We analyse the relationship between income volatility and inequality and the conditional role played by aid and remittances. Using a panel of 142 countries for the period 1973–2012, we confirm a well-established finding among the literature that income volatility has an adverse impact on inequality, and that the poorest people are the most exposed to these fluctuations. However, while aid and remittances do not seem to have a clear direct impact on inequality, we uncover robust evidence that suggests that aid helps dampen the negative effects of volatility on the distribution of income, while remittances do not.
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Adding the lagged dependent variable on the right-hand side of the equation allows controlling for time persistence in income inequality which seems to be the case when looking at Figures S1 in the supplementary appendix. Serial autocorrelation is confirmed using Wooldridge tests which all suggest persistence in our various inequality measures. Results available upon request.
It is also worth mentioning the work of Solt (2016), who, building on the WIID, proposed an interpolated version of the dataset, the “Standardized World Income Inequality Database (SWIID)”. The SWIID provides comparable estimates of the Gini index for 174 countries from 1960 to 2012, as well as measures of absolute and relative redistribution. Data points are fully interpolated and should be used cautiously.
Measures based on consumption data reflect more accurately income distribution, but would restrict our sample too much.
When the aid variable is introduced without the interaction term aid × volatility, the coefficient of the aid variable is not significantly different from zero. Results available from the authors upon request.
Which can be seen with implausibly good p-values of 1.000.
The underlying idea is that when destination countries experience significant economic growth, the migrant’s diaspora is likely to benefit from this growth and therefore could send more remittances in their home country.
Cf. Table S2 in the supplementary appendix.
Channel 3 (impact of aid on inequality) is not significant.
The same pattern appears when education is removed from the effect of income per capita (i.e. when we use predicted educational outcomes with respect to countries’ income level).
Aid to social sectors is from the Creditor Reporting System (CRS) dataset and includes aid to education, for health of population, and water and sanitation. It is only available for 2002 onwards.
Note that when looking at the primary school enrolment rate the effect of macroeconomic volatility is also negative and statistically significant. Results not reported here to save space but available on request from the authors.
When using the primary instead of the secondary school enrolment rate, the interaction term between aid and macroeconomic volatility is still positive, but less significant (results not shown).
Not shown but available upon request.
Aid counter-cyclicality is measured using the correlation of the cycles of aid with the cycles of exports. When the correlation is negative, aid is assumed to be counter-cyclical. Aid and exports are measured in constant US dollars deflated by US unit import prices.
Results are also robust to the unweighted measure of exports volatility. Results available upon request.
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Special acknowledgments are due to Pierre-Richard Agenor and Kyriakos Neanidis and to participants at the BCEAO seminar held in Dakar on November 9, 2016 as well as to the anonymous referees for helpful comments and suggestions. The authors are grateful to Maddalena Agnoli and Sosso Feindouno for their assistance at the early stage of this work. Financial support from the DFID-ESRC Growth Research Programme, under Grant No. ES/L012022/1, is gratefully acknowledged. The views expressed in this paper are only those of the authors.
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Chauvet, L., Ferry, M., Guillaumont, P. et al. Volatility widens inequality. Could aid and remittances help?. Rev World Econ 155, 71–104 (2019). https://doi.org/10.1007/s10290-018-0331-7