Political Connection, Government Patronage and
Firm Performance: Evidence from Chinese
Manufacturing Firms
Bei Qin1
IIES, Stockholm University
May, 2012
Abstract: The paper tests whether politically connected firms receive preferential favor
from government, as measured by capital investment from central government and subsidy.
Matching the working experience of top leaders from the State Council (Chinese central
government) and Central Committee of Communist Party of China in power during 19982007 with the panel data of manufacturing firms in China, I got a set of firms and their
political connection. Using firm fixed effects estimator, I exploit the variation for the same
firm over time to get clear identification of the political preference. My results show that firms
connected with one more top leader from State Council tend to get more subsidy, amounting
for 9 percentage points more than the average. Firms connected with one more leader who
holds positions in both State Council and Central Committee tend to obtain 21 percentage
points more state capital than the average, and then gain 2 percentage points higher markup
than the average. And when extra state capital brought by the connection kicks in, other
domestic capitals are crowded out. Firms with more employees, but lower sales and lower
profit are more likely to receive more state capital given being connected, while firms with
higher sales tend to obtain more subsidy. When extra state capital and subsidy enter the firm,
they do not seem to improve firms performance, in terms of sales and profit.
JEL code: P16 P48
“From 1989 to 2002, China was led by a group of individuals imbued with heavy urban
biases in their views of economic development and with a strong industrial policy conviction..... They followed a typical career path in a communist system-first serving as chief
technicians and engineers at large SOEs and then ascending through the bureaucracy.”
—– Yasheng Huang, 2008
1 PhD
student at Institute for International Economic Studies, IIES. Stockholm University. I thank Jakob
Svensson, David Strömberg, Masayuki Kudamatsu; Frederico Finan, Nancy Qian, Philippe Aghion, Tao Zhang,
Zheng Michael Song, Maria Perrotta, Jinfeng Ge, Shuang Zhang, Shengxing Zhang, Konrad Burchardi, Pamela
Campa, Nathaniel Lane, Abdulaziz Shifa for the great comments and suggestions.
1
1
Introduction
Substantial literature in political economy has shown that political leaders use their power
to grant economic favors to connected firms(Fisman ,2001; Johnson and Mitton, 2003;Faccio,2006). Most of the literature studies the political connection as the personal connection
between politicians and specific firms, either via cronyism or shareholding or managers, and
often suggests that the diverted resource are used inefficiently, Khwaja and Mian(2005) for
example. The political connection, however, besides with specific firms, could be extended
to a group of firms sharing similar characteristics.When top leaders start their influential positions, not only firms but also the industries in the cities they arose from will gain more
attention from both the leader and the public. Political leaders may also show favor to the
firms in the same industry and city they ever worked for, which is the main question the paper
will answer.2
Existed studies of political connection usually deal with, at least institutionally defined as,
democrat countries, and the trade off happens between politician accountability and private
(pecuniary) benefit. Under the autocracy, without election system, accountability is thought
to be hardly hold, which thereby fades the development. How much the political connection
will affect the economy activity under an autocracy? Is it possible to discipline the politician
behavior towards to what people expect? The paper explores the evidence from China, an
autocrat developing country for answers to the above.
Taking advantage of a historical phenomenon in China and using the variation of top leaders in and out of power, the paper studies the impact of political connection on the resources
from government to firms, and the second order effect, impact on firms’ market power. The
paper defines that firms are connected with a top leader if they are in the industry and the
city the leader ever worked for and the leader is in power in the year, so the political connection for one specific firm will vary across years. Top leaders are defined as individuals
holding position high as or above the minister level, while the resources are measured by
state capital invested from central government and subsidy, the market power is measured by
the product markup. Before economy reform(1978-) in China, firms are all publicly owned,
de facto controlled by the central government (State-owned or controlled Enterprises, SOEs)
or local governments (Collective Enterprises) . It was not until the adoption of the Company
Law in 1994 that a distinction is made clear between firms and government units under the
bureaucracy. Before that, it was common to see people interchanging their jobs between
government offices and firms at the time. Since industrial sector is the central part of the
reform task, more and more individuals with working experience in industries are selected
2 In
the firm data used in the paper, firms top leader actually worked for only acocunt for 0.1% which is a
very small group so I focus the discussion on a broader group with more general characteristics, firms in the
industry and city top leader ever worked for.
2
into governments when the economy reform initiated. This is how the situation described in
the beginning came into being, and the trend even continued after 2003. In the data of the
paper, 153 out of 295 top leaders from 1998-2002 term, 119 out of 280 from 2003-2007 term
of central government and party organ indicate working experience in firms ever.
Once the individual enters the government, she/he will follow the Chinese officers promotion pattern: work in the local/city government for years, promoted into provincial government if perform well, and years later further promoted into central government if performed
well at provincial level. Then they finally get the chance to be promoted into minister or
above level positions. The promotion process from local to central government is usually
very long(more than 10 years) and the appointment of top Chinese leaders is actually a top
side down process, suggesting that individual top leader’s in-and-out of power is exogenous
to one specific firm. It provides the main identification for the paper.
Another endogeneity concern is the unobservable firm characteristics, which may endogenously determine their possibility of being connected with politicians and at the same
time obtain more resources from government. I collect a set of politician data which cover
the CV information of 350 top leaders in power during 1998-2007 from Central Committee
of Communist Party of China and economy related offices in State Council(the central government in China), and then match with the panel data of manufacturing firms to get a set
of firms and their political connection. By exploiting the panel data via firm fixed effects
estimator, I can reduce the unobservable firm characteristics concern, and use the variation
for same firm across years to get a clean identification.
Four mechanisms why leaders favor connected firms are suggested in the paper: social
networking, information view, reputation building and bribery view. When the politicians
arrive at the top level of the bureaucracy, connected firms might beat others in fight for more
resources from the government since they can access to the decision makers easier, given
other conditions equal. The mechanism is the so called social networking. By information
few, firms with higher quality tend to receive more resource when connected since the leader
knows them well, if the leader aim to maximize the government investment return. The public would owe some credits to the leader when observing places they ever worked for develop
well, and the public is also more likely to notice the good performance in places top leaders ever worked. To build up reputation, leaders would divert more resource to where they
ever worked to promote their development. Social networking view is established once the
political connection is observed to bring more resource to firms. If the information view established, we would observe good firms(firms with higher sales, profit for example) tend to
get more resources from governments; if the reputation building functions, we would observe
resources going to where the public expect, for example, large firms with more employees
and higher sales. Besides checking the effect of political connection on resources from government, the paper further checks what type of firm characteristics predict more resources
3
obtained given being connected, measured by the employment, sales, amount of capital and
profit. In lack of the bribery measurement, the paper is not able to test the bribery view.
Few studies of political connection track down the usage of the extra resources gained by
the connection. Limiting sample to firms who do not have connection in the beginning of
sample period but get it later, I check years before and after they switch into the connection
to explore the heterogeneous effect of political connection as well as the resource utilization
after more resources are rendered to firms.
The paper finds that one extra leader from State Council connected with the firm will bring
9 percentage points more subsidy than the average level for the firm, while being connected
with one more politician who holds positions in both State Council and Central Committee will bring the firm 21 percentage points more state capital than the average level, and
increase the firm’s markup by 2 percentage points. The results show that firms with larger
employee size but lower yearly sales and lower profit tend to get more state capital, and but
after firms get more state capital, none of the firm characteristics above get statistically significant change. When similar analysis is applied to subsidy, firms with higher sales predict
more subsidy given being connected with top leader from State Council, and profit increase
only in the year getting connected but decrease after that. The extra resources brought by
political connection has no long term effects on any firm characteristic The results lend more
support to the reputation building view than the information view.
