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APRIL 2017 W U E T A L . 255

Impacts of Typhoons on Local Labor Markets based on GMM: An Empirical Study of Province, China

XIANHUA WU Collaborative Innovation Center on Climate and Meteorological Disasters, University of Information Science and Technology, Nanjing, Jiangsu, China

LEI ZHOU School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

JI GUO Collaborative Innovation Center on Climate and Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

HUI LIU School of Economics and Management, Nanjing University of Information Science and Technology, Nanjing, Jiangsu, China

(Manuscript received 13 July 2016, in final form 9 December 2016)

ABSTRACT

What impacts do typhoons have on local labor markets? Few empirical researches have been conducted in China. By collecting the data of 23 quarters (3-month intervals) of Guangdong province from 2009 to 2014 and using the generalized method of moments (GMM), this paper analyzes the impacts of typhoons on labor markets from the perspectives of general effect, regional effect, intensity effect, and time effect. In addition, a comparative analysis is carried out between this study and similar studies of developed countries. The results show that 1) massive typhoons resulted in a 12.5% increase in employment but did not have a significant impact on Guangdong’s per capita em- ployee remuneration, and 2) there are periodic features to typhoons’ impacts on employment. Typhoons influence employment in a four-quarter cycle. In the quarter affected by a typhoon, the first quarter, the number of employees increased by 17.4%. The quantity of labor employed in the subsequent two quarters shows no significant change. In the last quarter, the number of employed people decreases by 17.0%, which returns to predisaster levels. Additionally, 3) the results of this study are different from those of studies involving developed countries, which may be caused by the distinctiveness of China’s labor market. Finally, conclusions and corresponding suggestions are presented.

1. Introduction indicates that in the Twelfth Five-Year Plan Period (from 2011 to 2015), the number of people affected by China is one of the countries affected most by natural disasters in China is 310 million annually. The number disasters in the world. China’s Actions for Disaster of persons killed or missing as a result of natural Prevention and Reduction during the 12th Five-Year disasters reaches 1500 annually. The direct economic Plan Period,1 a document issued by the General Office loss is more than 3800 billion (RMB). China of China National Commission for Disaster Reduction, has been severely affected by the devastating effects of typhoons, whose increased frequency and virulence as well as their deep socioeconomic impact cannot be 1 See http://news.163.com/16/1011/18/C3481MKF00014SEH.html. overlooked. About 27 typhoons are generated in the northwest Pacific region each year, and about seven Corresponding author e-mail: Xianhua Wu, wuxianhua@moe. of them land in China (Kang 2016). For instance, edu.cn in August 2015 Typhoon Sudiro affected nearly

DOI: 10.1175/WCAS-D-16-0079.1 Ó 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses). Unauthenticated | Downloaded 09/24/21 02:28 AM UTC 256 WEATHER, CLIMATE, AND SOCIETY VOLUME 9

