24(3), 581–598.

  11. Chamratrithirong, A., Archavanitkul, K., Richter, K., Guest, P., Thongthai, V., Bonochalaksi, W., et al. (1995). National migration survey of Thailand. Nakonpathom: Institute for Population and Social Research, Mahidol University.

  12. Cox, D. (1987). Motives for private income transfers. The Journal of Political Economy, 95(3), 508–546.

  13. Cox, D., Eser, Z., & Jimenez, E. (1998). Motives for private transfers over the life cycle: An analytical framework and evidence for Peru. Journal of Development Economics, 55(1), 57–80.

  14. Cox, D., & Jimenez, E. (1990). Achieving social objectives through private transfers: A review. The World Bank Research Observer, 5(2), 205–218.

  15. Curran, S., Garip, F., Chung, C., & Tangchonlatip, K. (2005). Gendered migrant social capital: Evidence from Thailand. Soc. F., 84, 225.

  16. de la Briere, B., Sadoulet, E., De Janvry, A., & Lambert, S. (2002). The roles of destination, gender, and household composition in explaining remittances: An analysis for the Dominican Sierra. Journal of Development Economics, 68(2), 309–328.

  17. Dubin, J., & Rivers, D. (1989). Selection bias in linear regression, logit and probit models. Sociological Methods and Research, 18(2), 360–390.

  18. Durand, J., Kandel, W., Parrado, E. A., & Massey, D. S. (1996a). International migration and development in Mexican communities. Demography, 33(2), 249–264.

  19. Durand, J., Parrado, E., & Massey, D. (1996b). Migradollars and development: A reconsideration of the Mexican case. International Migration Review, 30(2), 423–444.

  20. Foster, A., & Rosenzweig, M. (2001). Imperfect commitment, altruism, and the family: Evidence from transfer behavior in low-income rural areas. Review of Economics and Statistics, 83(3), 389–407.

  21. Fuchs-Schuendeln, N., & Schuendeln, M. (2009). Who stays, who goes, who returns? East-West migration within Germany since reunification. Economics of Transition, 17(3), 703–738.

  22. Funkhouser, E. (1995). Remittances from international migration: A comparison of El Salvador and Nicaragua. The Review of Economics and Statistics, 77(1), 137–146.

  23. Garip, F. (2008). Social capital and migration: How do similar resources lead to divergent outcomes? Demography, 45(3), 591–617.

  24. Garip, F. (2012). The impact of migration and remittances on wealth accumulation and distribution in rural Thailand. In Weatherhead Center for International Affairs Working Paper. Harvard University.

  25. Garip, F., & Curran, S. (2010). Increasing migration, diverging communities: Changing character of migrant streams in rural Thailand. Population Research and Policy Review, 29(5), 659–685.

  26. Heckman, J. (1979). Sample selection bias as a specification error. Econometrica: Journal of the Econometric Society, 47(1), 153–161.

  27. Hoddinott, J. (1994). A model of migration and remittances applied to western Kenya. Oxford Economic Papers, 46(3), 459–476.

  28. Jansen, K. (1997). External finance in Thailand’s development: An interpretation of Thailand’s growth boom. New York, NY: St Martin’s Press.

  29. Johnson, G., & Whitelaw, W. (1974). Urban-rural income transfers in Kenya: An estimated-remittances function. Economic Development and Cultural Change, 22(3), 473–479.

  30. Jones, R. C. (1998). Remittances and inequality: A question of migration stage and geographic scale. Economic Geography, 74(1), 8–25.

  31. Lauby, J., & Stark, O. (1988). Individual migration as a family strategy: Young women in the Philippines. Population Studies, 42(3), 473–486.

  32. Lee, Y., Parish, W., & Willis, R. (1994). Sons, daughters, and intergenerational support in Taiwan. American Journal of Sociology, 99(4), 1010–1041.

  33. Lillard, L., & Willis, R. (1997). Motives for intergenerational transfers: Evidence from Malaysia. Demography, 34(1), 115–134.

  34. Little, R. J. (1985). A note about models for selectivity bias. Econometrica, 53(6), 1469–1474.

  35. Lowell, B. L., & de la Garza, R. O. (2002). The development role of remittances in U.S. Latino communities and Latin America. In R. de la Garza & B. L. Lowell (Eds.), Sending money home: Hispanic Remittances and community development (pp. 3–27). Lanham, MD: Rowman and Littlefield.

  36. Lucas, R., & Stark, O. (1985). Motivations to remit: Evidence from Botswana. The Journal of Political Economy, 93, 901–918

  37. Massey, D., & Basem, L. (1992). Determinants of savings, remittances, and spending patterns among US migrants in four Mexican communities. Sociological Inquiry, 62(2), 185–207.

  38. Massey, D. S., Goldring, L., & Durand, J. (1994). Continuities in transnational migration: An analysis of nineteen mexican communities. American Journal of Sociology, 99(6), 1492–1533.

  39. Meng, C., & Schmidt, P. (1985). On the cost of partial observability in the bivariate probit model. International Economic Review, 26(1), 71–85.

  40. Mills, M. (1997). Contesting the margins of modernity: Women, migration, and consumption in Thailand. American Ethnologist, 24(1), 37–61.

