Financial Leverage and Employee Death:
Evidence from China’s Coalmining Industry
Huihua Nie and Huainan Zhao

September, 2015

Abstract
Examining a unique panel dataset of 22,076 firm-year observations for China’s
coalmining industry, we find that a firm’s leverage significantly determines its
coalmining fatality. We show, specifically, that leverage reduces a firm’s safety
investment and, hence, causes more fatalities. Our study draws important implicationsof financial policy to labor policy and calls for a more corporate-finance-based analysis
on the well-being of employees.

JEL classification: G30, G32, J81
Keywords: Leverage, Debt, Capital Structure, Stakeholder Interest, Workplace Safety,
Coalmining Accident

Huihua Nie is from the School of Economics, Renmin University of China, E-mail:niehuihua@gmail.com; Huainan Zhao is from Cranfield University School of Management, E-mail:huainanzhao@gmail.com. Special thanks to Ting Jiang for his detailed comments on the paper andto Minjie Jiang for his excellent research assistance. We are also grateful toVineet Agarwal, Kevin Amess, Sanjay Banerji, Xin Chen, Ziying Fan, Ning Gao, Igor Goncharov,Jieying Hong, Yanghua Huang, Qingyuan Li, Wen Li, Fangwen Lu, Di Luo, Simona Mateut, David
P. Newton, Bei Qin, Wing Suen, Yongxiang Wang, Yaqiong Yao, Steven Young, and seminar
participants at the Chinese Academy of Social Science, Lancaster University, Renmin University of
China, Shanghai Jiao Tong University, University of Hong Kong, University of Nottingham, andWuhan University for their helpful comments. Nie acknowledges financial support from the New
Century Excellent Scholar Program at Renmin University of China.

1Electronic copy available at: http://ssrn.com/abstract=2369185

1. Introduction
Coal mining is the most deadly job in China. 1 Its annual fatalities averaged 4,643
between 2000 and 2011, some 140 times higher than in the U.S., 2 whereas China’s
annual coal production was just twice that of the U.S. 3 In total, China accounts forapproximately 80 percent of coalmining deaths worldwide (Tu, 2007). While recent
papers shed light on the determinants of China’s coalmining fatalities, 4 they are
confined to cases, small samples, or provincial-level studies which overlook the crosssectional variations of firm characteristics.
In this paper, we conduct a comprehensive firm-level analysis to identify thedeterminants of China’s coalmining fatalities. Prior studies in the U.S. have suggested anegative relation between a firm’s financial leverage and its employee benefits. Highleverage reduces a firm’s cash flow through high interest payments and the reduction in
its internal (and external) resources would lead to an investment cut, especially theinvestment in employee benefits.5

1

According to Tu (2007), the number of coalmining workers killed by mining accidents in China
was over 250,000 since 1949. Wright (2004) note that coal mining accounts for less than 4% of theindustrial workforce in China but over 45% of industrial fatalities.
2
China’s data are obtained from the State Administration of Work Safety (SAWS) website:
http://www.chinasafety.gov.cn. The U.S. data are obtained from the Mine Safety and HealthAdministration (MSHA) website: http://www.msha.gov.
3
BP Statistics Review of World Energy 2013, http://www.bp.com/en/global/corporate/aboutbp/statistical-review-of-world-energy-2013/statistical-review.
4
Wright (2004) and Tu (2007) attribute the high fatality rate to the hiring of low quality workers inlocal town/village owned mines. Wang (2006) points to ineffective monitoring by the state. Fisman
and Wang (2014) show that executives’ political connections significantly increase workplace
fatalities. Jia and Nie (2015) find collusion between local government and coalmining firms to be
responsible for the high death toll.
5
High leverage not only directly reduces a firm’s internal resources but also makes its externalfinancing more difficult, since high leverage typically creates a debt overhang problem or leads toconflicts between shareholders and debtholders (risk shifting). As such, it reduces both a firm’s
internal and external resources.

2

Electronic copy available at: http://ssrn.com/abstract=2369185

Titman (1984) argues that firms’ stakeholders such as their employees, who do
not hold financial claims on the firms, bear many potential costs of debt. Maksimovic
and Titman (1991) show that if firms want to commit themselves credibly to providing
better employee benefits, they should have lower debt ratios than firms that do not
implement employee friendly policies. Hanka (1998) finds that high leverage isassociated with more frequent employment reductions, lower wages and reduced
pension.6
Recently, Benmelech, Bergman, and Seru (2011) show that employment levels
are sensitive to cash flow especially for highly-leveraged firms. Bae, Kang, and Wang(2011) find that firms that treat their employees fairly maintain low debt ratios. Agrawal
and Matsa (2013) show that firms increase leverage following exogenous increase in
unemployment benefits. Cohn and Wardlaw (2014) find that employee injury rates
increase with leverage and negative cash flow shocks, and decrease with positive cash
flow shocks.76

In addition, the literature on trade unions shows that, due to bankruptcy concerns, a firm could
strengthen its bargaining power with the trade union when negotiating workers’ wages if the firmchooses to use more debt in its financing, suggesting a negative relation between leverage andemployee benefits. See, for example, Bronars and Deere (1991), Perotti and Spier (1993), and Matsa(2010).
7
Cohn and Wardlaw (2014) is the closest work to our study. They use establishment-level data fromthe U.S. Bureau of Labor Statistics’s annual Survey of Occupational Injuries and Illnesses for 20022009, and examine a relation between employee injury rates and firms’ financial resources. Our
study, however, differs from Cohn and Wardlaw (2014) in several important ways. First, their study
has a broad coverage on financial constraints/resources and workplace injuries, while our work issolely focusing on leverage and employee death. Second, they use employee injury data, while we
use employee death data which is an extreme form of injury and can be more clearly defined. Third,
the regulatory environment and institutional settings between U.S. and China are vastly different.
Injuries or deaths are recognized and treated in entirely different ways. For example, punitive andreputational damages are far higher in the U.S. than in China. Fourth, their sample does not include
the coalmining industry while ours is focused solely on this industry (in their sample, injury rates arehighest in the candy and soda, fabricated products, and transportation industries). Overall, their

3

In the spirit of these earlier studies, we posit that a coalmining firm’s leverage issignificantly related to its employee fatality. 8 However, “China is an important
counterexample to the findings in the law, institutions, finance, and growth literature”(Allen, Qian, and Qian, 2005) and, as echoed recently by the Financial Times: “Fairenough in theory. But China is where theories go to die.”9 Thus, given its completely
different institutional framework and regulatory environment as compared to the U.S.,
the proposition may not hold for China. We therefore test what role, if any, leverage
plays in determining China’s coalmining fatalities.
To conduct this analysis, we hand collect coalmining accidents and deaths datafrom the State Administration of Work Safety (SAWS) and the State Administration of
Coal Mine Safety (SACMS). We then merge these data with the Chinese Industrial
Enterprise Database (CIED) to obtain firm-level data for coalmining firms. We form aunique panel dataset of 22,076 firm-year observations for 2001-2006. Our key finding isthat a firm’s leverage significantly determines its coalmining fatality.
Using multivariate regression analysis, we first establish a positive relation
between leverage and coalmining death, which is robust under Poisson, negative
binomial, and OLS estimation methods. We then investigate whether high leveragecauses high fatality.

results would probably be more applicable to developed countries, while our results are moreindicative to developing countries.
8
Anecdotal evidence seems to support this proposition. For example, the state-owned Huangling
Mining Group in Shaanxi Province experienced three coalmining accidents in 2004 alone, whichcaused 25 deaths. Its debt-to-asset ratio was then at 114%, while the average debt ratio of oursample (4,848 coalmining firms) for that year was at 51%. In the Group’s 2014 manager andemployee representatives meeting, the firm acknowledged that high debt ratio was the biggest
challenge faced by the firm. Information obtained from Huangling Mining Group’s official website:www.hlkyjt.com.cn/info/1016/8233.htm
9“Shanghai markets: border games” - - Financial Times, April 11, 2014.