Another interesting question is, when governments invest more to a connected firm, how
will other investors react? By checking the capital structure change since connection year,
the paper finds when extra state capital brought by political connection kicks in, collective
capital, domestic private capital, legal person capital, and Hongkong & Macao capital are
crowded out, while foreign capital maintains. However, 2 years after the connection, state
capital begin the decreasing trend while domestic private capital start increasing. Furthermore, the total amount of capital does not increase but actually decrease with the extra state
capital. It at least suggests that getting extra state capital does not signal as an attractive
investment for other investors.
The rest of the paper is organized as follows. Section 2 describes the background information of the government resources, enterprises and politicians, and summarizes the mechanisms through which the political connection works. Section 3 describes the data set building
and the main econometric methodology used in the paper. Section 4 presents the basic results
of political connection on the state capital, subsidy and markup, while section 5 explores
other firm characteristics change with the connection. Section 6 checks the heterogeneous
effect of the political connection and the firm performance after they get more resource from
government via the connection. Section 7 concludes.
4
2
Background of politician and firms in China
2.1
Firms and government resources
As a developing country, China has been allocating plenty of resources to support firms, including direct investment, subsidies, special funds, tax breaks etc. To have a rough idea of
how much the part of money is, figure 1 plots the total subsidy and additional working capital given to enterprises as ratio of the yearly total government revenue. The yearly subsidy
to enterprises, although decreasing across years, is still big, accounting for 0.5-3 percentage points of yearly government expenditure; similarly, working capitals to enterprises also
decreases over years, accounting for 0.05-0.5 percentage points of yearly government expenditure; however, the innovation funds to enterprises still remains 2.4-3.7 percentage points of
yearly expenditure. Government investment and subsidy to firms (including the innovation
funds)3 are the main focus of the paper.Moreover, given the choice to look at top leaders,
I will only analyze capital flows from the central government. Because there is no detailed
information on subsidy source, subsidy studied in the paper is the total amount from all governments.
Capital from central government is named state capital, and falls into two types: capitals
towards the central enterprises and capital towards other regional enterprises4 . The investment to central enterprises are directly managed by the State-owned Assets Supervision and
Administration Commission(SASAC) in the State Council, whose core mission is to carry
out the government’s functions as investor and owner of state assets. Another type of regular investment planned by the State Council are named as central government within budgetary investment, amounting to 2-3 hundred millions RMB(i.e. 30-45 million US dollars)
per year, which is managed and decided by the National Development and Reform Commission(formerly State Planning Commission and State Development Planning Commission
before 2003), while Ministry of Finance issues the funds. The budgetary investment is kind
of project investment funds, and there is no clear rules guiding which firms they should invest
but principally it functions as industrial and fiscal policy for the macro-economy adjustment.
There could also be irregular investment out from the offices list above.
There are many kinds of subsidies announced by various State council offices, and corresponding to each subsidy, there is a specific title for it. As discussed in Qin(2004), there are
3 For
individual firm, granted innovation funds is grouped into government subsidy.
Chinese economic system classifies enterprises according to their level of administrative supervision. Central enterprise is the one with its control rights-managerial appointments, asset disposals, strategic
directions-of the firms and some or all of the income rights reside with the central government. Regional enterprise is one where the same control and income rights belong to a regional government.(Huang, 2004). For
enterprises with more than one public share holder(central or local governments), the administrative supervision
goes with the biggest investor. If the enterprises are completely private or foreign invested, the relationship of
administrative supervision is decided by the level of government they registered with.
4 The
5
three types of subsidies toward SOEs : a) subsidies to help sustain and revive loss-making
SOEs b) subsidies to help privatize or restructure SOEs c) subsidies provided to foster key
SOEs. In more detail, if a SOE wants to layoff extra employees (resulted by the old command economy system), wants to reconstruct its production and business, plans to diversify
its ownership, or wants to purchase advance technology or replace the old machines, it can
apply for corresponding titles of subsidy to realize the plan. For subsidies to laid off extra
employees, Ministry of Human Resources and Social Security(formerly Ministry of Labour
and Social Security before 2003) is the office firms would interact with; subsidies for introducing advance technology and production machines, or science creative production projects
are managed by Ministry of Science and Technology. Other offices that manage subsidies
includes National Development and Reform Commission, State-Owned Assets Supervision
and Administration Commission, State Planning Commission, State Development Planning
Commission, State Commission for Economic Restructuring, Ministry of Finance as well as
Ministry of Commerce. Actually, not only SOEs but also regional Chinese firms(collective
firms) as well as foreign invested firms are qualified to apply for such subsidies. Figure
2 shows the share of subsidies directed to foreign enterprises, increasing across years and
amounting to 26% of the total subsidies to enterprises by central government. In the abovescale firm(with yearly sales larger than 5 million RMB, i.e. $0.9 million) data used in the
paper, all type of ownership firms report non-zero subsidy. Usually, firms that have administrative supervision relationship with higher level of government (closer to central government) respond more to the subsidy announced by the State Council offices. At the same
time,there are also many subsidy titles from local governments firms can apply for. Given the
vertical style of bureaucracy in China, we cannot deny that the top leaders from State Council
or Central Committee can also affect the local resource allocation decision.
From above description, we see that the state capital investment and subsidy allocation
are the responsibility of the related State Council offices. However, China is lead by the
unique party, Communist Party of China, and Central Committee is the top authority within
the CPC, leading all the work of the Party and represents the CPC outside the Party. Although
there is no clear channel for the party to intervene the economy directly, it is reasonably believed that Central Committee has the power to influence the decisions of State Council since
most important social and economic policies are decided by CPC, and CPC holds the nomenclature role for personnel appointments in State Council. According to the duty of Central
Committee members, legally, we should not observe the impact of the political connection
with a Central Committee member on the resources from central government to firms, but
since the Party influence is not clear, I leave the question open.
6
2.2
Top leaders arising from firms
By definition in this study, a firm is connected with a political leader in power if the firm lies
in the city/county and industry where the politician ever worked for. The politicians the paper
focuses on include the heads of the State Council and the offices mentioned in section 2.1,
and the politicians from Central Committee, in power between 1998 and 2007. In sum:
1. Members of Central Committee of the Communist Party of China except ones promoted from the army, since the administration system of the army is separated from
the normal government in China.
2. Heads of departments in the State Council(the central government) which might be
involved in the government decision of resources allocated to firms. Premiers, ministers, directors, secretary general or vice ones from National Development and Reform
Commission; State-Owned Assets Supervision and Administration Commission; State
Planning Commission; State Development Planning Commission; State Commission
for Economic Restructuring; Ministry of Finance; Ministry of Commerce; Ministry of
Labour and Social Security; Ministry of Human Resources and Social Security; Ministry of Science and Technology.
Political leaders of above two categories can be overlapping, that is, a leader can be a head of
the chosen office in State Council and at the same time a member in the Central Committee
of CPC, and the leaders that hold position in both are usually more powerful. Among current
top leaders, many of them have been working in SOEs in the past. It is worth notice that
many of the SOEs they worked for have been re-constructed in both ownership and corporate
governance aspects today(Clarke, 2003). A typical career pattern of such leader is, firstly
they were re-assigned to local government from the firms, subsequently as a common case,
they would serve in the local governments, possibly county to provincial level, for years and
were evaluated according to their performance there, and then if highly valued, they would be
promoted to the State Council and finally got the chance to be selected into the top positions,
or be elected into the Central Committee.
To become a member of Central Committee, the politician would need to win the voting of
the Party National Congresses composed by thousands of Party members across the country.
