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The impacts of workers’ remittances on poverty and inequality in developing countries

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Abstract

This study investigates the impacts of workers’ remittances on poverty and inequality by using data for 103 developing countries from 1990 to 2014. The main difficulty in assessing these impacts is the endogeneity of remittances and the difficulty in finding instruments to resolve this issue at the aggregate level. To address the endogeneity of remittances, I estimate bilateral remittances and use them to create weighted indicators of remittance-sending countries. These weighted indicators are used as instruments for remittance inflow to remittance-receiving countries. Results obtained in this study indicate that remittances decrease poverty. A 10% increase in per capita remittances leads to a 1% decrease in poverty headcount, 1.8% decrease in poverty gap, and 2.5% decrease in poverty headcount. Remittances also decrease inequality in developing countries. Remittances decrease the Gini coefficient, increase the income share held by the poorest decile and quintile, and decrease the income share held by the richest quintile and decile in developing countries.

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Notes

  1. The reason that region dummies are used in this research rather than country dummies is that for many countries, there is just one year of data available, and for using country-specific effects, those countries had to be dropped from the regression. To avoid dropping those countries, I used region-specific effects rather that country-specific effects.

  2. Ratha and Shaw (2007) propose a method of “Calculating Weights Based on Migrant Stocks, Per Capita Income in the Destination Countries, and Per Capita Income in the Source Countries”.

    The average remittance sent by a migrant from host country j to home country i (\(r_{ij}\)) is modeled as a function of the per capita income of the home country and the host country.

    $$\begin{aligned} \displaystyle {r_{ij}=f(\overline{Y}_i Y_j)={\left\{ \begin{array}{ll} \overline{Y}_i &{}\quad \hbox {if} \ \ Y_j< \overline{Y}_i\\ \overline{Y}_i + (Y_j - \overline{Y}_i )^ \beta &{}\quad \text {otherwise} \end{array}\right. }} \end{aligned}$$

    where \(Y_j\) is the average per capita GNI of host country j, \(\overline{Y}_i\) is the per capita GNI of the migrant’s home country, and \(\beta \) is a parameter between 0 and 1. The amount sent by an average migrant is assumed to be at least as much as the per capita income of the home country, even when the individual migrates to a lower-income country. The rationale is that the migration occurs in the expectation of earning a higher level of income for the dependent household than what the migrant would earn in her home country. To estimate bilateral remittances for all countries, they use the average \(\beta \) (equal to 0.75) for the top 20 remittance-receiving countries. Then, for each home country i, \(r_{ij}\) is used to build weights for each host country j as

    $$\begin{aligned} W_{ij}=\frac{r_{ij} M_{ij}}{\sum \nolimits _{j=1}^{214}{r_{ij} M_{ij}}}. \end{aligned}$$

  3. UN bilateral migration data includes just the following years: 1990, 1995, 2000, 2005, 2010, 2013, and 2015. I used linear interpolation to estimate bilateral migration for the remaining years. This should not raise any concerns as bilateral migration data, as well as GNI per capita of remittance-sending countries and GNI per capita of remittance-receiving countries, only are used to estimate bilateral remittances to use them as weights of remittance-sending countries in constructing weighted average economic variables of remittance-sending countries.

  4. The estimated bilateral data consist of 214 remittance-receiving countries, 214 remittance-sending countries, and 1,190,696 observations.

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  1. Purdue University Northwest, Hammond, IN, USA

    SeyedSoroosh Azizi

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Appendices

Appendix A

See Table 7.

Table 7 List of countries
Full size table

Appendix B

The results of the first-stage regressions are provided in Table 8. The dependent variable is the logarithm of per capita remittances. The explanatory variables are weighted average per capita GNI, unemployment rate, real interest rate, real exchange rate, and labor force participation rate of the remittance-sending countries. In each first-stage regression, the endogenous variable (per capita remittances) is regressed on all instruments and all exogenous variables of the original regression. In each first-stage regression, I test the joint significance of five instruments. The F-values are reported in the table. The rule of thumb is that if the F-value is greater than 10, then the instrument is strong.

The first column of Table 8 shows the result of the first-stage regression for original regressions with poverty measures as the dependent variables. The second column of Table 8 shows the result of the first-stage regression for original regressions with the Gini coefficient as the dependent variable and per capita GDP, inflation, broad money, and per capita FDI as control variables. This column also represents the results of the first-stage regression for the regressions provided in Table 6. The third column of Table 8 shows the result of the first-stage regression for original regressions with the Gini coefficient as the dependent variable and per capita GDP, broad money, and per capita FDI as control variables. The fourth column of Table 8 shows the results of the first-stage regression for original regressions with the Gini coefficient as the dependent variable and per capita GDP and broad money as control variables.

Table 8 First-stage regressions
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Azizi, S. The impacts of workers’ remittances on poverty and inequality in developing countries. Empir Econ 60, 969–991 (2021). https://doi.org/10.1007/s00181-019-01764-8

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