Crime Law Soc Change (2014) 61:309–333
DOI 10.1007/s10611-013-9504-4
Who pays more “tributes” to the government?
sectoral corruption of China’s private enterprises
Jiangnan Zhu & Yiping Wu
Published online: 17 December 2013
# Springer Science+Business Media Dordrecht 2013
Abstract Which industry sectors bribe the government and, in turn, are exploited by
the government the most in China? Or, as commonly satirized by the people, which
sectors pay the most “tributes” (shanggong) to government officials? This article
attempts to answer these questions by proposing a meso-level approach, which examines corruption in China at the sectoral level. We use a firm-level survey from 1997 to
2006 in China and treat two types of payments by private enterprises—public relations–building fees (yingchou) and forced apportionment of funds (tanpai)—as indicators of potential corruption in a sector. We find that the most corrupt sectors are those
that rely on scarce and less mobile resources controlled by the government. Thus,
further reform in the factor markets is necessary to reduce corruption caused by
government intervention in the allocation of important resources.
Introduction
Research has increasingly shown that corruption, an “economic and political evil,”
increases transaction costs, distorts allocation of resources, weakens private investment,
and undermines political trust [28]. To control corruption effectively, it is important to
understand the sources of corruption. Cross-country surveys, such as the World Bank
Country Diagnostic Surveys and the Transparency International, reveal that the private
sector provides rich ground both for corruption itself to occur and for the remedies
J. Zhu
Department of Politics and Public Administration, Faculty of Social Sciences, The University of Hong
Kong, Pokfulam Road, Hong Kong
e-mail: [email protected]
Y. Wu (*)
School of Public Economics and Administration, Shanghai University of Finance and Economics, 777
Guoding Road, Shanghai, China
e-mail: [email protected]
Y. Wu
e-mail: [email protected]
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J. Zhu, Y. Wu
needed to address it because, in most cases, this sector offers the gratification to public
officials, either passively or actively.1 Therefore, it is useful to identify the distribution
of corruption across the private sector to help curb corruption from the supply side.
Prior research has also indicated that anticorruption programs targeting vulnerable
sectors are more effective than broad programs because they can target the root causes
more directly and remain effective for longer periods ([29]: 6).
In China, the coexistence of serious corruption and rapid economic development has
been puzzling. On the one hand, scholars estimate that corruption has caused a great
amount of economic loss, accounting for 13.2 % to 16.8 % of annual gross domestic
product (GDP) since the second half of the 1990s [14]. Corruption is also widely
regarded as ultimately fatal to the Chinese Communist Party’s (CCP) rule. On the other
hand, corruption does not seem to have affected the momentum of economic growth.
Various domestic private and international investments have continued to inject vigor
into the Chinese economy. Thus, the short- and long-term impacts of corruption in
China remain unclear. We argue that because the spread of corruption in China mainly
comes from the transition of economic systems, many differences in corruption exist
from sector to sector. Therefore, to more accurately understand the potential effects of
corruption on Chinese socioeconomic development, we aim to identify the economic
sectors in which corruption tends to concentrate. Corruption is more likely to undermine the long-term economic performance of a country and generate severe consequences for future generations’ well-being if it remains mainly in sectors that are the
economic engines or involve some core resources of the country and infrastructure. 2
However, scarce research has examined the sources and status of corruption in China
from a sectoral perspective.
In this article, we attempt to fill this gap by shedding light on how corruption has
spread across the private sector. In particular, we answer the following two questions:
Which types of industries in the private sector bribe government officials the most? and
Which types of industries, in turn, are exploited the most by the government? Knowledge of the supply side of corruption should also help us identify the demand side of
corruption in the functional bureaucracies because most bribes are paid to the government offices with which these firms interact most often. In this way, this approach
complements and supports the findings of the World Bank surveys, which tend to focus
more on the demand side of corruption in government agencies or the public sector.
We also focus on corruption in the private sector because of the increasing importance of Chinese private enterprises. Despite some state repression caused by political
obstacles, the private sector has grown rapidly since the 1990s and has indisputably
become the most productive engine of the Chinese economy [15]. The development
and health of private enterprises, a core player in any true market economy, reflects the
degree to which Chinese economic reform—the transition from a planned economy to
a market economy—has been successful. Therefore, we conduct preliminary research
on sectoral corruption based on surveys of Chinese private enterprises because corruption is often considered a major obstacle of development and investment for most
1
For example, see the Governance & Corruption Diagnostic Survey in Malawi (http://siteresources.
worldbank.org/INTWBIGOVANTCOR/Resources/MalawiFinalMainReportGCBSurvey.pdf, accessed 3 July
2012).
2
Transparency International, Bribe Payers Index Report 2011.
Who pays more “tributes” to the government?
311
private enterprises in cross-country studies. We do not imply that sectoral corruption in
Chinese private enterprises also represents corruption in state- and foreign-controlled
sectors. Several strategic sectors, including finance, petroleum, and telecommunications, are actually monopolized by the state, and corruption in these sectors is well
known. Rather, private enterprises operate in most economic sectors today, and many
previously restricted sectors, such as high-technology and real estate, are now open to
private enterprises. Therefore, research on private sectors can to a large degree show the
general situation in China, despite the data limitations.
Following recent research, we define corruption as “publically unacceptable misbehavior committed by state functionaries for private gains at the expense of public
interests, and/or causing intentional and unintentional damage to public interests and
values” ([16]: 374). This definition pertains not only to misconduct by individual
officials but also to the integrity of the CCP and the government. Furthermore, it
encompasses both a wide range of illegal behaviors by government officials (e.g.,
kickbacks in public procurement, embezzlement, misuse of government funds) and
“immoral, irresponsible, and socially unacceptable behaviors of state functionaries”
([16]: 374). Among these misbehaviors, we pay special attention to bribes paid to
officials by firms and illegal and semilegal fees extracted from firms by the government. While bribery is “the most common type of major corruption” in China today
([12]: 357), arbitrary fees collection is a prototype of extortion by government agencies,
or “institutional corruption” as Wedeman [35] calls it. Institutional corruption involves
the pursuit of gain by the government “acting collectively and relying on the authority
or resources of the organization to generate or extract income improperly” ([35]: 805).
