1. Introduction
China is one of the countries affected most by natural disasters in the world. China’s Actions for Disaster Prevention and Reduction during the 12th Five-Year Plan Period,1 a document issued by the General Office of China National Commission for Disaster Reduction, indicates that in the Twelfth Five-Year Plan Period (from 2011 to 2015), the number of people affected by disasters in China is 310 million annually. The number of persons killed or missing as a result of natural disasters reaches 1500 annually. The direct economic loss is more than 3800 billion renminbi (RMB). China has been severely affected by the devastating effects of typhoons, whose increased frequency and virulence as well as their deep socioeconomic impact cannot be overlooked. About 27 typhoons are generated in the northwest Pacific region each year, and about seven of them land in China (Kang 2016). For instance, in August 2015 Typhoon Sudiro affected nearly 5.655 million people living in China’s coastal areas, led to 26 fatalities, and resulted in a direct economic loss of 13.77 billion RMB.2 With global warming, the generation frequency and intensity of typhoons will be further strengthened (Qin 2008; Qin and Luo 2008), which indicates that the effects of natural disasters on China will become more numerous and more severe.
China boasts the richest labor resources in the world.3 These rich labor resources have been the primary driving force of China’s fast economic growth over the past 40 years. However, because of the limitations of research perspectives, data, and methods, there has been hardly any study of the impact of typhoons on the Chinese labor market. By contrast, international scholars have adopted various ways to study typhoons’ impacts on labor markets.4 Most studies of the United States and other developed countries have found that hurricanes (typhoons) will cause labor to flee the region, so employers have to increase the pay to attract enough labor. Hurricanes (typhoons) in this respect have a negative impact on employment rates and a positive impact on wage levels. Taking a hurricane in Florida as an example, the study of Belasen and Polachek (2009) suggests that the industry structure of the labor market in hurricane-affected areas has changed. Workers with the highest-paying jobs tend to migrate to relatively safe areas, leaving low-wage workers behind. This in turn, results in a reduced employment rate of 4.76% and a 4.35% increase in wages in the affected areas. Using a vector autoregressive model, Ouattara and Strobl (2014) studied the impact of hurricanes on the migration of coastal cities in the United States and found hurricanes increased the outward mobility of the wealthier population. McIntosh (2008) analyzed the employment in labor market and industry distribution characteristics of the Houston area. Through the analysis, the research found that hurricanes (typhoons) led to a 0.5% drop in employment. Groen and Polivka (2008) found a 35% decrease in urban employment of New Orleans in the aftermath of Hurricane Katrina.
Because of the uniqueness of the Chinese labor market, this paper assumes that the impacts of typhoon on China’s labor market may be different from those of developed countries such as the United States. The main reasons are as follows: first, in China, the abundant and relatively cheap labor resources can make up for the “labor shortage” brought by typhoon disasters in a timely manner. Therefore, a typhoon will not have a significant impact on China’s employee remuneration. In contrast, the labor shortage in developed countries can only be resolved by rising labor remuneration (Belasen and Polachek 2008, 2009; Rodríguez-Oreggia 2013). Second, there is a difference in the mobility of the labor market between China and developed countries. Because of better mobility of labor markets in developed countries, it is easier for high-paid workers to flee the area and leave low-paid ones, resulting in inadequate labor supply and accordingly rising labor remuneration. In China, however, there is regional segmentation in such areas as the household registration system, medical security system, and normal enrollment, so labor mobility is impeded by many institutional obstacles and the cost of movement is quite high. Therefore, facing the threat of typhoon disasters, the labor may not migrate, and the number of employees may not necessarily decrease.
What impact will the typhoons have on China’s labor market? This paper attempts to verify the above speculations through empirical analysis. In light of this, by using a generalized method of moments and taking 21 prefecture-level cities of Guangdong Province as basic research units, this paper evaluates the impacts of typhoon disasters on the employment and wages of labor markets during 23 quarters from 2009 to 2014 from four effects of typhoons—a general effect, regional effect, intensity effect, and time effect—and compares itself with current similar studies of developed countries, so as to analyze the reasons behind the phenomenon and try to get some new inspirations. This study not only fills the gaps in the field of research on how natural disaster can affect human capital in China, but also provides results that can be compared with those for developed countries; furthermore, the current study can provide empirical evidence for the postdisaster restoration and reconstruction as well as disaster prevention and reduction management in China, therefore ultimately mitigating the adverse impacts of typhoons.