5.655 million people living in China’s coastal areas, led hurricanes increased the outward mobility of the to 26 fatalities, and resulted in a direct economic loss of wealthier population. McIntosh (2008) analyzed the 13.77 billion RMB.2 With global warming, the genera- employment in labor market and industry distribution tion frequency and intensity of typhoons will be further characteristics of the Houston area. Through the strengthened (Qin 2008; Qin and Luo 2008), which in- analysis, the research found that hurricanes (ty- dicates that the effects of natural disasters on China will phoons) led to a 0.5% drop in employment. Groen and become more numerous and more severe. Polivka (2008) found a 35% decrease in urban em- China boasts the richest labor resources in the ployment of New Orleans in the aftermath of Hurri- world.3 These rich labor resources have been the pri- cane Katrina. mary driving force of China’s fast economic growth Because of the uniqueness of the Chinese labor over the past 40 years. However, because of the limi- market, this paper assumes that the impacts of typhoon tations of research perspectives, data, and methods, on China’s labor market may be different from those there has been hardly any study of the impact of ty- of developed countries such as the United States. phoons on the Chinese labor market. By contrast, in- The main reasons are as follows: first, in China, the ternational scholars have adopted various ways to abundant and relatively cheap labor resources can study typhoons’ impacts on labor markets.4 Most make up for the ‘‘labor shortage’’ brought by typhoon studies of the United States and other developed disasters in a timely manner. Therefore, a typhoon will countries have found that hurricanes (typhoons) will not have a significant impact on China’s employee re- cause labor to flee the region, so employers have to muneration. In contrast, the labor shortage in de- increase the pay to attract enough labor. Hurricanes veloped countries can only be resolved by rising labor (typhoons) in this respect have a negative impact on remuneration (Belasen and Polachek 2008, 2009; employment rates and a positive impact on wage Rodríguez-Oreggia 2013). Second, there is a difference levels. Taking a hurricane in Florida as an example, in the mobility of the labor market between China the study of Belasen and Polachek (2009) suggests and developed countries. Because of better mobility that the industry structure of the labor market in of labor markets in developed countries, it is easier hurricane-affected areas has changed. Workers with the for high-paid workers to flee the area and leave low- highest-paying jobs tend to migrate to relatively safe paid ones, resulting in inadequate labor supply and areas, leaving low-wage workers behind. This in turn, accordingly rising labor remuneration. In China, results in a reduced employment rate of 4.76% and a however, there is regional segmentation in such areas 4.35% increase in wages in the affected areas. Using a as the household registration system, medical secu- vector autoregressive model, Ouattara and Strobl (2014) rity system, and normal enrollment, so labor mobility studied the impact of hurricanes on the migration is impeded by many institutional obstacles and the of coastal cities in the United States and found cost of movement is quite high. Therefore, facing the threat of typhoon disasters, the labor may not mi- grate, and the number of employees may not neces- 2 See http://www.weather.com.cn/zt/tqzt/368537.shtml. sarily decrease. 3 According to the China Statistical Yearbook 2015, the pop- What impact will the typhoons have on China’s ulation aged 15–64 years reached 1.004 69 billion in 2014. labor market? This paper attempts to verify the above 4 The socioeconomic effects of typhoon disasters have become speculations through empirical analysis. In light of the focus of domestic and foreign scholars, and quite a few research this, by using a generalized method of moments results have been achieved. According to the classification method of Xiao (2011), current relevant literatures can be divided into two and taking 21 -level cities of Guangdong categories: input–output and quantitative regression analysis, from Province as basic research units, this paper evaluates the perspective of research methods. Input–output techniques can the impacts of typhoon disasters on the employment be divided into input–output table models and CGE models. and wages of labor markets during 23 quarters from Input–output models analyze the comprehensive effects caused by 2009 to 2014 from four effects of typhoons—a general disasters based on ‘‘input–output table,’’ proposed by Leontief, an American economist (e.g., Rose et al. 2007; Ai and Polenske 2008; effect, regional effect, intensity effect, and time effect— Lin et al. 2012; Akhtar and Santos 2013; Wu et al. 2014, 2016a; and compares itself with current similar studies of Schulte in den Bäumen 2015). There are many papers evaluating developed countries, so as to analyze the reasons the loss of natural disasters by statistical and quantitative methods. behind the phenomenon and try to get some new in- For example, Skidmore and Toya (2002), Ewing et al. (2009), spirations. This study not only fills the gaps in the field Zissimopoulos and Karoly (2010), Rodríguz-Oreggia (2013), Olsen and Porter (2013), Ouattara and Strobl (2014), Asad (2015), Wu of research on how natural disaster can affect human et al. (2016b), Hamilton et al. (2016), and others also did related capital in China, but also provides results that can researches. be compared with those for developed countries;

Unauthenticated | Downloaded 09/24/21 02:28 AM UTC APRIL 2017 W U E T A L . 257 furthermore, the current study can provide empirical According to Belasen and Polachek (2009),ifthe evidence for the postdisaster restoration and re- disaster has an impact on the local labor market and construction as well as disaster prevention and re- causes migration of workers, the workforce in the duction management in China, therefore ultimately less-affected neighboring areas will increase. How- mitigating the adverse impacts of typhoons. ever, the labor force mobility in China is hindered by factors such as household registration and children’s education. Besides, the province of Guangdong is 2. Research hypothesis, data, and variable noted to have one of the most developed economies descriptions and best employment and living environment. a. Research hypothesis Therefore, despite typhoons’ destructive effects, the local labor still stays. Therefore, typhoons will not The impact of natural disasters on the labor markets lead to the migration of local labor. The second hy- can be studied from the perspectives of supply and pothesis is as follows: demand. In terms of labor supply, disasters can cause casualties and evacuation of residents, which directly H2: Typhoons will not lead to migration of labor force in Guangdong Province. reduce the number of people employed; disasters can also destroy transportation, communications, water Nordhaus (2006) believes the hurricane (typhoon) supply, and other key lifelines, and lead to water loss is associated with wind speed. Skidmore and Toya quality deterioration and air quality degradation (2002) also hold that only disasters of high intensity can (Zheng 2013). However, the typhoon-prone coastal affect economic development. In this case, the third cities in China have higher incomes, more employment hypothesis is shown below: opportunities, and better living conditions compared H3: The impact of a typhoon on the labor market in with the inland. After weighing the costs and benefits, Guangdong Province varies with its intensity. people may be reluctant to relocate after a disaster. Moreover, China is rich in labor resources, which In addition, the disaster is an external factor affecting means that the labor supply gap caused by typhoons social and economic development, so its disturbance in can be supplemented in time. Therefore, the negative the labor market will cause a labor supply and demand impact of typhoon on China’s labor supply may not be conflict. However, the labor market is self-adaptive and significant. may be gradually restored, since it is assisted by the In terms of disasters’ impact on labor demand, since government intervention. the Chinese government has the advantage of central- This recovery process can be defined as a process in ized power, it can invest a large amount of human and which the labor market of the disaster-affected area material capital and develop plans for emergency relief shifts from one equilibrium state to another due to and postdisaster construction quickly in the aftermath of the destruction caused by natural disasters (Wu et al. the disaster. During the process of emergency relief and 2013). The impact of disaster shows periodic char- postdisaster reconstruction, fields of industry construc- acteristics and changes over time. If the above hy- tion and transportation gradually recover, finding pothesis H1 is verified, then after the disaster, during themselves in desperate need of substantial labor force; the postdisaster recovery and reconstruction period, at the same time, other industries will also flourish, in- the employment of the labor force will not be nega- directly increasing the demand for labor (Ewing et al. tively affected. That is, the employment of labor 2009; Rodríguez-Oreggia 2013). Based on Joseph force may remain unchanged or increase. Thus, in the Schumpeter’s theory of creative destruction, Skidmore end of postdisaster reconstruction, the number of and Toya (2002) suggest that typhoons and other me- employed people in the labor market will gradually teorological disasters reduce the expected return in reduce and return to predisaster levels. Based on the capital and thus the investment is redirected to human above analysis, the fourth hypothesis is proposed as capital. As a result, typhoons may have positive im- follows: pacts on labor markets. H4: The impact of disasters on labor market varies over Taking Guangdong Province of China as an example, different stages of postdisaster recovery. this paper proposes the following hypotheses: b. Data H1: Typhoons will not have a negative impact on the quantity of labor employed and employee remuneration The disastrous weather effects of typhoons come in Guangdong Province. from the EM-DAT database, which is built and