Institutional corruption may have an equally or even greater negative effect than
individual official corruption on state integrity. In addition, we primarily investigate
corruption between state functionaries and private enterprises, rather than commercial
bribery between firms, because official corruption is more common and serious in
China.
In the next section, we discuss the data and methods of gauging corruption at the
sectoral dimension. Using extant research, we choose two types of payments as
indicators of potential corruption in a given sector. The first is the public relations–
building (yingchou) fees firms spend proactively, and the second is the forced apportionment of funds (tanpai) collected by the government. We further identify the
sum of the two fees as the indicator of overall sectoral corruption, or the total
“tributes” (shanggong) firms pay to the government—that is, the yingchou cost
as the indicator of bribery and the tanpai cost as the indicator of government
extortion. We apply some simple statistics to these corruption indicators to generate sectoral corruption indices.
Then, we present our major findings and analysis. After briefly exploring specific
reasons for different levels and forms of corruption across sectors, we offer two
arguments that help shed light on sectoral corruption in contemporary China. First,
we find that the degree of scarcity and mobility of state provisions on which the sectors
rely, such as state-controlled natural resources, services, and policy supports, greatly
influences sectoral corruption. The scarcer the state provision in a sector, the higher is
the level of corruption. This is especially evidenced by the high corruption in the real
estate, mining, and agricultural sectors. A common feature in these sectors is their
heavy reliance on state-controlled resources, such as land, mines, and even bank loans,
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which are all scarce. Land and mines are also immobile between localities, which
further increases investors’ reliance on the local government. Second, we also argue
that resource-oriented corruption in the most corrupt sectors reflects the necessity to
reform factor markets in China so as to limit government intervention in the allocation
of key resources. However, factor market reform likely involves the property rights of
scarce resources more profoundly than the product-market reform carried out so far and
therefore is more difficult. Finally, in the concluding section, we summarize our major
findings, discuss limitations of the study, and suggest future research.
Gauging corruption at the sectoral level
Prior research has examined the causes, changing patterns, and the effects of corruption
in China. A large body of literature has taken a macro perspective, investigating general
institutional determinants of corruption, which include cultural and traditional legacies,
administrative decentralization, marketization of the economy, judicial dependence,
and power concentration of the CCP. In brief, these factors have created an environment
that tolerates corruption, generates opportunities and incentives for corruption, and
hinders effective supervision of corruption [1, 10, 13, 20–23, 25, 27, 30]. Scholars have
also used corruption cases uncovered by government and media to illustrate the overall
patterns. They find that corruption has undergone a path from quantitative to qualitative
change, with more sophisticated forms, ever higher stakes and ever higher-level
officials, and longer latency periods [8, 12, 36]. At the same time, other scholars have
taken a micro approach, studying how corrupt networks are woven by individual
participants through detailed case studies. They provide insights into how exactly
bribery and manipulation take place and how bribers and bribees arrive at a deal [17,
34]. Little research has employed a meso perspective to probe how corruption spreads
across industrial sectors.
In this research, we use a survey of private firms conducted jointly by the All-China
Federation of Industry & Commerce (ACFIC), a semigovernmental organization for
Chinese industrialists and businesspeople, and other organizations. This survey has
been conducted every 2 to 3 years since 1991 among firms in all the provincial units in
China.3 Since 1997, the following two questions have appeared on the questionnaire:
(1) “How much money (in Y10,000) has your firm spent on social networking and
public relation building (yingchou) in the past year?” and (2) “How much money (in
Y10,000) has your firm spent for various forced apportionment of funds (tanpai)
collected by government in the past year?”
We take the yingchou and tanpai costs surveyed between 1997 and 2006 to measure
two types of corruption.4 Yingchou typically refers to firms’ public relations–building
activities, such as hosting and entertaining their suppliers and clients. Although some
yingchou costs are certainly legitimate, research and Chinese netizens’ anecdotal
reports both reveal that a large portion of this money is actually spent to build good
relationships with the government, especially functional departments (e.g., industry and
3
For information on the survey and the data used in this research, see Appendix A. This is a popularly used
data set on Chinese private enterprises [see [18, 19]].
4
Thus, our data include 1996, 1999, 2001, 2003, and 2005, because the survey reflects the previous year.
Who pays more “tributes” to the government?
313
commerce administrations, tax collection agencies), to maintain normal business operations.5 Some yingchou activities of private entrepreneurs are also intended to obtain higher
sociopolitical status, such as local or national legislative representatives, which helps them
gain access to larger political and business networks and various resources (e.g., bank
loans).6 However, yingchou is very costly for private entrepreneurs. According to a survey
conducted in Guangxi Province in 2006, approximately 22 % of the firms reported that
expenditures on government relations building were a heavy or very heavy burden for
them, and more than 80 % of the firms indicated that this burden had become heavier or
remained the same [5]. Moreover, existing research [2, 4] shows that firms’ expenditures
on yingchou are closely related to government corruption. Firms might spend a significant
amount of money on entertaining government officials (e.g., eating, drinking, gifts) and
hosting their expensive sports club membership fees and travel. Most of these expenditures are indeed bribes. Therefore, we treat yingchou costs as indicators of bribery that
firms proactively pay to government officials for enhanced patron–client relationships.
Firms also must pay tanpai costs, or forced apportionment of funds. This problem has
long harassed both state-owned enterprises (SOEs) and non-SOEs, despite periodic government sanctions.7 Various government agencies levy forced apportionment of funds on
firms under differing names, including fund-raising for public administration and government (luan jizi), arbitrary fines, national competition of non-SOEs, improper investigation,
contests, training, and financial sponsorship. Large amounts of these fees have been used to
enrich the coffers of local governments and functional departments. For example, it is
estimated that in 2004, revenues from different types of fines and fees reached Y936.7
billion for government units, courts, and procuratorates [40]. Although tanpai is, to some
degree, a forced option for local governments due to their limited autonomy in local taxes
and revenues for local development, it has resulted in obstacles for enterprise development.