2. Research hypothesis, data, and variable descriptions
a. Research hypothesis
The impact of natural disasters on the labor markets can be studied from the perspectives of supply and demand. In terms of labor supply, disasters can cause casualties and evacuation of residents, which directly reduce the number of people employed; disasters can also destroy transportation, communications, water supply, and other key lifelines, and lead to water quality deterioration and air quality degradation (Zheng 2013). However, the typhoon-prone coastal cities in China have higher incomes, more employment opportunities, and better living conditions compared with the inland. After weighing the costs and benefits, people may be reluctant to relocate after a disaster. Moreover, China is rich in labor resources, which means that the labor supply gap caused by typhoons can be supplemented in time. Therefore, the negative impact of typhoon on China’s labor supply may not be significant.
In terms of disasters’ impact on labor demand, since the Chinese government has the advantage of centralized power, it can invest a large amount of human and material capital and develop plans for emergency relief and postdisaster construction quickly in the aftermath of the disaster. During the process of emergency relief and postdisaster reconstruction, fields of industry construction and transportation gradually recover, finding themselves in desperate need of substantial labor force; at the same time, other industries will also flourish, indirectly increasing the demand for labor (Ewing et al. 2009; Rodríguez-Oreggia 2013). Based on Joseph Schumpeter’s theory of creative destruction, Skidmore and Toya (2002) suggest that typhoons and other meteorological disasters reduce the expected return in capital and thus the investment is redirected to human capital. As a result, typhoons may have positive impacts on labor markets.
H1: Typhoons will not have a negative impact on the quantity of labor employed and employee remuneration in Guangdong Province.
H2: Typhoons will not lead to migration of labor force in Guangdong Province.
H3: The impact of a typhoon on the labor market in Guangdong Province varies with its intensity.
In addition, the disaster is an external factor affecting social and economic development, so its disturbance in the labor market will cause a labor supply and demand conflict. However, the labor market is self-adaptive and may be gradually restored, since it is assisted by the government intervention.
H4: The impact of disasters on labor market varies over different stages of postdisaster recovery.
b. Data
The disastrous weather effects of typhoons come from the EM-DAT database, which is built and maintained by the Center for Research on Epidemiology of Disaster (CRED) of Katholieke Universiteit Leuven of Belgium.5 The EM-DAT database records the time of occurrence, name, location, death toll, and economic loss. But the location is recorded only at the level of provinces rather than precisely down to the prefectural-level cities. The detailed information about the prefectural-level cities affected by typhoons is from Baidu Encyclopedia.6 The EM-DAT database does not specify typhoons as a separate category. Based on the times and name of typhoons provided by the EM-DAT database, the corresponding categorical information is collected from the tropical cyclones data of the China Meteorological Administration, and then classified and verified.
According to the national standard classification of tropical cyclones (GBT19201–2006) issued by China Meteorological Administration, tropical cyclones are divided into six levels.7 The first three grades can be categorized as high-intensity typhoons (abbreviated as “H”) and the remaining three as low-intensity typhoons (abbreviated as “L”). Details are shown in Table 1.
Descriptions of typhoons affecting Guangdong during 2009–14. Information on affected municipal areas comes from Baidu Encyclopedia, levels of tropical cyclones from the Information Center for Tropical Cyclones of the China Meteorological Administration, and death tolls and economic losses from EM-DAT.
The research area is restricted to Guangdong Province for the following reasons. First, seen from the situation of disasters, Guangdong Province is located at a special geographical position subject to the threat of typhoons, with low latitudes and marked monsoon climate, facing the South China Sea on the south and the Pacific on the east and having a coastline of up to 8500 km, which accounts for more than one-third of the total of the country. According to incomplete statistics, from 1985 to 2006 typhoon disasters of Guangdong have caused 667 deaths, 20 873 injuries, 1 699 150 collapsed buildings, and an economic loss of as much as 250 billion Yuan. In horizontal comparisons, almost each disaster indicator of Guangdong ranks highest in the country, so the place can be considered as the province that has been most affected by the typhoon disasters in China (Deng 2011).
