There are at least four methodological challenges that confront any economic (or social science) work on international migration and remittances. These problems include: simultaneity, reverse causality, selection bias and omitted variables. This introduction reviews each of these challenges, and suggests possible solutions (for more, see McKenzie and Sasin, 2007).
First, many of the decisions on international migration are made at the same time as other household decisions. For example, a household may decide to send its oldest male to work abroad at the same time that it decides to send its youngest daughter to school. As a result, variables that “cause” international migration may also “cause” household patterns of consumption and education.
The second problem is reverse causality. For instance, while international remittances may help reduce poverty in the developing world, the level of poverty may also influence the amount of remittances received by a particular country. Thus, any attempt to analyze the impact of remittances on poverty that fails to consider the reverse causality between these two variables might lead to erroneous conclusions.
The third problem is selection bias, which refers to the “selectivity” of people who tend to migrate and to receive remittances. If, for example, households with more education or income are more likely to produce migrants, then it is impossible to identify the effects of migration by simply comparing the characteristics of migrant and non-migrant households.
Fourth, when households produce migrants or receive remittances on the basis of unobservable characteristics – characteristics like the risk averseness of the household head – then the problem of omitted variable bias arises. For example, it is possible that households with more risk averse heads will be less likely to produce migrants, but it is very difficult to collect data on this issue.
To meet these various methodological challenges, at least five possible solutions have been proposed in the literature. Most of the studies included in this anthology employ one or more of these solutions.
The first, and perhaps best, solution is to use a randomized, “natural” experiment whereby individuals desiring to pursue international migration are denied the right to migrate (by a lottery system, for example), thereby creating a “control group” of would-be-migrants to compare with a group of actual migrants (see e.g. McKenzie, Gibson and Stillman, 2006). Comparing the characteristics of would-be-migrants to those of actual migrants would then yield accurate information on the causal motives for migration. Unfortunately, however, it is very difficult to conduct such randomized, “natural” experiments in the developing world to such an extent that the only real example at this time of such a natural experiment is MeKenzie, Giibson and Stillman (2006).
A second, and slightly less difficult, solution is to use panel data. Panel data, which includes repeated observations on the same household over two or more time periods, is a good solution because by taking “first differences” between various variables it becomes possible to eliminate many of the methodological problems discussed above. Unfortunately, however, panel data sets on international migration and remittances in the developing world are relatively rare.
A third solution is to construct a “counterfactual” situation, that is, to artificially construct what the status of a migrant household would have been had that household not produced a migrant. For example, if the topic is remittances and income, then it would be necessary to estimate the income of a migrant household by imputing the value of that migrant had he stayed and worked at home (see e.g. Barham and Boucher, 1998).
A fourth solution to use econometric procedures to regress the outcome of interest (for example, poverty) on a set of independent variables, and then supplement this approach with a sample selection procedure, like the two-stage Heckman model (see e.g. Acosta et al., 2007). Here the selection model is used to estimate the size and direction of the selection bias. However, the difficulty comes in specifying an exogenous variable that “causes” migration or the receipt of remittances in the first-stage equation, but has no direct impact on the dependent variable in the second-stage equation.
A fifth, and quite common, solution is to use instrumental variables. A good instrumental variable, one that is correlated with the explanatory variable but uncorrelated with the outcome variable, can eliminate many of the biases that arise from endogeneity, selection bias and omitted variables. In practice, however, selecting a good instrumental variable in migration and remittances work can be difficult. For example, assume that migration is the explanatory variable and poverty is the outcome variable of interest. The challenge is then to find an instrumental variable (like distance, for example) that is correlated with migration but exogenous to the outcome variable, poverty.
As noted above, many studies employ one or more of these solutions to the problems of simultaneity, reverse causation and selectivity. It is not uncommon, for example, to find instrumental variables used in conjunction with panel data. Other studies estimate counterfactual situations with the use of instrumental variables (see Acosta et al, 2007).