The heads of departments in State Council are nominated by Premier each term, and passed
by the Party, while the vices are elected in a more flexible fashion and not restricted by the
term timing. It is impossible for one individual firm to lobby the various persons who are
involved in the assignment. Based on the complicated and long screening process before the
politician gets to the top positions, I would argue that whether and when the politicians come
into power is independent of the individual firms they ever worked for. Furthermore, for firms
7
with more than one directly connected or indirectly connected politicians, it is even harder to
predict the total number of politicians they connected with in certain year.
Although when and whether individual politician are able to be in power are arguably
exogenous, industries in cities where the politicians ascend from may be important for the
(local or national) economy in certain period of economy development, and thus the government want to enroll them in. The study will try to take the aspect into account by controlling
for interaction terms of industry and year dummies, of province and year dummies.
2.3
Why such political connection works?
Four mechanisms may make firms with political connection favored by the government.
1) social networking. The previous colleagues or friends the leader got to know may
still work in the same industry and city where the leaders were years ago. Therefore, the
maintained social capital make such firms easier to access to the leaders.
2) information view. Similar with the related lending discussed in La Porta, Lpeiz-desilanes and Zamarripa(2003), leaders may know more about the firms in the industries in the
cities they ever worked for, and thus better assess the cost-benefit of the investment projects.
The information view will predict the resources are more likely diverted to higher quality
firms and generate higher return, if leaders want to make an efficient investment for the government.
3) reputation building. Once the leader became an important, well-known figure, industries and places they ever worked for will also get attention from the public. If firms there
develop well, people or superior decision makers will nevertheless owe some points to the
leader. Such good image would do good to the leader for her/his future career. Similarly,
given the public attention, if the top leader acts in these places, which will be quickly noticed
by people. Therefore, the leader might want to divert more resource to the industry/city or
county so as to push up their development, or more immediate effect, enlarge the employment.
4) bribery view. Firms interchange private benefit with the top leaders for the resource
from governments.
When firms get connected with top leaders, more resources from government can be
immediately observed, and the market power of the firm will be the second order effect. When
governments show preferential treatment. to some firms, it may strengthen firms competitive
ability comparing with exist firms, deter new entrance and thus increase their market power.
So market power,measured by markup is also checked in the paper.
8
3
3.1
Data & Methodology
Data
Firm data is from the Annual Surveys of Industrial Production, collected by the Chinese
government’s National Bureau of Statistics every year since 1998. It is a census of all manufacturing firms with more than 5 million RMB, approximately 0.9 million US dollars yearly
sales revenue in mainland China, and firms with all types of ownership are included. The
panel data start with over 160,000 firms in 1998, with new firms joining every year, and in
2007 in total over 330,000 firms are included. The surveys cover the very detailed information
of the firms: names, addresses, age, ownership, capital amount and their sources, employees,
wage, equity, tax , subsidy as well as other financial statistics. The paper limits the leaders
under study to the top leaders in central government and Central Committees, assuming the
influencing power of top politicians decreasing along down the government cadre. Therefore, in the study, only firms with the administrative supervision relationship to the central,
provincial and prefecture governments, at least 1 year during the data period, are chosen, i.e.
86827 distinct firms with ten years panel from 1998 to 2007, 406774 obs. in total.
The politician data is collected by the author: first, the name list of members of CPC
Central Committee, top leaders of State Council, and (vice) Ministers/ secretaries of State
Council departments that might interact with firms in terms of government resource allocation
in power during 1998-2007 are found, in total 350 politicians; next, politician’s individual
CV are collected from Dictionary of Central Committee Members of Communist Party of
China 1921-2003, www.baidu.com, www.renwu360.cn/, www.xinhuanet.com/. Information
of individual characteristics, education, majors, working experience, province/city/county
ever worked in are all collected.
3.2
Matching Politicians to Firms
Among the 350 politicians, around 200 of them indicate ever working in firms, but only 173
politicians state clearly the firm names they ever worked for, and thus 277 distinct firm names
are got (not all of them are manufacturing firms). I then use the 173 politician records to build
up the political connection with firms from the firm data.
I check out the two digit industry code firms where the politician ever worked belong
to and the corresponding city/county. For firms exist in the firm data, I use the industry
code given there(the matched firm can have more than one industry code across year, all
are recorded); for firms not found in the sample, I first check their online profile to get the
industry they claim; for firms that do not state that clearly, I search in the manufacturing
firm data for the similar firms by products and take the top three frequently stated industry as the industry for them. Therefore, one firm could correspond to more than one industry.
9
Some politicians from Central Committee are at the same time in provincial governments, the
city/county associated with them are regarded as connected if it lies inside the same province
where the politician assumes the position; otherwise, regarded as not connected. The indirect connection is thus coded by the pair of city/county + industry, which finally is shaped
into a table with number of politicians of each kind across year associated with each pair
of city/county + industry. It finally gives out 194 city/county + industry connection pairs.
When merging with the manufacturing firm data, 170 pairs associated with 131 politicians
are matched. In total 10,287 out of 86,827 distinct firms ever had political connection during
1998-2007
3.3
Summary Statistics
Table 1 presents the summary statistics for the variables of interest for the firm data and
matching with politicians. State capital (capital from central government), and subsidy are the
measures of preferential treatment from government, and was obtained by taking logarithm
of the amount of yearly state capital and subsidy received5 .As we can see, that the state
capital for one single firm can be as large as 2000 billion RMB and yearly subsidy can be
large as 13.8 billion RMB. Markup is the measurement for firms’ market power, calculated by
dividing the total manufacturing products profit by the total cost of manufacturing products
(obtained by minus total profit from total sales). The range of the markup is huge, from -199.1
to 16826.4. To give a picture of the difference across different type of firms, and also for later
checks the paper will do, other firms’ characteristics, total amount of employee, yearly sales,
profit, total number of actual received capital as well as the year the current firm established
are also described in table 1.
Panel B and Panel C separate the sample into never connected and ever connected firms.
Firms never and ever indirectly connected have closer mean value and standard deviation of
and subsidy, but firms ever connected have much lower mean of logarithm of state capital.
Firms that ever had connection show much lower markup than the ones never had, while they
also have much lower standard deviation, .39 against 28.9. In terms of the market power,
firms ever connected are more similar with each other. For type of firm characteristics, firms
ever connected and never show similar mean values and standard deviations for logarithm of
employee amount, logarithm of yearly sales and total received capital. Firms that ever had
political connection have mean profit three times more than ones never connected. To have a
better control group, I will limit the analysis to firms ever had connection during 1998-2007.
That a firm was ever connected with a politician does not mean it has political connection
every year because the politician can be in and out of the top positions across years due
to various reasons. The number of connected politicians from both Central Committee and
5 The
two measures are logarithm of the number plus 1 so as to get rid of the negative infinite value.
10
State Council ranges from 0 to 2 for a given firm in a given year; the number of connected
politicians from Central Committee ranges from 0 to 9; the number of connected politicians
from State Council range from 0 to 3.
Table 2 shows all the politicians statistics across year: the in/out of power status of the
131 top leaders across year. For the three type of politicians, connected politicians from both
Central Committee and State Council are always the fewest, the number for ones from Central
committee is the highest, and ones from State Council only account for around one fourth to
half of the ones from Central Committee. The data period covers two official terms for the two
type of politicians respectively, which are 1997-2002 and 2002-2007 for Central Committee
of CCP; 1998-2003 and 2003-2008 for State Council. Therefore, year 2002-2004 should be
the years when most changes were expected and observed. From table 2 we can see that year
2002 and year 2003 are the most volatile years, and we can also tell less leaders from the
State Council change compared to the ones from Central Committee. Departments in State
Council concerning the economy usually require professional knowledge and experience so
that the replacements of the leaders out of these offices are less frequent.