Second, seen from the social conditions, Guangdong province, which is adjacent to Hong Kong and Macao, has distinct location advantages and is one of China’s most economically developed coastal provinces. Each year Guangdong attracts a wealth of labor resources that have the following characteristics. First, the majority or workers are migrant workers. According to the results of the sixth national census in 2010, Guangdong is the province with the largest inflow of population. Second, with fast economic development and a relatively developed labor market, Guangdong is a typical developed region and a development benchmark for relatively backward provinces. Therefore, a study of the impacts of natural disasters on Guangdong’s employment and labor remuneration has great demonstrative significance.
Because of data availability, quarterly data of unit employment and per capita remuneration of 21 prefecture-level cities of Guangdong from the first quarter of 2009 to the third quarter of 2014 have been selected to reflect the changes in the labor market. The statistical scope of labor forces includes state-owned units, urban collective-owned units, joint ventures, joint-stock ventures, and foreign investments (including Hong Kong, Macao, Taiwan, etc.). The descriptive results of data are shown in Table 2.
Quarterly data of unit employment and per capita remuneration of the cities in Guangdong during 2009–14. Data come from the Statistical Information Network of Guangdong Province. Among the data, per capita remuneration is obtained by dividing total remuneration and the number of employees and goes through price deflator processing, with 2009 as the base period.
Seen from the table above, from 2009 to 2014, Guangdong Province was hit by 15 severe typhoons. Zhanjiang is the city that suffered typhoon invasions most frequently (11 times), whereas Shaoguan, QingYuan, Huizhou, Guangzhou, and Dongguan were not affected. The number of prefecture-level cities hit by typhoons reached 11. During the 23 seasonal quarters from 2009 to 2014, Shenzhen had the most employment, reaching 4 623 100, while Chaozhou had the least, at 200 900; Guangzhou had the highest average employment at 2 849 700 while Chaozhou had the least, at 135 500. As for per capita remuneration per quarter, Guangzhou’s average topped the list, reaching 13 153.12 Yuan while Yangjiang’s average was the least, 6913.63 Yuan.
c. Variable descriptions
To study the relationship between typhoon disasters and Guangdong’s employment and labor remuneration, a regression model has been established with a generalized method of moments (GMM). Based on the previous studies on labor markets (e.g., Belasen and Polachek 2008, 2009; Groen and Polivka 2008; Ewing et al. 2009, etc.) and taking into account data availability, the following variables have been selected as the control variables of regression from the economic, seasonal and policy factors affecting labor markets.
1) Economic factors
The employment of labor markets is closely related to economic development. With economic growth, investment, production, and consumption will increase accordingly, thereby creating more employment opportunities and stimulating rising employment rate; otherwise, there will be a decline in the employment rate. For instance, Zhang (2009) believed that the gross domestic product (GDP) is the preferred measure of economic development. Therefore, GDP8 is chosen as a control variable, the data of which come from Statistical Information Network of Guangdong. To eliminate the impacts of such price factors as inflation, GDP data go through price deflator processing, with 2009 as the base period.
2) Seasonal factors
The supply and demand of labor can be affected by seasonal variations, especially in Guangdong province, which is rich in migrant labor resources. In the new year and the spring festival of the first quarter, for example, there are large and expected fluctuations in labor. Therefore, seasonal dummy variables are applied to exclude the impacts of seasonal labor return.
3) Policy factors
The labor market of China, in contrast to common commodity markets, is greatly influenced by the country’s intervention. For example, as an important method adopted by the government to protect the rights and interests and labors, the minimum wage standard plays a significant role in the relationship of labor supply and demand objectively. Ma et al. (2012) believed that every 10% increase in the minimum wage gave rise to a decline about 0.6% in the employment of manufacturing enterprises. In the present study, the minimum wage standard variables of different years in different prefectural-level cities are taken as the control variables in the equation.