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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.

Economic loss Name of Level of (millions of Landing date typhoon Affected areas tropical cyclones Intensity Death toll U.S. dollars) 1 Aug 2009 Goni STS L 9 70 14 Sep 2009 Koppu , TY H 13 295.001 13 Oct 2009 Parma Zhanjiang TS L 3 35 20 Sep 2010 Fanapi , , Zhaoqin, Yangjiang, STY H 75 298.285 , , , Zhanjiang 19 Jun 2011 Haima Zhanjiang, Yangjiang, TS L 0 0 24 Jul 2012 Vicente , Zhuhai, , , Meizhou, TY H 8 329 Jiangmen, , Yangjiang, Shanwei, Maoming, , , Yunfu 17 Aug 2012 Kai-tak Zhanjiang TY H 2 262 28 Oct 2012 Son-Tinh Zhanjiang STS L 1 197 14 Jul 2013 Soulik Zhanjiang STY H 9 460 15 Aug 2013 Utor Zhanjiang, Maoming, Yangjiang STY H 88 2120 22 Sep 2013 Usagi , HeYuan, Meizhou, Shanwei, STY H 20 0 Chaozhou, Jieyang 30 Sep 2013 Wutip Zhanjiang, Maoming TS L 74 0 14 Jun 2014 Hagibis Shantou, Shanwei TS L 0 131 18 Jul 2014 Rammasun Yangjiang, Maoming, Zhanjiang Super TY H 71 4232.973 10 Sep 2014 Kalmaegi Zhanjiang, Maoming STY H 9 2900 maintained by the Center for Research on Epidemi- divided into six levels.7 The first three grades can be ology of Disaster (CRED) of Katholieke Universiteit categorized as high-intensity typhoons (abbreviated as Leuven of Belgium.5 The EM-DAT database records ‘‘H’’) and the remaining three as low-intensity ty- the time of occurrence, name, location, death toll, and phoons (abbreviated as ‘‘L’’). Details are shown in economic loss. But the location is recorded only at the Table 1. level of provinces rather than precisely down to the The research area is restricted to Guangdong Province prefectural-level cities. The detailed information about for the following reasons. First, seen from the situation of the prefectural-level cities affected by typhoons is from disasters, Guangdong Province is located at a special Baidu Encyclopedia.6 The EM-DAT database does not geographical position subject to the threat of typhoons, specify typhoons as a separate category. Based on the with low latitudes and marked monsoon climate, facing times and name of typhoons provided by the EM-DAT the on the south and the Pacific on the database, the corresponding categorical information is east and having a coastline of up to 8500 km, which ac- collected from the tropical cyclones data of the China counts for more than one-third of the total of the country. Meteorological Administration, and then classified and According to incomplete statistics, from 1985 to 2006 verified. typhoon disasters of Guangdong have caused 667 deaths, According to the national standard classification of tropical cyclones (GBT19201–2006) issued by China Meteorological Administration, tropical cyclones are 7 The six levels are as follows: 1) super typhoon: the maximum wind speed on the ground near the center reaches more than 2 51.0 m s 1 (equivalent to wind grade 16 or above); 2) severe ty- phoon: the maximum wind speed on the ground near the center 2 5 The records in EM-DAT database must meet the following reaches 41.5–50.9 m s 1 (equivalent to wind grade 14–15); 3) ty- conditions: 1) 10 or more deaths have been reported; 2) 100 or phoon: the maximum wind speed on the ground near the center 2 more people have been reported affected, wounded, or homeless; reaches 32.7–41.4 m s 1 (equivalent to wind grade 12–13); 4) severe 3) disaster-stricken areas have declared a state of emergency; and tropical storm: the maximum wind speed on the ground near the 2 4) a call for international assistance. From the EM-DAT database center reaches 24.5–32.6 m s 1 (equivalent to wind grade 10–11); 15 disastrous weather events of typhoons have been selected that 5) tropical storm: the maximum wind speed on the ground near the 2 hit Guangdong from 2009 to 2014. center reaches 17.2–24.4 m s 1 (equivalent to wind grade 8–9); and 6 For details of Typhoon Swan (the seventh tropical storm in 6) tropical depression: the maximum wind speed on the ground 2 2009) and others, see http://baike.baidu.com/item/%E5%8F% near the center reaches 10.8–17.1 m s 1 (equivalent to wind B0%E9%A3%8E%E5%A4%A9%E9%B9%85/9115336. grade 6–7).