Politicians could also be placed in the positions out of the term switching years. Such irregular appointments are more likely to happened with State Council due to contingent needs
and more likely to happen with the vice positions. The irregular appointments in Central
Committee can happen only when some leaders are out of the board due to unpredicted reasons(sick, dead, checked/arrested due to malpractice, sick etc.) and in order to maintain the
fixed number of CC members, new politicians are elected to fill out the absence. To explore
the change of different type of politicians across years, table 3 list the number of change each
year for connected politicians, which are grouped by reason of change. Among the reasons,
dismissed/arrested/fled away due to malpractice, term limit, retired can be regarded as totally
exogenous shock, which account for 8% of the total variance. Given the selection process of
the top leaders in national government, plus the shock of being replaced out of offices, the
amount of politicians firms connected each year is arguably exogenous to firms unobservable
characteristics.
3.4
Methodology
The core difficulty in identifying the effect of political connection on preferential treatment
received from government is the endogeneity concern: some firms are important and influential to the country so that politicians ever worked there are more likely to be promoted and
at the same time more government resources are directed to the firms; or such firms have received government benefit for long time, and would further lobby the related decision makers
to elect the politicians connected with them into the position. Given this concern, a convincing estimation strategy is to exploit the variation for the same firm across year by using firm
11
fixed effects ( Khwaja and Mian (2005)):
The main estimator in the analysis will be fixed effect estimator for panel data,
yit = ci + λt + Political_connectionit τ + Industryit ∗ λt
(1)
+provincei ∗ λt + Gi ∗ λt + Ownershipit ∗ λt + εit
where yit are results of interest, state capital, subsidy and markup of firm i in year t.
yit can also be other firm characteristics variables in auxiliary regressions and checks. The
reason I do not scale the two measure by yearly sales or total number of employee is that
either sales or employees can also be the result of the political connection. Similarly, since
any individual firm characteristics related with its performance can affected by the political
connection the firm associated, I exclude them from the regressions. λt is the vector of
time period dummies, subscript t denotes year 1998 to 2007.Political_connectionit is the
vector of political connection indexes for firm i in year t, the key independent variables of
interest: # o f CCSC politicians, # o f CCSC politicians, # o f SC politicians. CC denotes
Central Committee, SC denotes State Council, and CCSC denotes Central Committee and
State Council. Since one politician can be in and out of office across years, the number of
politicians connected changes across years. ci is the firm fixed effect representing unobserved
firm characteristics.
Industryit ∗ λt , provincei ∗ λt , are interaction terms between industries(2 digit industry
code, 40 categories), provinces(24 provinces for ever connected firms) and years respectively,
representing the provincial trend and industry trend that may affect the resources allocation
decision and the promotion of politicians ascending from that industry and region. The fact
that the politicians were selected from original SOEs to governments can be because of the
industry they worked in become important for the economy at the time so that government
want some experts on that. Although the years they were promoted from firms to the government are years before the sample years, it is possible that some industries have been and
are still important for the national economy during the sample years. Similar concerns apply
to provinces.The industry trend and provincial trend in the regressions will take care of these
concerns.
Although the sample is limited to firms ever connected, the connection patterns differ
across firms. There are four categories of firms: firms switch at least twice between connected
and not connected during the sample period-type 1; firms have no connection in the beginning
of the sample period but gain it later on-type 2; firms have the connection in the beginning
but lose it later-type 3; firms have connection all the sample years-type 4 firms. It is possible
that firms in same connection pattern share some unobservable characteristics differing from
others that will vary across years. So I will try to control for the different connection pattern
trend by the interaction term between four types of firms and year dummy, Gi ∗ λt . Gi is the
12
vector of the connection pattern dummies.
According to Huang(2004), Chinese government has political pecking order: government
holds certain political preferences towards different ownership type of firms, and they are
translated into economic policies, regulatory practices, financial support decisions etc. In the
order, state-owned or state controlled enterprises are ranked first, and then collective firms
class, foreign invested firms are next, while the domestic wholly private firms are the last6
. I will control for the ownership type trend. In all the regressions, standard errors are
corrected to account for correlation of the error term across observations in different years
that correspond to the same firm and thus are clustered at firm level.
4
Main Results: Impact of Political Connection on State
Capital , Subsidy and Market Power
Table 4 presents the results of the estimation of equation (1). One more leader holding positions in both Central Committee and State Council connected with the firm in one year will
bring 21 percentage points more state capital compared with the average level of firms, and
increase the markup by 2 percentage points. One more leader from State Council connected
with the firm tend to bring 9 percentage points subsidy more than the average. Estimates for
connection with Central Committee member on all three measurements, show little effect, in
terms of both economic and statistic significance. The connection with leaders from State
Council here do not help with the state capital, small negative point estimate and too large
standard errors. The connection with leaders from both seem not to help with the subsidy.
Usually the leader holding positions in both Central Committee and State Council ranks
on the top of the hierarchy. Hence, it is not surprising to see they influence the big amount
(state capital investment) rather than the small amount(subsidy). The targeted number of
firms for state capital investment is much smaller than the one for subsidy. And compared
with the subsidy, state capital is much larger amount for an individual firm. The chance to
get more investments from central government is much lower but the amount is much bigger
than subsidy. Therefore, if a top leader plans to favor preferred firms, she/he would not
bother to check the small amount of subsidy but rather they will consider the investment. If
the decision for state capital is influential and noticeable, when the firm in question is not
eligible to obtain, a lower leader, compared with the leader holding position in both State
Council and Central Committee, would not risk to divert the capital to firms they preferred.
However, will extra capital brought by the connection come with more or better projects,
and will it boost the firm production and improve their performance in the subsequent years?
We will look into the former question in section 5 and the latter question in section 7.
6 In
total, 23 ownership types exist in the data.
13
5
Is the state capital accompanied by more projects?
Bertrand, Kramarz, Schoar and Thesmar (2007) find politically connected CEOs hire more
employees during the election periods but such employees increase does not go with more
projects or contracts. Similarly, I would ask what is the goal of more investment from central
government to the enterprises. Is it because the top leaders think the firms from the industry
and city/county they once worked for are promising by the information view, so that they
would like to build more projects there and gather the best return for the government investment? If so, the state capital increase should be associated with more projects planned by
the government. In this case, we would observe bigger production size and more profit when
they are connected with at least a leader who hold position in both State Council and Central
committee. It is also possible that the extra state capital will come with or be invested to
some long run projects and the return will be able to see only years after. For this possibility,
I will discuss in the part how firms use the extra resources they obtained in section 7.
Replacing the dependent variable with number of employees, yearly sales, profits and
total amount of capital, I re-estimate equation (1). Table 5 presents the estimates of political
connection on the above four firm practice measures. From column (1), (2) and (4), we
cannot reject the zero effect of the indirect connection with a leader holding positions in both
top authorities, which suggests the connection does not necessary come with more and better
projects that will function immediately. The estimate of top leaders from Central Committee
and State Council do have positive sign on sales and profit, which help explain the positive
effect on markup(show in section 5). It is not obvious and significant that such connection
increase sales and profit, but the combined effect is salient.
However, no statistically significant effect on firm employee, sales and profit can also be
because of the heterogeneity of the connected firms. Firms differ too much and then drive the
mean estimation down. Section 6.1 will further discuss the heterogeneous effects of political
connection on more resources, and section 6.2 limit the sample to only firms benefit from the
political connection and study the dynamic effects of connection on firm performance.
Column (3), surprisingly, presents no effect of connection with leaders from Central Committee and State Council on the total capital, negative and statistically insignificant. Why
extra state capital invested into firms do not increase the total capital? Given the negative
sign of state capital on the actual received capital, an explanation would be: when the state
capital is increased with the connection with top leaders, it at the same time crowds out other
sources of capitals, by at least equivalent or even more amount.
14
6
What characteristics predict the favor and what happens
later on?