Nontaxation fees and fines constitute one-third to one-half of enterprises’ financial burdens
[40]. Prevalent and arbitrary tanpai also reflects the expansion of the “grabbing” hand of
the government, which exploits public power to obtain monetary gains from the society.
Large amounts of tanpai and extra-budgetary fees have gone to the “small treasuries”
5
For example, see “Shandong liaocheng xin’ao ranqigongsi 09 niandu gonggong guanxi weihu
jihua biao, shuji shizhang minglie qizhong” [“Shandong liaocheng xin’ao gas company 2009 yearly
public relation maintaining plan, local party secretary and mayor are both listed”], China Economic
Network (http://www.paihang360.com/phfy/xiangqing.jsp?record_id=345173&op=op_browse). In this
case, yingchou expenses to local government agencies accounted for up to Y0.78 million, which is
73.1 % of the firm’s total yingchou costs. See also “Wangbao chunjie lidan she 500 yuming
guanyuan, sheshi gongsi cheng zheng diaocha” [“Netizens exposed that Spring Festival gift list
involved more than 500 officials, relevant company claimed that investigation is undergoing”],
Chinanews.com (http://news.cn.yahoo.com/ypen/20110313/255960.html).
6
See “Zhu Siyi ruhe bianzhi ‘guanshang da wang’ songli sanbu zou guojiefei tingzuo 5 wan” [“How did Zhu
Siyi weave ‘large official-business networks’? Three steps of sending gifts, festival fees surpassing Y50,000 to
bureau officials”], Nanfang Ribao [South China Daily] (http://fanfu.people.com.cn/GB/12416702.html).
7
The central government has issued several orders to stop the “three disorders” or sanluan (i.e., improper
levying of fees, luan shoufei; arbitrary fines, luan fakuan; and forced apportionment of funds, luan tanpai).
These orders include the State Council, Jinzhi xiangqiye tanpai zanxing tiaoli [Tentative rule on restrictions of
collecting apportionment of funds from enterprises], 28 April 1988; Zhonggong zhongyang, guowuyuan,
guanyu jianjue zhizhi luanshoufei, luanfakuan, he luantanpai de jueding [Decisions of the Party center, the
State Council on steadily stopping luanshoufei, luanfakuan, and luantanpai], 16 September 1990; Zhonggong
zhongyang guowuyuan guanyu zhili xiangqiye luanshoufei, luanfakuan, he gezhong luantanpai deng wenti de
jueding [Decisions of the Party Center, the State Council on regulating luanshoufei, luanfakuan, and various
luantanpai toward firms], 7 July 1997.
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(xiaojinku) of government units, allocated as bonuses for government employees,
misappropriated for luxurious car purchases, or even directly embezzled by government
officials [3]. Thus, we use tanpai as a proxy for government extortion.
Both yingchou and tanpai costs are sources of official corruption. Together, they
measure the total tributes firms pay to the government, or shanggong, as commonly
satirized by the Chinese people. Just as surrounding kingdoms—either voluntarily or
cowed by the might of the Central Kingdom—paid their tributes to China historically,
private firms today also must pay bribes and extra fees to government officials. In place
of paternal protection, these modern tributes are exchanged for state patronage, better
government services, or simply less hassle with the government.
Yingchou and tanpai costs measure two types of corruption—bribery and extortion
of government agencies, respectively—and have some inherent differences. The former
cost often includes bribes sent actively or voluntarily to government officials, while the
latter is money or revenue that firms passively, and often involuntarily, pay to the
government. At times, government officials even actively solicit bribes themselves,
rather than waiting for firms to offer them, and thus we must delve deeper into these
costs. As reviewed previously, yingchou costs, including bribes, are more likely to flow
directly into the personal pockets of government officials. Therefore, yingchou pertains
more to private corruption of individual officials or groups of officials pursuing their
own self-interests. Conversely, revenues from tanpai usually stay under the control of
government agencies and are often allocated to their members as benefits or bonuses.
Therefore, tanpai costs pertain more to corruption or extraction by an entire government agency. In addition, bribery, commonly considered illegal, is usually conducted
covertly, and the amount of the bribe generally depends on the potential profits of a
project, instead of being publicly regulated. In contrast, tanpai, which often appears
official and legal, is practiced more overtly by government agencies through exploitation or usurpation of their public authorities. Not only is tanpai openly collected much
of the time, but its amount is also explicitly defined, sometimes regardless of firms’
revenues, though it is also sometimes negotiable.8
Therefore, the two types of corruption add different kinds and levels of financial
costs to firms and damage regime legitimacy in different ways. They also reflect
different business–government relationships. Yingchou costs mainly measure the dependence of a firm or a sector on the government. We expect that the more a firm or
sector needs government support, either for resources or administrative approval, the
more likely it is to pay bribes of higher value to government officials. Tanpai costs
measure the degree of government exploitation and fiscal reliance on a firm or sector.
We expect that sectors on which the government relies more for revenues pay more fees
than less important sectors. Thus, we also explore the distribution of bribery and
government extortion, respectively, across sectors.
Method
First, we classify firms into major sectors. The main sectors from 1997 to 2006 include
agriculture (i.e., farming, forestry, livestock husbandry, and fishing), mining,
8
Even tax payments are sometimes negotiable in China, not to mention the informal fees [6].
Who pays more “tributes” to the government?
315
manufacturing, energy (i.e., electricity and gas), construction, infrastructure, transportation, restaurant & retail, finance & insurance, real estate, social services (e.g., resident
services, renting), science & technology, and the area of culture, education, public
health & sports (CEHS). However, the finance & insurance and infrastructure sectors
usually include no more than three firms per survey, as these sectors are largely beyond
the reach of private enterprise. We therefore omit these sectors, which leaves 11 sectors
in the study. Appendix A provides more detailed information on the data set, the
number of firms classified by industries (Table 2), and a discussion of missing values.