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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.

Unit employment (million persons) Per capita remuneration () Maximum Minimum Mean Maximum Minimum Mean Chaozhou 20.09 11.78 13.55 10 383.24 5297.11 7107.14 243.86 21.29 58.78 16 888.33 1275.67 9550.53 Foshan 168.63 52.43 75.22 12 716.34 3954.63 8826.70 366.39 219.77 284.97 16 780.07 11 159.49 13 153.12 HeYuan 25.36 23.13 24.27 12 891.77 5407.38 7871.83 92.80 70.87 83.36 12 121.49 5685.06 8349.14 Jiangmen 60.33 34.71 48.03 9437.70 5563.57 6848.64 Jieyang 37.74 20.52 23.79 8873.78 4410.33 6206.42 Maoming 43.59 29.61 34.64 8764.91 5147.35 6929.25 Meizhou 29.38 22.49 24.36 10 272.33 5002.22 7410.66 31.07 24.30 27.46 11 852.57 6099.43 8373.55 Shantou 52.48 30.74 38.34 8427.53 5605.02 6960.66 Shanwei 23.23 13.24 16.15 12 906.81 3950.15 7119.44 37.98 29.67 32.36 11 189.00 6193.13 8350.32 Shenzhen 462.31 200.94 278.95 17 341.22 8158.15 12 078.78 Yangjiang 23.33 16.67 18.77 10 710.24 4512.41 6913.63 Yunfu 21.52 16.16 17.78 8741.08 5024.75 7086.26 Zhanjiang 49.66 24.24 41.68 12 047.24 5356.49 7520.97 Zhaoqing 40.77 27.06 31.07 12 347.61 3946.95 8005.26 Zhongshan 86.01 23.40 37.94 14 331.60 3903.16 9072.80 Zhuhai 73.29 52.08 62.01 15 189.64 6954.69 9427.16

20 873 injuries, 1 699 150 collapsed buildings, and an investments (including , Macao, , etc.). economic loss of as much as 250 billion Yuan. In hori- The descriptive results of data are shown in Table 2. zontal comparisons, almost each disaster indicator of Seen from the table above, from 2009 to 2014, Guangdong ranks highest in the country, so the place can Guangdong Province was hit by 15 severe typhoons. be considered as the province that has been most affected Zhanjiang is the city that suffered typhoon invasions most by the typhoon disasters in China (Deng 2011). frequently (11 times), whereas Shaoguan, QingYuan, Second, seen from the social conditions, Guangdong Huizhou, Guangzhou, and Dongguan were not affected. province, which is adjacent to Hong Kong and Macao, has The number of prefecture-level cities hit by typhoons distinct location advantages and is one of China’s most reached 11. During the 23 seasonal quarters from 2009 to economically developed coastal provinces. Each year 2014, Shenzhen had the most employment, reaching Guangdong attracts a wealth of labor resources that have 4 623 100, while Chaozhou had the least, at 200 900; the following characteristics. First, the majority or Guangzhou had the highest average employment at workers are migrant workers. According to the results of 2 849 700 while Chaozhou had the least, at 135 500. As for the sixth national census in 2010, Guangdong is the per capita remuneration per quarter, Guangzhou’s aver- province with the largest inflow of population. Second, age topped the list, reaching 13 153.12 Yuan while with fast economic development and a relatively de- Yangjiang’s average was the least, 6913.63 Yuan. veloped labor market, Guangdong is a typical developed c. Variable descriptions region and a development benchmark for relatively backward provinces. Therefore, a study of the impacts of To study the relationship between typhoon disasters and natural disasters on Guangdong’s employment and labor Guangdong’s employment and labor remuneration, a re- remuneration has great demonstrative significance. gression model has been established with a generalized Because of data availability, quarterly data of unit em- method of moments (GMM). Based on the previous ployment and per capita remuneration of 21 prefecture- studies on labor markets (e.g., Belasen and Polachek 2008, level cities of Guangdong from the first quarter of 2009 to 2009; Groen and Polivka 2008; Ewing et al. 2009,etc.)and the third quarter of 2014 have been selected to reflect the taking into account data availability, the following vari- changes in the labor market. The statistical scope of labor ables have been selected as the control variables of re- forces includes state-owned units, urban collective-owned gression from the economic, seasonal and policy factors units, joint ventures, joint-stock ventures, and foreign affecting labor markets.

