The Impact of Natural Disasters on Remittances to Low- and Middle-Income Countries

Abstract

In this paper, we offer novel empirical evidence on the impact of natural disasters on remittance flows towards low- and middle-income countries. We consider a panel of 98 countries over the period 1990–2010. Our findings show that remittances increase after a disaster, thus contributing ex post to the reconstruction process. At the same time, we find that remittances play a key role in terms of ex ante risk preparedness for those countries that experienced more disruptive events in the past. Finally, when taking into account the interaction with the level of development of the local financial sector, remittances seem to substitute for less efficient financial systems both in terms of ex post response to disasters and in terms of ex ante risk management strategy.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. See http://www.emdat.be/natural-disasters-trends.

2. Various aspects such as education, openness, institutional quality and financial development (Kahn, 2005; Noy, 2009; Rasmussen, 2004; Toya & Skidmore, 2007) all seem to be effective at reducing the disruptive impact of natural disasters.

3. Among others, see Attzs (2008), Halliday (2006), Harvey and Savage (2007) and Weiss Fagen (2006), with regard to Central America, Yang and Choi (2007), Wu (2006) and Le De, Gaillard, and Friesen (2015), with regard to South Asia.

4. A related strand of literature have documented that migrants’ remittance transfers contribute to mitigate vulnerability of developing countries to adverse food-price shocks and global financial crises (Combes et al., 2014; Sirkeci et al., 2012) and to sustain efforts for reconstruction from armed conflicts (Harris & Terry, 2013; Naudé & Bezuidenhout, 2014).

5. The dataset is publicly accessible at http://www.cred.be/emdat/.

6. Formally, an event is classified as a disaster and enters the database whenever it fulfils at least one out of four selection criteria: 10 or more people killed; 100 or more people affected, injured or homeless following the disaster; declaration of a state of emergency; call for international assistance. See http://www.emdat.be/criteria-and-definition.

7. Since annual country-level data on the overall size of the diaspora are not available, we use the total number of migrants residing in OECD countries to proxy for it.

8. The inclusion of the squared term has also been driven by the output of a Reset test, where the null hypothesis of linearity was strongly rejected (statistic 91.04, p–value 0.00).

9. The database is accessible at http://stats.oecd.org/Index.aspx?DatasetCode=MIG.

10. Alternatively, we built EXPOSURE by considering the average number of disasters in the period between 1970 and t-3, with no appreciable differences in estimation results.

11. All specifications include two lags (the third and the fourth) of remittances, financial development, aid, per capita GDP and its square as instruments.

12. The result of the Hansen test of overidentifying restrictions shows that the moment conditions assumed for GMM estimation are valid. Moreover, the AR2 test rejects the presence of second order serial correlation.

13. In Table 2, column 1, for example, the threshold is equal to 6.9 (in natural logarithm), while the sample mean is 7.01.

14. In order to take into account potential endogeneity issues, estimates have been carried out also by excluding GDP per capita and its square from the baseline equation. Results do not change substantially and are available upon request.

15. The formula to compute this effect is *100 per cent, where α₁ is the coefficient associated to DISASTER_t,t-1 in Equation (1).

16. When extreme temperature or precipitation are defined as those events that fall farther than 1.5 standard deviations away from the mean, we take into account approximately the lower/upper 6.5 per cent of the distribution as extreme events. When moving to 2 standard deviations, extreme events correspond approximately to the lower/upper 2.5 per cent of the distribution. In the latter case, estimation results confirm a positive response of remittances to either extreme temperature or precipitation although the stricter criterion results in non significant coefficients.