Second, we aggregate the yingchou and tanpai costs for each sector in each of the
5 years. These costs are represented by the variables BRIBEit and EXTORTit, respectively, where i is the ith sector and t is the year. We sum these two variables to
determine the overall corruption in each sector and each year, CORRUPTit.9
Third, for each of these three indicators, we combine two approaches to generate
final corruption indices across sectors. In the first approach, we take the frequency with
which each sector falls into the above-average corruption level across the 5 years.
Frequency captures the consistency or stability of corruption for a sector. High frequencies of above-average levels of corruption mean that corruption in these sectors is
constant, not accidental, and that it results from systematic, rather than random, factors.
Appendix B illustrates sectors with above- and below-average corruption levels year by
year during this period. In the second approach, we examine the annual average of
sectoral CORRUPTi, BRIBEi, and EXTORTi across the 5 years and call them
AVECORRUPTi, AVEBRIBEi, and AVEEXTORTi. Using AVECORRUPTi as an
example, we calculate it as follows:
X
CORRUPT it
AVECORRUPT i ¼
; t ¼ 1996; 1999; 2001; 2003; 2005:
5
If frequency measures which sector pays tributes to the government most often,
AVECORRUPT gauges which sector pays the most tributes on average during the
5 years. It measures the potential or the seriousness of corruption in a sector. For
comparison, we also check the standardized AVECORRUPTi, AVEBRIBEi, and
AVEEXTORTi to determine how many standard deviations a sector’s corruption level
is above or below the average. If a sector is substantially above average, this indicates
that corruption in this sector is very serious or, at least, that the sector has a high
potential for corruption. For both approaches, we roughly categorize sectors into high-,
medium-, and low-corruption levels. Combining both methods helps balance out the
possible bias caused by the limited number of years observed in this sample.
Thus, we take the absolute amount of potential bribes and funds paid by a sector as
the indicator of corruption, instead of the percentage of revenues paid by firms, as in
some previous research, such as the World Business Environment survey. We take this
approach for several reasons. First, it is consistent with the common measure of severity
9
For example, for BRIBEit, the bribery of the real estate sector (RE) in 2001 is BRIBERE,2001 =
(yingchouREFirm1,2001 +yingchouREFirm2, 2001 +…+yingchouREFirmN, 2001)/N, where N is the number of firms
in this sector. The indicator CORRUPT also helps to capture a potential fusion of yingchou and tanpai.
Sometimes there is no clear-cut between yingchou and tanpai, for examples, private firms may be forced to
entertain government officials. While we cannot detect whether firms tend to count this as yingchou or tanpai,
it is in the general category of corruption.
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of corruption. In criminal law, the penalty for corruption tends to increase with the
amount of money involved in the case. Second, as Table 2 shows, many firms did not
report their sales revenues, and thus measuring corruption by this ratio would result in a
large amount of missing data and fewer observations. However, no measurement of
corruption is perfect, and sums of tributes may be correlated with sectoral profitability.
We found that sectors with higher average sales revenues tended to have higher levels
of corruption and subcategories of corruption (see Appendix C). 10 Thus, the ratio
measurement does have the advantage of controlling the size of industrial profitability.
In Appendix D, we provide the rank of sectoral corruption measured by ratios to
compare with the findings based on our major indicators. Not surprisingly, several
sectors with a smaller absolute amount of tributes are ranked higher in the ratio index
because their sales revenues are lower. This likely indicates that tributes, even small
amounts, can be a heavy burden for sectors with less profit. At the same time, two
industries that have high levels of corruption in our major indicators (i.e., real estate and
mining) also appear problematic in the ratio index. Appendix E shows that these two
sectors are especially plagued by government extortion. In general, the different results
from these measurements suggest that the notion of corruption can differ depending on
differing perspectives of corruption.
Understanding corruption at the sectoral level
Table 1 reports the AVECORRUPT, AVEBRIBE, and AVEEXTORT of each sector and
their respective standardized values, as well as the frequency with which each sector is
above the yearly average across the 5 years. In each of the three categories of
corruption, we classify sectors with both high average values of corruption and high
frequencies above the yearly corruption average (i.e., above 3) as high-corruption
sectors. Several sectors also have low absolute values and frequencies; we categorize
these as low-corruption sectors. Sectors in the middle have a medium level of
corruption.
To provide a systematic understanding of corruption at the sectoral level, we take the
following three steps. First, we describe the general patterns demonstrated in Table 1.
Second, we focus on the most corrupt sectors and analyze the potential reasons for
corruption sector by sector. Third, on the basis of the first two steps, we derive some
general explanations for the larger picture of sectoral corruption in China.
General patterns
Columns 2–4 in Table 1 show that real estate, agriculture, and mining have the highest
overall corruption. For example, a total of Y434,740 was paid as tribute per real estate
firm per year on average between 1996 and 2005. This figure is about two and a half
standard deviations higher than the industrial average of all the 11 sectors. Furthermore,
10
However, the sales revenue seems to exert smaller effects on government extortion than on bribery. This is
consistent with our previous discussion that tanpai costs are oftentimes coerced from firms regardless of
profitability. Our interviews with taxation bureaus also reveal that tanpai does not necessarily correlate with
firms’ revenues (interviews were conducted in January 2011 in Henan province).
Who pays more “tributes” to the government?
317
Table 1 Average, frequency, and levels of sectoral corruption
Overall corruption
No.