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1) ECONOMIC FACTORS economic factor (GDP) and seasonal factor (seasonal dummy variable) and minimum wage standard as The employment of labor markets is closely related control variables, and regression analysis is carried out to economic development. With economic growth, in- based on the establishment of panel model. Specific vestment, production, and consumption will increase models are shown below: accordingly, thereby creating more employment op- portunities and stimulating rising employment rate; Q 5 a 1 a TC 1 a GDP 1 åS 1 a MWS otherwise, there will be a decline in the employment it 10 11 it 12 it 13 it rate. For instance, Zhang (2009) believed that the gross 1 n 5 ::: 5 ::: it,(i 1, 2, , 21; t 1, 2, 23), domestic product (GDP) is the preferred measure of (1) economic development. Therefore, GDP8 is chosen PW 5 a 1 a TC 1 a GDP 1 åS 1 a MWS as a control variable, the data of which come from it 20 21 it 22 it 23 it Statistical Information Network of Guangdong. To 1 y i 5 ::: t 5 ; ::: it,( 1, 2, , 21; 1 2, 23). (2) eliminate the impacts of such price factors as inflation, 9 GDP data go through price deflator processing, with In the models, Qit represents the unit employment of 2009 as the base period. region i in the period t;PWit the per capita remuneration of region i in the period t;TC the dummy variable of 2) SEASONAL FACTORS it typhoon, whose value is 1 when it is in experimental The supply and demand of labor can be affected by group where region i in the period t is hit by typhoons and seasonal variations, especially in Guangdong province, 0 when it is in control group free from typhoons; GDPit which is rich in migrant labor resources. In the new year the gross product of region i in the period t;andS the and the spring festival of the first quarter, for example, following three quarterly variables: S1it, whose value is 1 there are large and expected fluctuations in labor. when period t is the first quarter in region i and 0 when it is

Therefore, seasonal dummy variables are applied to other quarters, S2it, whose value is 1 when period t is the exclude the impacts of seasonal labor return. second quarter in region i and 0 when it is other quarters, and S3 , whose value is 1 when period t is the third quarter 3) POLICY FACTORS it in region i and 0 when it is other quarters. Also, MWSit The labor market of China, in contrast to common indicates the minimum-wage standard in region i during commodity markets, is greatly influenced by the the period of t; amn (m 5 1, 2; n 5 0, 1, 2, 3) is the co- country’s intervention. For example, as an important efficient of regression and both nit and yit are error terms. method adopted by the government to protect the All the equations in this paper are estimated using the rights and interests and labors, the minimum wage generalized method of moments estimation method pro- standard plays a significant role in the relationship of posed by Arellano and Bond (1991) and Blundell and Bond labor supply and demand objectively. Ma et al. (2012) (1998). The GMM is adopted for three main reasons: first, believed that every 10% increase in the minimum wage GDP is likely to have reverse causality between employ- gave rise to a decline about 0.6% in the employment of ment and wages (i.e., GDP and employment, and GDP and manufacturing enterprises. In the present study, the wages, affect each other). The systematic GMM estimation minimum wage standard variables of different years in method can reduce the endogeneity of GDP by using the different prefectural-level cities are taken as the con- lagged variable of the endogenous explanatory variables as trol variables in the equation. the instrumental variables. Second, systematic GMM esti- mation can deal with the original equation using first-order 3. Empirical analysis differential treatment, which can parse the time-invariant observables and unobservable city-specific effects. Third, in a. General effect of typhoons on labor market theory, the change of employment and wages in the labor According to the analysis above, the unit employ- market is a continuous dynamic process, and systematic ment and per capita remuneration of the cities of GMM estimation introduces a lagged dependent variable Guangdong Province are selected as dependent vari- as an independent variable, changing the original static ables, typhoon disasters as an independent variable, the

9 The unit employment refers to the total number of the em- ployed who work in state-owned units, urban collective-owned 8 Since there are no data of cities’ quarterly GDP per capita and units, joint ventures, joint-stock economy, foreign investment the only data available are undifferentiated GDPs, this paper economy, and the economy invested by Hong Kong, Macao, adopted cities’ quarterly GDPs. Taiwan, etc.

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TABLE 3. 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.

Direct effect Indirect effect Variables Coefficient P value Coefficient P value Typhoon 0.0158 0.7349 20.0758 0.3386 GDP 20.0933 0.3938 0.3365** 0.0214 The minimum wage standard 20.6842 0.1329 1.1436*** 0.0180 model into a dynamic one, which is more consistent with To avoid the pseudoregression generated by data the actual situation. instability, an augmented Dickey–Fuller test (ADF) Whether or not the adoption of GMM system is ef- method has been applied to test data stability for fective is determined by two tests. One is the Arellano- all the variables to enter the model and shown that Bond AR test. This test will examine if the model set is all the variables are stable (specific results are appropriate. The original hypothesis is of no first-order omitted and the same below). In addition, the loga- or second-order correlation in the model residual se- rithm of the continuous variables in the above Eqs. quence. The other is the Sargan test. It is used to (1) and (2) is taken to reduce the possibility of ab- determine whether there is overidentification in in- normal points; the ‘‘annual variable’’ is added in the strumental variables. The original hypothesis is that above Eqs. (1) and (2) to test year effect, with the instrumental variables are not overidentified. following results:

D 5 a 1 a D 1 a D 1 a D 1 åD 1 a D 1 åD lnQit 10 11 lnQit21 12 TCit 13 ln(GDPit) S 14 ln(MWSit) YEAR 1 n 5 ::: 5 ::: it,(i 1,2, , 21; t 1,2, 23), (3)

D 5 a 1 a D 1 a D 1 a D 1 åD 1 a D 1 åD ln(PWit) 20 21 ln(PWit21) 22 TCit 23 ln(GDPit) S 24 ln(MWSit) YEAR 1 y 5 ::: 5 ::: it,(i 1,2, , 21; t 1,2, 23). (4)

