The effect of natural and man-made disasters on countries’ production efficiency

Figure 1 illustrates graphically countries’ production efficiency curves both for conditional and unconditional cases over the period 1980–2011.⁸ The results presented are based on countries’ income classification and are obtained under the assumption of variable returns to scale (VRS).⁹ Figure 1a examines the original production efficiency scores over the examined period. It indicates that there was a trend towards similar levels of production efficiencies for high-income and upper-medium-income countries over the period 1980 to 1996.

However, after this point, there is evidence that countries’ production efficiency levels divert. A similar phenomenon is also observed for the larger period for lower-medium- and lower-income countries. However, after 2007, it appears that there is a trend towards similar efficiency levels. Figure 1c presents countries’ the original production efficiency estimates of the Order-α frontier. Since these estimates are not bounded by 1, then we observe extremely high efficiency scores compared to the full frontiers (i.e., VRS frontier in our case).

The results show that high-income countries have steadily increased their efficiency estimates over the years with a production efficiency stabilization period from 2007 to 2011. This might be attributed due to the initiation of Global Financial Crisis. Furthermore, as can be observed, there is a trend of similar production efficiency levels for upper-medium and lower-medium income countries (especially over the period 2006–2011), whereas lower-income countries appear to increase their production efficiency levels over the years but with lower estimated efficiency scores compared to the other three income groups.

Furthermore, Fig. 1b, d presents diachronically countries’ efficiency curves when accounting both for the number of disaster occurrences and time (i.e., time-dependent conditional efficiency estimates). For the case of full frontiers (Fig. 1b), we can find that high-income countries have greater efficiency scores compared to the other three groups. It is also observed that under the effect of time and the number of disaster occurrences, lower-medium and lower-income countries’ production efficiencies have similar trend from 1980s to early 1990s. However, after that point, a trend of similar efficiency levels is observed between upper-medium- and lower-medium- income countries, whereas the group of lower-income countries seems to divert in terms of their production efficiency levels. For the case of robust measures (Fig. 1d), again it is observed that the group of high-income countries has significant higher time-dependent conditional efficiency scores compared to the other three groups. Finally, when examining the group of high-income countries both for the full (Fig. 1b) and partial frontier (Fig. 1d), we observe that during the 1990s, their efficiency levels have decreased. This phenomenon is more pronounced when looking the conditional robust estimators. Since these two graphs account directly for the effect of time (alongside with the effect of the number of disaster events), our finding supports the findings by Marone (2009) that during the 1990s, high-income countries have decreased their growth rates. This in turn reflects upon their conditional efficiency estimates. Regardless, the efficiency gap between high-income countries and the other groups remains. Furthermore, the trend of similar levels of production efficiencies is observed between lower-medium- and lower-income countries. Clearly we can observe from Fig. 1 that when we account both for the effect of time and disasters, countries’ production efficiency curves seem to be highly fluctuated, this is especially more intense for the case of high-income countries.

In addition to the previous finding, Fig. 2 examines both the time-dependent conditional and unconditional efficiency estimates of the seven major economies (known also as G7) both under the full and robust frontiers. Specifically under the full frontier (Fig. 2a), USA is reported as the top performer. It seems that there was a similar level of efficiencies with the GBR at early 2000s, but after that period, a diversion of efficiencies is reported. Furthermore, it seems that DEU, FRA, ITA, and GBR have similar production efficiency levels especially during the beginning of Global Financial Crisis (i.e., 2008 to 2011), with JPN to divert slightly from that group. Under the robust frontier analysis (Fig. 2c), the results are different.

USA again outperforms the other countries with an increasing efficiency trend up to the beginning of Global Financial Crisis. Then the second performer is JPN, whereas, it appears the existence of similar efficiency levels among the European countries (DEU, FRA, GBR, and ITA). Finally, the last performer is CAN which seems to divert from the European group. Figure 2b, d is presented here only for the purpose of comparison and to demonstrate how the effects of time and disasters influence the largest economies’ production efficiency curves. In both cases (i.e., for the full and robust frontiers) and in comparison with the original estimates, it is observed that G7 countries’ efficiency curves are highly fluctuated under the effect of time and disaster events presenting a highly nonlinear path.

There is only one exception and that is the case of the USA, which appears to have a fairly steady increasing production efficiency path in the longer part of the examined period. This indicates that the USA responds better to man-made and natural disasters compare to the other economies (with the exception over the period 1980 to 1987).