Sector
1
Real estate
2
Agriculture
3
Mining
4
Construction
5
Energy
6
Manufacturing
7
Transportation
8
Science &
technology
9
CEHS
10
Restaurant &
retail
11
Social services
AVE
CORRUPT
43.474
(2.47)
27.28
(0.952)
23.59
(0.606)
15.968
(-0.108)
14.481
(-0.248)
13.633
(-0.327)
13.3
(-0.358)
11.208
(-0.554)
9.847
(-0.682)
8.703
(-0.789)
6.866
(-0.961)
B ri b e r y
Frequency
above yearly
average
Sector
4
Real estate
3
Agriculture
3
Mining
2
Construction
2
Energy
1
Manufacturing
1
Science &
technology
2
Transportation
0
CEHS
0
Restaurant &
retail
0
Social services
AVE
BRIBE
24.129
(1.964)
23.640
(1.888)
11.260
(-0.043)
10.913
(-0.097)
10.657
(-0.137)
9.755
(-0.277)
9.107
(-0.378)
8.270
(-0.509)
7.910
(-0.565)
6.567
(-0.775)
4.665
(-1.071)
Frequency
above yearly
average
3
3
2
2
2
2
2
1
2
1
0
Government extortion
Frequency
AVE
Sector
above yearly
EXTORT
average
19.345
Real estate
5
(2.547)
12.329
5
Mining
(1.228)
5.055
2
Construction
(-0.141)
5.029
2
Transportation
(-0.146)
4.716
2
Agriculture
(-0.205)
3.908
0
Manufacturing
(-0.357)
3.825
1
Energy
(-0.372)
2.995
Social services
0
(-0.528)
Restaurant &
2.598
0
retail
(-0.603)
Science &
2.100
0
technology
(-0.697)
1.936
CEHS
0
(-0.727)
1. Data in parentheses are standardized AVECORRUPT, AVEBRIBE, and AVEREXTORT. For calculation of
standard values, see Note 4 of Appendix A
2. Cells shaded in dark gray are sectors with high levels of corruption. Cells shaded in light gray are sectors
with low levels of corruption. Cells not shaded are sectors with medium levels of corruption
the real estate industry falls into the above-average corruption level four times in the
five surveys. The construction, energy, manufacturing, transportation, and science &
technology industries also fall into the above-average corruption level one to two times
in the five surveys, indicating that these sectors are also periodically vulnerable to
corruption. Note that the real estate, mining, construction, energy, and manufacturing
industries are among China’s primary economic engines and contribute more than 50 %
of GDP annually. Manufacturing alone contributed up to 35 % of GDP during the
2004–2008 period.11 Compared with many of the low-corruption sectors in column
2, including CEHS, restaurant & retail, and social services, these sectors involve greater
capital investment. This means that since the mid-1990s, corruption has expanded into
many capital-intensive sectors, such as the real estate industry, large-scale urban construction projects, and the financial area, rather than being limited to commodity supply,
such as official profiteering in the 1980s. Prior research on corruption has also confirmed
this changing trend since the economic reform [11, 30]. The expansion into capitalintensive sectors means that corruption naturally becomes more complicated and involves larger sums of money.
Columns 5–10 in Table 1 further illustrate the subcategories of corruption. We find
that all 11 industries spend larger sums on bribery than on government extortion,
indicating that bribery, or building relations with the government, is a heavier burden
for private firms than paying forced apportionment of funds. This is probably due to the
different nature of the two types of corruption, as discussed previously. Forced apportionment of funds is often overtly collected with a publicly defined amount, which helps
11
This percentage of GDP includes sectors with all different ownerships because contributions solely by
private sectors are not available (see http://www.stats.gov.cn/).
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constrain the total volume of extortion from private firms. In contrast, under-the-table, or
covert, bribery deals are more difficult to control. We also find that bribery and
government extortion are simultaneously higher in the real estate industry than in the
other sectors. This implies that a strong symbiotic business–government relationship
likely exists in this sector. In such a relationship, firms must rely on the government for
resources and support, while the government must depend on these firms for revenue.
This situation seems to match the extant findings on real estate corruption [41]. In
contrast, the other two sectors with high levels of overall corruption exhibit slightly
different trends. The agricultural industry has a high level of bribery and an average level
of extortion, while the mining industry has a high level of government extortion and a
medium level of bribery. We also find that most sectors with low overall corruption also
have low levels in the corruption subcategories. Together, these variations imply varied
business–government relationships across sectors, probably due to different sectoral
characteristics, and different levels of government intervention into these industries.
Sector-by-sector analysis
In the real estate sector, land is the key productive factor, and most corruption in this
sector centers on it. In the past two decades, the inherent scarcity of land in China has
been intensified by the rapid economic development and market demand for housing.
Appendix B shows that corruption in the real estate industry was not rampant in 1996
but became salient after the survey of 1999, which matches the timing of the housing
reform and the boom of the real estate industry in China. To protect basic land use for
agricultural cultivation, the state, which owns land theoretically, approves only limited
areas of constructive land annually. In practice, local governments exercise the actual
disposal right of land by deciding supply, transaction modes, zoning, prices, and the
requisition of land at the local level, within the autonomy given by higher-level
governments. This privilege encourages real estate developers to send bribes to government officials for land to earn tremendous industrial profits [9]. Even with new
means of land transfer through tender, auction, and listing, corruption still occurs
during land transactions comprising more complicated forms; these types of transactions have a dual system that consists of both submarket and market configuration of
land, which makes government intervention possible. Other functional units, such as
urban planning bureaus, also affect the approval of a real estate project and its final
profits. Therefore, firms tend to spend large amounts of money, or yingchou, to build
relationships with or actually bribe various government agencies [41].
Corruption in the mining sector bears many similarities to the real estate industry but
relies on state resources even more strongly. The inherent scarcity of mines has
increased along with the rising demand for energy and various scarce minerals. Local
government, as the de facto owner of local mines, has the actual right of disposal. It
also determined the transfer mode during privatization of state-owned mines early in
the 2000s, when systems of property rights exchange and state property management
were incomplete. The yearly comparison in Appendix B also indicates that the level of
overall corruption in the mining industry began to increase in 1999. Local government’s intervention not only made bribery common in the sector but also gave many
local officials opportunities to become the actual owner or shareholders of private
mines, sometimes even by “investing” with their political power rather than money
Who pays more “tributes” to the government?