4

One might argue that it is the high death tolls that force a firm to borrow more inorder to meet employee compensation, government fines, litigation and other associatedcosts. We show that, due to the ultra-low costs incurred, this reverse causality is
unlikely to hold for China’s coalmining firms. This also makes less likely an alternative
hypothesis, that coalmining firms with limited financial resources may increase safety
investment in order to avoid high costs of employee deaths.
Some unobservable firm characteristics could be responsible for our results. To
partially address this, we identify an instrumental variable (provincial leverage) andapply the two-stage least squares (2SLS) regression in our analysis. Our results hold up
to this test.
One might also argue that this year’s coalmining fatalities could be affected by theprevious year’s death toll. Hence, we control for the number of deaths during theprevious year and use the system GMM (generalized method of moments) regression
method to test our results. The GMM results are consistent with our main findings.
Notwithstanding our evidence, leverage and deaths could still be related for otherreasons which might be responsible for both the level of leverage in a firm and thenumber of fatalities. For instance, high leverage could be a result of poor management
and/or fast asset growth and that could also cause high fatalities. We control for these
additional factors and our results remain unchanged.
We investigate a channel through which leverage may potentially impact
coalmining fatalities. Specifically, we examine how leverage affects firms’ safety
investment and, hence, impacts the death toll. We find that high leverage reduces safety
investment and, consequently, causes more fatalities. This suggests that cash constraints

5

limit coalmining firms’ ability to meet/raise safety standards and lends support to a
causal interpretation between leverage and employee death.
Ours is the first paper, of which we are aware, to analyse coalmining deaths froma corporate financing point of view, while current literature focuses mainly on
regulation, political connection, collusion, or property rights (e.g., Fisman and Wang,
2014; Jia and Nie, 2015). We document a channel through which a firm’s leverage canaffect the well-being of its employees.
Our work also draws new implications for the study of labor economics.
Although current literature points out that managers try to weaken unions’ negotiation
power by increasing debt and lowering workers’ wages (e.g., Bronars and Deere, 1991;
Matsa, 2010), it does not discuss the issue that high leverage could also increase the risk
to employees’ lives. Our finding, together with Cohn and Wardlaw (2014), documents asignificant relation between leverage and workplace injury or death.
The rest of the paper is organized as follows: Section 2 briefly introduces thecharacteristics of China’s coalmining industry. Section 3 describes the data and presents
sample statistics. Section 4 presents and discusses our empirical results. Section 5
concludes the paper.

2. Coal Mining with Chinese Characteristics
Coal is the dominant energy source in China, providing around 70% of the country’s
energy needs and underpinning China’s meteoric economic rise during the past three
decades (Wang, 2006).
Compared to coal mines in the U.S. and other major coal producing countries,
China’s coalmining industry exhibits some unique features. First, underground mines6

(which are more dangerous) dominate surface mines. Second, the underground mines
are very deep, with many old state-owned mines drilling beyond 1,000 meters at very
high temperature. Third, over one-third of the mines fall into the category of ‘high gas
content,’ one-third have seams that are prone to self-ignition, and one-half face high riskof dust explosions.10 These features make coalmining in China not only more risky but
also more expensive to invest in both production and safety equipment, and also more
complicated when implementing safety procedures (Wang, 2006).
Coalmining firms in China are broadly classified into three categories: key stateowned mines, local state-owned mines, and township and village mines (TVMs) where
the majority of accidents and deaths occur. The bulk of its labor force comes frompoverty-stricken rural regions. Many coalmining firms take advantage of thisvulnerability and push the workers into the most dangerous working conditions without
adequate training and equipment. 11 The miners are poorly compensated with wages
ranked among the lowest among all industries. Thus, being a coal miner is, in mostcases, not a career choice but a sign of desperation.12
The industry places little value on the lives of their miners due largely to the low‘price’ tag imposed on human life. Prior to 2005, the compensation for each coalmining
death was between 30,000 and 50,000 RMB, with a maximum compensation of 80,000

10Source: http://www.chinasafety.gov.cn/file/2004-01/kjgh-3.doc.
Employee interests are, in general, badly recognized in China. Lu, Zhong, and Kong (2009) show
that, even for the 100 largest listed companies, 50 percent of them mentioned nothing about
employees’ safety and benefits in their annual reports. The situation is much worse for smaller
companies.
12
In the aftermath of the 2001 Xuzhou disaster where 92 miners were killed in an explosion, awoman who lost her husband said: “The mines won’t stay closed, and when they open again, I will
work in them too. It’s not safe, but what else can I do? I don’t think of it as good or bad. There’s justno other way.” - - Washington Post, September 9, 2001.
11

7

RMB ($10,000 USD). 13 In 2005, starting with Shanxi province (the epicentre ofcoalmining accidents) and gradually followed by other provinces, the compensation was
raised to 200,000 RMB ($25,000 USD), a mere quarter of the annual salary of anexperienced U.S. coal miner (Tu, 2007).
According to data published by the State Administration of Coal Mine Safety,
coalmining deaths are mainly a result of gas explosions (40%) and roof falls (30%). In
order to prevent such accidents, firms need to make significant investment on safety
equipment and training. Because the government controls coal prices so as to subsidize
the power producers, the profitability of state-owned coalmines remains low. This limits
their ability to make adequate safety investment. For smaller town and village mines,
safety standard/investment largely gives way to profit maximization.14
Thus, despite the central government’s repeated pledges, China’s coalmining
industry has persistently underinvested in safety measures. In 2006 (our sample endingyear), safety measures for China’s coal mines were underinvested by at least 68.9
billion RMB ($8.6 billion USD),15 and the investment made was only about 1.9% of thetotal coal sales revenue.16

13

The exchange rate between the U.S. dollar and the Chinese RMB was around 1 US dollar to 8
RMB during our sample period (2001-2006).
14
Many TVMs did not even follow the most elementary safety guidelines such as the installment ofgas ventilation in mines. As a result, a small spark could set off a huge explosion (Wang, 2006).
15“68.9 billion are short paid for coal mine safety,” Safety and Health, Issue 3, 2006, p.24.
16“People’s Republic of China: Coal mine safety study (Final report),” 2008, Asian Development
Bank, TA 4849-PRC.

8

3. Data and Descriptive Statistics
3.1. The Sample
We hand collect the data on coalmining accidents and fatalities from the State
Administration of Work Safety (SAWS) and the State Administration of Coal Mine
Safety (SACMS) of China. Since 2001, the SAWS and SACMS started to publish
coalmining accident news bulletins on their official websites, which include the dates ofcoalmining accidents, the names of the firms involved, the death tolls for each accident,
and the total casualties (the number of deaths plus the number of people injured andmissing) for each accident.
We collect these data for each coalmining firm across 22 provinces for the period
2001-2006.17 Our sample ends at 2006, since SAWS and SACMS only publish data on
accidents involving more than 3 casualties from 2007, which leaves the bulk ofaccidents/deaths unreported.18
We obtain all firm-level data from the Chinese Industrial Enterprise Database(CIED),19 which is constructed by the National Bureau of Statistics and is the largest
official database of Chinese industrial firms. The CIED covers all state-owned andsizable non-state-owned enterprises,20 which accounts for 90% of the total sales of allindustrial firms in China.21
17

Not all the 31 provinces in China have coal mines or coalmining firms. Nine provinces orprovincial-level cities are not included in the sample, since they either do not have any coalmining
firms or only have an insignificant number of coalmining firms. The nine provinces are: Shanghai,
Tianjin, Tibet, Zhejiang, Jiangsu, Guangdong, Guangxi, Qinghai, and Hainan provinces.
18
The average death toll per accident is 1.8 in our sample.
19
The CIED contains 130 firm-level data items. It is also called China Annual Survey of Industrial
Firms or China Annual Survey of Manufacturing Firms by some studies. For recent studies which
use this database, see, for example, Hsieh and Klenow (2009) and Song, Storesletten, and Zilibotti(2011).
20
The sizable non-state-owned enterprise refers to firms with annual sales of no less than 5 million
RMB ($625,000 US dollars).

9

We take all the coalmines’ firm-level data from CIED and merge it with thecoalmining accidents data hand-collected from SAWS and SACMS. We form a panel
dataset of 22,076 firm-year observations for 2001-2006.
We also collect some provincial-level data which, we believe, are important to ouranalysis. Coal price in each province is obtained from the China Coal Industry
Yearbook. The GDP per capita for each province is collected from the China Statistical
Yearbook. The measure of the level of corruption for each province is taken from the
China Procuratorial Yearbook. The number for total circulation of newspapers is
obtained from the China Statistical Data of Press and Publication.
Table 1 presents the annual distribution of coalmining accidents and fatalities forthe full sample. It shows that the number of coalmining firms increased year on year.
The number of firms in 2006 (5,898) was three times larger than in 2001 (1,747). The
number of firms with coalmining accidents also increased year on year except for 2006.
The number of accidents reached over 350 per year during 2003-2005, followed by a
sharp decline in 2006 (84 accidents).22
The number of fatalities from 2001 almost doubled every year to 2003 (755deaths), then fell gradually and dropped to 267 in 2006. The death toll was around 2
deaths per accident (except for 2006) despite the variations in other indicators. Although
we have seen some improvements in 2006, it was the worst year in terms of deaths per
accident (at 3.2). This indicates that it is no easy task to deal with the fatality problem in21

According to the first National Economic Survey conducted in 2004, the total sales for allindustrial firms were at 218 billion RMB, while the total sales for all CIED firms were at 196 billion
RMB.
22
As noted in Sections 2 and 4, starting from 2005 the death compensation has been raised 2.5 times
from $10,000 to $25,000 and the total fine has been raised 7 times from $18,750 to $125,000. The
higher level of compensation and fine is likely to be one of the main reasons driving down thecoalmining accidents and deaths in 2006.

