Does Nationalization Save the Miners?
Jiateng Wang
Abstract
The casualty rate per million tons of coal in China has dropped by almost 20 times from 2002 to
2015. Many attribute this to the controversial nationalization project launched by the Chinese
government around 2008. Are state owned enterprises (SOEs) doing better in protecting workers’
lives? Is the improvement of mining safety a result of nationalization? With panel data of major
coal producing provinces from 2002 to 2015, this paper indicates that nationalization has a
significant effect in bringing down the death rates of coal mines.
Keywords: nationalization, coal, mining accident, safety, China
Introduction
[The death rate in Chinese coal mining industry once was extremely high.]The abundant
reserve of coal is a gift to China. The black ore has been bringing light and heat to Chinese
people for thousands of years. However, coal does come with a price. Coalmine accidents,
including gas explosions, tunnel collapses, and water leakages and so on, cause heavy casualties
every year in China. In the first ten years of the 21st century, strong demand for coal fueled the
coalmining industry, pushing coal accidents to the peak in 2002, when 6,995 coalmine workers
were killed in 3,112 accidents in a single year. In these days, media were occupied by coalmine
disasters. Shocked again and again by fatality reports, the public finally became somewhat
accustomed or even indifferent to these tragedies. Coal thus seems not a blessing, but also a
curse to China.
[In order to avoid mining accidents, the government began nationalization private mines
around 2008.] Partially in reaction to the appalling safety condition of coal mines, the Chinese
government launched a mass reform project in the coalmining industry around 2008, especially
in Shanxi, the most important coal producing province of China. Measures included closing
small mines that are responsible for most of the deaths, and instruct merges that state owned
mining giants take over private mines. After taken over, these private mines, which were usually
small and crowded together, were integrated and upgraded into larger mines.
[The death rate has been significantly reduced in recent years.]Things seem to get better
after that. Coal mine accidents are rarely seen now. News of mining tragedies that used to fill up
the newspaper and TV are disappearing, and the casualty numbers are usually quite small
compared to the past. In 2015, the deaths per million tons has dropped to 0.25, almost 20 times
lower than the peak of 5 that occurred in 2002.
[Can nationalization explain this improvement?]What is the reason of this improvement?
Does this prove that the SOEs did better in protecting the workers? Shall we attribute this
improvement to the recent nationalization project, at least to some extent? In order to answer
these questions, we have to correctly assess the impact of nationalization policy and evaluate the
safety performance of SOEs. This paper aims at assessing the impact of nationalization on
mining safety. This research will offer more concrete empirical evidence for the debate on “Guo
Jin Min Tui” and the behavioral of the SOEs.
Literature Review
[Existing literature lacks quantitative researches on this question.]Unfortunately, few
studies have been made about the impact of nationalization mergers on coalmine safety. Existed
literature generally considers nationalization and mining safety separately as two isolated topics.
Although there are some researchers that try to explain the economic dynamic behind the
changes in death rates of coal mines, the data they use are usually obsolete, failing to consider
the impact of the most recent and relevant nationalization policy. What is more, only simple
statistical and econometric methods are used in that research, hampering their explanatory power.
On Coalmine Safety
There are a great number of papers discussing the effects of a particular kind of protection
instrument or mining technique. But they are more about engineering, while the question we are
more interested is why or why not these technologies are applied by the mines. This requires the
discussion on the behavioral characteristics of state owned and private mines.
Some research has been made to analyze coalmining accidents by using statistical analysis. Yin
et al. (2013) examined statistical data of coalmining from 2001 to 2012 and pointed out some
interesting characteristics of mining accidents. Similar is Chen et al. (2012), who used data from
2001 to 2010 and described the general trend of mining accidents. Their works confirmed the
improving safety condition in recent years, not only in total number of accident casualties, but
also in deaths per million tons of coals. However, this period of time was treated as a steady
process without deeper changes or essential reforms in the industry. They did not consider the
nationalization project that was launched around 2008 and its influences. As a result, their study
focused on statistical descriptions of accidents without deeper inquiries into the mechanism of
declining casualties.
Comparatively, economists’ research reveals more about the reason behind the rising and
declining coalmining accidents. He (2012) focused on the impact of closing small mines. His
econometric analysis indicated that the decline in the death rate was mainly because that the
government closed down small mines. He was right in pointing out that owners of small mines
have no incentive to invest in safety protection. However, he does not go further to inquire why
they tend to act in this way. In fact, when supervised properly, small mines could also be safe,
1
and big mines will also be dangerous if run merely after profits.
The most prominent research was by Nie et al(2011, 2013) , who explained the coal mine deaths
with the collusion between the businessmen and the local officials. He argued that when the
supervising power moved from state government to local governments (e.g. the provincial
governments) between 1998 and 2002, more accidents happened because this opened the door
for the government officials and coal bosses to collude.
