Abstract
Remittances are growing substantially in the developing world, surpassing foreign aid and, in several nations, reaching more than 20 per cent of their Gross Domestic Product. The relevance of these capital inflows for the recipient economies has been discussed in previous work, but the evidence is, at best, inconclusive. This article contributes to the literature by providing new findings on the effects of remittances on growth through social capital development. Using a more inclusive and complete set of social capital indicators, our results show that the effectiveness of remittances on long-term growth is enhanced by the presence of social capital indicators. Our results are consistent and robust when using different econometric techniques.
Abstract
Les transferts de fonds augmentent sensiblement dans les pays en développement et dépassent l’aide étrangère; dans plusieurs pays, ils représentent plus de 20 per cent du produit intérieur brut. La question de l’importance de ces flux de capitaux pour les pays receveurs a déjà été abordée dans d’autres études, mais les résultats sont, dans le meilleur des cas, peu concluants. Cet article contribue à la littérature existante en apportant de nouveaux éclairages sur les effets des transferts de fonds sur la croissance à travers le développement du capital social. En nous appuyant sur un ensemble plus exhaustif d’indicateurs de capital social, nos résultats montrent que les effets positifs des transferts de fonds sur la croissance à long terme sont renforcés par la présence d’indicateurs de capital social. Nos résultats concordent avec ceux obtenus en mobilisant d’autres techniques économétriques et mesures de robustesse.
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Notes
Equation (1) is derived from the theoretical analysis on endogenous growth models developed by Romer (1986), Lucas (1988) and others. Following Burnside and Dollar (2000), we also selected 4-year time period to increase the number of observations per country. We selected 1990 as the starting year due to data limitations. For instance, from the 28 social capital indicators, only 6 can be traced before 1986. The list of countries and the remittance value for the most recent year is shown in Appendix B.
We use a standard set of control variables such as fixed capital formation or investment, a measure of education or human capital formation and an indicator of trade openness. Refer to Appendix A for details.
We could have expanded the model by combining multiple social capital indictors in a single equation and testing the effect of remittances on growth in the presence of a set of social capital indicators; however, we lack a theoretical support to justify such arrangements. Another alternative could be the design of an overall social capital index that combines all or most of the 28 indicators. Although this approach may simplify the interpretation of the parameter values, it faces several limitations: (i) it may conceal information about specific social capital indicators, (ii) it may cancel out effects and (iii) there is no theoretical basis to justify the weight that each indicator will hold in the overall index.
The interactive term (β3) can take positive or negative values, but our focus is on the marginal values, β2+β3 *SCj>0. Some studies point towards a complementary relationship between remittances and the interactive variable if β3 >0, and a substitutability nature if β3 <0 (Giuliano and Ruiz-Arranz, 2005).
Several empirical studies have verified that domestic growth might determine the level of remittances in a country, raising concerns about the endogeneity of remittances (Borja, 2012b). Under this condition, the error term might be correlated with remittances and estimated coefficients might be biased. A similar problem is observed with some of the social capital indicators used in this study. Chong and Calderon (2000) explored the correction of endogeneity using time-series analysis. Boulila et al (2008) assessed the stability of the results by running more than 4000 regressions per variable. Persson and Tabellini (2003) applied numerous approaches including a propensity score matching method. Any of these techniques is imperfect, but they point to positive advances in the endogeneity issues typically encountered in the empirical economic growth analysis.
We focus on the endogeneity of remittances, leaving for further research the exploration of IVs for all or some of the 28 social capital indicators, mostly to keep the econometric analysis simple but also recognising the trade-off between clarity of exposition and possible presence of endogeneity. Even with these limitations, we proceed to test our hypotheses using the IV-2SLS method.
Dynamic models tend to be superior to OLS or IV-2SLS because (i) they internally address the bias arising from endogeneity through instrumentalisation of their own lagged variables and (ii) they provide a larger set of potentially valid instruments. These models also offer comparable estimates to those from IV-2SLS as a way to test for possible violations of the exclusion restriction, granting some validity to the IVs (Bazzi and Clemens, 2009). However, these models have limitations. First, the assumption that the error term and remittances are uncorrelated for all t>s must hold. Second, first-differences and lagged values might considerably reduce the number of observations. Dynamic GMM models do not fully prevent the exposure to endogeneity bias or violation of the exclusion restriction; nonetheless, following Ashley’s (2009) sensitivity analysis approach, we apply multiple econometric tools and observe the discrepancies in the estimates as a way to provide robustness to the study.
The main results remain fairly similar across the four econometrics models. Complete tables using IV-2SLS and SGMM are available upon request from the authors.
Parameters under a multiplicative interactive model must be interpreted with caution. The parameter of remittances can no longer be inferred as the unconditional marginal effect of remittances on growth except when SCj=0.
Model 2 is slightly different from the one developed by Chami et al (2008) since the authors use a remittances-to-GDP ratio instead of total remittances as dependent variable.
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Appendices
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Appendix B
Appendix C
Appendix D
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Borja, K. Social Capital, Remittances and Growth. Eur J Dev Res 26, 574–596 (2014). https://doi.org/10.1057/ejdr.2013.32
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DOI: https://doi.org/10.1057/ejdr.2013.32