Evidence for sharp increase in the economic damages of extreme natural disasters

Abstract

Climate change has increased the frequency and intensity of natural disasters. Does this translate into increased economic damages? To date, empirical assessments of damage trends have been inconclusive. Our study demonstrates a temporal increase in extreme damages, after controlling for a number of factors. We analyze event-level data using quantile regressions to capture patterns in the damage distribution (not just its mean) and find strong evidence of progressive rightward skewing and tail-fattening over time. While the effect of time on averages is hard to detect, effects on extreme damages are large, statistically significant, and growing with increasing percentiles. Our results are consistent with an upwardly curved, convex damage function, which is commonly assumed in climate-economics models. They are also robust to different specifications of control variables and time range considered and indicate that the risk of extreme damages has increased more in temperate areas than in tropical ones. We use simulations to show that underreporting bias in the data does not weaken our inferences; in fact, it may make them overly conservative.

Keywords: climate change; economic damages; natural disasters; tail effects.

Fig. 1.

(A) Stylized representation of a convex, upwardly curved transfer mechanism from stressor to damages. In A, Inset, the horizontal axis shows the largest damages (upper range of the vertical axis in the main image) on the log scale. (B) Time-trend estimates from quantile (upward of 80%) and OLS (mean) regressions for a simulated dataset. The horizontal axis represents percentiles. The time-trend estimate from OLS is shown as a constant. Quantile regression estimates are obtained through the modified Barrodale–Roberts algorithm (38). The mean of a hypothetical Gumbel-distributed stressor ($GEV\left(\mu ,\sigma ,\xi \right)$ with shape parameter $\xi =0$) undergoes equal yearly shifts for 55 “years” (the time range of the data in Fig. 2). For each year, we generate i = 1,...1,000 draws and the corresponding damage values ${\text{Da}}_i$. We then consider the simple model ${\text{Da}}_i=\alpha +\beta t_i$ (where $t_i$ is the year of $i$) and fit quantile and OLS regressions. An alternative visualization is provided in SI Appendix, Fig. S1.

Fig. 2.

Empirical distributions of economic damages from natural disasters (A) and estimated time trends from Model 2 (B). (A) Yearly distributions of economic damages (US$ billion) associated with n = 10,901 disasters occurred worldwide between 1960 and 2015 (see data description in Data and Code). We show partial boxplots colored by decade. Lower and upper hinges correspond to medians and 90th percentiles, respectively; middle lines to 75th percentiles; and upper whiskers to 99th percentiles; the top 1% single-event damages amounted to US$482 million in 1970 and to US$9.92 billion in 2010—an ∼20-fold increase. The red dashed line tracks the time progression of the 99th percentiles (kernel smooth), illustrating the marked increase in damages due to extreme events. A, Inset zooms into the right tails of the distributions and shows their progressive fattening over time [Gaussian kernel density estimates on log-transformed damages aggregated by decade, bandwidth fixed with Silverman’s rule (42)]. (B) Quantile and OLS (mean) regressions for the same data (but in US$ million and restricted to n = 9,495 disasters occurred between 1960 and 2014 after preprocessing; Data and Code). The model used is 2. The horizontal axis represents percentiles and the vertical one estimated time trends; e.g., at the 99th percentile, we estimate the top 1% single-event damages to increase by US$26.4 million every year. The time trend estimate from OLS (statistically nonsignificant at 5% level) is shown as a constant, with its standard 95% CI. Quantile regressions estimates are obtained through the modified Barrodale– Roberts algorithm (38) and a 95% confidence band around them is produced with r = 1,000 bootstrap samples [joint resampling of response and predictor pairs (40)]. Full results on estimates and standard errors are given in SI Appendix, Table S2.

Fig. 3.

Quantile regressions for damages (US$ million) associated with n = 9,495 disasters occurred between 1960 and 2014 in different Köppen–Geiger climate zones (excluding the polar class). The model used is 3. The horizontal axis represents percentiles and the vertical one estimated time trends: e.g., at the 99th percentile, we estimate the top 1% single event damages to increase by US$17.9 million every year in tropical areas and by US$46.5 million in temperate ones. Quantile regressions estimates are obtained through the modified Barrodale–Roberts algorithm (38) and a 95% confidence band around them is produced with r = 1,000 bootstrap samples [joint resampling of response and predictor pairs (40)]. Time trend estimates for cold and arid zones are shown with dashed lines and without confidence bands because they were statistically nonsignificant for most percentiles. Full results on estimates and standard errors are given in SI Appendix, Table S5.

Fig. 4.

Empirical distribution of deaths from natural disasters (A) and estimated time trend from Model 4 (B). (A) Yearly distributions of deaths associated with n = 10,901 disasters occurred worldwide between 1960 and 2015. We show boxplots colored by decade. Lower and upper hinges correspond to the 25th and 75th percentiles, respectively; middle lines to medians and upper whiskers to 90th percentiles; the top 10% single-event deaths amounted to 585 in 1970 and to 114 in 2010—an ∼5-fold decrease (the top 1% single-event deaths decreased by more than 80-fold from 156,744 to 18,51; not shown in the graph). The red dashed line tracks the time progression of the 90% percentile (kernel smooth), illustrating the marked decrease in deaths due to extreme events. A, Inset zooms into the right tails of the distributions and shows their progressive thinning [Gaussian kernel density estimates on log-transformed deaths aggregated by decade, bandwidth fixed with Silverman’s rule (42)]. (B) Quantile and OLS mean regressions for the same data (but restricted to n = 9,495 disasters between 1960 and 2014 after preprocessing; Data and Code). The model used is 4. The horizontal axis represents percentiles and the vertical one estimated time trends; e.g., at the 99th percentile, we estimate the top 1% single-event deaths to decrease by 52.1 every year. The time trend estimate from OLS (statistically nonsignificant) is shown as a constant, with its standard 95% CI. Quantile regression estimates are obtained through the modified Barrodale–Roberts algorithm (38), and the 95% confidence band around them is produced with r = 1,000 bootstrap samples [joint resampling of response and predictor pairs (40)]. Full results on estimates and standard errors are given in SI Appendix, Table S6.

Fig. 5.

Quantile regressions for simulated datasets. The widening distance between estimates obtained on data from the “True Model” and on data with a decreasing “Logistic Truncation” shows how past damage underreporting can induce a downward bias for upper percentiles, making our assessments of mounting damages conservative. To create the simulated data labeled “True Model” in the plot, we pooled damages from 2000 to 2014 (a period when underreporting was negligible) and used this empirical distribution “stationarily” to draw 318 disasters (the average number of disasters per year in the 1960–2014 span covered by our study) for 55 consecutive years. To create the simulated data labeled “Logistic Truncation,” we pooled damages in the same fashion, but left-truncated them with an intensity decreasing over time (the percentage of bottom values removed starts at 50% in the first year, and coverage is increased according to a logistic, reaching 96% in the last year). This mimics a progressive reduction in underreporting. The horizontal axis represents percentiles and the vertical one estimates time trends. The model used is ${\text{Da}}_i=\alpha +\beta t_i$ (where $D{a}_i$ and $t_i$ are damage and year of disaster $i$). Quantile regressions estimates are obtained through the modified Barrodale–Roberts algorithm (38). The 95% confidence bands are produced through 500 Monte Carlo replications of the whole procedure.