10

the coalmining industry. Overall, there are 998 firms involved in 1,531 accidents and2,814 deaths averaging about 2 deaths per accident.
[Table 1]

3.2. Main Variables
We first define the main variables used in this study. All variables are constructed atannual frequency and measured as of the end of the year.23 We adjust all the relevant
variables to the 2001 prices by using various price indices obtained from the China
Statistical Yearbook.
The main dependent variable employee death is the total number of coalmining
deaths per firm per year. We focus on the number of death rather than the death rate (e.g.
death per thousands of employees) in our study, since punishment and social impact are
based on the absolute number of deaths and not on the death rate.
According to government classification, an accident with fewer than 3 fatalities isdefined as “general”, 3-9 as “relatively serious”, 10-29 as “very serious”, and 30+ as“extremely serious”. Suppose we have two coalmining firms: the first employing 1,000
coalminers and having a death toll of 10; the second employing 100 coalminers andhaving a death toll of 1. Clearly, although both firms have the same death rate (1%), theformer (with 10 deaths) is classified as “very serious” and have a more severe impact onsociety. Both the government and the public are much more concerned about the23

Definitions for all the variables used in this study are provided in Appendix Table A1.

11absolute number of deaths and do not necessarily pay attention to how many workers
are employed by the firm. 24
Our main variable of interest is the firm’s debt ratio which is defined as the firm’s
total liabilities divided by its total assets. We initially observe a positive relation
between debt ratio and employee deaths (Appendix Table A2).
Other firm-level explanatory variables can be classified into three groups. The
first group, in addition to the debt ratio, contains other financial indicators. The tax rateis the total amount of taxes effectively paid by a firm divided by its total sales. The
subsidy rate is the total amount of government subsidies received by a firm divided by
its total sales.
The second group controls for key firm-specific characteristics. Size is defined as
the natural logarithm of total assets. 25 Age is the natural logarithm of the number ofyears since the formation of the firm. Return on equity (ROE) is the net profit divided
by the book value of equity. Asset turnover rate is defined as total sales divided by total
assets. If a firm produces more, it is likely to have more accidents.
The third group controls for equity ownership.26 State ownership is the percentage
equity ownership by the state. Private ownership is the total percentage equity
ownership by all individual investors. Foreign ownership is the total percentage equity
ownership by foreign investors.27

24

In unreported results, we use death rate (death per thousand employees) as the dependent variable
and run similar regressions as performed in the study. We observe a positive relation between
leverage and death, though it is only marginally significant.
25
Alternatively, we control for the total number of employees as a proxy for size, and results are
similar.
26
In addition to the three different ownerships controlled in the study, others include collective
ownership and legal-person ownership.
27
Foreign ownership also includes investors from Hong Kong, Macau, and Taiwan, since they aretreated as foreign investors under Chinese law.

12In addition to the firm-level measures, we also construct four provincial-levelvariables to control for the variations across different provinces. Coal price is thenatural logarithm of average coal price per ton in each province. As mentioned earlier(Section 2), the low coal prices imposed by the government is a key driving force
behind the underinvestment in safety measures and thus a key determinant of
coalmining death.
GDP per capita is the natural logarithm of average GDP per capita for eachprovince. Corruption is measured as the corruption cases filed per 10,000 government
officials in that province, similar to Fisman and Gatti (2002), Adsera, Boix, and Payne(2003), and Glaeser and Saks (2006). High level of corruption increases the possibilityof collusion between local government and firms.
Media exposure is the per capita print of newspapers in each province, as in
Besley and Burgess (2002), Nie, Jiang, and Wang (2013). Large circulation ofnewspapers facilitates the flow of information especially bad news such as coalmining
deaths. Egorov, Guriev, and Sonin (2009) argue that the media can play a significant
role even in an authoritarian system since they help the government monitor the
behaviour of bureaucratic officials.

3.3. Descriptive Statistics
Table 2 presents the descriptive statistics for the explanatory variables. The averagedebt ratio is 51.6% (median = 51.4%) with a standard deviation of 0.30. The debt ratio
is 28.3% for the 25th percentile and rises to 74.0% for the 75th percentile.
The tax effectively paid to total sales is, on average, at 9.7% with a standard
deviation of 0.06. In our case, how much tax is effectively paid by a firm not only

13

captures a firm’s profitability but also likely to reflect its connection with thegovernment. Closely connected firms, in China, are likely to pay less tax or receive
more tax credit. Thus, tax rate might capture a firm’s degree of connection with thegovernment.
The average subsidy received is rather low at 0.5% of total sales (standard
deviation = 0.04). Even this low figure is driven by relatively few firms receiving verylarge subsidies, since the rate at the 75th percentile is still at 0%. We take a closer lookat the data and find that large state-owned coalmining firms receive most of thesubsidies. Subsidy rate could also, therefore, proxy for government connection.
[Table 2]
The average size of a coalmining firm is 179 million RMB ($22 million USD),
which is two times larger than the average size of 85 million RMB for all the industrial
firms recorded in CIED database for the same period. The average firm is 15 years old
with the oldest 58 years and youngest 1 year old. While the average ROE is 39.7%, themedian is just 16.5%. The average asset turnover rate is 2.0 with a median of 1.1.
In terms of equity ownership, individual investors, on average, collectively own
36.3% of the firm compared to 16.7% state ownership. This is mainly because 80% of
the sample firms are classified as non-state-owned firms by CIED. In addition, foreign
investors, on average, own just 0.3% of the firm.
The provincial-level data show that the average coal price is 372 RMB ($47 USD)
per ton (median 300 RMB) with a standard deviation of 257. The average GDP per
capita is 7,346 RMB ($918 USD), while a large disparity exists: the minimum is just
2,524 RMB and the maximum is 23,988 RMB. The degree of corruption for eachprovince is similar. There are, on average, 4.6 corruption cases filed for every 10,000

14government officials (standard deviation = 1.5). The level of media exposure (per capita
print of newspapers in a year) is not even, with a mean of 19.1 and a standard deviate of
14.6.
Appendix Table A2 presents the correlation matrix for the firm-level variables. It
shows that leverage is positively correlated with tax rate, subsidy rate, firm size, age,
ROE and state ownership, while negatively related to asset turnover rate, private andforeign ownerships. This suggests that if a firm is large, old, receiving more subsidies
from the government, or largely owned by the state, it is more likely to have a high debt
ratio.
One interesting relationship is between leverage and ROE, since a potential driverbehind a firm’s choice of high leverage is to maximize ROE. However, the correlation
between the two is merely 0.005 which suggests, at best, a weak relation. Appendix
Table A3 shows that when the debt ratio increases from 25th to 99th percentile, the ROE
remains mostly constant.
In untabulated results, we run multiple regressions with the ROE as the dependent
variable and debt ratio and other control variables as the explanatory variables. We findthat the coefficients on debt ratio are statistically insignificant for all the regressions.28
Thus, a coalmining firm’s leverage decision is not as simple as profit maximization.29
28

Results are similar if we use return on assets (ROA) or return on sales (ROS) as the dependent
variable instead.
29
Some argue that high leverage simply reflects the close relationship between a coalmining firm
and the government, which not only enables the firm to obtain an operating licence under low safety
standard but also helps it to obtain more bank loans. This would suggest that it is, in fact, thepolitical connection or collusion with the government that causes the coalmining fatalities, rather
than the high leverage itself. We recognize that political connection or collusion imperils production
conditions in coal mines (e.g., Jia and Nie, 2015), but we argue that high leverage is not simply areflection of strong political connection or collusion. If high leverage largely reflects a close relation
with the government, then those high-levered coalmining firms should be more likely to earn highprofits since, in China, if you are blessed by the government, you can expect to enjoy lower funding

15

Identifying the determinants of the debt policy of Chinese coalmining firms is, however,beyond the scope of this paper.
Table A2 also shows that coalmining fatality is positively related to leverage,
subsidy rate, firm size, age, state and foreign ownerships, while negatively related to tax
rate, ROE, asset turnover rate, and private ownership. It would be interesting to see how
many of these relations are held in a multivariate regression analysis, which is the focusof the rest of the study.

4. Financial Leverage and Employee Death
In this section we perform multivariate regression analyses in an attempt to establish acasual relation between leverage and coalmining deaths. Each specification includes anintercept, which is omitted from the tables.

4.1. Baseline Regression Results
Our dependent variable, the number of coalmining deaths, is the annual total number ofdeaths for a coalmining firm. This variable is nonnegative and takes on relatively few
values including zero, known as the count variable. In our case, 99% of the observations
take the values of zero, one, two, and three.
A count variable does not follow a normal distribution since it is not a continuous
variable taking on a large range of values. The distribution can be significantly different
from normal if the variable only takes on very few values. Given normality is theand production cost and better marketing and sales channel than your competitors, which should
result in high profits. However, our data show that leverage is negatively related to profitability(ROE, ROA, or ROS). This is at odds with the above proposition. Thus, high leverage is not simply
a result of strong political connection or collusion.