The year term can be divided into five annual vari- The third column of Table 3 shows that typhoons do ables: Year09it indicates the data are from the year 2009 not have significant impacts on the per capita em- (and so on for Year10it for year 2010, etc.) If positive the ployee remuneration. This can be attributed largely value is 1; otherwise it is 0. to China’s relatively weak economy, in which low- The regression result of column 1 of Table 3 shows income households account for a large proportion that typhoons have brought about an increase in the compared with developed countries (Liu 2004). number of local workers per unit by 6.31%, the reason Comparatively speaking, people living in typhoon- of which may be that China’s abundant labor re- prone coastal cities enjoy higher economic income sources can compensate for the negative impacts of and social welfare, more employment opportunities, typhoon disasters in a timely manner. Especially in and better living conditions. Therefore, in the post- Guangdong, a place with relatively developed econ- disaster recovery and reconstruction activities, there omy and a large number of migrants, the threat of is no need to attract the labor force by increasing typhoon disasters is still not enough to cause a local wages. Again, hypothesis H1 is verified, and typhoons labor shortage. Since the inflow of labor forces in inGuangdongProvincewillnothaveanegativeim- Guangdong comes basically from underdeveloped pact on labor employment remuneration. areas (Liu 2004), the workers are willing to assume b. Regional effect of typhoons on labor market the risk of typhoon disasters for their survival (Belasen and Polacheck 2008). In addition, post- On the basis of H2, this paper further divides the ty- D disaster recovery and reconstruction requires a large phoon variable into TCit , a variable indicating direct I amount of labor resources. Therefore, hypothesis H1 effects of a typhoon, and TCit for indirect effects (in- in this paper is validated and typhoons in Guangdong cluding neighboring areas of typhoon-stricken areas). Province will not have a negative impact on the These two variables are used to measure whether or not quantity of labor employed. the labor force will migrate due to the typhoon, which

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TABLE 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.

Direct effect Indirect effect Variables Coefficient P value Coefficient P value Typhoon 20.0659 0.1731 0.0290 0.7067 GDP 0.0497*** 0.0004 0.3028*** 0.0000 The minimum wage standard 0.5525*** 0.0004 20.5398*** 0.0023 may bring changes to the labor market in the neigh- effects of typhoons on labor markets. The models are boring area. The purpose is to discuss the regional shown as follows:

D 5 a 1 a D 1 a D D 1 a D I 1 a D 1 a åD 1 a D lnQit 10 11 lnQit21 12 TCit 13 TCit 14 ln(GDPit) 15 S 16 ln(MWSit) 1 a åD 1 n 5 ::: 5 ::: 17 YEAR it,(i 1,2, , 21; t 1,2, 23), (5)

D 5 a 1 a D 1 a D D 1 a D I 1 a D 1 a åD 1 a D ln(PWit) 20 21 ln(PWit21) 22 TCit 23 TCit 24 ln(GDPit) 25 S 26 ln(MWSit) 1 a åD 1 y 5 ::: 5 ::: 27 YEAR it,(i 1,2, , 21; t 1,2, 23). (6)

D In the models, TCit represents the variable of whether This method uses the direct effect to represent the mean region i in the period of t is directly (D) affected by ty- effect of the independent variable on the region, and the I phoons; TCit indicates whether region i in the period of t indirect effect represents the mean effect of the in- is indirectly (I) affected by typhoons (or whether the dependent variable on other regions (Hu and Li 2015). neighboring areas of region i is directly affected). The results are consistent with the abovementioned study The results in the columns of Table 3 that describe (i.e., typhoons will not bring spillover effects). Details are regional effects show that in the regions either directly provided in Tables 3 and 4. or indirectly affected by typhoons, the total employ- ment and per capita labor remuneration are not af- c. Intensity effect of typhoons on labor market fected; in other words, the typhoon will not cause labor migration. Based on hypothesis H3, the typhoon variable is Furthermore, we employed the Spatial Dubin Model further divided into high-intensity typhoons and low- (SDM) to calculate the regional effects of the typhoon intensity typhoons, so the impacts of typhoons of dif- and measure whether they have caused labor migration ferent intensity will be discussed. The models are and led to changes in labor markets in neighboring areas. shown as below:

D 5 a 1 a D 1 a D 1 a D 1 a D 1 a åD 1 a D lnQit 10 11 lnQit21 12 HTCit 13 LTCit 14 ln(GDPit) 15 S 16 ln(MWSit) 1 a åD 1 n 5 ::: 5 ::: 17 YEAR it,(i 1,2, , 21; t 1,2, 23), (7)

D 5 a 1 a D 1 a D 1 a D 1 a D 1 a åD 1 a D ln(PWit) 20 21 ln(PWit21) 22 HTCit 23 LTCit 24 ln(GDPit) 25 S 26 ln(MWSit) 1 a åDYEAR 1 y ,(i 5 1,2,:::, 21; t 5 1,2,::: 23). 27 it (8)

In the models, HTCit (LTCit) represents the variable typhoons have no effect on the number of employees, which of whether region i in the period of t is affected by high- may be explained by their less destructive impacts that intensity (low-intensity) typhoons. are insufficient to affect normal socioeconomic activities. The regression results in the columns of Table 5 that only In addition, neither higher-intensity nor lower-intensity higher-intensity typhoons will result in a 12.5% increase typhoons have significant impacts on per capita labor in the total number of employees, while lower-intensity remuneration.

