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