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Natural disasters and economic growth: a quantile on quantile approach

  • S.I.: Regression Methods based on OR techniques
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Abstract

Natural disasters have caused over a million of deaths and $3 trillion in economic losses during the last 20 years. However, theoretical and empirical studies have not reached a conclusion as for their effect on economic growth, and the results can best be described as mixed. The present study proposes the use of a quantile on quantile (QQ) approach to shed more light on this complex relationship. This approach combines the standard quantile regression analysis with nonparametric estimations and allows us to examine how different quantiles of natural disasters affect different quantiles of GDP growth. Using data from over 100 countries over a 30-years period, we confirm that the results of the QQ approach differ from the ones obtained by standard approaches like fixed effects regressions. We document that the relationship between the intensity of natural disasters and economic growth is mostly negative. Nonetheless, there are some exceptions to this. Our findings reveal that the effect of natural disasters can be occasionally positive, depending on the quantiles that we examine. The magnitude of the effect also differs across different combinations of the quantile of economic growth and the quantile of natural disasters. Finally, we obtain somewhat different results when we estimate separate QQ regressions for groups of countries that differ in terms of climate, economic and democratic development, and across different year lags of the natural disaster index.

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Notes

  1. The costs are even higher considering the underreporting of direct economic costs in relevant databases. The report highlights that The direct economic costs of disasters have been systematically under-reported worldwide for decades, both in wealthier countries and, most especially, in poorer ones. Throughout the period 1998–2017, economic losses data only exist for 37% of disasters; the direct cost of the majority of disasters (63%) is unknown or not well documented (p. 14).

  2. Studies finding a negative impact are, among others, the ones of Noy (2009), Raddatz (2009), Strobl (2011), Felbermayr and Gröschl (2014). In contrast, Skidmore and Toya (2002) find that higher frequencies of climatic disasters are corelated with economic growth. Additionally, Loayza et al. (2012) conclude that moderate disasters (such as moderate floods) can have a positive growth effect in some sectors. Others claim that the impact is not significant. For instance, Albala-Bertrand (1993) concludes that The main conclusions are that capital loss is unlikely to have an important effect on growth and that a very moderate response expenditure may be sufficient to prevent the growth rate of output from falling (p. 1417). Felbermayr and Gröschl, (2014) review fourteen studies and highlight that from the 368 point estimates of the effect of disasters on GDP per capita, about 38% of all estimates are statistically insignificant at the 10% level, about 44% of the statistically significant effects are positive, while the remaining 56% of significant estimates are negative.

  3. This literature has so far employed quantile regression to examine the growth effect of social capital (Deng et al. 2012), financial development (Andini and Andini 2014), political stability (Uddin et al. 2017), foreign direct investment (Cai et al. 2018), and energy consumption (Gozgor et al. 2018).

  4. Existing studies in the field use a QQ approach to examine the relationship between oil prices and US equities (Sim and Zhou 2015), whether Bitcoin can hedge global uncertainty (Bouri et al. 2017), whether a partisan conflict predicts stock market volatility (Gupta et al. 2018), the relationship between economic policy uncertainty and equity premium (Raza et al. 2018), the association between uncertainty and currency performance (Han et al. 2019), the inflation—hedging property of gold (Shahzad et al. 2019) and the relationship between income distribution and environmental quality (Mallick et al. 2019).

  5. More precisely, the work of Shahzad et al. (2017) examines the relationship between tourism and economic growth, whereas the study of Shahbaz et al. (2018) concentrates on the association between energy consumption and economic growth.

  6. The data are available online at: https://www.cesifo-group.de/ifoHome/facts/EBDC/Ifo-Research-Data/Ifo_GAME_Dataset.html.

  7. The convenience of using asymmetric Laplace Distribution (ALD) is to easily allow the estimation of the τ-th conditional using the maximum likelihood methods.