Unauthenticated | Downloaded 09/24/21 02:28 AM UTC APRIL 2017 W U E T A L . 263 d. Time effect of typhoons on labor market (9) and (10) are established to analyze the recovery time of the labor market after disasters in order to According to hypothesis H4, typhoon lagged vari- measure the restoring force of Guangdong’s labor ables are introduced—TC 2 ,TC 2 ,andTC 2 —that it 1 it 2 it 3 market hit by typhoon disasters: represent the one-, two-, and three-phased lagged variables of region i in the period of t, respectively. Eqs.

D 5 a 1 a D 1 a D 1 a D 1 a D 1 a D 1 a D 1 a åD lnQit 10 11 lnQit21 12 TCit 13 TCit21 14 TCit22 15 TCit23 16 ln(GDPit) 17 S 1 a D 1 a åD 1 n 5 ::: 5 ::: 18 ln(MWSit) 19 YEAR it,(i 1,2, , 21; t 1,2, 23) (9)

D 5 a 1 a D 1 a D 1 a D 1 a D 1 a D 1 a D ln(PWit) 20 21 ln(PWit21) 22 TCit 23 TCit21 24 TCit22 25 TCit23 26 ln(GDPit) 1 a åDS 1 a D ln(MWS ) 1 a åDYEAR 1 n ,(i 5 1,2,:::, 21; t 5 1,2,::: 23) 27 28 it 29 it (10)

The time-effect column of Table 5 shows that typhoons effect, regional effect, intensity effect, and time effect. have caused a 17.4% increase in the number of employees The results are presented as below: first, high-intensity in quarter t. No significant effect on the number of em- typhoons brought about an increase of 12.5% in the ployees is found in the next quarter t 1 1 and the quarter quantity of labor employed. Second, the typhoon did not after next t 1 2. The number of employees decreases by have a significant impact on the per capita employee 17.0% in quarter t 1 3. That is, in the third quarter after remuneration in Guangdong. Third, over time, the im- the disaster, the number of employed people decreased by pacts of typhoons will subside and employment levels 17.0%, which recovers to predisaster levels. Then, what will return to predisaster levels. The first and the second will be the impact of the typhoon in quarter t 1 4onthe conclusions are markedly different from those obtained number of local workers per unit? After introducing the by current studies abroad. For instance, McIntosh (2008), lagged variable of quarter t 1 4intoEq.(9), it is found Zissimopoulos and Karoly (2010),andBelasen and that the coefficient of the variable’s impact on the number Polachek (2008, 2009) believed that hurricanes reduced of employees is 20.117, although it failed the t test the local employment rate; Ewing et al. (2009), Olsen (t statistic 521.15, prob. 5 0.249). From the above and Porter (2013), and Belasen and Polachek (2008) analysis, it is concluded that the t 1 4 quarter no longer pointed out that disasters increased the local per capita has a significant impact on the quantity of labor employed. remuneration. As noted in the introduction, there is a In this case, the delayed effect of typhoon on employment contradiction in research conclusions between studies of will not exceed four quarters. After a year’s adjustment, China and studies of other developed countries, which the impact caused by typhoons will gradually disappear. may be attributed to the distinctiveness of the Chinese At the same time, typhoons have no significant im- labor market. pacts on per capita labor remuneration, which is con- Based on the research above, this paper puts forward sistent with the results obtained by the models of the the following three suggestions regarding reducing general effect, regional effect, and intensity effect. barriers to labor mobility, eliminating the minimum wage standard in the region, and increasing the con- struction of disaster prevention and mitigation facilities 4. Conclusions and discussion in less-developed areas. In this paper, the impact of typhoon on the labor market The first is to reduce barriers to labor mobility. In in cities of Guangdong province is studied for the first time. China, because of the region segmentation in the house- Based on quarterly data of unit employment and labor re- hold registration system, healthcare systems, and student muneration of 21 prefecture-level cities of Guangdong enrollment, labor mobility is subject to many constraints. from 2009 to 2014, this paper applies a generalized method Therefore, maybe it is time to gradually abandon the of moments (GMM) to establish a regression model household registration system. Instead, we can increase between disastrous weather events of typhoons and such the diversity and quality of supply in public services such variables as employment and per capita labor re- as education and health care, and help migrant workers in muneration, and does calculations from four aspects of healthcare insurance and their children’s school enroll- the impacts of typhoons on labor markets: general ment. The labor market employment insurance system

Unauthenticated | Downloaded 09/24/21 02:28 AM UTC 264 TABLE 5. 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.