319
[32]. When the “referee” becomes the “player,” regulations are difficult to enforce
because mine owners and administrators often collude with each other. Thus, both real
estate and mining corruption are essentially concerned with property rights reform of
key natural resources in factor markets.
In addition to tremendous corruption opportunities in these two sectors, the local
government has incentives to tolerate and even participate in real estate and mining
corruption. For many local governments, selling land has become the number-one
revenue resource [33]. Profits made by real estate companies also partially turn into
tributes to the government through heavy tanpai, in addition to taxes. According to a
proposal submitted by the National Political Consultative Conference in 2009, at the
local level approximately 25 different government departments can collect up to 50
different types of fees during a real estate development project. These fees account for
15 % to 20 % of final housing prices. Similarly, localities with rich mining resources
often have few other sources of income on which local governments can rely. This is
probably why tanpai costs in the mining sector are so high and rank only after the real
estate industry. This finding is also consistent with Zhan [39], who finds that provinces
with rich mineral endowments in China suffer from poor governance and more
corruption. Some local governments, to pursue economic growth and fiscal revenue
in the short run, laxly enforce production safety and environmental protection regulations for certain private—sometimes illegal—mines, causing mining disasters [37].
Real estate and mining corruption also tends to involve groups of officials because both
sectors require a series of administrative approvals for resource allocation. While mining
corruption more often involves lower-level officials, real estate corruption often includes
senior officials and extremely large bribe amounts. In a sample of 120 provincial-/ministerial-level corrupt officials from 1987 to 2010, 38 % were arrested for real estate corruption.12
According to the Supreme People’s Procuratorate, in 2009, among the 1305 officials
arrested for corruption and other misconduct in the state land and resource management
system, 1,149 worked in land transaction departments and 98 worked in mining approval
and management departments. This number increased in 2010, proving that government
departments that manage land and mines are very vulnerable to corruption [32].
Agriculture ranks as the third highest corruption sector, directly after the real estate
and mining industries. This might be somewhat surprising because agriculture is
usually considered a lower-profit industry than real estate and mining. Furthermore,
bribery in agriculture ranks the second highest among the 11 sectors, while government
extortion places only fifth. In other words, agriculture seems plagued by yingchou costs
more than tanpai, which also contradicts the common perception that this sector bears
various financial burdens. Agricultural firms actually depend heavily, probably no less
than the other two industries, on government support for resources, such as land and
bank loans, and policy support. Although the land market is still in its formative stage,
the capital market in China is even less developed, and enterprises, especially private
ones, must pay extensive costs to obtain loans from state-controlled banks. In a survey
of 29 cities, nearly 82 % of respondents reported that rent-seeking during capital
allocation in Chinese banks was prevalent or very common [38]. Agricultural enterprises are more disadvantaged in terms of securing bank loans.
12
“Gaoguan tanfulu” [“High level officials’ corruption record”], Caijing Magazine, 25 October 2010
(http://magazine.caijing.com.cn/2010-10-24/110550933.html, accessed 31 August 2012).
320
J. Zhu, Y. Wu
The agricultural sector is highly labor intensive with low skill requirements. Firms need
to rent land to grow or process products and to purchase seed and fertilizer, which require
large amounts of initial capital. Moreover, agricultural production is vulnerable to climate
change, often needs relatively long investment cycles, and thus faces more business risks.
This low-technology, high-risk feature makes it more difficult for agricultural firms to
procure bank loans than others. Many firms must “lobby” agricultural departments at
various administrative levels by any means, including bribery, to rate their firms as leading
enterprises (longtou qiye) in the sector because such a title can bring low-interest rate bank
loans and other preferential policies that small to medium-sized enterprises cannot get.13
Since 2001, several senior agricultural department officials have been caught accepting
bribes to rate firms higher.14 Thus, the agricultural sector might need to spend more time
and money in building government relationships to obtain local government support. The
2002 ACFIC survey shows that agricultural firms, on average, spent 57 % more, excluding
interests, than other sectors to obtain bank loans. A large portion of these extra expenditures
are likely yingchou expenses and even bribes. Agricultural firms may also need to pay
yingchou to local government officials to overcome various internal trade barriers caused
by local protectionism when trying to sell products across regional borders.15
These particularly high demands for more support, better services, and protections
from government have likely also increased agricultural entrepreneurs’ desire for
political participation. The ACFIC survey shows that during the five years, more than
63 % of sampled agricultural entrepreneurs reported that they were representatives of
the local or National People’s Congress and political consultative conference. This ratio
is higher than all other sectors (47 % on average). As Dickson [7] argues, absorbing
private entrepreneurs into the CCP and its formal institutions is indeed CCP’s
“cooptation” strategy. Why might local governments favor agricultural sector entrepreneurs more than others? The answer most likely lies with the entrepreneurs, given the
preceding discussion. From the entrepreneurs’ perspectives, if the needed resources are
essentially controlled by the government, it is better to stay close to political power.
Thus, these entrepreneurs are more eager to participate in the government decisionmaking process to make their voices heard in the formal channel. Positions, such as
People’s Congress representatives, also provide private entrepreneurs the opportunities
to cultivate and enhance their relationships with government officials and, by doing so,
significantly benefit their businesses. However, to seize these political advantages,
private entrepreneurs sometimes need to reach out to local government officials
actively and even bribe them. Therefore, the high overall corruption and bribery cost
in the agricultural sector might also be caused by entrepreneurs’ networking activities
geared toward formal political positions.16
For example, see “Xi nongwei: guanyu yinfa wuxishi 2010 niandu nongye chanyehua longtou qiye fuchi
zijin guanli banfa de tongzhi” [“Wuxi Agricultural Committee: About the notice of financial sponsorship
management of leading firms in agricultural sector in Wuxi in 2010”] (http://www.wxagri.cn/web101/pages/
nlzx/tzgg/423119.shtml).