3. Empirical analysis
a. General effect of typhoons on labor market
In the models, Qit represents the unit employment9 of region i in the period t; PWit the per capita remuneration of region i in the period t; TCit the dummy variable of typhoon, whose value is 1 when it is in experimental group where region i in the period t is hit by typhoons and 0 when it is in control group free from typhoons; GDPit the gross product of region i in the period t; and S the following three quarterly variables: S1it, whose value is 1 when period t is the first quarter in region i and 0 when it is other quarters, S2it, whose value is 1 when period t is the second quarter in region i and 0 when it is other quarters, and S3it, whose value is 1 when period t is the third quarter in region i and 0 when it is other quarters. Also, 
All the equations in this paper are estimated using the generalized method of moments estimation method proposed by Arellano and Bond (1991) and Blundell and Bond (1998). The GMM is adopted for three main reasons: first, GDP is likely to have reverse causality between employment and wages (i.e., GDP and employment, and GDP and wages, affect each other). The systematic GMM estimation method can reduce the endogeneity of GDP by using the lagged variable of the endogenous explanatory variables as the instrumental variables. Second, systematic GMM estimation can deal with the original equation using first-order differential treatment, which can parse the time-invariant observables and unobservable city-specific effects. Third, in theory, the change of employment and wages in the labor market is a continuous dynamic process, and systematic GMM estimation introduces a lagged dependent variable as an independent variable, changing the original static model into a dynamic one, which is more consistent with the actual situation.
Whether or not the adoption of GMM system is effective is determined by two tests. One is the Arellano-Bond AR test. This test will examine if the model set is appropriate. The original hypothesis is of no first-order or second-order correlation in the model residual sequence. The other is the Sargan test. It is used to determine whether there is overidentification in instrumental variables. The original hypothesis is that instrumental variables are not overidentified.
The year term can be divided into five annual variables: Year09it indicates the data are from the year 2009 (and so on for Year10it for year 2010, etc.) If positive the value is 1; otherwise it is 0.
The regression result of column 1 of Table 3 shows that typhoons have brought about an increase in the number of local workers per unit by 6.31%, the reason of which may be that China’s abundant labor resources can compensate for the negative impacts of typhoon disasters in a timely manner. Especially in Guangdong, a place with relatively developed economy and a large number of migrants, the threat of typhoon disasters is still not enough to cause a local labor shortage. Since the inflow of labor forces in Guangdong comes basically from underdeveloped areas (Liu 2004), the workers are willing to assume the risk of typhoon disasters for their survival (Belasen and Polacheck 2008). In addition, postdisaster recovery and reconstruction requires a large amount of labor resources. Therefore, hypothesis H1 in this paper is validated and typhoons in Guangdong Province will not have a negative impact on the quantity of labor employed.
Direct and indirect effects of typhoon on the quantity of labor employed in Guangdong Province (2009–14). One, two, and three asterisks indicate significance at the levels of 10%, 5% and 1%, respectively.
The third column of Table 3 shows that typhoons do not have significant impacts on the per capita employee remuneration. This can be attributed largely to China’s relatively weak economy, in which low-income households account for a large proportion compared with developed countries (Liu 2004). Comparatively speaking, people living in typhoon-prone coastal cities enjoy higher economic income and social welfare, more employment opportunities, and better living conditions. Therefore, in the postdisaster recovery and reconstruction activities, there is no need to attract the labor force by increasing wages. Again, hypothesis H1 is verified, and typhoons in Guangdong Province will not have a negative impact on labor employment remuneration.
b. Regional effect of typhoons on labor market
In the models, 
The results in the columns of Table 3 that describe regional effects show that in the regions either directly or indirectly affected by typhoons, the total employment and per capita labor remuneration are not affected; in other words, the typhoon will not cause labor migration.
Furthermore, we employed the Spatial Dubin Model (SDM) to calculate the regional effects of the typhoon and measure whether they have caused labor migration and led to changes in labor markets in neighboring areas. This method uses the direct effect to represent the mean effect of the independent variable on the region, and the indirect effect represents the mean effect of the independent variable on other regions (Hu and Li 2015). The results are consistent with the abovementioned study (i.e., typhoons will not bring spillover effects). Details are provided in Tables 3 and 4.
Direct and indirect effects of typhoon on per capita labor remuneration in Guangdong Province (2009–14). One, two, and three asterisks indicate significance at the levels of 10%, 5%, and 1%, respectively.
c. Intensity effect of typhoons on labor market
In the models, 
The regression results in the columns of Table 5 that only higher-intensity typhoons will result in a 12.5% increase in the total number of employees, while lower-intensity typhoons have no effect on the number of employees, which may be explained by their less destructive impacts that are insufficient to affect normal socioeconomic activities. In addition, neither higher-intensity nor lower-intensity typhoons have significant impacts on per capita labor remuneration.