  8. The lagged natural logarithm of GDP per capita is included to account for the initial wealth per capita in a country (Felbermayr and Gröschl 2014; Strobl 2011). As opposed to Felbermayr and Gröschl (2014); Graham et al. (2015) clearly describe the expectations-based transformation used to sweep out fixed effects; for that reason, no endogeneity arises when the lagged dependent variable is introduced. Consequently, there is no biasness due to endogeneity when using a lagged dependent variable on the right-hand side of Eq. (3). Moreover, one avoids data transformation that could affect the interpretation of results. Nevertheless, Felbermayr and Gröschl (2014) clearly mentioned that they do not explicitly deal with the bias that arises from the inclusion of a lagged endogenous variable on the right-hand-side of the equation since the bias is small in panels drawn over a long time period like the one of our study (Nickell 1981; Judson and Owen 1999).In more detail, Nickell (1981) shows that the problem with the fixed effects model arises because the demeaning process subtracts the individual’s mean value of y and each X from the respective variable, creating a correlation between regression and error. Nickell demonstrates that the inconsistency of \( \widehat{\text{p}}\;{\text{as}}\;{\text{N}} \to \infty \) is of order 1/T. For reasonably large values of T, the limit of \( (\widehat{\text{p}} - {\text{p)}} \) as \( {\text{N}} \to \infty \) will be approximately − (1 + p)/(T − 1). Additionally, using a Monte Carlo approach, Judson and Owen (1999) reach several interesting conclusions. First, they mention that with the exception of OLS all the other estimators (least squares dummy variable—LSDV, Anderson-Hsiao—AH, GMM, Corrected LSDV) generally perform better as N and T increase, and therefore the differences in efficiency, bias and RMSES of the different techniques become quite small for large N and T. Second, using an RMSE criterion, the LSDV performs just as well or better than the viable alternatives when T = 30. Third, while GMM produces the lowest RMSEs when T = 20, the difference in performance is not that great and computation issues may become more important.

  9. The literature on the macroeconomic effects of disasters can be divided into studies looking into the short- to-medium term and the longer term, with almost all studies taking a shorter-term perspective (Hochrainer, 2009). Long-run analysis raises questions of endogeneity in disaster impact that are, to a large extent, not relevant for the short-run (Noy 2009). Therefore, as in Noy (2009), Strobl (2011) and Felbermayr and Gröschl (2014), among others, we examine the impact of the disasters occurring in time t on the GDP growth over t − 1 to t (i.e. the change from year t − 1 to year t). In further analysis in Sect. 3.2.2. we extend the analysis to the medium term using up to five lags of the natural disaster index.

  10. Felbermayr and Gröschl (2014) use one more index from the insurance firm Munich Re, called NatCatSERVICE. However, this is not included in the dataset that they make available online. Most likely this is due to the fact that the database NatCatSERVICE-in contrast to EM-DAT and GeoMet—is not publicly available.

  11. The decision rule for large-scale disasters from EM-DAT and NatCatSERVICE bases on the convention of ‘great natural disasters’ by Munich Re (2006) and the United Nations. Disasters are defined as large if (i) 1000 or more were killed, or (ii) at least 1 billion US dollar monetary damage (made comparable over time using the deflator on US dollars fromWDI), and with EM-DAT also if (iii) 100,000 or more affected.

  12. For example, GEOLAND ranges between 0.000 (e.g. Australia, Brazil) to 2.155 (Singapore in 1985), while GEOSDL ranges between 0.000 (e.g. Australia, Brazil) and 2.228 (Singapore in 1984).

  13. Fisher (1925) proposed the combination of the p-values from independent tests to obtain an overall test statistic and is frequently called a Fisher-type test. We perform the test in Stata which uses the p-values from the panel-specific unit-root tests based on the methods proposed by Choi (2001). The null hypothesis being tested is that all panels contain a unit root. For a finite number of panels, the alternative is that at least one panel is stationary.

  14. All the p-values (available upon request) are equal to either 0.000 or 0.001.

  15. Felbermayr and Gröschl (2014) also include the natural logarithm of population, which we drop for two reasons. First, when we examine the within country correlation of GDP and population, we find that in several occasions there are large correlations which raise concerns about multicollinearity issues. Second, having both elements of the dependent variable (i.e. GDP and population) in the right-hand side of the regression could raise additional concerns. In unreported Fixed effects regressions we also estimated the specifications of Table 2 with the inclusion of the logarithm of population. The only difference is that with the inclusion of the logarithm of population, GEOLAND was also statistically significant (at the 5% level) in the specification in Column 5. However, given the multicollinearity concerns, these results should be treated with caution.

  16. As in the case of the country-fixed effects model, the QQ specifications include lagged values of the following control variables, which are not shown to conserve space: LCGDP, POLITY, OPEN, INTEREST, CAPITAL, FDI, CPI, BALANCE.

  17. In the case of the “Disaster index” (i.e. GEOLAND) they find that it has a negative and statistically significant impact on both non-OECD countries (at the 1% level) and OECD countries (at the 10% level), the low/middle economies (at the 10% level) and in autocratic countries (at the 10% level), and an insignificant impact in the case of high income and democratic economies.

  18. It should be noted that each cell in Tables 3, 4 and 5 presents the coefficient of a QQ regression that includes lagged values of all the control variables which are not shown to conserve space.