Topic 2 – Articles
Acosta, Pablo, Pablo Fajnzylber, and Humberto Lopez. 2007. The Impact of Remittances on Poverty and Human Capital: Evidence from Latin American Household Surveys. In International Migration, Economic Development & Policy, edited by C. O. a. M. Schiff. Washington, DC: World Bank.
This paper uses nationally-representative household surveys from 11 Latin American countries to examine the impact of international remittances on poverty, education and health. Since remittances may be endogenous, the authors estimate counterfactual incomes for migrants had they stayed and worked at home, and they control for selection bias using a two-step Heckman procedure. The authors find evidence of selection bias in the migration process, suggesting that households with a higher propensity to not migrate also have higher per capita incomes. Results from the counterfactual income estimates suggest that the impact of remittances on poverty is positive but modest: in most countries poverty headcounts fall by no more than 5 percent when remittances are included in household income.
While remittance flows to developing countries are very large, it is unknown whether migrants desire more control over the uses to which remittances are put. This research uses a randomized field experiment to investigate the importance of migrant control over the use of remittances. In partnership with a large Salvadoran bank, we offered US-based migrants from El Salvador facilities for channeling remittances into savings accounts in their home country. We randomly varied migrant control over El Salvador-based savings by offering different types of accounts across treatment groups. The treatment that offered migrants the greatest degree of control over savings had the highest impact on savings accumulation at the partner bank, compared to comparison groups offered less or no control over savings. Effects of this treatment on savings are concentrated among migrants who express demand for control over remittances in the baseline survey. We also find positive spillovers of our savings intervention in the form of increased savings at other banks (specifically, banks in the U.S.), which is likely due to the financial education implicitly conveyed by our intervention. Our findings point to the potential for future innovations to enhance migrant control over remittance uses in other areas such as financing for education, health, housing, or micro-enterprises.
This study uses a small, non-representative household survey from Nicaragua (152 households) to examine the effects of international migration on income distribution. Since remittances may be endogenous, the authors estimate counterfactual incomes for migrants had they stayed and worked at home, and they control for selection bias using a two-stage Heckman procedure. Controlling for human capital and networks, the authors find no evidence of selection bias in the migration process, suggesting that migrants are selected randomly from the population. With respect to income inequality, the authors find that when the observed income distribution is compared with two no-migration counterfactual situations, income inequality is higher when international remittances are included in household income.
McKenzie, David, and Sasin J. Marcin. 2007. Migration, Remittances, Poverty and Human Capital: Conceptual and Empirical Challenges. In World Bank Policy Research Paper 4272. Washington, DC: World Bank.
This paper reviews common methodological problems faced by social scientists interested in measuring the impact of migration and remittances on poverty, inequality and human capital formation. It briefly reviews methodological problems such as endogeneity, reverse causality, selection bias and omitted variables. The paper also proposes a number of solutions to these problems, including: conducting “natural” experiments, constructing counterfactuals, using panel data and creating instrumental variables. Since many researchers use instrumental variables, the paper pays particular attention on how to create and test for the validity of instrumental variables.
McKenzie, David, John Gibson, and Steven Stillman. 2006. How Important Is Selection? Experimental Versus Non-Experimental Measures of the Income Gains from Migration. In World Bank Policy Research Working Paper 3906. Washington, DC: World Bank.
This study uses a small, non-representative household survey from Tonga (438 households) to examine the income gains from international migration. All empirical studies that analyze the income gains from migration face the methodological problem of the non-random selection of migrants. To meet this problem, the authors use a migrant lottery system whereby New Zealand allows a certain quota of Tongans to migrate each year. This allows the authors to estimate the income gains from migration by comparing the incomes of 3 groups: migrants who were selected in the lottery, and migrated; those who were selected in the lottery, but did not migrate; and those who did not apply to the lottery. Results suggest that Tonga-to-New Zealand migrants are positively selected in terms of both observable and unobservable skills. Results also show that an instrumental variables approach works best in estimating the income gains from migration.