GMM estimation of the effects of typhoons on local labor markets in Guangdong Province, China 2009–14. The data in parentheses are robust standard errors. One, two, and three asterisks indicate significance at the levels of 10%, 5% and 1%, respectively.
d. Time effect of typhoons on labor market
The time-effect column of Table 5 shows that typhoons have caused a 17.4% increase in the number of employees in quarter t. No significant effect on the number of employees is found in the next quarter t + 1 and the quarter after next t + 2. The number of employees decreases by 17.0% in quarter t + 3. That is, in the third quarter after the disaster, the number of employed people decreased by 17.0%, which recovers to predisaster levels. Then, what will be the impact of the typhoon in quarter t + 4 on the number of local workers per unit? After introducing the lagged variable of quarter t + 4 into Eq. (9), it is found that the coefficient of the variable’s impact on the number of employees is −0.117, although it failed the t test (t statistic = −1.15, prob. = 0.249). From the above analysis, it is concluded that the t + 4 quarter no longer has a significant impact on the quantity of labor employed. In this case, the delayed effect of typhoon on employment will not exceed four quarters. After a year’s adjustment, the impact caused by typhoons will gradually disappear.
At the same time, typhoons have no significant impacts on per capita labor remuneration, which is consistent with the results obtained by the models of the general effect, regional effect, and intensity effect.
4. Conclusions and discussion
In this paper, the impact of typhoon on the labor market in cities of Guangdong province is studied for the first time. Based on quarterly data of unit employment and labor remuneration of 21 prefecture-level cities of Guangdong from 2009 to 2014, this paper applies a generalized method of moments (GMM) to establish a regression model between disastrous weather events of typhoons and such variables as employment and per capita labor remuneration, and does calculations from four aspects of the impacts of typhoons on labor markets: general effect, regional effect, intensity effect, and time effect. The results are presented as below: first, high-intensity typhoons brought about an increase of 12.5% in the quantity of labor employed. Second, the typhoon did not have a significant impact on the per capita employee remuneration in Guangdong. Third, over time, the impacts of typhoons will subside and employment levels will return to predisaster levels. The first and the second conclusions are markedly different from those obtained by current studies abroad. For instance, McIntosh (2008), Zissimopoulos and Karoly (2010), and Belasen and Polachek (2008, 2009) believed that hurricanes reduced the local employment rate; Ewing et al. (2009), Olsen and Porter (2013), and Belasen and Polachek (2008) pointed out that disasters increased the local per capita remuneration. As noted in the introduction, there is a contradiction in research conclusions between studies of China and studies of other developed countries, which may be attributed to the distinctiveness of the Chinese labor market.
Based on the research above, this paper puts forward the following three suggestions regarding reducing barriers to labor mobility, eliminating the minimum wage standard in the region, and increasing the construction of disaster prevention and mitigation facilities in less-developed areas.
The first is to reduce barriers to labor mobility. In China, because of the region segmentation in the household registration system, healthcare systems, and student enrollment, labor mobility is subject to many constraints. Therefore, maybe it is time to gradually abandon the household registration system. Instead, we can increase the diversity and quality of supply in public services such as education and health care, and help migrant workers in healthcare insurance and their children’s school enrollment. The labor market employment insurance system should be improved, so that workers with different levels of proficiency can enjoy the same employment services. Thus, even faced with typhoons, labor mobility can still ultimately increase the overall social welfare.
The second is to gradually eliminate the minimum wage standard in the region. The minimum wage standard set by the Chinese government was intended as a protection to maintain the daily life of low-income people, which may, to a certain extent, have also impaired the market regulation of labor supply and demand. In disaster-prone areas such as Guangdong province, agriculture, process manufacturing, construction, transportation, and service industries are labor-intensive industries and are highly sensitive to the impact of typhoons. The implementation of the minimum wage standard will force companies to make up for the losses caused by minimum wage system by reducing worker’s training and education opportunities, since it is impossible to reduce the wage cost (Hu 2013). This practice will eventually reduce the workers’ income in essence. At the same time, because the minimum wage standard system has increased the labor cost of companies, some were forced to relocate, thereby reducing the local employment opportunities. To let the market regulator fully play its due role in labor supply and demand, the minimum wage can be eliminated to create favorable conditions for postdisaster recovery and reconstruction.