  41. Moffitt, R. (2003). Causal analysis in population research: An economist’s perspective. Population and Development Review, 29(3), 448–458.

  42. Mora, J. J. (2005). The impact of migration and remittances on distribution and sources of income. United Nations Population Division Working Paper.

  43. Phongpaichit, P., & Baker, C. (1996). Thailand’s boom!. New South Wales: Allen and Unwin.

  44. Piotrowski, M. (2006). The effect of social networks at origin communities on migrant remittances: Evidence from Nang Rong District. European Journal of Population/Revue Europienne de Demographie, 22(1), 67–94.

  45. Poirine, B. (1997). A theory of remittances as an implicit family loan arrangement. World Development, 25(4), 589–611.

  46. Rapoport, H., & Docquier, F. (2006). The economics of migrants’ remittances. Handbook on the Economics of Giving, Reciprocity and Altruism, 2, 1135–1198.

  47. Ratha, D., & Xu, Z. (2008). Migration and remittances factbook 2008. Washington, DC: World Bank Publications.

  48. Reardon, T. (1997). Using evidence of household income diversification to inform study of the rural nonfarm labor market in Africa. World Development, 25(5), 735–747.

  49. Reed, W. (2000). A unified statistical model of conflict onset and escalation. American Journal of Political Science, 44(1), 84–93.

  50. Regmi, G., & Tisdell, C. (2002). Remitting behaviour of Nepalese rural-to-urban migrants: Implications for theory and policy. Journal of Development Studies, 38(3), 76–94.

  51. Rempel, H., & Lobdell, R. (1978). The role of urban-to-rural remittances in rural development. Journal of Development Studies, 14(3), 324–341.

  52. Rindfuss, R., Kaneda, T., Chattopadhyay, A., & Sethaput, C. (2007). Panel studies and migration. Social Science Research, 36, 374–403.

  53. Rosenzweig, M. (1988). Risk, implicit contracts and the family in rural areas of low-income countries. The Economic Journal, 98(393), 1148–1170.

  54. Russell, S. (1986). Remittances from international migration: a review in perspective. World Development, 14(6), 677–696.

  55. Sana, M. (2005). Buying membership in the transnational community: migrant remittances, social status, and assimilation. Population Research and Policy Review, 24, 231–261.

  56. Spencer, D. F., & Berk, K. T. (1981). A limited information specification test. Econometrica, 49, 1079–1085.

  57. Staiger, D., & Stock, J. H. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586.

  58. Stark, O. (1991). The migration of labor. Cambridge, MA: Basil Blackwell.

  59. Stark, O., & Bloom, D. (1985). The new economics of labor migration. The American Economic Review, 75(2), 173–178.

  60. Stark, O., & Levhari, D. (1982). On migration and risk in LDCs. Economic Development and Cultural Change, 31(1), 191–196.

  61. Taylor, J. (1999). The new economics of labour migration and the role of remittances in the migration process. International Migration, 37(1), 63–88.

  62. Taylor, J., Arango, J., Hugo, G., Kouaouci, A., Massey, D., & Pellegrino, A. (1996). International migration and national development. Population Index, 62(2), 181–212.

  63. Taylor, J., Rozelle, S., & De Brauw, A. (2003). Migration and incomes in source communities: A new economics of migration perspective from China*. Economic Development and Cultural Change, 52, 75–101.

  64. van de Ven, W., & van Praag, B. (1981). The demand for deductibles in private health insurance: A probit model with sample selection. Journal of Econometrics, 17(2), 229–252.

  65. VanWey, L. (2003). Land ownership as a determinant of temporary migration in Nang Rong, Thailand. European Journal of Population, 19(2), 121–145.

  66. VanWey, L. K. (2004). Altruistic and contractual remittances between male and female migrants and households in rural Thailand. Demography, 41(4), 739–756.

  67. Warr, P., & Nidhiprabha, B. (1996). Thailand’s macroeconomic miracle: Stable adjustment and sustained growth. Washington, DC: World Bank Press.

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This research was funded by grants from the Clark and Milton Funds at Harvard University and a Junior Faculty Synergy Semester Grant from the Weatherhead Center for International Affairs. I am grateful to Peter Azoulay, Sara Curran, Cedric Deleon, Paul DiMaggio, Andrew Foster, Stine Grodal, Alya Guseva, Emily Heaphy, William Kandel, Nancy Luke, Doug Massey, Sigrun Olafsdottir, Kenneth Wachter, Mary Waters, Bruce Western and Viviana Zelizer for helpful advice. I thank the research team from the Carolina Population Center at the University of North Carolina and the Institute for Population and Social Research at Mahidol University for their data collection efforts and the villagers of Nang Rong district, Buriram province, Thailand for their cooperation.

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Correspondence to Filiz Garip.

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Garip, F. An Integrated Analysis of Migration and Remittances: Modeling Migration as a Mechanism for Selection. Popul Res Policy Rev 31, 637–663 (2012). https://doi.org/10.1007/s11113-012-9246-5

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  • Migration
  • Remittances
  • Selectivity
  • Thailand