Nie’s series of papers are of particular importance. First, he rightly clarified the question: what
shapes the behaviors of coal mining enterprises? Second, Nie hand-collected data from accident
announcements by the National Safety Bureau of many provinces and created a new data set that
had never made or published by the government before. Finally, he used panel data, a relatively
advancedconometric technique in this paper. This makes it distinct from other research , often
with only descriptive statistics.
However, there are two defects that weaken the explanatory power of Nie’s work. First, he
identified that the most important reform happened around 2000, when the central government
gave the power of managing national owned mines to the provinces. But, this reform was
relatively trivial compared to ownership reforms, such asthe large scale opening-up to private
capital in the 1990s and the recent nationalization between 2009 and 2011. Secondly, he used
data between 1995 and 2005. As a result, his research could not cover the recent drastic changes
of ownership in the nationalization movement.
In conclusion, although economists have given some attention to coalmine safety problems, the
existing works offers no convincing explanations to the substantial fatality changes. However, as
we will soon see, they apparently have more to say about the other half of the question: “Guo Jin
Min Tui”, or nationalization policy.
Guo Jin Min Tui: the state triumphs, “the people” retreat
The reform in coal mining industry was usually described as a nationalization movement and
was soon entitled a popular term, “GuoJin Min Tui（国进民退）”. It literally means that that
the state (Guo) is stepping forward, but “the people” (Min) are pushed back. Although this
1
Especially when they are integrated and upgraded into bigger mines, as what happened after the nationalization.
famous term was also used to describe similar changes in some other industries, such as in the
steel industry, but the changes in the coalmining industry were the most dramatic, and the
government took more explicit actions. Therefore, the nationalization of the coalmines became
the center of the debate of nationalization. In essence, this debate is about the justification of
state owned enterprises in the almost fully capitalist economy of China.
Admittedly, people have been complaining a lot about the bureaucratic, slow and cold services of
the SOEs, but it is generally acknowledged that theythey have better social responsibility records.
That is because the SOEs are believed to have less incentives of making profits, and can act more
in accordance with the interest of the society. At least they are less likely to break the laws for
profits. This is one of the major grounds for the supporters of SOEs in China. This partially
explains why the government try to rely on nationalization to reduce the death rates. It had tried
to regulate the private bosses, but never succeeded. Finally it gave up and
[Views against nationalization]
However, not everybody agrees with this conventional idea, especially some economists
For the advocates of privatization, nationalization is unacceptable. Their basic logic is that,
because of some intrinsic defects, state-owned enterprises must be inefficient compared to
private ones, thus their survival can only rely on the monopoly market power granted by the
government. They regard state-owned enterprises as awful zombies from the old planned, and
are gue that the SOEs are to blame for all the bad things happen around China. Naturally, they
denounce the mergers of small private mines dominated by local governments as robbery. The
article “The Dreadful Coalmine Reform" in <Cai Jing>, a famous market-advocating business
journal, is a good example of the prevaling hostility towards nationalization. It begins with a
metaphoric description of the biting winter of Shanxi province. 2 Then it tells us a classical story
of how the decent, legitimate businessmen got their hard-earned money robbed by the greedy
government in the name of public interest.
But their critics on SOEs go further than the efficiency side. They even deny the conventional
idea that the SOEs works better in labor protection. They argue that SOEs are also agents that try
to maximize their own privileges, which is even worse than maximizing the profit, because they
will end up with both loss of social responsibility and inefficiency. For example, Zhang
Shuguang 3(2010) argued that the nationalization in Shanxi was “repelling against the 30 years’
market reform”. He claimed that coal mine safety had nothing to do its ownership. He doubted if
the large coal mines were really safer, claiming the large coal mines could hide casualties easier.
4
He quoted (without announcing sources) data to prove that the state owned mines was not safer
“Shanxi is covered with snow. But this time, it is no longer a harbinger of peace and harmony.” The Dreadful
Coalmine Reform, Cai Jing, April 2010.
3
He is a member of the Economic Research Institute of the China Social Science Academy, and a member of the
Beijing Tianze Economic Research Institute, a famous pro-privatization and pro-lassie-faire scholar.
4
Did he imply that it is easier to bury dead bodies in a large mine’s tunnels without being discovered? Well, the
authors of this paper think that is a brilliant idea. Unfortunately, large coalmines usually attract more monitoring.
2
than private mines. However, there is plenty of evidence showing that the state owned mines
overwhelms private ones on safety, both in total number of accidents and casualties, but in
deaths per million tons as well. He then concluded that the coal bosses’ images were “defiled” by
the government.