The chance to obtain more state capital investment is small in the whole sample, and the
connection variation occurs at industry and city level, it would not be possible that all firms
within the connected industry in the city gained the preferential treatment. Certain type of
firms are more likely targeted by the leaders. By information view, firms with higher sales or
profit should be targeted; by reputation building, firms with larger employee size should be
targeted. To explore what type of characteristics helps given being connected, I use the firm
characteristic one year before getting the connection to interact with the first connected year
dummies, and firm characteristics one year before losing the connection to interact with the
first disconnected year dummies to investigate the heterogeneous effects.
To explore further what happens with the extra resources brought by political connection,
I limit the analysis to only firms who actually obtain (lose) more resources in the year or one
year after they gain (lose) political connection. By standardizing the year into ith years before
or after getting(losing) connection years and exploring the variation from different timings, I
could draw a more clear graph indicating what is going on in the timing axis. When analyze
the state capital, I construct the getting (losing) connection year timing according connection
with leaders from Central Committee and State Council; when it apply to subsidy, I construct
according to connection with leaders from State Council.
6.1
State Capital
6.1.1
Heterogeneous effects of connection with leaders from CCSC on obtaining more
state capital
Before trying to find out the firm characteristics predicting de facto more state capital when
connected, I need to identify when the increase(decrease) will happen. After check, I find the
extra resources obtaining(losing) are more likely happened in the same year getting(losing)
connection, and a much smaller change in one year after.7 So additional to equation (1)
specification, I add the interaction terms between the firm characteristics one year before
connection (disconnection) and the getting connected (disconnected) year dummies. Run the
equation (2) as the following:
yit = ci + λt + Political_connectionit τ + # o f CCSCit∗ α + # o f CCSCit∗ ×Charactit∗−1 Φ
+CCSCit 0 β +CCSCit 0 ×Charactit 0 −1 Ω + Industryit ∗ λt
+provincei ∗ λt + Gi ∗ λt + Ownershipit ∗ λt + εit
7 Results
are provided by request.
15
(2)
where subscription t∗denotes the year firms gain connection, and t 0 denotes the year firms
lose connection. # o f CCSCit∗ denote the number of connected leaders that holds positions
in both Central Committee and State Council. CCSCit 0 is the dummy for losing the connection with leaders holding positions in both CC and SC. Charactit∗−1 is the vector of firm
characteristics(employees, profit, sales, and total other type of capital)one year before the
connection, and similarly Charactit 0 −1 is the vector of firm characteristics before the year
losing the connection. We would expect to observe exactly opposite sign for the interactions
of # o f CCSCit∗ ×Charactit∗−1 and CCSCit 0 ×Charactit 0 −1 , if these characteristics sensitive
to every change of the political connection.
Results are reported in table 6 column (1). Firms with more employees are more likely
to gain more state capital in the connection year. However, yearly sales and profit predict
the state capital on the opposite way, firms with lower yearly sales and profit are more likely
to get the state capital in the the connection year. Assume one firm gets 1 leader connected
in year t∗, if the firm has 1 percentage points higher employee than the average, it will
obtain 0.12 percentage points more state capital; if the firm has 10,000 RMB lower profit,
it will obtain e − 05 percentage points more state capital than the average; if the firm has 1
percentage points lower sales than the average, it will obtain 0.08 percentage points more state
capital. The effect of the employee size is quite considerable and suggest the big weight of
the employee size in the state capital investment consideration. The level of total other capital
does not seem to help attract more investment from the central government (no statistic power
for the estimate).
When checking the interaction terms between firm characteristics and losing connection
year, except for employees, interaction terms with yearly sales and profit both show opposite
signs. It suggest that firms with lower profit or lower sales lose state capital in the year they
lose the connection. Firms with more employees still maintain the state capital when they
lose the connection. The estimates for the interaction term with the year losing connection,
except for the profit, do not have the statistic power.
The results suggest that the state capital diverted by the top leaders does not go to the
firms with obvious better performance, but the larger firms in terms of employee number.
The purpose that the top leader disposes the state capital to firms in the industry and city
they once worked for is not likely for maximizing the profit as an investor. The reputation
building view is supported here, which predicts that the leaders would want to build up a
good image for themselves by boosting the employment of the industries and cities they ever
worked. More employment and more firms exist in the place, no mater efficiency or not, will
definitely be a good image in front of the public. In fact, if the central government care more
about big firms (with more employees), such preference of the leaders is consistent with this
aim. If it is not true, the national economy will pay for the individual leaders’ reputation.
No matter what the true aim of the central planner is, state capital do go for employment
16
enlarging under the functioning of the political connection. Leave the efficiency aside, higher
employment is always the public would like to see. Hence, in an autocracy and developing
country like China, officials’ career concern will discipline the leaders behave somewhat as
the public expect.
6.1.2
Capital structure change across years around connection (disconnection)
From section 5, I do not find any increase with the total capital when state capital increase. I
will thus check what happens to the capital structure when firms obtain extra state capital due
to political connection. To check this, I limit the study sample to firms who obtain (lose) state
capital in the year or 1 year after they gain (lose) connection with at least a leader holding
position in both Central Committee and State Council, and run the following estimate.
Kit = ci + λt +CCSCit 0 −1 α1 +CCSCit∗ α2 +CCSCit∗+1 α3 +CCSCit∗+2 α4
+CCSCit 0 β1 +CCSCit 0 +1 β2 +CCSCit 0 +2 β3 + Industryit ∗ λt
(3)
+provincei ∗ λt + Gi ∗ λt + Ownershipit ∗ λt + εit
Kit denotes one of the certain types of capital: state capital, collective capital(from local
government), domestic private capital, legal person capital (capital invested from domestic
companies), Hongkong or Macao capital, foreign capital, total actual received capital.The
subscription t∗ denotes the year getting connection and t 0 denotes the year losing connection.
Results are reported in table 7. From table 7, except for foreign capital, all other types of
capital show decreasing in the year or one to two years after getting connection, and the
decrease maintained even to the year losing connection except for the collective capital. In
one to two years after losing the connection, other types of capital show increasing. To see
the pattern clearly, I plot the coefficient estimates α1 -α4 , and β1 -β3 for each type of capital
regression in figure 3.
From figure 3, we can see that the state capital level increase in the beginning of the
connection but almost fall back to the level as before the connection in two years after getting
the connection. When extra state capital kicks in, all other domestic capitals are crowded
out and capital from local government (collective capital) and domestic legal person capital
decrease all the time even till the two years after the connection year, but domestic private
capital finally go back and beyond the level before getting connection in two years after the
connection year. Hongkong & Macao capital and foreign capital has the most flat pattern,
slightly affected by the extra state capital. The most crowded out capital are legal person
capital and collective capital when extra state capital kicks in, while legal person capital can
hardly go back to the original level.
17
6.1.3
Usage of the extra state capital.
Another question the study tries to answer is how firms will deal with the extra state capital
brought by the political connection; will it be used to increase the efficiency of the production? To answer the question, also by limiting samples to firms who actually obtain more
state capital in the year or 1 year after getting connection, I replaced Kit with employees,
yearly sales, profit in equation (3),and re-estimate the model. Results are reported in table 8.
In the connection year and one year after, all the three index show decrease. The sales and
profit show increase in the two years after the connection year. All the three characteristics
show mixing signs in years around the year losing connection. However, all of these estimate
have very large standard errors to be statistically significant, and we cannot deny the zero
effects for all of them.
In sum, I did not find any statistically change on firms employee, yearly sales and profit in
the year and till two years after the firm obtaining connection with leaders holding positions
in both Central Committee and State Council, when they obtain more state capital because
of the connection. Combing this results with the result from section 5, I can conclude that
the extra state capital really does not come with more or better projects. Extra state capital at
least does not improve production size, nor yearly sales and profit.