16

standard distributional assumption for linear regression, a linear model is, therefore, notthe best approach for our study.
Since the nominal distribution for count data is the Poisson distribution, a Poisson
regression model is more appropriate for our analysis. In addition, the economic
interpretation is straightforward under the Poisson regression, which also helps us
assess the economic significance of our study.
In Table 3, we run Poisson regressions on coalmining death by controlling for alarge set of explanatory variables defined in Section 3.2. In specification 1, we run the
regression without controlling for any fixed effects. The coefficient on the debt ratio is0.372 which is statistically significant at the 1% level. In specifications 2 and 3, we
control for year fixed effects, and year and province fixed effects, results are similar. In
general, specification 3 suggests that a 10% increase in the debt ratio leads, on average,
to a 4.3% increase in the number of death toll.
In specification 4, we control for both year and firm fixed effects where, as aresult, we lose 90% of the observations. This is because that controlling for firm fixed
effects in Poisson regression can cause large losses of observations in an unbalanced
panel dataset such as ours. In order to be included in the firm fixed-effects Poisson
estimation, a firm must appear in at least two years in the data and must have positive
death tolls in at least one of these years. Nonetheless, the coefficient on debt ratio
remains positive and significant at the 1% level.
[Table 3]
Due to the large losses of observations, we do not, therefore, control for firm fixed
effects in the rest of the Poisson estimations. Instead, we use the province fixed effects

17

when firm fixed effects are absent. Hence, specification 3, which controls for both year
and province fixed effects, is the standard estimation model going forward.
Table 3 establishes an initial positive relation between leverage and coalmining
death. One concern is, however, that high leverage firms may experience financial
distress and our results could, therefore, simply pick up a special firm circumstanceunder financial distress rather than reflect a general scenario with moderate leverage.
To address this, we sort our sample into three subsamples based on the level ofleverage. Low leverage subsample comprises firms with a debt ratio below the 25th
percentile; moderate leverage is between 25th and 75th percentile; high leverage consistsof firm above the 75th percentile.
Table 4 reports the subsample regression results. It shows that the positive relation
between leverage and fatality is highly significant for both low and moderate debt
subsamples, while it is insignificant for high debt ones, which suggests that our result is
not a special firm circumstance under financial distress but a general scenario withmoderate leverage.
[Table 4]

4.2. Endogeneity Issues
We have, so far, observed a positive relation between a firm’s leverage and the numberof employee deaths. This seems to suggest that high debt burden leads to highcoalmining fatalities. However, endogeneity issues prevent us from drawing aconclusion that high leverage causes high fatality. Leverage and coalmining death could
still be related for other reasons, which might be responsible for both the level ofleverage in a firm and the number of fatalities. We discuss and test these issues below.

18

4.2.1. Reverse Causality
One form of the endogeneity problem is reverse causality. One might argue that it is thehigh death tolls that force a firm to borrow more in order to meet employeecompensation, government fines, litigation and other associated costs. In addition,
Pouliakas and Theodossiou (2013) show that employees require a compensating wage
premium for exposure to injury risk. Thus, coalmining firms may also have to bear ahigh cost of wages which could as well drive up its leverage.
While this could be true for many developed countries where the costs for an
employee’s life can be extremely high,30 we argue this is less likely to be the case for
China. First, the bulk of coalmining firms’ labor force is peasants coming from povertystricken rural regions. Many firms take advantage of this vulnerability and compensate
the workers poorly by paying them the lowest industrial wages.
Second, in terms of the costs of human life, prior to 2005, the compensation foreach coalmining death was between 30,000 and 50,000 RMB, with a maximum
payment of 80,000 RMB ($10,000 USD) and a maximum total fine of 150,000 RMB($18,750 USD). From 2005, starting from Shanxi province (the epicentre of coalmining
accidents) and gradually followed by other provinces, the compensation level was raised
to 200,000 RMB ($25,000 USD) plus one million RMB ($125,000 USD) fine for eachcoalmining death.

30

Take the U.S. for example. In April 5, 2010, a massive explosion happened at the Upper Big
Branch coal mine in West Virginia which led to 29 miners’ deaths. Each family of the victims wasoffered case settlements for $3 million. The families were also entitled to receive other benefits
including health insurance coverage, life insurance (five times the annual salary of the mineworker),collegetuition,andongoing
weeklypaychecks(untilwidowsremarry).
http://www.npr.org/templates/story/story.php?storyId=126397976.
In terms of fines made by the government, a Massey Energy subsidiary was fined $2.5 million by a
U.S. District Judge for a fire that killed two West Virginia coal miners on January 19, 2006.
http://www.insurancejournal.com/news/southeast/2009/04/17/99728.htm.

19

Using 2005 data from China’s official Xinhua News Agency,31 the death toll per
million tons of coal was at about 3 people and the gross profit for one million tons ofcoal produced was about 100 million RMB. Thus, even under the new scheme (post2005), the total compensation and fine for the 3 deaths was at 3.6 million RMB($450,000 USD), which was less than 4% of the gross profit. For 2001-2004, the bulk of
our sample, using the maximum compensation of 80,000 RMB and a total fine of
150,000 RMB per death, 3 deaths would cost a firm just over half million RMB($62,500 USD), which is less than 1% of its gross profit.32
Further, as discussed in Section 2, given the special features of China’s coal mines,
it is more expensive to invest in safety equipment and more complicated to implement
safety procedures. Firms would, therefore, have little incentive to invest in safety given
the ultra-low costs of employee’s life.
Thus, for Chinese coalmining firms, the reverse causality scenario noted above isless likely to hold.33 In addition, an alternative hypothesis that coalmining firms withlimited financial resources may choose to invest more in safety in order to avoid highcosts of employee death is also unlikely to hold.

31

The Xinhua News Agency is the official press agency of China. It is subordinate to the StateCouncil and reports to the Communist Party’s Propaganda and Public Information Departments.
Xinhua’s website is: http://www.news.cn/english/
32
Tu (2007) uses the 2005 fatality data in China and finds that the penalty represents only 1% of thegross profits earned by the mine owners. Similarly, the Asian Development Bank 2008 report (TA4849-PRC) states that: “the penalty imposed by the new regulation (on compensation to coalmining
death) represented only 1% of the gross profits collected by mine owners.”
33
In untabulated results, we run multiple regressions with debt ratio in year t as the dependent
variable and the number of coalmining deaths reported in year t-1 (and other control variables) as the
explanatory variable. Our results show that coalmining death in year t-1 does not significantly affectthe firm’s debt ratio in year t.

20

4.2.2. Omitted Variables
Some unobservable firm characteristics could be responsible for our results. To partiallyaddress this, we identify an instrumental variable (IV) and use the two-stage leastsquares (2SLS) regression method in our analysis.
Specifically, we use the value-weighted debt ratio for all firms in a province as the
instrumental variable (IV) for a firm’s debt ratio in that province.34 Our choice of the
instrumental variable is based on two points: First, if a province has, on average, a high(low) level of leverage, it is likely that this overall high (low) leverage will affect theleverage decisions of individual firms (instrument relevance). Second, it is exogenous tothe coalmining death of any particular firm (instrument exogeneity).
For instrument relevance, the influence can be passed on through at least twochannels: first and foremost, virtually everything in China is politically connected. The
overall leverage in a province is likely to reflect the political intention of the provincial
government. Given that debt, in China, is predominantly in the form of bank loans, if aprovincial government embraces a high or low debt policy, it would guide the banks tolend more or less to its firms. Second, if a province has an overall high level of leverage,banks in that region can achieve economy of scale more easily. As a result, they are able
to reduce operating costs, which, in turn, feed into more lending in that area.
Table 5 specifications 1 and 2 reports the two-stage least square (2SLS) regression
results. The first stage results indicate that the provincial-level average debt ratio
significantly affects the individual firm’s leverage, which is consistent with our earlier

34

In the economics literature, it is common to use an average indicator from an area as the
instrumental variable (IV) for firms in that area. For example, Fisman and Svensson (2007) use
industry-location averages as an instrument for firm-level bribery. Also see a review by Krueger andAngrist (2001).

21

argument. The second stage results show that, consistent with the main evidence, thecoefficient on debt ratio is positive and significant at the 5% significance level.
[Table 5]
Another concern is that one year’s coalmining fatalities could be affected by thedeath toll in the preceding year. To address this problem, we control for the number ofdeaths in the preceding year, which also captures some persistent and unobservablefactors.35 We use the system GMM (generalized method of moments) regression in thisanalysis, as it is the most suitable approach for examining dynamic panel data. It uses
only internal instruments to cope with potential endogeneity of the debt ratio (Arellano
and Bond, 1991 and Blundell and Bond, 1998).36
Specification 3 shows the GMM regression results. Similar to other tests, thecoefficient on debt ratio is positive, though significant at the 10% level. The
insignificance of the lagged death could be partially due to our unbalanced panel dataset.
As can be seen, we lose half of the observations by introducing the lagged variable.

4.2.3. Alternative Explanations: Management Quality and Asset Growth
Poorly-managed firms are more likely to be less profitable or even loss-making and may
have to increase borrowing to finance their operations. They may suffer from highfatalities because of poorly-organised production lines and poor oversight or being lesslikely to hire well-qualified workers. Firms with rapid growth in assets are likely toissue more debt to finance their expansions. Rapid growth may also cause more deaths

35

For example, both the coalmining firm and the government may take some immediate actions
following the coalminers’ deaths at year t-1, which may affect the results at year t.
36
If we assume the debt ratio is endogenous and is correlated with the contemporaneous error term,
we can use the second and further lagged debt ratios as instruments for the first-differenced equation,and use the lagged first-differenced debt ratios as instruments for the level equation.