Dependent variable General effect Regional effect Intensity effect Time effect Independent Per capita Per capita Per capita Per capita variable Employment remuneration Employment remuneration Employment remuneration Employment remuneration Typhoon 0.0631* 20.179 0.174* 0.188 (0.0379) (0.193) (0.0966) (0.648) Direct impacts of 20.0571 22.381 typhoons (0.146) (2.434) Indirect impacts of 0.275 4.407

typhoons (0.246) (3.685) V SOCIETY AND CLIMATE, WEATHER, High-intensity 0.125* 21.799 typhoons (0.0758) (2.123) Low-intensity 0.100 5.580 typhoons (0.141) (9.291) One-phase lagged 20.0144 0.881 typhoons (0.0921) (0.615) Two-phase lagged 0.0451 0.507 typhoons (0.0473) (0.882) Three-phase lagged 20.170* 21.414 typhoons (0.102) (1.185) One-phase lagged 0.955*** 1.056*** 0.955*** 0.967*** employment (0.0463) (0.110) (0.0267) (0.0271) One-phase lagged per 1.213* 20.356 0.610 1.482** capita remuneration (0.672) (0.396) (0.588) (0.672) GDP 0.0396 21.106 20.135 20.731 0.0773 21.210 20.0270 20.457 (0.0680) (0.864) (0.195) (0.718) (0.0531) (1.018) (0.0453) (0.778) The minimum wage 0.0776 5.537 0.559 5.122 20.108 6.747 0.387 1.897 standard (0.423) (3.926) (0.653) (3.574) (0.217) (5.133) (0.240) (3.475) Unauthenticated |Downloaded 09/24/21 02:28 AMUTC The first quarter 20.00288 20.968** 20.0666 20.479 0.0130 20.769** 20.0491** 20.407 (0.0270) (0.422) (0.0761) (0.418) (0.0226) (0.360) (0.0210) (0.345) The second quarter 20.0359*** 20.505*** 20.0862 20.676*** 20.0282** 20.686* 20.0165 20.0232 (0.0120) (0.163) (0.0544) (0.183) (0.0121) (0.401) (0.0218) (0.224) The third quarter 20.0320*** 20.239*** 20.0738 20.547 20.0435 0.215 20.0841* 0.101 (0.0108) (0.0621) (0.0450) (0.428) (0.0284) (0.600) (0.0465) (0.383) Year 2009 0.00627 2.535 0.216 2.172 20.0782 3.036 0.104 1.140 (0.195) (1.825) (0.297) (1.746) (0.102) (2.422) (0.131) (1.719) Year 2010 20.0143 1.634 0.133 1.470 20.0587 2.170 0.0659 0.633 (0.124) (1.155) (0.194) (1.080) (0.0670) (1.753) (0.0769) (1.070) OLUME 9 APRIL 2017 W U E T A L . 265

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 14.86 Per capita 2 ultimately increase the overall social welfare. remuneration 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, 0.00522 0.287 2.333 to a certain extent, have also impaired the market 2 2 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

36.28 implementation of the minimum wage standard will Per capita 2

remuneration Employment 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 0.0494 0.933 0.0545 1.242 0.0122 0.228 2 2 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 em-

) ployment opportunities. To let the market regulator fully play its due role in labor supply and demand, the Dependent variable minimum wage can be eliminated to create favorable 19.04 0.508 Continued Per capita

2 conditions for postdisaster recovery and reconstruction. remuneration Employment 5. ( The third is to increase the construction of disaster pre- vention and mitigation facilities in less developed areas. In ABLE

T the less prosperous regions, workers’ income are low, and they are inadequate in skill, which all together increase their

3.231 vulnerability to natural disasters. In the aftermath of the 2 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 ef- 0.0525 0.0246 0.303 0.00580 0.277 0.00148 0.0135

33.63 forts to strengthen disaster prevention and mitigation edu- 2 Per capita 2 remuneration Employment cation, and improve the comprehensive ability to deal with disasters, so as to respond more effectively to disasters and facilitate the sustainable development of society. General effect Regional effect Intensity effect Time effect Acknowledgments. This research was supported by the 0.0261 0.746 0.0532 0.793* 0.0245 0.818 0.0639 0.997** 0.554 (0.0562) (0.504) (0.0953) (0.465) (0.0343) (0.730) (0.0449) (0.439) (0.0611) (0.539) (0.106) (0.508) (0.0394) (0.984) (0.0452) (0.586) (0.0213) (0.134) (0.0349) (0.264) (0.0105) (0.303) (0.0122) (0.117) (2.487) (27.30)Natural (3.731) Science (22.78) Foundation (1.248) of China (33.17)(71373131, 91546117), (1.440) (24.60) 2 2 2 Employment the National Social and Scientific Fund Program (16ZDA047), the National Soft Scientific Fund Program (2011GXQ4B025), National Industry-Specific Topics (GYHY200806017; GYHY 201506051), and the Min- value 0.268 0.103 0.594 0.217 0.571 0.431 0.589 0.483 valuevalue 0.237 0.342istry of Education 0.265 0.827 Scientific 0.161 0.609 Research 0.235 0.965 Foundation 0.181 0.400 for 0.453 0.800 0.128 0.345 0.177 0.232 P P P returned overseas students (2013-693; Ji Guo). This

variable research was also supported by the Priority Academic Independent Program Development of Jiangsu Higher Education Year 2011 Year 2012 Year 2013 0.00469 Constant term AR(1) test AR(2) test ObservationsNumber of code 462 21 462 21 462 21 462 21 462 21 462 21 420 21 420 21 Sargan test Institutions.

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