How does disaster affect development

But instead of following this path, an unexpected natural disaster hits our country at some point in time and things change. The big question now is: what will happen to the growth of GDP per capita in the long-run?

To answer that question we need to think about the different direct and indirect impacts that natural disasters have. Their direct effect is obviously the destruction of capital and in the worst cases also human casualties. Intuition might suggest that destroying capital would make it harder for our country to produce goods and services and growth would therefore temporarily be negatively affected.

The economy would then start to recover and growth would return to its original level, but the GDP per capita itself would be permanently negatively impacted. The new trajectory would be parallel, but below the baseline trend. The area between the two lines represents the size of the long-term productivity loss due to the natural disaster.

We can think of this scenario in a way as moving the country back in time undoing the growth that happened during some period of time.

An even more pessimistic hypothesis suggests that not only the level of GDP per capita would be reduced in the long-run, but also the growth rate of GDP per capita. While there will always be some recovery, the country might be so much negatively affected that the growth rate will stabilize at a lower level than before. This is the worst case scenario. But there are also other theories. According to neoclassical economic growth theory, the country would experience a temporary drop in GDP per capita due to the destruction of productive capital such as damaged roads or destroyed buildings.

But the lower capital levels would also allow for higher productivity gains and the disaster would therefore be followed by higher growth rates than what the country experienced previously. These findings suggests that natural selection is at work both inside and outside the affected areas. Second, the estimated marginal effects in Table 1 are comparable between the firms in damaged Column A and undamaged Column B areas, and the curvatures of the two lines in Figure 1 are comparable although the levels of the two lines differ, which will be discussed below.

This finding means that the Tohoku Earthquake neither promotes nor demotes the natural selection of firms. In reality, it is evident that the firms in the affected area do suffer substantial damage, especially from tsunamis or the serious accident at the nuclear plant in Fukushima. Hence, the finding of a smaller probability of bankruptcy in the affected area is seemingly counterintuitive. However, there is one potential reason for the lower probability in the affected area — the enormous amount of public aid to firms.

Relatedly, the descriptive statistics show another intriguing finding. These firms may have voluntarily closed down. This story is convincing because the Tohoku area is the epitome of a shrinking Japan due to its aging population. Therefore, the firms in this area would have sooner or later faced long-run problems such as difficulty in business succession and a decline in the local economy.

Indeed, we have little evidence to support these conjectures, and there remain many empirical questions to resolve. As the above discussion suggests, the exit of firms — or more generally their dynamics and the recovery of the local economy after natural disasters — is closely intertwined with the policy measures taken and the underlying economic conditions.

Thus we should have a broader and long-run perspective in examining their economic impact and the policy measures needed to deal with them — beyond just focusing on the direct and devastating damage that attracts much of our attention. Cole M A. De Mel, S. McKenzie and C. Saito ed. For the mechanism through which the market eliminates inefficient firms during the recession period, see e. Using very detailed plant-level information for the amount of damage, this study finds that plants with more damage are more likely to exit.

Ono et al examine the relocations of firms after the Tohoku Earthquake and find that damage by the earthquake increased the likelihood of relocations. Using this budget, the government has implemented a variety of measures, such as different forms of subsidies, public credit guarantees by credit guarantee corporations, and loans by public financial institutions.

Saito et al inspect from an economics viewpoint the policy-making process behind this huge budget and conclude that the budget was excessive.

This article is published in collaboration with VoxEU. Publication does not imply endorsement of views by the World Economic Forum. To keep up with Agenda subscribe to our weekly newsletter.

Short-term impacts of disasters are discussed more widely. Albala-Bertrand looked at statistics of 28 natural hazard-induced disasters in 26 countries for the period — using a simple before-and-after comparison of variables, including Gross Domestic Product GDP , its growth rate, and inflation rate up to 3 years after disasters. He aggregated all rapid-onset disaster events in a year, weighted by month of occurrence. Hochrainer took an approach that developed a counterfactual projection of GDP and then compared this to the actual value of GDP after disasters.

A majority of multicountry studies confirms that more intense disasters have a larger negative impact on output and growth Raddatz ; Hochrainer ; Strobl ; Cavallo et al. Noy and Nualsri found that procyclical fiscal policy in response to disasters may aggravate negative outcomes on the macroeconomy in developing countries. Part of the research uses econometric methods for the multicountry studies. In addition to these examples, a couple of sector-specific studies try to detect the winners and losers of natural disasters Hsiang ; Loayza et al.

Limited by the fact that reliable and higher-resolution data for different natural hazard-induced disasters at the subnational regional scale are not available in every country, however, the relationship between different disasters and output growth in a single country have rarely been discussed.