Somehow, the term “Guo Jin Min Tui” has some kind of democratic sense, for it reminds people
of a picture of helpless citizens facing a brutal, leviathan-like bureaucratic system. Many free
market advocates are happy to strengthen this impression. However, as some left wing critics
point out, this term itself is a trick. As Zhang (2011) and Wei(2001) argued, this is a fake
question. First, there were no evidence that the SOEs taking up a larger portion of the economy.
In fact, the portion of state owned assets keeps dropping, so what happened in the coal industry
is more like a single case. Second, “Guo Jin Min Tui” implies that the contradiction is between
an expanding government and the whole people, but in fact the nationalization happens between
the government and the coal bosses. Clearly, the coal bosses cannot represent the people.
Facts to be clarified
But both parties seem to forget the trigger of the nationalization: the horrible safety and working
condition of the coalmines in China, and the tragedies that filled out the screens.
Specification of the Model
In our regression model, our dependent variable is a “deaths per million tons”, which is
measured as a one death per one million of coal produced within each province per year. There
are four dependent variables: “nationalization rate”, “coal yield”, “investment” levels, and “gdp
per capita”. We take natural logs for deaths per million tons, coal production and investment. But
we don’t take logs for nationalization rate, because percentage change of a ratio will be
misleading. The model is :
deaths per million tons= β0 + β1lnationalization+ β2lncoalyield+ β3investment+
β4gdppercapita+u
We expect negative sign on nationalization rate, because we argue that state ownership would
better provide organizational ties and increase safety regulations among different small private
coal-mining companies. This, in turn it would positively affect safety conditions and death rates
would decrease. Next, we also claim that increase in production of coal would decrease death
rates. First of all, increase in coal production we interpret as an increase in the scale of
production, which might be achieved through the state merge of different private enterprises,
which otherwise difficult to significantly increase production for separate small coal producers.
Thus, again better communication and optimization among small entities would create better
conditions to some sort of large corporation, which would control solid market share, and
therefore, would decrease death rate. At the same time, we need to control for some additional
What is more, since the number of workers is larger, it will be hard to block the information when so many people
get evacuated when accidents happen—unless you bury them all in the tunnel, too.
exogenous factors as well. We hypothesize that increase in investment levels into region would
increase death rates. We come to this conclusion, because we think that increase in investments
into regions would demand more coal production, which causes an increase in large number of
small coal producers, which will maximize profits and will not obey safety rules. Therefore, we
expect positive sign for “investment” variable. Finally, we expect a negative sing on “gdp per
capita”. So, if gdp per person would increase in a particular province, people would be able to
switch to more environmental-friendly sources of energy, rather than relative cheap coal. Thus, it
could cause a low demand in coal in average, which would lead to a decrasae in death rates.
Data
We use provincial panel data of death rates from 2000 to 2015.
The dependent variable is the indicator of coalmine safety. We choose the most commonly used
measurement, deaths per million tons of coal produced. They are published in the Statistical
Yearbooks of most major coal producing provinces after 2006. Death rates before 2006 could be
found in the Yearbooks (different from the Statistical Yearbooks because they are published by
different agencies…) of each province. Sometimes only total numbers of deaths are provided,
and there are missing data. We take average of previous and next years when available. If the
gap is too wide, then we leave the blanks as they are. 5
The indicator of nationalization is the portion of a province’s coal yield that is produced by the
state owned “backbone” (key) mines. This number is calculated based on two figures. First, the
total coal yield in each province, which can be found in both the Yearbooks and the Statistical
Yearbooks of these provinces, sometimes in the annual social and economic development reports
of each province. Second, the coal produced by state owned backbone mines, which can be
found in the China Coal Industry Yearbooks. The backbone mines are major coalmines that used
to be directly controlled by the central planning system. They are the base stones of those large
state owned large coal groups who take over private mines. There are other national owned
mines, but usually smaller, and a lot of them have been rented to privates, so they are not that
“pure” state owned. The higher the portion is, the further the nationalization had gone.
Other independent variables, such as GDP per capita, fixed capital investment are available in
the Statistical Yearbooks and sometimes in the annual social and economic development reports
of each province.
There are 26 major coal producing provinces in China. We exclude inner Mongolia, where most
mines are on the surface and generally have nothing to do with coal mine accidents. We also
include some other provinces that we have relative small coal out puts, or the data is unavailable.
In the end, we keep 13 provinces. The top 8 coal out put provinces are all included. In fact, they
5
In the original data set, all data calculated by the author are colored in RED.
take up most of the coal out put so this omit will not cause severe problems.
Figure 1Top 8 Coal Producing Provinces in China. None of them is omitted.