14
Cases include Ding Li from Agricultural Department of the State Council and Deng Cichang of Hunan
Provincial Agricultural Department. See http://cpc.people.com.cn/GB/64093/64371/5868446.html and http://
politics.people.com.cn/GB/14562/12494379.html.
15
This is possible especially when considering that most agricultural firms in the survey are located in east China.
16
See, for example, “Anhui: liang qiyejia xinghui dangshang de renda daibiao daibiaole shui?” [“Anhui: two
entrepreneurs bribed to be People’s Congress representatives; who do they represent?”], Zhongguo Qingnian
Bao [Chinese Youth Post], (http://fanfu.people.com.cn/GB/12343333.html).
13
Who pays more “tributes” to the government?
321
Another sector worthy of attention is construction, which ranked just behind the
three most corrupt sectors of real estate, mining, and agriculture. It is also fourth in
terms of bribery and third in terms of government extortion. While this sector’s 5-year
average of overall corruption, bribery, and government extortion fell into the medium
level, corruption in all three categories in this sector fell into the above-average level
twice in the 5 years. This sector relies less on state-controlled natural resources than the
real estate and mining sectors, but official corruption is also closely related to government intervention. The government often initiates and controls large construction
projects, ranks construction companies, and certifies architects. In addition, the construction sector is a labor-intensive sector with a low entry bar, which prompts intense
competition in an industry in which firms large and small, qualified and unqualified all
directly compete with each other for business. Therefore, many firms bribe the government to secure profitable projects, higher ratings, and certification of architects.
Corruption in this sector has also increased in recent decades, leading to several
notorious accidents caused by low-quality construction projects, including the fatal
accidents of the high-speed railways.
General explanations: what types of sectors pay more tributes?
To shed more light on sectoral corruption in China, we derive some general explanations of the types of sectors that tend to pay more tributes to the government.
Comparing the 11 sectors, especially the sectors with high and low overall corruption
in Table 1, we conclude that the degree to which a sector relies on scarcity and mobility
of government provisions, such as state-controlled natural resources, services, and
support, determines how much that sector is willing to and/or must pay the government
in tributes. The scarcer and less mobile the state provisions a sector needs, the higher is
the level of corruption in that sector. This is especially the case in real estate, mining,
agriculture, and even the energy sectors. Land, mine, and energy resources are not only
scarce but also immobile between different localities. Competition for these resources is
often exclusive, as use of these factors, such as a plot of land, can only benefit one firm
at a time, at the expense of others. Thus, entrepreneurs are more willing to bribe
government officials for these scarce state provisions, which can bring a privilege of
high rent in a sector. The government is also more likely to solicit a large amount of
bribes and extract more revenues from these sectors. Immobility and scarcity of statecontrolled resources can give local governments more bargaining leverage over enterprises when attracting investment because these firms have fewer choices when
selecting investment destinations in these sectors. Thus, scarce and immobile resources
are often the major fiscal source for local governments. In addition, some resources,
such as land, mines, energy, and bank loans, are scarce naturally, while others are scarce
by design, such as the rankings of construction and agricultural firms. These rankings
are preconditions to attain competitive opportunities and scarce productive factors.
Thus, enterprises in many highly corrupt sectors compete essentially for scarce and/or
immobile basic productive factors, mainly land, natural resources, and capital. In
comparison, sectors that rely less on scarce state provisions face this kind of competition to a lesser degree. For example, in the restaurant & retail sector, although
enterprises also need government-approved licenses, these licenses usually are not
provided in a competitive way. As long as an enterprise meets the requirements, the
322
J. Zhu, Y. Wu
60
Real Estate
Agriculture
40
Energy
Mining
Construction
20
Transportation
Social Services
Manufacturing
Restaurant & Retail
Science & Technology
CEHS
0
4
6
8
10
12
Dependence on Land (%)
Total tributes (Y10,000)
Fig. 1 Correlation between tributes and sectoral dependence on land. Source: ACFIC Surveys of 2003 and
2005. In both surveys, entrepreneurs were also asked their firms’ dependence on land as their basic production
factor. We averaged their answers in the 2 years. Total tributes are also the average of the absolute value of the
tributes in 2003 and 2005
50
Agriculture
40
Real Estate
30
20
Energy
Construction
Manufacturing
Transportation
10
Social Services
Mining
Science & Technology
Restaurant & Retail
CEHS
0
4
6
8
10
Dependence on Land (%)
Bribery measured by yingchou (Y10,000)
Fig. 2 Correlation between bribery and sectoral dependence on land. Source: Same as Fig. 1
12
Who pays more “tributes” to the government?
323
25
Real Estate
20
15
10
Mining
Transportation
5
Social Services
Energy
Construction
Manufacturing
Agriculture
Restaurant & Retail
CEHS
Science & Technology
0
4
6
8
10
12
Dependence on Land (%)
Extortion measured by tanpai (Y10,000)
Fig. 3 Correlation between government extortion and sectoral dependence on land. Source: Same as Fig. 1
licenses are approved. Even if there is corruption in these sectors, it tends to be petty
corruption.
Scarcity of state provisions does not necessarily mean corruption, as long as the
government is transparent and factor markets are effective. Corruption in the real estate,
mining, and agricultural sectors, however, shows that the government still deeply
intervenes in Chinese land, natural resources, and capital factor markets. In contrast
with the “big bang” policy of reform in Russia and Eastern Europe, China has taken an
approach of “evolutionary reform” [24], in which marketization began with product
markets by introducing competition, restructuring SOEs, encouraging the establishment
of non-SOEs, and maintaining steady economic growth. This reform only gradually
moved down to “the more fundamental, and more sensitive, development of markets
for land, labor, capital, and finance” ([26]: 248). As the sector-by-sector analysis shows,
there are many local interests, departmental interests, and ambiguity of property rights
in these key factor markets, which makes their reform difficult. In a positive situation, a
dual system might exist, comprising administrative (or submarket) and market transactions of factors such as land. In a negative scenario, such as the capital market,
essentially no real market exists. Liu Wei, a famous Chinese economist, commented
that in China, the labor market is more disordered than product markets; the land
market is more backward than the labor market; and the capital market, which is
basically monopolized by the state, is even more underdeveloped than the land market.
