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The Impact of Migration and Remittances on Wealth Accumulation and Distribution in Rural Thailand

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This article studies the impact of internal migration and remittance flows on wealth accumulation and distribution in 51 rural villages in Nang Rong, Thailand. Using data from 5,449 households, the study constructs indices of household productive and consumer assets with principal component analysis. The changes in these indices from 1994 to 2000 are modeled as a function of households’ prior migration and remittance behavior with ordinary least squares, matching, and instrumental variable methods. The findings show that rich households lose productive assets with migration, potentially because of a reduction in the labor force available to maintain local economic activities, while poor households gain productive assets. Regardless of wealth status, households do not gain or lose consumer assets with migration or remittances. These results suggest an equalizing effect of migration and remittances on wealth distribution in rural Thailand.

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  1. 1.

    Household land is measured inconsistently across survey waves and is also excluded from the asset index computation. Although the 1984 and 1994 surveys captured both the total amount of land owned and land used, the 2000 survey asked about only the latter. The exclusion of land does not affect the main results. Alternative models of productive asset change (where the asset index includes land owned in 1994 and land used in 2000) produce qualitatively similar results (available upon request) to those presented here.

  2. 2.

    Some of the consumer assets can be considered productive. For example, household members may use a car or motorcycle for work, or a sewing machine to produce clothing to be sold. This alternative classification does not change any of the results.

  3. 3.

    Migrants are defined as “temporarily absent” household members, whose reason for moving is reportedly related to education or work.

  4. 4.

    Because migration, by definition, precedes remittance behavior, simultaneity bias is not a concern.

  5. 5.

    I restrict the analysis to migration decisions reported in the 1994 survey to ensure that the decisions are strictly prior to the changes in wealth from 1994 to 2000. I exclude from the sample 835 households that reported no migrants in the 1994 survey but had a migrant in the 2000 survey (final N = 4,614). Thus, I compare households with a migrant in the 1994 survey with those without a migrant in both the 1994 and 2000 surveys. Similarly, in testing the effect of remittances on wealth change, I take out 531 households that reported no remitters in 1994 but had a remitter in 2000 (final N = 2,687).

  6. 6.

    Endogenous selection is especially problematic for remittance receipts because households with a migrant can exercise the option of asking for remittances under economic duress. However, the matching method used here, along with the descriptive analysis testing the equal trends assumption, reduces its viability to households that do not show any visible signs of wealth change prior to 1994 but still expect one between 1994 and 2000 and receive remittances as a result. The IV method applied later further reduces the potential sources of endogeneity to time-variant unobservables that affect both wealth change and the selected instruments (that is, the percentage of remitters among sibling and village ties).

  7. 7.

    A common concern with the one-to-one nearest-neighbor matching is that it can discard a large number of observations that are not selected as matches (Stuart 2010). An alternative method—kernel matching—includes all observations, matching treated units with a weighted average of all controls. The weights are inversely proportional to the distance between the treated and control pairs. The estimates from this method (available upon request) are very similar to those from one-to-one nearest-neighbor matching.

  8. 8.

    Abadie and Imbens (2008) questioned the use of the bootstrap for calculating standard errors and provided an alternative estimator (Abadie and Imbens 2006, 2011). The results obtained with this estimator (available upon request) are very similar to those estimated with the bootstrap standard errors.

  9. 9.

    The sibling network was measured in 1994, but I compute the aggregate migration or remittance behavior in that network in 1984. Some of the network ties in 1994 may be absent in 1984. To consider this possibility, I exclude the ties to siblings who were younger than 35 in 1984 because those siblings may still be living in the individual’s household then. The results, however, are robust to their inclusion.

  10. 10.

    I thank an anonymous reviewer for suggesting this explanation.


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This research was funded by grants from the Clark Fund, Milton Fund, Weatherhead Center for International Affairs (Synergy Semester Grant), and the Center for Population and Development Studies at Harvard University. I thank Sara Curran, Paul DiMaggio, Douglas Massey, Michael White, Viviana Zelizer, and the participants at the Conference on Immigration and Poverty at UC Davis for helpful suggestions.

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Correspondence to Filiz Garip.


Appendix A. Robustness of the Results to Alternative Wealth Categories

Table 6 checks the robustness of the results to alternative wealth categorizations. The first row of Panel A reproduces the matching estimates presented in Table 5, where the wealth categories are based on the tertiles of the household productive index in 1994. The second row uses an alternative categorization, where the poorest one-fourth in productive assets is compared with the richest one-fourth and the remaining middle half. The third row employs the tertiles of household land owned in 1994 to determine wealth categories. The fourth row uses a simple sum of assets, where binary indicators for having land greater than 10 rai (4 acres), a tractor, an itan, and a car are added and top-coded at 2 to create three categories (0, 1, and 2) that correspond to poor, medium-wealth, and rich households, respectively.

The effect of having a migrant on productive assets is positive and significant for poor households in the first two categorizations based on the tertiles and quantiles of the productive asset index. The effect is negative and significant for medium-wealth households in the first categorization only. The effect is negative and significant for rich households in three of the four categorizations. The effect of having a remitter (among households with migrants) on productive assets is positive for poor households in the first two categorizations and negative for rich households across all categorizations except for the one based on land alone. Models in Panel B replicate the analysis for consumer assets. Regardless of the categorization, having a migrant or a remitter has no effect on the changes in consumer assets for all wealth groups. These results show certain consistency across alternative categorizations, establishing the robustness of the conclusions to various definitions of wealth.

Table 6 The effect of having a migrant or remitter in the 1994 survey on the change in household assets from 1994 to 2000, matching estimates with alternative wealth categoriesa

Appendix B. Sensitivity of the Matching Estimates to Caliper Size

Table 7 The effect of having a migrant or a remitter in the 1994 survey on the change in household productive assets from 1994 to 2000, sensitivity of the matching estimates to caliper size

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Garip, F. The Impact of Migration and Remittances on Wealth Accumulation and Distribution in Rural Thailand. Demography 51, 673–698 (2014). https://doi.org/10.1007/s13524-013-0260-y

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  • Migration
  • Remittances
  • Wealth distribution
  • Thailand