Moving Forward, Looking Back: the Impact of Migration and Remittances on Assets, Consumption, and Credit Constraints in the Rural Philippines

Abstract

This paper investigates the impact of migration and remittances on asset holdings, consumption expenditures, and credit constraint status of households in origin communities, using a unique longitudinal data set from Bukidnon, Philippines. Taking into account the endogeneity of the number of migrants and remittances received, a larger number of migrant children reduces the values of nonland assets and total expenditures per adult equivalent. However, remittances have a positive impact on housing, consumer durables, nonland assets, total expenditures (per adult equivalent), and educational expenditures, enabling asset accumulation and investment in human capital. Neither migration nor remittances affects current credit constraint status.

Acknowledgements

This research was funded by the Food and Agriculture Organization of the United Nations through American University. Funding for data collection was provided by the US Agency for International Development through the Broadening Access to Input Markets and Services Collaborative Research Support Program (BASIS-CRSP) at the University of Wisconsin-Madison, and the Department for International Development (UK) through its support of IFPRI's rural–urban linkages programme. We thank Alan de Brauw, Ben Davis, Ricardo Faini, Courtland Robinson, Paul Winters, workshop participants at the FAO and the Population Association of America Annual Meetings, and an anonymous referee for helpful comments and discussions. We thank Celine Castillo-Macy for help in finalising the document. All errors and omissions are ours.

Notes

1. The cut-off of 15 years old could overstate the ‘non-migrant’ population because migration may occur more often at an older age, but this age is consistent with other demographic studies. An older cut-off would not change the results substantially. However, our analysis will focus on migrants 21 years and older, because the period between 15–21 years is typically characterised by continuation in school as well as early labour market experience, which may make it difficult to disentangle the net impact of migration.

2. Interestingly, there are very few remittances from spouses who have migrated.

3. There are some shortcomings in using these three questions to capture credit constraints. The terms of the hypothetical loan that would be made available to the household are not clearly specified in question 1. Feder et al. (1990) and Barham et al. (1996) add a phrase like ‘at going rates of interest’ to this question. However, even when such phrases are included, it is unclear how the respondent chooses the loan characteristics (interest rates, length of repayment, collateral requirements) on which to judge his desire for more credit. From these questions, we do not know if the respondent considers the average terms of loans recently taken or the likely terms of his next best, or marginal, source of credit, which would be less favourable. For respondents with little or no recent experience in the credit market, errors in judging the probable terms of this hypothetical loan may be great. Moreover, the hypothetical nature of the question may lead to inflated reports of credit constraints because respondents are not immediately faced with the burden of paying back the hypothetical loan. Finally, the context in which these questions were asked in the Bukidnon survey (loans for production of specific crops) suggests that some households that were credit constrained for consumption or other purposes were inaccurately classified as unconstrained.

4. Despite attempts to ensure comparability, the new questions differ slightly. The 1984/1985 survey repeats the credit constraint questions, referring to the prior four months, for each of the four rounds for each crop under production. In contrast, the 2003 asks the questions only once per household in reference to an entire agricultural year.

5. The 1984/1985 figures are an average over the relevant rounds, each of which has a recall period of four months, while the 2003 figures refer to the past 12 months.

6. Yang (2008) uses exchange rate shocks to characterize shocks faced by Filipino migrants to international destinations. However, since most of the migrants in our sample are internal migrants, we use the percentage deviation from regional GDP.

7. Fitzgerald et al. (1998a) show that weighted least squares can generate consistent parameter estimates when selection is based on observables, even when they are endogenous; the attrition correction used in this paper draws from Godquin and Quisumbing (2007). A fuller discussion of attrition between rounds in the 1984/1985 study and between 1984/1985 and 2003 is found in McNiven and Gilligan (2005).

8. Stata does not allow us to correct tobit regressions for attrition weights, but this does not affect our estimates of impact because we use attrition weights in the IV regressions.