Raddatz and Loayza et al. We focused on the short-term postdisaster state in China for several reasons. First, along with global climate change and the rapid development of the economy and rapid urbanization, China is one of the countries most affected by natural hazard-induced disasters. The variety of disaster events, the high frequency of disasters, and the vast areas affected make results more robust in a regional examination and less susceptible to the impact of outliers Noy and Vu Due to the instability of the monsoon, meteorological disasters such as floods and typhoons occur frequently in China, with an annual average of approximately seven typhoons making landfall in the southeastern coastal areas Xu et al.

Local or regional droughts occur in most years. Most of China is located at the intersection of the Asia-Europe plate, the Indian plate, and the Pacific plate. The active geotectonic movements cause frequent earthquakes. The data analyzed in this study are natural hazard-induced disaster impact records and socioeconomic data. To account for the impact of disaster measures and disaster types, we estimate two sets of equations for showing the impact of economic development levels on macroeconomic consequences in eastern, central, and western China.

We used two types of data in this study Table 1. In this study, we treat all of these occurrences, except earthquakes, as meteorological disasters Wu et al. Next, we wanted to obtain the impact records of both meteorological disasters and earthquakes, respectively.

Earthquake disaster event impact records are available from the China Earthquake Yearbook China Earthquake Administration We used the method of Wu et al. It is formulated as. Earthquake impact records from the China Earthquake Yearbook do not include information on the number of people affected. Therefore, our impact records of two natural hazard-induced disaster types can only be expressed through direct economic losses.

Considering that the impact of a specific natural hazard-induced disaster on the macroeconomy depends on the magnitude of the disaster relative to the size of the economy, we divide the number of people affected by the provincial population size in the year prior to the current year and divide the direct economic loss by provincial GDP values the year before.

The second type of data is the provincial data for other macroeconomic variables, including provincial GDP growth, retail sales as a proxy for trade, highway mileage as a proxy for infrastructure, school enrollments as a proxy for education, and the proportion of the primary industry, which are available from the Chinese Socioeconomic Development Statistical Database. Footnote 2 Data on trade were divided by the provincial GDP values and were expressed as a percentage of output.

For school enrollments, we sum up primary, secondary, and college enrollments and divide them by population. Data on highway mileage were also divided by population. Footnote 3 Information about the variables and their sources is summarized in Table 1.

Considering that the level of development and the geographic characteristics of the 31 provinces are quite different, China can generally be divided into three regions. We then aggregate data and report descriptive statistics for variables in Table 2 for ready reference. The benchmark estimation equation of this study is from Noy and Vu , in which a provincial-level analysis in Vietnam is presented. Our starting estimation is characterized by the following equation:.

As such, the first set of regressions is specifically characterized by:. As such, the second set of regressions is specifically characterized by:.

In these disaster impact and development regression equations, taking the lagged term of all the control variables can control the endogeneity problem, which makes the estimation results more interpretable Noy In our estimated model, the lagged dependent variable is included in the independent variables, which make our panel data dynamic.

Therefore, in these cases, the estimation method of the difference-GMM difference generalized method of moments or the system-GMM system generalized method of moments are usually adopted Vu and Noy Table 3 shows our baseline results. The coefficient estimates for AFP and its lagged values are reported in Columns 1a , 2a , and 3a , respectively, whereas the coefficient results for DEL and its lagged values are reported in the corresponding b columns.

From this table, the impacts of the population affected AFP and the direct economic loss DEL present a similar direction of influence only in central China [see Columns 2a and 2b ]. Here, we also sum the coefficients for the current and lagged values and report the p value for the significance of this sum.

The sum of two values can be interpreted as a composite effect of a disaster in the short-term Noy and Vu ; Vu and Noy These results are provided after each specification in the next column. However, in Table 3 , the composite effect of both disaster measures in the three regions is not significant. As for eastern China, the impact of the number of people affected AFP on the growth rate is an immediate negative, whereas the impact of the amount of damage DEL on the growth rate is a lagged positive [see Columns 3a and 3b ].

In the western region, the impact of the affected population AFP on the growth rate is positive; this difference between the eastern and western regions could be ascribed to different types of dominant disasters Jaramillo Therefore, we estimate the impact of direct economic loss from the different disaster types Table 4.

The signs of direct damage on the economic growth rate, as shown in Table 4 , are almost the same as reported in Table 3. However, in this case they are reflected in specific disaster types. In the central and eastern regions of China, the impact of meteorological disasters is significantly correlated with the growth rate, whereas in western China the impact of earthquakes is significant.

Because of the positive significance of meteorological disaster impacts on growth in eastern China, we want to know if a province that is more seriously or frequently stricken is getting faster growth. We set Shanghai Municipality as the base group.