The third is to increase the construction of disaster prevention and mitigation facilities in less developed areas. In the less prosperous regions, workers’ income are low, and they are inadequate in skill, which all together increase their vulnerability to natural disasters. In the aftermath of the high-intensity typhoon, people in the afflicted area tend to cut spending on education and production to make up for the loss caused by the disaster, thus being caught in a vicious circle of poverty leading to disaster leading to poverty. We must invest in infrastructure facilities, take forward our efforts to strengthen disaster prevention and mitigation education, and improve the comprehensive ability to deal with disasters, so as to respond more effectively to disasters and facilitate the sustainable development of society.
Acknowledgments
This research was supported by the Natural Science Foundation of China (71373131, 91546117), the National Social and Scientific Fund Program (16ZDA047), the National Soft Scientific Fund Program (2011GXQ4B025), National Industry-Specific Topics (GYHY200806017; GYHY 201506051), and the Ministry of Education Scientific Research Foundation for returned overseas students (2013-693; Ji Guo). This research was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions.
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According to the China Statistical Yearbook 2015, the population aged 15–64 years reached 1.004 69 billion in 2014.
The socioeconomic effects of typhoon disasters have become the focus of domestic and foreign scholars, and quite a few research results have been achieved. According to the classification method of Xiao (2011), current relevant literatures can be divided into two categories: input–output and quantitative regression analysis, from the perspective of research methods. Input–output techniques can be divided into input–output table models and CGE models. Input–output models analyze the comprehensive effects caused by disasters based on “input–output table,” proposed by Leontief, an American economist (e.g., Rose et al. 2007; Ai and Polenske 2008; Lin et al. 2012; Akhtar and Santos 2013; Wu et al. 2014, 2016a; Schulte in den Bäumen 2015). There are many papers evaluating the loss of natural disasters by statistical and quantitative methods. For example, Skidmore and Toya (2002), Ewing et al. (2009), Zissimopoulos and Karoly (2010), Rodríguz-Oreggia (2013), Olsen and Porter (2013), Ouattara and Strobl (2014), Asad (2015), Wu et al. (2016b), Hamilton et al. (2016), and others also did related researches.
The records in EM-DAT database must meet the following conditions: 1) 10 or more deaths have been reported; 2) 100 or more people have been reported affected, wounded, or homeless; 3) disaster-stricken areas have declared a state of emergency; and 4) a call for international assistance. From the EM-DAT database 15 disastrous weather events of typhoons have been selected that hit Guangdong from 2009 to 2014.
For details of Typhoon Swan (the seventh tropical storm in 2009) and others, see http://baike.baidu.com/item/%E5%8F%B0%E9%A3%8E%E5%A4%A9%E9%B9%85/9115336.
The six levels are as follows: 1) super typhoon: the maximum wind speed on the ground near the center reaches more than 51.0 m s−1 (equivalent to wind grade 16 or above); 2) severe typhoon: the maximum wind speed on the ground near the center reaches 41.5–50.9 m s−1 (equivalent to wind grade 14–15); 3) typhoon: the maximum wind speed on the ground near the center reaches 32.7–41.4 m s−1 (equivalent to wind grade 12–13); 4) severe tropical storm: the maximum wind speed on the ground near the center reaches 24.5–32.6 m s−1 (equivalent to wind grade 10–11); 5) tropical storm: the maximum wind speed on the ground near the center reaches 17.2–24.4 m s−1 (equivalent to wind grade 8–9); and 6) tropical depression: the maximum wind speed on the ground near the center reaches 10.8–17.1 m s−1 (equivalent to wind grade 6–7).
Since there are no data of cities’ quarterly GDP per capita and the only data available are undifferentiated GDPs, this paper adopted cities’ quarterly GDPs.
The unit employment refers to the total number of the employed who work in state-owned units, urban collective-owned units, joint ventures, joint-stock economy, foreign investment economy, and the economy invested by Hong Kong, Macao, Taiwan, etc.