22

due to lack of adequate number of workers (overburdening) or appropriate supervisionand training.
To test these alternative explanations, we control for management quality andasset growth in the analysis. Following Morck, Shleifer, and Vishny (1990) and Masulis,
Wang, and Xie (2007), we use operating income growth as a proxy for management
quality.37 In line with Cooper, Gulen, and Schill (2008), we use the annual percentagechange in total assets as a measure of asset growth.38 In addition, we also measure thegrowth of fixed assets, 39 which would be more important to coalmining firms sinceinvestment in fixed assets may directly improve the working environment and reduce
the number of accidents. Results are shown in Table 6.
[Table 6]
Specifications 1 to 3 control for management quality, total assets and fixed assets
growth, respectively. We lose a large number of observations and hence power in theseestimations. Since the measures of management quality or asset growth requires datafrom year t-2, which is a significant restriction on our unbalanced panel dataset.
Nonetheless, we find that the coefficients on debt ratio are highly significant across all
three models. Management quality and total assets growth are insignificant, while fixed
assets growth negatively and significantly relates to coalmining death.
In specification 4, we control for both management quality and total assets growth.
Both factors are insignificant, while leverage remains significant at the 1% level. We
control for both management quality and fixed assets growth in specification 5. Our key
result on leverage is unchanged, and fixed assets growth again negatively and37(Operating incomet-1 – Operating incomet-2)/Operating incomet-2(Total assetst-1 – Total assetst-2)/Total assetst-2
39(Fixed assetst-1 – Fixed assetst-2)/Fixed assetst-2
38

23

significantly relates to coalmining death. Although management quality is significant inthis specification, it is not robust across different specifications.
All in all, given the well-known difficulties in addressing the endogeneity
problem with regard to leverage, it is hard, if not impossible, to find a clean instrument
for it. Consequently, alternative explanations cannot be ruled out completely.

4.3. A Channel: Leverage, Safety Investment, and Coalmining Fatality
In this sub-section, we examine how leverage affects firms’ safety investment and hence
impacts death tolls. High leverage reduces a firm’s resources and leads to a reduction in
its investment. 40 The investment cut would likely to hit the safety investment
particularly hard, since its benefits are difficult to quantify and it is normally notcontractually obligated.
Although we do not have data on firms’ investment in safety equipment, thisinvestment should be shown in firms’ investment in fixed assets. However, not all fixed
assets are related to safety and we, therefore, choose the per capita investment inproductive fixed asset as a proxy for investment in safety equipment; it is the closest
measure we can obtain from our dataset. We posit that high leverage reduces safety
investment and hence causes more fatalities.
Table 7 reports the results. Specification 1 is the linear regression with theinvestment in safety equipment as the dependent variable and all other variables as the
explanatory variables. It shows that high leverage reduces the investment in safety
equipment. It also shows that high coal prices lead to a high level of safety investment.
Since the Chinese government controls coal prices to subsidize the power producers,
40

Denis and Denis (1993) and Lang, Ofek, and Stulz (1996) find that firms with high leverage tend
to invest less.

24

our evidence suggests that the low coal prices imposed by the government is also one of
the key driving forces behind the underinvestment in safety measures.
Specifications 2 and 3 are Poisson regressions with the number of deaths as thedependent variable. Specification 2 shows that investment in safety equipment
significantly reduces the death toll. In specification 3, we add debt ratio as the
explanatory variable, the coefficient on safety investment remains significantly negative,
while the results for leverage stay the same.
Overall, Table 7 identifies a channel through which leverage impacts coalmining
fatality. Specifically, we show that leverage reduces safety investment which, in turn,leads to higher death tolls. This evidence suggests that cash constraints limit coalmining
firms’ ability to meet/raise safety standards and lend support to a causal interpretation
between leverage and employee death.
[Table 7]

4.4. Robustness Tests
In this subsection, we conduct robustness tests on our main results where we try
different estimation methods, use alternative measures for coalmining death, account forstate ownership status, changes in costs of employee life, and the geographicaldifferences in coal mines.

4.4.1. Negative Binomial and OLS Models
Another natural treatment for count data as dependent variable is the negative binomial
model. In Appendix Table A4, we run negative binomial regressions similar to the
Poisson regressions performed in Table 3. We find that all the coefficients on debt ratio

25

are positive and significant at the 1% level. In Appendix Table A5, we run OLS
regressions similar to that of Table 3.41 Our main results stay the same while at a lower
significance level.

4.4.2. Alternative Measures of Employee Death
In Table 8 we use alternative measures for employee death. First, the government
classifies that an accident with a fatality fewer than 3 as general, 3 or more as relatively
or very serious. In Specification 1 we set a dummy variable ‘Death3’ as our dependent
variable,42 which equals one if an accident involves 3 or more deaths and zero otherwise.
Second, instead of using the total number of deaths per firm per year, in Specification 2,
we use the fatality number per accident as the dependent variable.43 We find similar
results under these alternative measures.
[Table 8]

4.4.3. State-owned vs. Non-state-owned Coalmines
It is well known that the Chinese non-state-owned enterprises have many difficulties ingetting debt financing from the banks and the majority of coalmine accidents occur insuch firms. It is, therefore, important to group the firms by state ownership status andstudy them separately. Although we have controlled for state ownership in our
regressions, it may still be inadequate since it only captures the linear effects. In Table 9,41

Although a liner model for count data might not provide the best fit over all values of the
explanatory variables, Wooldridge (2013) suggests that it is, nonetheless, always informative toperform a liner analysis.
42
Fisman and Wang (2014) also investigate a control sample using only major accidents whichcaused 3 or more deaths to prevent their results being skewed by the under-reporting problem.
43
Table 1 shows that the average death toll per accident for the full sample is about 2 people.

26

we sort the sample into state-owned and non-state-owned firms and run separate
regressions. Our main results stay the same regardless the state ownership status.
[Table 9]4.4.4. The Value of Human Life: Changes in Compensation and Fine
As stated earlier, prior to 2005 the maximum compensation for each coalmining death
was 80,000 RMB ($10,000 USD) and the maximum fine was 150,000 RMB ($18,750
USD). From 2005, the maximum compensation and fine were raised to 200,000 RMB($25,000 USD) and one million RMB ($125,000 USD), respectively.
In Table 10, we divide the sample into two sub-periods: low (2001-2004) and high(2005-2006) compensation and fines periods, and run two separate Poisson regressions.
Our results are not affected by this new classification.
[Table 10]

4.4.5. Geographic Difference: The Shanxi Effect?
Shanxi province is China’s centre for coal production and the epicentre for coalmining
accidents, which accounts for a quarter of our total firm-year observations. It is possiblethat we may just pick up a Shanxi effect and our results may not hold for other regions.
In Table 11, we sort our sample into Shanxi province and other regions, and run Poisson
regressions separately. Our results show that the leverage effect is significant at 1% forboth subsamples, while the economic impact is larger for Shanxi province.
[Table 11]27

4.5. Limitations of the StudyFirst, the vast majority of our sample firms are non-public. There are just 25 listed
coalmining firms in China.44 We are, thus, not able to collect the market-based publicdata for our sample firms, and have to rely on the information provided by the Chinese
Industrial Enterprise Database. The encouraging news is that the CIED is an official
dataset which is constructed by the National Bureau of Statistics and is widely used foracademic research in China. Researchers outside China also use the CIED dataset. For
instance, Hsieh and Klenow (2009), and Song, Storesletten, and Zilibotti (2011).
Second, other explanatory variables, that might be important for explaining thecoalmining deaths, are not available to us. For example, the status of the coal mines,
information at the individual mine level could be more useful than the firm level data;
the quality of worker, firm with better trained and more experienced workers may havefewer accidents/deaths; the number of hours worked, firm imposing on long working
hours may induce more accidents.
Third, non-state-owned small and medium mines account for the bulk of thesample. It is widely perceived that small and medium coalmining firms may underreport their death tolls. In addition, state-owned firms may also have incentives andeven means to manipulate data on death tolls. This is indeed a concern to us. There are,however, also good reasons to have confidence in the reporting system.
Firstly, hiding or manipulating data on coalmining accidents/deaths is severely
punished by the law. The State Law requires coalmining firms to report to theirprovincial governments and the relevant departments of the State Council immediately
following coalmining accidents. Delayed reporting or underreporting are subject not

44

See also Fisman and Wang (2014).