  19. To classify countries as tropical or non-tropical ones, we use information on the percentage of population in each country living in tropical and subtropical zones, taken from Ashraf and Galor (2013).

  20. As before, we estimate a model with country fixed effects, time dummies and robust errors clustered at the country level. Estimating this model without time dummies does not influence the results.

  21. One could expect the results for the high vs medium/low income group countries to be identical to the ones of the OECD versus non-OECD countries. The estimations in Tables 4 and 5 show that this is not necessarily the case. The reason for this is that despite similarities, these group are not identical. The correlation between the dummy for OECD membership and the one for High income group membership is 0.786. In more detail, the group of high-income countries includes numerous countries like Cyprus, Israel, South Korea, Singapore, Kuwait, Oman, Slovenia, Trinidad and Tobago that are not OECD members, and at the same time most of them appear to be among the countries that experience the highest GDP difference (e.g. South Korea, Kuwait, Oman, Singapore, Trinidad and Tobago) among the high-income ones.

  22. There exist many studies that test the conditional convergence of GDP per capita across countries. For discussions of the literature and alternative methodologies see de la Fuente (1997), Rassekh (1998) and Diaz del Hoyo et al. (2017). Conducting such an analysis does not fall into the purpose of the present study.

  23. For example, Becerra et al. (2012) conclude that, controlling for the magnitude of the disaster, countries with higher real GDP per capita receive less post-disaster foreign aid. Strömberg (2007) also reports that there is a negative association between the real GDP per capita of a country and whether the Office of U.S. Foreign Disaster Assistance (OFDA) provided relief for a given disaster between 1968 and 2002.

  24. The results for the group of non-tropical countries are, to some extent, close to the ones of the high-income group. This could be partially justified by the fact that many high − income countries are located in non-tropical areas.

  25. See Zhang et al. (2019) for a similar model, and Hoskins and Medal (2019) for a stochastic programming solution for the placement of satellite ground stations. Additionally, for a discussion on the recent use of big data in humanitarian supply chain management in the case of disaster relief see Gupta et al. (2019).

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Acknowledgements

We are thankful to Professor Nicholas Sim for providing us with the code developed in Sim and Zhou (2015), which has been used in this study to extend the QQ regression to panel data. In this regard, special thanks are also due to Professor Naji Jalkh for his valuable comments and suggestions. We would also like to thank two anonymous referees for insightful comments that helped us improve an earlier version of the manuscript. Any remaining errors are our own. Montpellier Business School (MBS) is a founding member of the public research center Montpellier Research in Management, MRM (EA 4557, Univ. Montpellier).

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Authors and Affiliations

  1. School of Production Engineering and Management, Technical University of Crete, Chania, Greece

    George S. Atsalakis

  2. USEK Business School, Holy Spirit University of Kaslik, Jounieh, Lebanon

    Elie Bouri

  3. Montpellier Business School, Montpellier, France

    Fotios Pasiouras

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  1. George S. Atsalakis
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Appendix: Definition of variables

Appendix: Definition of variables

Acronym

Definition

GDP growth

Per capita GDP PPP growth, calculated as first difference in ln of GDP per capita from year t− 1 to year t

EMALL

EM-DAT All disasters index, defined as the sum of all disasters in EM-DAT database normalized by land area

EMLAR

EM-DAT Large disasters index, defined as the sum of Large disasters in EM-DAT database normalized by land area

GEOLAND

GeoMet Disaster Index, defined as the unweighted sum of disasters types in GeoMet database, normalized by land area

GEOSDL

GeoMet Disaster Index_weighted, defined as the sum of disaster types in GeoMet database, weighted by the country specific inverse of standard deviation of a disaster type within a country over all years, and normalized by land area

LCGDP

ln of per capita GDP PPP

POLITY

Polity index from Polity IV (2010), capturing the degree of democracy/autocracy in a country, normalized between 0 (strongly autocratic) and 1 (strongly democratic)

OPEN

Indicator of trade openness calculated as imports plus exports over GDP

INTEREST

Real interest rate

CREDIT

Domestic credit in banking sector as a share of GDP

CAPITAL

Gross capital formation (share of growth)

FDI

Foreign direct investment, net inflows as a share of GDP

CPI

Inflation, consumer prices

BALANCE

Current account balance as a share of GDP

  1. All the data are from Felbermayr and Gröschl (2014)

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Atsalakis, G.S., Bouri, E. & Pasiouras, F. Natural disasters and economic growth: a quantile on quantile approach. Ann Oper Res 306, 83–109 (2021). https://doi.org/10.1007/s10479-020-03535-6

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