Although Shanghai is commonly considered one of the cities most vulnerable and at risk to floods, it experienced the lowest direct damage of meteorological disasters among the eastern region provinces during our study period. We generate slope dummies for all other provinces and regress GDP growth on the direct economic losses of meteorological disasters Meteor with all control variables added, including the lagged value of GDP growth.

Table 5 shows the results of benchmark variables. The coefficient of the Meteor in Column 1 reports the effect of meteorological disasters on the growth of Shanghai. Other results in Column 1 show the difference in coefficients of each province relative to the base group. A positive coefficient implies that a province enjoys higher GDP growth than the base group in the case of the same loss rate of meteorological disasters and vice versa.

Additionally, the effects of meteorological disasters on the growth of the other provinces are calculated by adding up the coefficient of each province to that of the base group. These province regression results and their p values are calculated in Column 2. Then, the sum of the current value and the lagged value for all provinces are reported in Column 3 , including their p values for significance. Column 2 notes that the eastern provinces can also be suppressed by meteorological disasters.

For example, Hainan Province is quite unusual for having significantly benefited from meteorological disasters. Similarly, according to the results in Table 4 , we did the same analysis for the central see Table 6 and western regions see Table 7. We set Henan Province as the base group since it was the least stricken area in the central region. Table 6 reports more information about the central provinces.

It is clearly indicated that provinces with higher damage rates of meteorological disasters do have a lower GDP growth than the base group. The results of Column 1 show that meteorological disasters are definitely one of the factors hindering the development pace for the central provinces. Last, we turn our attention to western China. We set Guangxi, Guizhou, Shaanxi, and Ningxia together as the base group because earthquakes there are quite rare compared to the other seven western provinces in the past three decades.

Table 7 shows the results. Column 1 shows that not all the provinces with higher earthquake damage enjoy higher GDP growth than the base group.

The negative signs imply that the growth rate of Chongqing, Gansu, and Xinjiang might be decreased in the case of earthquake occurrences. Nevertheless, the effect of earthquakes on the macroeconomic growth rate in the three frequently and seriously stricken provinces see He et al.

The possible reason is that postearthquake aid increases investment, which then brings prosperity. Furthermore, we investigated the intraregional effects of significant natural hazard-induced disasters across the western, central, and eastern regions in China.

We found that, from the perspective of either disaster measures or disaster types, natural hazard-induced disasters have distinct spatially heterogeneous effects on the regions. According to previous empirical studies on the macroeconomic impacts of natural hazard-induced disasters, the studies can be grouped into two substrands van Bergeijk and Lazzaroni In this study, we use two disaster measures—affected population AFP and direct economic loss DEL , as direct disaster costs to detect indirect disaster costs.

The result of the van Bergeijk and Lazzaroni meta-analysis found that different measures of disasters do not influence the tendency to report a negative or positive impact except for disaster intensity among indirect cost studies.

However, our results in Table 3 demonstrate a spatial heterogeneity of disaster measure sensitivity. The nonmonetary term—population affected—is significant in the western region, while both nonmonetary and monetary terms show significant impact in the eastern region—although the two influences act in opposite ways. The consistency of the two disaster measures is reflected in the central region, whether from the sign or the ratio of current value and lagged value.

This means that a better level of development exacerbates the absolute consequences of disasters but increases the ability to maintain a relatively low rate of costs. In China, it is generally accepted that the western and the central regions belong to the underdeveloped or less-developed areas while the eastern region belongs to the developed area Zhou et al. The spatial pattern of natural hazard-induced disaster variables in China is consistent with that of the IPCC report Fig.

Provincial average of the number of people affected a and the amount of direct economic loss b in the western, central, and eastern regions of China. A 5-year moving average is shown. Theoretically, the expansion of the affected population could reduce the efficiency of labor production and hinder the process of human capital accumulation and thus bring a reduction in growth. The Toya et al.

However, our results show a positive effect of the affected population AFP in the western and central regions of China, the underdeveloped regions, albeit a lagging effect [see Columns 1a and 2a in Table 3 ]. Compared with eastern China, differences in the degree of economic development and geographical environmental conditions as well as the dominant disaster types discussed in the next section should be a significant driver behind this effect Zhou et al.

Jaramillo believed that different natural disasters could create different macroeconomic impact scenarios. This is related to the mechanism of damage caused by disasters. Compared with earthquakes, meteorological disasters occur more frequently and often at specific times of the year, which makes them easier to predict Skidmore and Toya Conversely, a drought may not bring much effect on economic growth because the loss is generally restricted to annual or seasonal production.