28

only to administrative penalties and but also criminal charges.45 Secondly, the press isvigilant to coalmining accidents, especially the ones outside the accident-hit area. Given
the rapid development of the internet and social network sites, it becomes much harder
to hide or manipulate data.46
Last but not least, the CIED includes firms with annual sales of more than 5
million RMB ($625,000 USD) and drops them if they do not meet this criterion. The
entering and exiting is not random and hence could have implications to our study. Thisshould not, however, have a material impact on our results. In our study, we show thatleverage reduces safety investment and causes high fatalities (i.e., the channel). If we
include all the small firms (i.e., < 5 million RMB sales) in the analysis, the results
should not change in a significant way unless high leverage is not causing a reduction insafety investment in small firms. In fact, there are good reasons to believe that small
coalmining firms are more likely to cut safety investment when burdened with highleverage.

45

See Article 92 of “The Production Safety Law” issued in 2002.
Jia and Nie (2015) argue that if the coalmining death data are being manipulated, we should thenobserve a clustering of the number of reported deaths at some key classification thresholds such as 2
and 9; since the government defines coalmining accident with a fatality less than 3 as “general”, 3 to
9 as “relatively serious”, while 10 and more as “very serious”. They find, however, this is not the
case.
46

29

5. Conclusion
Past studies of China’s coalmining fatalities focus on regulatory environment andinstitutional framework. Although they have shed light on the issue, they largely
overlook its micro foundation at the firm level, presumably due to the difficulty inobtaining such a firm-level database.
In this paper, we bridge the gap by hand-collecting and constructing a uniquedataset for China’s coalmining firms. We show that leverage, a neglected factor in thecoalmining safety studies but a fundamental element in corporate finance, significantly
determines coalmining fatalities. Specifically, we document a channel through which afirm’s leverage can affect the well-being of its employees.
While we focus on China’s coalmining industry in this study, our intuition can beapplied more widely across industries and other developing countries where workplacesafety is a particular concern. Our study draws important implications of financial
policy to labor policy, and calls for a more corporate-finance-based analysis on the wellbeing of employees.

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33

Table 1: Coalmining accidents and employee fatalities
This table presents the statistics on the annual distribution of China’s coalmining accidents andemployee fatalities for the period 2001-2006. The data on coalmining accidents and fatalities are handcollected from the State Administration of Work Safety (SAWS) and the State Administration of Coal
Mine Safety (SACMS). The SAWS and SACMS publish information on the date of the coalmining
accident, the name of the firm involving an accident, and the number of death tolls for each accident.
Year

No. of firms

2001
2002
2003
2004
2005
2006
01-06

1,747
2,126
2,549
4,655
5,101
5,898
22,076

No. of firms
with accident
74
151
209
238
250
76
998

No. ofaccidents
106
252
368
368
353
84
1,531

34

No. of deaths
251
423
755
583
535
267
2,814

Deaths per
accident
2.4
1.7
2.1
1.6
1.5
3.2
1.8

Table 2: Descriptive statistics
This table provides the coalmining firm-level and provincial-level data for the period 2001-2006. The firm-level data are obtained from the
Chinese Industrial Enterprise Database (CIED), which is constructed by the National Bureau of Statistics and is the largest database for
Chinese industrial firms. The CIED covers all state-owned and sizable non-state-owned enterprises, which accounts for 90% of the total
sales of all industrial firms in China. For the provincial-level data: Coal price in each province is obtained from the China Coal Industry
Yearbook. GDP per capita for each province is collected from China Statistical Yearbook; the measure for the level of corruption for eachprovince is recorded in China Procuratorial Yearbook; the number of total circulation of media newspapers is obtained from China
Statistical Data of Press and Publication. The sample is winsorized at 1% based on the debt ratio. All variables are defined in Appendix
Table A1.
Variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Firm size (RMB mil)Firm age (year)
Return on equity (ROE)
Asset turnover rate
State ownership
Private ownership
Foreign ownership
Province-level variables:
Coal price (RMB)
GDP per capita (RMB)
Corruption
Media exposure

Obs.

Mean

S.D.

Min

25%

Median75%

Max22,07622,07622,07622,07622,07622,00423,07622,07622,07622,076

0.516
0.097
0.005
179.2
14.986
0.397
1.964
0.167
0.363
0.003

0.299
0.060
0.036
1,311
13.544
5.847
3.257
0.362
0.462
0.045

0
0
0
1.00
1
-1.699
0.015
0
0
0

0.283
0.056
0
6.27
2
0.026
0.587
0
0
0

0.514
0.092
0
13.203
10
0.165
1.126
0
0
0

0.740
0.129
0
34.524
21
0.472
2.296
0
1
0

1.524
0.853
1.363
49,60058463.33202.381
1
1
1

121
132
132
132

372
7,346
4.56
19.14

256.744262.87
1.45
14.57

61.83
2,524
1.31
4.65

205.97
4,887
3.51
10.85

299.97
6,073
4.53
14.56

444.16
7,893
5.52
20.74

1280.14
23,988
10.18
79.97

35

Table 3: The determinants of coalmining fatality: Baseline regressions
This table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The dependent
variable is the total number of coalmining deaths per firm per year. All explanatory variables
are defined in Appendix Table A1. All regressions include an intercept term, which is omitted
from the table. P-values are reported in parentheses. ***, **, * denote significance at the 1%,5%, and 10% level, respectively.
Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
Firm fixed effects
No. of obs.
Log pseudolikelihood

Poisson (1)Poisson (2)Poisson (3)Poisson (4)0.372***(<0.001)-6.228***(<0.001)1.710***(<0.001)
0.360***(<0.001)
0.416***(<0.001)
0. 0005(0.905)-0.069***(<0.001)
0.591***(0.004)0.185***(<0.001)
0.432(0.322)0. 397***(<0.001)-6.019***(<0.001)
1.699***(<0.001)
0.369***(<0.001)
0.406***(<0.001)-0. 002(0.619)-0. 055***(0.003)0. 531***(<0.001)
0. 227***(<0.001)
0. 464(0.288)0.430***(<0.001)-2.062***(<0.001)-0.033(0.883)0.416***(<0.001)0.307***(<0.001)-0.003(0.363)-0.052***(0.005)0.229***(<0.001)-0.058(0.382)0.379(0.350)1.114***(<0.001)-6.505***(<0.001)
1.442***(0.002)-0.099(0.261)0.108**(0.039)-0.0005(0.871)-0.071*(0.086)0.131(0.285)0.454***(<0.001)-1.581(0.373)0.197***(<0.001)-0.591***(<0.001)
0.095***(<0.001)-0. 001(0.768)0. 143***(<0.001)-0. 424***(<0.001)
0.099***(<0.001)-0.005(0.237)0.166***(<0.001)-2.167**(0.011)
0.025(0.511)
0.002(0.732)0.321***(<0.001)-2.536**(0.012)-0.704*(0.093)0.002(0.689)

NoNo
No
21,131-8915.834

Yes
No
No
21,131-8684.976

Yes
Yes
No
21,131-7751.498

Yes
No
Yes
2,160
-2241.253

36

Table 4: The determinants of coalmining fatality: The level of debt
This table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The dependent
variable is the total number of coalmining deaths per firm per year. Low debt subsample
comprises firms with a debt ratio below the 25th percentile; Moderate debt is between 25th and75th percentile; High debt consists of firm above the 75th percentile. All explanatory variables
are defined in Appendix Table A1. All regressions include an intercept term, which is omitted
from the table. P-values are reported in parentheses. ***, **, * denote significance at the 1%,5%, and 10% level, respectively.
Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

Low Debt
Poisson (1)Moderate Debt
Poisson (2)High Debt
Poisson (3)3.045***(<0.001)-4.795***(<0.001)-3.756(0.476)0.190***(0.003)
0. 129*(0.060)-0.165(0.443)-0.086**(0.031)-0.336(0.227)-0.066(0.650)2.419(<0.001)

0. 667***(0.003)-1.990***(0.004)0. 860***(0.002)0.415***(<0.001)
0.482***(<0.001)
0. 235***(<0.001)-0. 121***(0.001)
0. 206**(0.012)
0. 238**(0.013)-0. 279(0.728)-0.284(0.238)-0.782(0.322)-1.132**(0.014)0.385***(<0.001)
0.092**(0.023)-0.0005(0.914)-0.047(0.203)0.253**(0.014)-0.608***(<0.001)-10.540(0.535)-0.376**(0.010)-1.006(0.726)-0.428***(0.002)-0. 045*(0.073)0. 317***(<0.001)-3.575***(0.001)0.121**(0.016)0.003(0.644)0.063(0.444)
0. 463(0.776)-0.036(0.640)0.015(0.166)

Yes
Yes
5,184
-1069.454

Yes
Yes
10,694-3784.470

Yes
Yes
5,253
-2509.742

37

Table 5: Two-stage least square (2SLS) and GMM regressions
This table presents the regression results on the robustness of the determinants of coalmining
fatality by performing the two-stage least square (2SLS) and the generalized method of
moments(GMM) regressions. The sample consists of 22,076 firm-year observations for theperiod 2001-2006. The dependent variable is the natural logarithm of (one plus) the number of
coalmining deaths per firm per year. All explanatory variables are defined in Appendix Table
A1. All regressions include an intercept term, which is omitted from the table. P-values arereported in parentheses. ***, **, * denote significance at the 1%, 5%, and 10% level,
respectively.
Independent variables
First stage
Additional variables:
Province average debt ratio (IV)