6.2
6.2.1
Subsidy
Heterogeneous effects of connection with leaders from SC on obtaining more
subsidy
As described in section 2, many purposes are associated with subsidy, laid off subsidy, production reconstructing and innovation subsidy etc. It is difficult to predict what type of firms
are more favored before looking into the data. Revising the equation (2) a bit and I run the
following equation (4).
yit = ci + λt + Political_connectionit τ + # o f SCit∗ α + # o f SCit∗ ×Charactit∗−1 Φ
+SCit 0 β + SCit 0 ×Charactit 0 −1 Ω + Industryit ∗ λt
(4)
+provincei ∗ λt + Gi ∗ λt + Ownershipit ∗ λt + εit
The key timing in equation (4) is defined as the year t∗ (t 0 ) when the firm get connected
with a leader holding position in State Council, because we only observe such connection
help firm obtain more subsidy in section 4. Results are reported in table 6 column (2). From
that, we can observe only yearly sales get the statistically significant estimate: firms with
higher yearly sales are more likely to receive the subsidy. It suggests that leaders in State
Council seem to care more about business active firms. Other firm characteristics, however,
seem to be trivial in the subsidy decision, both the economic size and statistic significance
18
are small.
6.2.2
Usage of extra subsidy
Compared with state capital, subsidy is more like cash flow and in small amount, which is
supposed to be spent all out right after they obtain it. So the effect of extra subsidy, if there
is any, should be observe immediately after the firm obtain (lose) the connection. Revise the
equation (3), I run the estimate for the equation (5) as following:
Charactit = ci + λt + SCit 0 −1 α1 + SCit∗ α2 + SCit∗+1 α3 + SCit∗+2 α4
+SCit 0 β1 + SCit 0 +1 β2 + SCit 0 +2 β3 + Industryit ∗ λt
(5)
+provincei ∗ λt + Gi ∗ λt + Ownershipit ∗ λt + εit
Charactit denotes the firm characteristics of interest, employee, yearly sales and profit.
Results are reported in table 9. From table 9, we can see that in the year when the firm get the
connection with leader from State Council, the firm increase their profit by 23.572 million
RMB more. Given the average level of yearly profit is 28.473 million RMB, this number is
really huge, accounting for almost 83% of the average level. The yearly sales in the same
timing at most will increase by 9.6 percentage points than the average, and the employee
at most increase by 5.4 percentage points than the average(if we assume the statistic power
is enough, though it is not), it is hard to believe the increased profit is generated by the
enlarged production and sales caused by connection with SC leaders. And the number drops
dramatically in one year later and even turns to negative in two years after the connection.
Therefore, the increased profit is probably driven by the simply increased income source subsidy. Still, no sustainable effects on firm performance is found for extra subsidy obtained.
7
Conclusion
A special historical fact of the leader component of Chinese government this decade is that
many persons with working experience in enterprises are enrolled and promoted into the government offices. (Huang, 2008). Taking advantage of it, I collect the individual information
of the top leaders from top authorities of government and party in poer during 1998-2007 and
then match it with the above scale manufacturing firm survey to get a set of firms and political connection. I investigate the effects of the political connection -defined as a top leader
from the offices I choose ever worked in the same industry and city/county the firm lies- on
the preferential favor of government, measured by state capital and subsidy, as well as the
market power measured by markup.
My results suggest that firms connected, with a leader from State Council are more likely
to obtain more subsidy from governments, but it doesn’t seem to help firm get more state
19
capital. A connection with leaders from Central Committee does not show help on the two
measure of government favor nor the firm markup, somehow indicating the party do not cross
the line and intervene the government decision on allocating resource to firms. Connection
with a politician who holds position in both State Council and Central committee would
bring more state capital to the firm. Given the extra investment, I further check whether
this increased capital comes with more projects or contracts planned by the government.
However, the result does not support it.
Checking firm characteristics right before (after) the connection (disconnection), I found
that larger (with more employee) and worse performed firms(lower yearly sales and profit)
are more likely to obtain the state capital brought by the connection with top leader holding positions in both Central Committee and State Council. The fact support the reputation
building view discussed. For firms who did get more state capital, no statistically significant
firm characteristics change is found in the 1st year till 3rd year into the connection, nor in the
1st year till 3rd year out of the connection.
Firms with larger sales size are more likely to obtain more subsidy when they get connection with leaders from State Council. In the first year of connection, profit show incredible
increase but dramatically decrease after that without obviously sales or employee increase.
It suggests the extra subsidy brought by the connection actually does not bring sustainable
improvement to firms.
However, corruption channel is not discussed given the data I have cannot test the story,
but it would be an interesting topic for future study.
References:
Bertrand Marianne, Kramarz Francis, Schoar Antoinette, and Thesmar, 2007, “Politicians, Firms and the Political Business Cycle: Evidence from France” , working paper version, http://www.crest.fr/ckfinder/userfiles/files/Pageperso/kramarz/politics_060207_v4.pdf
Faccio Mara, 2006, “Politically Connected Firms”, The American Economic Review, Vol
96, No.1
Fisman Raymond, 2001, “Estimating the Value of Political Connections”, American Economic Reviews, Vol. 91, 1095-1102,
Fisman Raymond and Wang Yongxiang, 2011, “Evidence on the Existence and Impact of
Corruption in State Asset Sales in China ”, working paper,
http://www2.gsb.columbia.edu/faculty/rfisman/papers/new/transfers%20paper%20-%20March272011.pdf
Huang yasheng, 2004, Selling China-Foreign Direct Investment During the Reform Era,
Cambridge University Press
Huang yasheng, 2008, Capitalism with Chinese Characteristics: Entrepreneurship and the
State, Cambridge University Press
20
share of yearly governm ent expenditure, %
Johnson Simon, and Todd Mitton, 2003, “Cronyism and Capital Controls: Evidence from
Malaysia”, NBER working paper, w8521
Khwaja Asim Ijaz and Mian Atif, 2005, “Do Leaders Favor Politically Connected Firms?
Rent Provision in an Emerging Financial Market”, Quarterly Journal of Economics, Vol 120,
Issue 4
La Porta Rafael, Lopez-De-Silanes Florencio and Zamarripa Guillermo, 2003, “Related
Lending”, Quarterly Journal of Economics, February 2008
Qin Julia Ya , 2004, “WTO regulation of Subsidies to State-Owned Enterprises(SOEs)-A
Critical Appraisal of the China Accession Protocol”, Journal of international Economic Law,
Vol 7, No.4
Figure 1: Yearly Subsidies, Innovation funds, Working Capital to Enterprises
3
2
1
1
.5
.1
.05
0
0
1998
1999
2000
2001
2002
2003
2004
2005
year
subsidies
working capital
innovation funds
Source: calculated from government finance part, Premium China Database, CEIC data.
21
2006
2007
25
20
15
10
1998
2000
2002
year
2004
2006
Source: calculated from government finance part, Premium China Database, CEIC data.