Guo et al. During our study period, more earthquakes occurred in the western region, while more meteorological and climatic disasters occurred in the central and eastern regions. The perception of these disasters may affect the propensity of government fiscal expenditures. To provide a basis for this conjecture, we collected data on transfer income from urban and rural households in each province from to Fig.

Footnote 4 Here, transfer income refers to various transfer payments made by the state, work units, and social groups to households, including disaster relief funds, pensions, production subsidies, living allowances, and the reimbursement of medical expenses National Bureau of Statistics of China In both urban and rural households, the provincial average relative transfer income as the ratio of GDP per capita is highest in the western region, followed by the eastern and lowest in the central regions.

We believe that the occurrence of the lowest transfer income in central China should be related to the general lack of main sudden-onset disasters in the region, such as earthquakes. The long-lasting disaster periods of droughts and extreme temperatures, for example, brought huge losses but did not result in matching relief funds that could be reflected in household transfer income.

Urban a and rural household b transfer income levels in the western, central, and eastern regions of China over the period from to Natural hazard-induced disasters caused considerable losses to the central region see Fig. The ultimate goal of a large number of empirical studies over the past decade has been to portray a macroeconomic response to natural hazard-induced disasters, though the real situation is absolutely complex.

Klomp and Valckx found empirical support for scenarios for climatic, geological, and hydrometeorological disasters. They attributed each disaster type to one of the four scenarios summarized by Chhibber and Laajaj Based on our empirical results mainly Table 4 , we also attempt to describe the specific scenarios of the three regions in China Fig.

Scenarios of the short-term per capita output impact of meteorological disasters and earthquakes in the western, central, and eastern regions of China. The slope of the dotted lines represents the current average GDP growth rate in each region. The points tangent to the dotted line are consistent with the estimation results in Table 4 —that is, there is no statistically significant difference between current GDP growth rate at this point and the benchmark growth rate.

First, by the end of —the end of our research period, the provincial average per capita GDP of eastern China was about twice that of the western region, and that of the central region was slightly higher than the western region. The western region developed slightly faster, at a rate of about 6. Therefore, the base level and benchmark slope of growth scenarios in the western, central, and eastern regions are identified.

Earthquakes in the central and eastern regions cause an initial drop in output because of the destruction of both human and financial capitals.

However, due to the stimulated inflow of external investment and the higher return on postdisaster capital, the output trajectory of the year can return to the baseline. Earthquakes will have a longer impact in the west, because it will still enjoy the positive stimulus of reconstruction investment until the end of the second year.

Meteorological disasters have temporary effects on growth in the west. Compared with the east, the central region spends a longer time on implementing reconstruction investments—possibly due to financial and technical capacity constraints Loayza et al. Note that financial constraints can be confirmed by the regional transfer incomes gap that was discussed earlier.

In addition, Hallegatte et al. These scenarios, however, are based on the inclusion of the Wenchuan Earthquake in our sample. When we exclude the outlier year from the sample, the positive impact of earthquakes in the western region is increased and advanced. Therefore, an outlier event could be a strong impediment to economic development in underdeveloped regions. Overall, short-term economic development scenarios are still robust even without excluding the outlier year.

There are some limitations in our study. Disaster impacts may often be systematically underreported in underdeveloped provinces, especially prior to the early s Zhang et al.

Such biases are also serious in multicountry studies Noy It is one of the reasons that we do regional scale estimation. Wu et al. How disasters will change the pace of development in different regions in the future will rely on the form and effectiveness of current disaster management measures.

It was generally recognized by previous studies that natural hazard-induced disasters have negative impacts on national macroeconomic development. The impact of such disasters has become prominent in China; however, the relationship between the effects of different hazards and regional growth is still poorly understood.

Such a heterogeneous response becomes more evident when we examined intraregional effects in the central and eastern regions, which demonstrates the robustness of our research results. The high correlation between development levels, types of disaster, and regional growth in China challenges our understanding of the disaster management behaviors of local governments.

In particular, the identified need based on our findings is in sharp contrast to the low transfer-income level of households in the central region. Furthermore, considering that the central region mainly experiences slow-onset but long-lasting disasters, the actual responses to natural hazard-induced disasters in the region indicate that the level of socioeconomic development and the type of disasters affect the development of the region.

From a policy perspective, people who manage disaster responses should understand that higher disaster losses do not necessarily imply that a province would have growth in the following year, although our empirical results show such tendencies in certain provinces. Loss compensation is the driving force of the postdisaster recovery, and social productivity and sustainable economic development are the economic basis of compensation for disaster losses.

To this end, economic development is the most effective way to compensate for disaster losses. Note that government behavior objectively adjusts or restricts the relationship between natural hazard-induced disasters and macroeconomic development, even under market-economy conditions.