In order to investigate the effect of time and disasters on countries’ production efficiency, we follow the approach by Bădin et al. (2012) by presenting the three-dimensional pictures of the joint effect. The left-hand side (Fig. 3a, c, e, g, and i) on Fig. 3 represents the effect of time and the number of disaster occurrences on countries’ technological change (shifts in the frontier), whereas the right-hand side (Fig. 3b, d, f, h, and j) represents the effect on countries’ efficiency levels (technological catch-up). The overall picture suggests that there is a nonlinear relationship between the number of disaster occurrences and countries’ productive efficiency levels. Specifically, when examining the entire sample (Fig. 3a, b), an inverted “U” shape between countries’ technological change and technological catch-up with disasters is revealed.

This finding suggests that lower number of disasters tend to increase countries’ technological change and technological catch-up up to a certain point. After that point (threshold number), the effect is negative. The positive effect maybe attribute to the fact that for lower number of disasters, countries engage restructuring and investment, which in turn accelerates both their technological change and technological catch-up levels. In fact, this finding is in the same lines with Skidmore and Toya (2002) suggesting that the disaster occurrences are positively correlated with economic growth. Other studies (McDermott et al. 2014; Raddatz 2007; Noy 2009) suggest that the overall effect of disasters is negative; however, mild disaster shocks can stimulate reconstruction activity and can impact positively countries’ economic growth. In our case, the negative influence maybe attribute due to the fact that a large number of disaster occurrences or small number of disasters with high impact can have a negative impact on countries’ production factors which reflects on their technological change and technological catch-up levels. Also it must be mentioned that the threshold value in which the influence from positive becomes negative is much lower for the case of technological catch-up (i.e., 15 disaster occurrences) compared to technological change (i.e., 25 disaster occurrences). This suggests that the negative influence of the number of disaster occurrences first affects countries’ production efficiency and then their technological change levels. The effect of time is positive for the case of technological change indicated by an increasing nonparametric line. For the case of technological catch-up, the effect is positive for the larger period; however, towards the Global Financial Crisis, the effect becomes rather neutral on countries’ technological catch-up levels. Finally, it must be emphasized that according to McDermott et al. (2014), the combination of the type and severity of the events along side with countries’ socioeconomic characteristics is very crucial and can shape the effect of disasters on countries’ efficiency levels.¹⁰ In our case and in order to account for countries’ different economic characteristics, we continue our analysis in a similar manner into four subsamples which are based on their income characteristics (high-income countries (Fig. 3c, d); upper-middle-income countries (Fig. 3e, f); lower-middle-income countries (Fig. 3g, h); lower-income countries (Fig. 3i, j)).

It is evident that regardless of countries’ different income levels, the effect of time and the number of disasters on countries’ technological catch-up and technological change is similar compared to our previous findings. However, an interesting observation is that the turning points (i.e., the threshold values) which the effect turns form positive to negative both for countries’ technological catch-up and technological change is based on countries’ income level. For instance, when examining the group of high-income countries, we can observe that the turning point of the effect on technological change (Fig. 3c) is 39 disaster occurrences, whereas, of the effect on technological catch-up (Fig. 3d) is 20 disaster occurrences. However, for the upper-middle-income countries (Fig. 3e, f), the critical points are indicated for 12 and 10 disaster occurrences, for lower-middle-income countries (Fig. 3g, h) for 20 and 17 disaster occurrences, and finally, for lower-income countries (Fig. 3i, j) for 20 and 8 disaster occurrences. This finding suggests that higher-income countries have higher turning points compared to the rest of the country groups. This can be attributed to the fact that lower-income countries’ are not able to respond to disasters in the same way as high-income countries, and as a result, the negative effect comes quicker for those countries. Noy (2009) provides evidence that the disaster effects are more severe for countries with a weak financial sector. Therefore, since high-income countries have stronger financial sectors compared to the lower-income countries, the negative effect of disaster occurrences impacts first the lower-income countries. Moreover, it is also evident that for all the country groups, the turning points for the effect on technological catch-up are much lower compared to the turning points of the effects on technological catch-up. This finding as has been mentioned previously implies that regardless of the income level of a country, the negative effect of disaster occurrences impacts first country’s ability to catch-up and then its technology change.

Finally, when examining the effect of time, it is observed that in most of the cases, the influence is positive but in a nonlinear manner. Two exceptions can be observed for the case of upper-middle- and high-income countries’ technological catch-up (Fig. 3f, d), in which the effect of time is neutral especially during the initiation period of Global Financial Crisis.¹¹