C oefficient
0
1
2
Figure 3 Capital structure change during years since connectin
-1
share of total yearly subsidies to enterprises, %
figure 2, Yearly subidies to foreign enterprises, by central government
t*-2
t*-1
t*
t*+1
t*+2
Timing
state capital
private capital
foreign capital
t0
t0+1
collective capital
legal person capital
Hongkong or Macao capital
t * : year into connection. t0 : year off connection
22
t0+2
Table 1 Summary Statistics of Firms
VARIABLES
N
mean
sd
min
max
Panel A: Full Sample
State Capital (log 10,000 RMB)
406,629
3.987
4.612
0
19.07
Subsidy (log 10,000 RMB)
406,109
1.094
2.506
-2.671
14.02
Markup
391728
.0177757
26.90143
-199.1
16826.4
Employee (log)
406,773
5.159
1.645
0
12.18
Yearly Sales (log 10,000 RMB)
406,747
9.557
2.839
0
18.98
Total actual received capital (log 10,000 RMB)
389,365
9.069
1.941
-0.119
19.07
Yearly profit (10,000 RMB)
406,773
11,444
405,358
-5.194e+06
1.108e+08
# of CCSC connected
406,774
0.0364
0.213
0
2
# of CC connected
406,774
0.0967
0.520
0
9
# of SC connected
406,774
0.0392
0.227
0
3
Having connection with CCSC
406774
0.031531
0.1747481
0
1
Having connection with CC
406774
0.0525279
0.2230894
0
1
Having connection with SC
406774
0.0307689
0.1726913
0
1
Year the firm established
401,345
1,982
19.36
1,600
2,007
Number of firms
86,827
Panel B: Never had Political Connection
State Capital (log 10,000 RMB)
352,280
4.119
4.626
0
18.22
Subsidy (log 10,000 RMB)
351,832
1.097
2.510
0
14.02
Markup
338709
.0220536
28.92998
-199.1
16826.4
Employee (log)
352,420
5.175
1.641
0
12.02
Yearly Sales (log 10,000 RMB)
352,398
9.509
2.864
0
18.89
Total actual received capital (log 10,000 RMB)
336,542
9.049
1.921
-0.119
18.22
Yearly profit (10,000 RMB)
352,420
8,818
174,529
-5.194e+06
2.742e+07
Year the firm established
347,367
1,982
19.40
1,600
2,007
Number of firms
76,540
Panel C Ever had Political Connection
State Capital (log 10,000 RMB)
54,349
3.131
4.425
0
19.07
Subsidy (log 10,000 RMB)
54,277
1.077
2.478
-2.671
13.64
Markup
53019
-.0095539
.3886813
-53.66667
10
Employee (log)
54,353
5.051
1.667
0
12.18
Yearly Sales (log 10,000 RMB)
54,349
9.865
2.657
0
18.98
Total actual received capital (log 10,000 RMB)
52,823
9.196
2.059
-0.119
19.07
Yearly profit (10,000 RMB)
54,353
28,473
1.016e+06
-4.436e+06
1.108e+08
# of CCSC connected
54,354
0.273
0.526
0
2
# of CC connected
54,354
0.724
1.252
0
9
# of SC connected
54,354
0.294
0.556
0
3
Year the firm established
53,978
1,983
19.08
1,837
2,007
Number of firms
10,287
Note: CCSC, Central Committee & State Council; CC, Central Committee; SC, State Council, same in all tables.
Amount is deflated by CPI, all in 1998 RMB currency value.
Table 2 Type of Politicians Connected with Firms across Years
Not in power
Central Committee
State Council
Both CC & SC
Total
1998
57
49
19
6
131
1999
56
49
20
6
131
2000
51
53
21
6
131
23
2001
49
54
22
6
131
2002
26
81
18
6
131
2003
53
44
23
11
131
2004
58
46
19
8
131
2005
58
47
18
8
131
2006
60
46
16
9
131
2007
61
48
13
9
131
Table 3 Reasons for Connected Leaders Varying across years
Type of change
1999 2000 2001 2002 2003 2004 2005 2006 2007 Total
placed in CC
1
25
1
27
placed in SC
3
2
2
1
7
1
2
18
leave due to position change
2
1
1
2
2
1
2
3
3
17
change from Central Committee to
Both
5
1
1
7
change from State Council to
Central Committee
1
1
change from State Council to Both
2
2
change from Both to Central
Committee
2
1
2
5
change from Both to State Council
1
1
dismissed due to abusing power
1
1
flee away due to corruption case
1
1
arrested due to corruption
1
1
retire
4
1
5
Leave after term terminated
28
2
30
1
1
term limit
Total
5
4
3
33
50
8
4
5
5
117
Note: the number of politicians between year n and n-1 corresponding to perspective reason are list in the table.
Table 4 Impact of political connection on resources obtained and market power
(1)
(2)
(3)
State Capital
Subsidy
Markup
0.207***
0.0250
0.0198***
(0.0746)
(0.0481)
(0.00588)
# of CC politicians connected
0.0461
-0.0170
0.00181
(0.0368)
(0.0213)
(0.00355)
# of SC politicians connected
-0.0588
0.0897**
0.00615
(0.0706)
(0.0373)
(0.00615)
Observations
52,048
52,052
52,046
Number of firms
9898
9898
9,897
R-squared
0.104
0.048
0.015
Year dummy
YES
YES
YES
Firm FE
YES
YES
YES
Industry*Year dummy
YES
YES
YES
Province*year dummy
YES
YES
YES
Connection pattern * year dummy
YES
YES
YES
Ownership type*year dummy
YES
YES
YES
F-test for connection variables jointly zero
3.088
2.469
4.267
Prob>F
0.0260
0.0600
0.00510
Robust standard errors clustered by firm in parentheses. *** p<0.01, ** p<0.05, * p<0.1
VARIABLES
# of CCSC politicians connected
24
Table 5 Political connection and other firm characteristics
(1)
(2)
(3)
VARIABLES
Employee
Yearly Sales
Total Capital
# of CCSC politicians connected
-0.00685
0.0263
-0.0228
(0.0146)
(0.0199)
(0.0157)
# of CC politicians connected
0.0191**
0.0187*
0.00923
(0.00800)
(0.0105)
(0.00724)
# of SC politicians connected
0.00454
-0.00610
0.0204
(0.0134)
(0.0175)
(0.0127)
Observations
52,052
52,049
52,044
R-squared
0.151
0.144
0.069
Number of idpanel
9,898
9,898
9,898
Year dummy
YES
YES
YES
Firm FE
YES
YES
YES
Industry*Year dummy
YES
YES
YES
Province*year dummy
YES
YES
YES
Connection pattern*year dummy
YES
YES
YES
Ownership type*year dummy
YES
YES
YES
Robust standard errors clustered by firm in parentheses *** p<0.01, ** p<0.05, * p<0.1
25
(4)
Profit
30,309
(23,919)
-478.8
(3,185)
-7,405
(6,626)
52,052
0.262
9,898
YES
YES
YES
YES
YES
YES
Table 6 Heterogeneous effects: firm Characteristics and more resources
(1)
State Capital VARIABLES
# of CCSC politicians connected
0.252***
(0.0852)
# of CC politicians connected
# of CC politicians connected
0.0477
(0.0371)
# of SC politicians connected
# of SC politicians connected
-0.0626
(0.0721)
# of CCSC politicians connected_t*
# of SC politicians connected_t*
-0.124
(0.310)
CCSC_t 0
-0.287
CCSC_t 0
(0.460)
# of CCSC politicians connected_t* X
0.123**
# of SC politicians connected_t* X
employee_t*-1
(0.0608)
employee_t*-1