2SLS
Second stage

0.300***(<0.001)

No. of death last year

0.008
(0.783)Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure
Year fixed effects
Firm fixed effects
Number of obs.
Adjusted R2
F-statistic
AR(2)

GMM

-0.159***(<0.001)
0.030(0.687)-0.006(0.164)-0.00006(0.986)0.0005*(0.081)-0.002**(0.015)0.0003(0.978)-0.014**(0.019)-0.053(0.308)-0.007*(0.069)-0.176**(0.019)-0.001(0.659)0.0003(0.543)
Yes
Yes
21,1310.012
4.52

0.510**(0.013)-0.020(0.750)0. 353***(0.001)0.008(0.155)0.002(0.717)0.0001(0.801)0.0006(0.553)0.017(0.248)0.014(0.116)0.012(0.865)0.023*(0.077)-0.147***(<0.001)
0.887***(<0.001)
0.017***(<0.001)
0.017***(<0.001)-0.0003(0.413)0.001**(0.041)
0.053***(<0.001)
0.017***(0.001)
0.049(0.304)0.013**(0.035)0.086(0.438)0.007(0.127)-0.0005(0.515)
Yes
Yes
21,1310.010(0.132)-0.053***(0.005)0.0006(0.864)0.0005(0.513)
Yes
Yes
12,0420.191

38

Table 6: The determinants of coalmining fatality: Alternative explanations
This table presents the Poisson regression results on tests of alternative explanations to therelation between leverage and coalmining fatality. The full sample consists of 22,076 firm-year
observations for the period 2001-2006. The dependent variable is the total number of
coalmining deaths per firm per year. All explanatory variables are defined in Appendix Table
A1. All regressions include an intercept term, which is omitted from the table. P-values arereported in parentheses. ***, **, * denote significance at the 1%, 5%, and 10% level,
respectively.
Independent variables
Alternative explanations:Management quality

Poisson (1)Poisson (2)-0.00005(0.927)

Asset growth (Total)-0.020(0.447)

Asset growth (Fixed)
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

Poisson (3)Poisson (4)Poisson (5)-0.00004(0.931)-0.020(0.447)0.117***(<0.001)-0.027*(0.072)-0.038**(0.020)0.570***(<0.001)-1.250*(0.069)-0.687*(0.058)0.469***(<0.001)
0.146***(<0.001)
0.001(0.901)
0.024(0.170)0.311***(<0.001)-0.165(0.118)-68.337(0.137)0.568***(<0.001)-1.236*(0.072)-0.692*(0.056)
0.469***(<0.001)
0.143***(<0.001)
0.002(0.897)0.024(0.176)0.310***(<0.001)-0.164(0.120)-68.672(0.136)0.505***(<0.001)-3.511***(<0.001)-0.685*(0.088)0.477***(<0.001)0.171***(<0.001)-0.004(0.712)0.006(0.807)0.201**(0.021)-0.403***(0.001)-70.242(0.134)0.568***(<0.001)-1.237*(0.072)-0.692*(0.056)
0.469***(<0.001)
0.143***(<0.001)
0.002(0.898)0.024(0.176)0.310***(<0.001)-0.164(0.120)-68.682(0.136)0.517***(<0.001)-3.355***(<0.001)-0.638(0.114)0.474***(<0.001)
0.194***(<0.001)-0.005(0.688)-0.003(0.912)0.190**(0.029)-0.415***(<0.001)-68.766(0.130)0.243***(0.007)-2.523*(0.084)0.057(0.263)0.014*(0.055)0.241***(0.007)-2.487*(0.089)0.058(0.260)0.014*(0.056)0.167*(0.069)-6.257***(<0.001)
0.012(0.828)0.005(0.560)0.241***(0.007)-2.486*(0.089)0.057(0.261)0.014*(0.056)0.171*(0.063)-6.428***(<0.001)-0.0008(0.988)0.004(0.604)

Yes
Yes
7,095
-3527.370

Yes
Yes
7,095
-3527.041

Yes
Yes
6,627
-3071.515

Yes
Yes
7,095
-3527.037

Yes
Yes
6,627
-3054.927

39

Table 7: A Channel: Safety investment and coalmining fatality
This table presents the results for the fixed-effect estimation and the Poisson estimation. The
sample consists of 22,076 firm-year observations for the period 2001-2006. For specification(1), the dependent variable is the per capita investment in productive fixed assets. For
specifications (2) to (3), the dependent variable is the number of coalmining deaths per firm
per year. All explanatory variables are defined in Appendix Table A1. All regressions include
an intercept term, which is omitted from the table. P-values are reported in parentheses. ***,**, * denote significance at the 1%, 5%, and 10% level, respectively.

Independent variables
Firm-level variables:
Debt ratio
Investment in safety
equipment (ln)Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Adjusted-R2
Log pseudolikelihood

Safety Inv.

Death

Death

OLS (1)

Poisson (2)Poisson (3)-145.120***(<0.001)-77.240***(<0.001)
23.554***(<0.001)-12.938***(<0.001)
0.081**(0.042)0.366**(0.027)5.290*(0.099)2.854**(0.047)
192.582**(0.032)-0.002***(<0.001)-2.239***(<0.001)-0.108(0.634)0.438***(<0.001)
0.283***(<0.001)-0.003(0.382)-0.065***(0.001)0.283***(<0.001)-0.087(0.189)0.333(0.402)0.409***(<0.001)-0.002***(<0.001)-2.212***(<0.001)-0.158(0.484)0.446***(<0.001)
0.277***(<0.001)-0.003(0.303)-0.054***(0.004)0.234***(<0.001)-0.061(0.359)0.433(0.277)

4.122***(0.002)-33.829(0.346)-2.361**(0.018)-0.391**(0.042)0.172***(<0.001)-2.344***(0.006)0.019(0.611)0.003(0.600)0.168***(<0.001)-2.214***(0.009)0.021(0.579)0.002(0.634)

Yes
Yes
21,131
0.112

Yes
Yes
21,131

Yes
Yes
21,131-7742.214-7728.369-24.132***(<0.001)40

Table 8: Alternative measures of employee death
This table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. Death3 is adummy variable which equals one if an accident involves 3 or more deaths (i.e., relatively
serious or more severe) and zero otherwise. All explanatory variables are defined in Appendix
Table A1. All regressions include an intercept term, which is omitted from the table. P-values
are reported in parentheses. ***, **, * denote significance at the 1%, 5%, and 10% level,
respectively.

Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

Death3

Death per accident

Logit (1)Poisson (2)0.695***(0.008)-4.708***(0.005)0.035(0.964)
0.523***(<0.001)
0.316***(<0.001)
0.009(0.381)-0.169*(0.050)0.274(0.181)0.155(0.499)0.828(0.491)

0.558***(<0.001)-2.352***(<0.001)-0.760**(0.023)0.257***(<0.001)
0.244***(<0.001)
0.0002(0.965)-0.095***(<0.001)0.260***(<0.001)-0.135*(0.062)0.655(0.109)0.139(0.348)-1. 968(0.509)-0.098(0.421)-0.096(0.453)0.157***(0.003)-1.867*(0.064)-0.010(0.825)-0.007(0.297)

Yes
Yes
21,126-1001.767

Yes
Yes
21,131-6356.258

41

Table 9: State-owned vs. Non-state-owned mines
This table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The dependent
variable is the number of coalmining deaths per firm per year. All explanatory variables aredefined in Appendix Table A1. All regressions include an intercept term, which is omitted fromthe table. P-values are reported in parentheses. ***, **, * denote significance at the 1%, 5%,
and 10% level, respectively.
Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

State-owned
Poisson (1)Non-state-owned
Poisson (2)0.563***(<0.001)-4.623***(<0.001)
0.152(0.538)0.412***(<0.001)
0.263***(<0.001)-0.018**(0.034)-0.0008(0.987)-2.024***(<0.001)-1.092*(0.090)0.848(0.748)0.553***(<0.001)-0.781(0.187)1.160(0.266)0.280***(<0.001)
0.262***(<0.001)
0.002(0.650)-0.118***(<0.001)
2.054***(<0.001)-0.067(0.347)0.570(0.176)0.185***(0.004)-4.760***(<0.001)
0.006(0.902)0.002(0.748)0.109(0.101)1.435(0.255)0.054(0.399)-0.003(0.717)

Yes
Yes
3,582
-3054.819

Yes
Yes
17,549-4395.190

42

Table 10: Changes in compensation and fineThis table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The dependent
variable is the number of coalmining deaths per firm per year. All explanatory variables aredefined in Appendix Table A1. All regressions include an intercept term, which is omitted fromthe table. P-values are reported in parentheses. ***, **, * denote significance at the 1%, 5%,
and 10% level, respectively.
Independent variables

Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

Low Compensation &Fines (2001-2004)
Poisson (1)High Compensation &Fines (2005-2006)
Poisson (2)0.338***(<0.001)-2.995***(<0.001)
0.463**(0.046)
0.414***(<0.001)
0.256***(<0.001)-0.004(0.314)-0.080***(0.002)0.250***(<0.001)-0.007(0.930)0.552(0.181)0.639***(<0.001)-0.423(0.605)-3.717***(0.001)
0.412***(<0.001)
0.429***(<0.001)
0.005(0.627)-0.024(0.399)0.306**(0.011)-0.047(0.674)-2.577(0.290)0.249***(<0.001)-3.000*(0.078)-0.163***(0.007)-0.009(0.158)0.127(0.613)-4.291(0.284)
1.075***(<0.001)0.520***(<0.001)

Yes
Yes
10,867-5217.344

Yes
Yes
10,264-2369.379

43

Table 11: Shanxi province and other regions
This table presents the Poisson regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The dependent
variable is the number of coalmining deaths per firm per year. All explanatory variables aredefined in Appendix Table A1. All regressions include an intercept term, which is omitted fromthe table. P-values are reported in parentheses. ***, **, * denote significance at the 1%, 5%,
and 10% level, respectively.

Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)

Shanxi province
Poisson (1)Other regions
Poisson (2)2.348***(<0.001)-3.653**(0.022)-8953.603(0.130)0.529***(<0.001)
0.205**(0.036)0.002(0.877)-0.010(0.945)-0.474**(0.027)-7.530***(0.007)-60.118(0.998)0.321***(<0.001)-1.660***(0.001)-0.030(0.894)0.417***(<0.001)
0.316***(<0.001)-0.004(0.257)-0.044**(0.020)0.300***(<0.001)
0.053(0.446)0.501(0.213)0.673(0.387)-3.908**(0.019)0.279(0.731)0.017(0.739)0.158***(0.001)-2.562***(0.004)0.039(0.319)0.002(0.714)

Yes
No
5,704
-694.057

Yes
Yes
15,427-6952.371

Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

44

Appendix Table A1: Variable Definitions
Variable
Coalmining death
Debt ratio
Tax rate
Subsidy rate
Size
Age
Return on equity (ROE)
Asset turnover rate
State ownership
Private ownership
Foreign ownership
Coal priceGDP per capita
Corruption
Media exposure
Investment in safety equipment
Management quality

Asset growth (Total)
Asset growth (Fixed)

Definition
The number of coalmining deaths per firm per year.
Total liabilities divided by the total assets.
The annual total amount of taxes paid by a firm divided by its
total sales.
The annual total amount of government subsidies received bya firm divided by its total sales.
The natural logarithm of a firm’s total asset.
The natural logarithm of the number of years since theformation of the firm.
Net profit divided by the book value of equity.
The firm’s total sales divided by its total assets.
The percentage equity ownership by the state.
The percentage total equity ownership by the collective
individual investors.
Total equity ownership by foreign investors including
investors from Hong Kong, Macau, and Taiwan
The natural logarithm of average coal price per ton in eachprovince.
The natural logarithm of average GDP per capita for eachprovince.
It is measured as the corruption cases filed per 10,000
government officials for each province.
It is measured as the per capita print of newspapers in a year
for each province.
A firm’s per capita investment in productive fixed asset ineach year.
Operating income growth over the past year, defined as(Operating incomet-1 – Operating incomet-2)/Operating
incomet-2
Annual percentage change in total assets, defined as (Total
assetst-1 – Total assetst-2)/Total assetst-2
Annual percentage change in fixed assets, defined as (Fixed
assetst-1 – Fixed assetst-2)/Fixed assetst-2

45

Appendix Table A2: Pearson correlation matrix
This table presents the pair-wise correlations between the key variables used in the study. All variables are defined in Appendix Table A1.

Death
Debt ratio
Tax rate
Subsidy rate
Size
Age
ROE
Asset turnover
State ownership
Private ownership
Foreign ownership

0.036-0.015
0.095
0.148
0.114
-0.003-0.0310.108-0.0390.0004

Debt
ratio
0.019
0.078
0.038
0.177
0.005
-0.1430.220-0.143-0.019

Tax rate-0.030
0.016
0.106
0.006
-0.1850.074-0.120-0.012

Subsidy
rate

0.038
0.207
-0.012-0.0560.222-0.083-0.008

46

Size

Age

ROE

0.175
-0.005-0.0610.201-0.0930.005-0.007-0.1570.455-0.283-0.0210.118-0.017-0.004-0.010Asset
turnover-0.174
0.107
0.013

State
ownership

Privateownership-0.343-0.020-0.045

Appendix Table A3: Leverage and Profitability
This table presents the percentile distribution for the mean and median of return on equity (ROE).

Debt Ratio
25th Percentile
50th Percentile
75th Percentile
90th Percentile
99th Percentile

Return on Equity (ROE)
MeanMedian
0.362
0.180
0.366
0.179
0.369
0.170
0.390
0.166
0.401
0.167

47

Appendix Table A4: Negative binomial regressions
This table presents the negative binomial (NB) regression results on the determinants ofcoalmining fatality. The sample consists of 22,076 firm-year observations for the period
2001-2006. The dependent variable is the number of coalmining deaths per firm per year.
All explanatory variables are defined in Appendix Table A1. All regressions include anintercept term, which is omitted from the table. P-values are reported in parentheses. ***,**, * denote significance at the 1%, 5%, and 10% level, respectively.
Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
No. of obs.
Log pseudolikelihood

NB (1)NB (2)NB (3)

0.427***(0.005)-5.660***(<0.001)
2.671**(0.028)0.269***(<0.001)
0.314***(<0.001)
0.002(0.871)-0.097***(0.001)0.854***(<0.001)
0.258**(0.018)0.523(0.508)0.478***(0.002)-5.592***(<0.001)
2.484**(0.043)0.292***(<0.001)
0.333***(<0.001)
0.001(0.923)-0.075***(0.009)0.803***(<0.001)0.331***(0.003)0.568(0.465)0.675***(<0.001)-1.894**(0.025)-0.132(0.888)0.348***(<0.001)
0.266***(<0.001)
0.002(0.834)-0.069**(0.018)0.320**(0.017)-0.109(0.345)0.472(0.514)0.464***(<0.001)-0.468**(0.044)0.001(0.980)-0.016(0.056)0.324***(<0.001)-0.274(0.243)-0.013(0.793)-0.018**(0.031)0.068(0.499)0.930(0.602)-0.108(0.206)0.003(0.837)

NoNo
21,131-5027.236

YesNo
21,131-4963.337

Yes
Yes
21,131-4775.363

48

Appendix Table A5: OLS regressions
This table presents the OLS regression results on the determinants of coalmining fatality.
The sample consists of 22,076 firm-year observations for the period 2001-2006. The
dependent variable is the natural logarithm of (one plus) the number of coalmining deaths
per firm per year. All explanatory variables are defined in Appendix Table A1. All
regressions include an intercept term, which is omitted from the table. Robust standard
errors are clustered at the provincial level. P-values are reported in parentheses. ***, **, *denote significance at the 1%, 5%, and 10% level, respectively.
Independent variables
Firm-level variables:
Debt ratio
Tax rate
Subsidy rate
Size (ln)Age (ln)
Return on equity (ROE)
Asset turnover rate
State ownership (%)
Private ownership (%)
Foreign ownership (%)
Province-level variables:
Coal price (ln)GDP per capita (ln)Corruption
Media exposure

Year fixed effects
Province fixed effects
Firm fixed effects
No. of obs.
Adjusted-R2

OLS (1)

OLS (2)

OLS (3)

OLS (4)0.011*(0.074)-0.175***(<0.001)
0.871***(<0.001)
3.03e-8***(<0.001)
0.017***(<0.001)
0.00002(0.945)-0.002(0.004)0.071***(<0.001)0.010**(0.012)0.038(0.320)0.010*(0.100)-0.172***(<0.001)0.870***(<0.001)
3.04e-8***(<0.001)
0.017***(<0.001)
0.00001(0.967)-0.001**(0.010)0.069***(<0.001)
0.012***(0.003)0.038(0.310)0.014**(0.018)-0.026(0.386)0.719***(<0.001)
3.10e-8***(<0.001)
0.016***(<0.001)
0.00007(0.796)-0.002***(0.005)0.056***(<0.001)-0.008*(0.072)0.029(0.437)

0.097*(0.073)-0.396*(0.089)2.508*(0.083)-3.81e-8(0.664)0.027(0.377)0.0003(0.519)-0.0009(0.426)0.047(0.677)0.022(0.438)-0.080(0.428)0.015***(<0.001)-0.038***(<0.001)-0.001(0.545)-0.00001(0.973)0.011***(0.001)-0.025***(0.007)-0.0003(0.884)-0.0003(0.435)0.009**(0.045)0.094(0.225)0.004(0.218)-5.92e-06(0.992)0.048(0.130)-0.489(0.254)0.029(0.526)0.001(0.718)

NoNo
No
21,131
0.072

Yes
No
No
21,131
0.076

Yes
Yes
No
21,131
0.105

Yes
No
Yes
21,1310.006

49