We run general OLS model with “Death rates” with our dependent variable, and four
independent variable: the portion of coal produced by SO backbone mines, aggregate yield
amounts within that province, level of “investments” flown in province, and the nominal “GDP
per capita”, which is also provincial data. Thus, our model is now can be expressed as
ln deaths per million tons= β0 + β1nationalization+ β2lncoalyield+ β3investment+
β4gdppercapita+u.
We take logarithms of the following variables: “coalyield”, “investment”, “gdpcc” which will
give us elasticity measurements, and so we can better interpret our estimates, “stateowned”
variable remains linear.
After running our regression model on “death rate” in STATA we have obtained the following
results:
Source
SS
df
MS
Model
Residual
241.651662
101.947486
18
147
13.4250923
.693520315
Total
343.599148
165
2.08241908
lndeath
Coef.
coalyield
stateowned
gdppc
dum_yr2
dum_yr3
dum_yr4
dum_yr5
dum_yr6
dum_yr7
dum_yr8
dum_yr9
dum_yr10
dum_yr11
dum_yr12
dum_yr13
dum_yr14
dum_yr15
dum_yr16
_cons
-.0000287
-.0230291
.0000119
-.1143819
-.3096231
-.3697606
-.7490445
-.6755165
-1.030884
-1.516257
-1.810558
-1.988338
-1.98538
-2.464733
-2.782451
-2.872063
-2.712776
-2.895409
3.266821
Std. Err.
3.65e-06
.0027393
5.69e-06
.3926193
.3929734
.3937412
.3950282
.3868222
.3829109
.3806035
.387081
.3880484
.4040717
.4092419
.424083
.4262841
.4313907
.530365
.3180899
t
-7.87
-8.41
2.10
-0.29
-0.79
-0.94
-1.90
-1.75
-2.69
-3.98
-4.68
-5.12
-4.91
-6.02
-6.56
-6.74
-6.29
-5.46
10.27
Number of obs
F( 18,
147)
Prob > F
R-squared
Adj R-squared
Root MSE
P>|t|
0.000
0.000
0.038
0.771
0.432
0.349
0.060
0.083
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
=
=
=
=
=
=
166
19.36
0.0000
0.7033
0.6670
.83278
[95% Conf. Interval]
-.0000359
-.0284426
6.99e-07
-.8902893
-1.08623
-1.147885
-1.529712
-1.439967
-1.787605
-2.268418
-2.57552
-2.755212
-2.78392
-3.273491
-3.620537
-3.7145
-3.565304
-3.943534
2.638201
-.0000215
-.0176155
.0000232
.6615255
.466984
.4083639
.0316233
.0889344
-.2741628
-.7640956
-1.045596
-1.221464
-1.18684
-1.655976
-1.944364
-2.029627
-1.860247
-1.847284
3.895441
lndeath
Coef.
coalyield
stateowned
gdppc
dum_yr2
dum_yr3
dum_yr4
dum_yr5
dum_yr6
dum_yr7
dum_yr8
dum_yr9
dum_yr10
dum_yr11
dum_yr12
dum_yr13
dum_yr14
dum_yr15
dum_yr16
_cons
-.0000287
-.0230291
.0000119
-.1143819
-.3096231
-.3697606
-.7490445
-.6755165
-1.030884
-1.516257
-1.810558
-1.988338
-1.98538
-2.464733
-2.782451
-2.872063
-2.712776
-2.895409
3.266821
Std. Err.
3.65e-06
.0027393
5.69e-06
.3926193
.3929734
.3937412
.3950282
.3868222
.3829109
.3806035
.387081
.3880484
.4040717
.4092419
.424083
.4262841
.4313907
.530365
.3180899
t
-7.87
-8.41
2.10
-0.29
-0.79
-0.94
-1.90
-1.75
-2.69
-3.98
-4.68
-5.12
-4.91
-6.02
-6.56
-6.74
-6.29
-5.46
10.27
P>|t|
0.000
0.000
0.038
0.771
0.432
0.349
0.060
0.083
0.008
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
0.000
[95% Conf. Interval]
-.0000359
-.0284426
6.99e-07
-.8902893
-1.08623
-1.147885
-1.529712
-1.439967
-1.787605
-2.268418
-2.57552
-2.755212
-2.78392
-3.273491
-3.620537
-3.7145
-3.565304
-3.943534
2.638201
-.0000215
-.0176155
.0000232
.6615255
.466984
.4083639
.0316233
.0889344
-.2741628
-.7640956
-1.045596
-1.221464
-1.18684
-1.655976
-1.944364
-2.029627
-1.860247
-1.847284
3.895441
(to be revised)
Conclusion
Nationalization do have a positive effect on improving mining safety.
For every 1% increment in the nationalization rate, the deaths per million tons of coal produced
drops by 2.3%
If the neoliberal advocating economists really cares about human rights as they claim, then they
should applaud the nationalization instead of scolding at it.
(to be completed)
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