# of CCSC politicians connected_t* X
-8.84e-08** # of SC politicians connected_t* X
profit_t*-1
(3.52e-08)
profit_t*-1
# of CCSC politicians connected_t* X
-0.0799*
# of SC politicians connected_t* X
yearly sales_t*-1
(0.0464)
yearly sales_t*-1
# of CCSC politicians connected_t* X
0.0318
# of SC politicians connected_t* X
total other capital_t*-1
(0.0196)
total other capital_t*-1
0.0539
SC_t 0 X employee_ t 0-1
CCSC_t 0 X employee_ t 0-1
(0.0864)
2.40e-06*
SC_t 0 X profit _ t 0-1
CCSC_t 0 X profit _ t 0-1
(1.39e-06)
0
0
0.0340
SC_t 0 X yearly sales _ t 0-1
CCSC_t X yearly sales _ t -1
(0.0582)
-0.0237
SC_t 0 X total other capital _ t 0-1
CCSC_t 0 X total other capital _ t 0-1
(0.0347)
Observations
51,531
Observations
R-squared
0.104
R-squared
Number of idpanel
9,829
Number of idpanel
Year dummy
YES
Year dummy
Firm FE
YES
Firm FE
Industry*Year dummy
YES
Industry*Year dummy
Province*year dummy
YES
Province*year dummy
Connection pattern* year dummy
YES
Connection pattern* year dummy
Ownership type*year dummy
YES
Ownership type*year dummy
Robust standard errors clustered by firm in parentheses *** p<0.01, ** p<0.05, * p<0.1
Note: t* denotes the year into connection, t*-1 denotes one year before get into connection
t 0 denotes the year out of connection, t 0-1 denotes one year before lose connection
VARIABLES
# of CCSC politicians connected
26
(2)
Subsidy
0.0262
(0.0485)
-0.0164
(0.0216)
0.0944**
(0.0422)
-0.671***
(0.225)
0.376
(0.236)
-0.0309
(0.0379)
-6.26e-08
(8.76e-08)
0.0784***
(0.0288)
0.00241
(0.0126)
-0.0550
(0.0462)
-2.55e-08
(1.56e-07)
-0.0169
(0.0328)
0.0151
(0.0124)
51,535
0.049
9,829
YES
YES
YES
YES
YES
YES
Table 7 Capital structure change around years into and out of connection with CCSC
VARIABLES
CCSC__t*-1
CCSC__t*
CCSC__t*+1
CCSC__t*+2
CCSC_t 0
CCSC_t 0+1
CCSC_t 0+2
(1)
State K
-0.851**
(0.421)
2.243***
(0.528)
2.114***
(0.549)
0.289
(0.507)
0.576
(0.529)
0.431
(0.565)
-0.721
(0.485)
3,748
0.325
524
YES
YES
YES
YES
(2)
Collective K
-0.393*
(0.234)
-0.583**
(0.294)
-0.264
(0.295)
-0.102
(0.234)
0.200
(0.309)
0.360
(0.407)
0.334
(0.360)
3,748
0.331
524
YES
YES
YES
YES
(3)
Private K
-0.00816
(0.156)
-0.184
(0.229)
-0.0510
(0.223)
0.208
(0.214)
-0.299
(0.195)
-0.0177
(0.287)
0.286
(0.177)
3,748
0.397
524
YES
YES
YES
YES
(4)
Legal person K
1.027**
(0.445)
-1.389**
(0.545)
-1.395**
(0.546)
-0.290
(0.482)
-0.668
(0.502)
-0.697
(0.588)
0.0856
(0.506)
3,747
0.299
524
YES
YES
YES
YES
(5)
HK & Makao K
-0.139
(0.127)
-0.205
(0.173)
-0.0102
(0.189)
0.00623
(0.124)
-0.0214
(0.148)
0.0709
(0.230)
-0.0619
(0.206)
3,748
0.589
524
YES
YES
YES
YES
(6)
Foreign K
0.167
(0.140)
0.126
(0.188)
0.0786
(0.207)
-0.137
(0.109)
0.0891
(0.144)
-0.328*
(0.194)
0.0422
(0.205)
3,748
0.568
524
YES
YES
YES
YES
Observations
R-squared
Number of firms
Year dummy
Firm FE
Industry*Year dummy
Province*year dummy
Connection pattern*
year dummy
YES
YES
YES
YES
YES
YES
Ownership type*year
dummy
YES
YES
YES
YES
YES
YES
Robust standard errors clustered by firm in parentheses *** p<0.01, ** p<0.05, * p<0.1
Note: t* denotes the year into connection, t*-1 denotes one year before get into connection, t*+1 one year after, etc.
t 0 denotes the year out of connection, t 0+1 denotes one year after the year losing connection, etc.
Sample limit to firms who do get(lose) more state capital when step into(out of) connection with CCSC
27
(7)
total K
-0.0576
(0.0475)
-0.121**
(0.0603)
-0.00113
(0.0644)
-0.0916
(0.0657)
-0.0421
(0.0636)
-0.121
(0.0912)
-0.0112
(0.0626)
3,747
0.206
524
YES
YES
YES
YES
YES
YES
Table 8 Firm Characteristics change around year into/out of connection with CCSC
(1)
(2)
(3)
Employee
Yearly sales
Profit
0.0340
-0.0256
-9,227
(0.0525)
(0.0677)
(6,945)
CCSC__t*
-0.0375
-0.00967
-25,152
(0.0647)
(0.0878)
(21,471)
CCSC__t*+1
-0.0164
-0.000982
-22,156
(0.0788)
(0.0909)
(23,041)
CCSC__t*+2
-0.0181
0.0665
2,071
(0.0582)
(0.0743)
(8,475)
0.0186
0.0669
-17,421
CCSC_t 0
(0.0507)
(0.0883)
(15,418)
-0.000117
0.0447
-43,304
CCSC_t 0+1
(0.0684)
(0.0986)
(28,067)
0.0189
0.0740
-28,790
CCSC_t 0+2
(0.0548)
(0.0867)
(31,900)
Observations
3,748
3,748
3,748
R-squared
0.282
0.264
0.440
Number of idpanel
524
524
524
Year dummy
YES
YES
YES
Firm FE
YES
YES
YES
Industry*Year dummy
YES
YES
YES
Province*year dummy
YES
YES
YES
Connection pattern*year dummy
YES
YES
YES
Ownership type*year dummy
YES
YES
YES
Robust standard errors clustered by firm in parentheses *** p<0.01, ** p<0.05, * p<0.1
Note: t* denotes the year into connection, t*-1 denotes one year before get into connection, t*+1 one year after, etc.
t 0 denotes the year out of connection, t 0+1 denotes one year after the year losing connection, etc.
Sample limit to firms who do get(lose) more state capital when step into(out of) connection with CCSC
VARIABLES
CCSC__t*-1
28
Table 9 Firm Characteristics change around year into/out of connection with SC
(1)
(2)
(3)
VARIABLES
Employee
Yearly sales
Profit
SC__t*-1
-0.00427
0.119
5,048
(0.0770)
(0.0741)
(8,096)
SC__t*
0.0528
0.0917
23,572**
(0.0756)
(0.0825)
(11,187)
SC__t*+1
-0.0291
0.0681
11,546
(0.0760)
(0.0757)
(15,501)
SC__t*+2
-0.0292
0.0231
-1,128
(0.0678)
(0.0731)
(17,542)
SC_t 0
-0.00404
-0.165*
-21,591
(0.0708)
(0.0863)
(13,139)
-0.0561
-0.203*
-18,282
SC_t 0+1
(0.0764)
(0.112)
(27,864)
-0.0110
-0.177*
-2,374
SC_t 0+2
(0.0924)
(0.0960)
(13,344)
Observations
2,878
2,878
2,878
R-squared
0.353
0.350
0.467
Number of idpanel
389
389
389
Year dummy
YES
YES
YES
Firm FE
YES
YES
YES
Industry*Year dummy
YES
YES
YES
Province*year dummy
YES
YES
YES
Connection pattern*year dummy
YES
YES
YES
Ownership type*year dummy
YES
YES
YES
Robust standard errors clustered by firm in parentheses *** p<0.01, ** p<0.05, * p<0.1
Note: t* denotes the year into connection, t*-1 denotes one year before get into connection, t*+1 one year after, etc.
t 0 denotes the year out of connection, t 0+1 denotes one year after the year losing connection, etc.
Sample limit to firms who do get(lose) more state capital when step into(out of) connection with SC
29