17
In the absence of effective factor markets, the government naturally plays an
17
See “Liu Wei: zhongguo xuyao yaosu shichanghua gaige” [“Liu Wei: China needs marketization of
factors”], Wangyi [NetEase], 15 March 2011, (http://money.163.com/11/0315/17/6V736IMT00254LK2.html).
324
J. Zhu, Y. Wu
influential role in allocating scarce resources. State functionaries, including officials,
can take the opportunities to create rent and extract rent by determining who gets what
[23]. Because firms that obtain these basic factors are likely to profit, and those that do
not are greatly disadvantaged, entrepreneurs naturally tend to bribe officials to grab the
rent. In this way, officials actually share the rent created by administrative power with
enterprises.
In evidence of these arguments, consider land, which is scarce, immobile, and
essentially allocated by the government. Figures 1, 2 and 3 show that among the 11
sectors, those that rely more on land as their basic productive factor tend to incur greater
corruption than those that rely less on it. Thus, although market reform in China has
created more opportunities and incentives for official corruption, further marketization
is greatly required to limit government intervention in resource allocation to decrease
corruption.
Conclusion
In this article, we conducted preliminary research on official corruption from a sectoral
dimension. Our main objective was to identify which sectors engage more in official
corruption, perhaps enabling future research to focus more on the most corrupt sectors
and further explore the risk points in a given sector. We treat firms’ yingchou and tanpai
costs as indicators of bribes sent proactively to government officials and revenues
extorted by the government, respectively. We assume that when a sector’s yingchou
costs are significantly higher than other sectors, potential bribery in this sector is high.
Future research might try to separate regular business activities from corruption
behaviors more accurately. In this research, we find that sectoral corruption is, to a
large degree, influenced by the degree of scarcity and mobility of state provisions on
which a sector depends, such as resources, services, and policy supports. The scarcer
and less mobile these provisions, the more tributes a sector must pay to the government.
Because sectoral corruption is also related to the quality of factor markets, factor
markets reform is urgently needed in China to reduce corruption opportunities for
government officials. Our in-depth investigation of the high-corruption sectors also
confirms this point.
Although these high-corruption sectors, along with the forms of corruption and
manipulation within them, differ from one another in many ways, a common
feature is that the government is in charge of key decisions and resources. For
example, the real estate, mining, and even agriculture sectors all rely on the
government for approval of large projects and for key scarce resources. Although
these resources are becoming increasingly desired as the economy develops, their
factor markets are still developing as a result of lingering effects of the planned
economy, the incomplete legal system, and local governments’ private interests. A
dual system consisting of both submarket and market transaction modes of state
resources leaves the government with broad autonomy to intervene in the
supply of those productive factors influencing firms’ profits. Such autonomy
generates opportunities and incentives for corruption. Thus, to curb corruption
oriented toward state-controlled resources, it is important to further complete factor
markets reform.
Who pays more “tributes” to the government?
325
We also find that official corruption tends to occur more often in economically
active sectors, such as real estate and mining, followed by construction, energy,
and manufacturing. Not surprisingly, this bears some similarity to practices in
many other countries18 because active sectors might involve more profit opportunities and, therefore, more incentive for corruption. Many of these newly rising
sectors are capital intensive, and thus corruption in these sectors would naturally
involve large sums of money and likely take on complicated forms. Corruption in
economic sectors also tends to have more long-lasting effects on development and
brings greater challenges for anticorruption campaigns in China. In contrast,
corruption in sectors such as CEHS, restaurant & retail, and social services stays
at a relatively low level, showing that corruption in these sectors is relatively
controlled. However, this does not mean that anticorruption can be ignored in
these sectors. Corruption measured by ratios shows that these seemingly lowcorruption sectors pay higher percentages of their sales revenues as tributes to the
government.
Finally, future research using larger samples could further examine the regional
distribution of sectoral corruption, especially in large countries, such as China.
The regional disparities, natural endowment of different localities, and institutional differences across regions in China likely lead to clusters of different types of
industries in different geographical areas. Consequently, corruption may not be
concentrated in the same sectors or exhibit the same format across China. Limited
by our sample size, we cannot deeply explore the regional differences of sectoral
corruption. Our tentative results in Appendix F show that the amount of tributes in
each of the three categories of corruption—overall tributes, bribery, and government extortion—is higher in east China than in west China on average in the 5year period. This is likely due to the economic and income disparity between east
and west China. Although socioeconomic institutions in east China are betterdeveloped than those in west China, the fundamental problems in the factor
markets occur nationwide. In these cases, the more rapid economic growth in
the east may end up creating more corruption opportunities and “inflating” the
corruption sums to a greater degree. More specifically, we find that for the three
high-corruption sectors, corruption in the real estate industry stays high regardless
of regions and types of corruption, while bribery is more serious in agriculture in the
east and the mining industry suffers more from government extortion in the west.
Sectors such as social services, CEHS, and restaurant & retails still appear to have
low levels of corruption across regions. Thus, further research on regional differences
would be worthwhile to design specific anticorruption strategies targeting different
sectors in different localities.
Acknowledgments The authors appreciate the valuable comments from Prof. Ting Gong, Dr. Jing Vivian
Zhan, Xin Sun, the three anonymous reviewers, and participants of the Bi-week Forum at Zhejiang University
of Finance and Economics and the Conference of Local Government Building in the New Era at Nankai
University. All errors remain responsibilities of the authors.
18
According to the Transparency International Bribe Payer Index’s 2008 report, internationally, public works,
contracts/construction, real estate and property development, oil and gas, and heavy manufacturing and mining
bribe officials in their business dealings more often than other industries.
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J. Zhu, Y. Wu
Appendix A About the survey
We obtained survey data from the Universities Service Center (USC) at Chinese
University of Hong Kong. This survey has been conducted every 2 to 3 years since
1991, in cooperation with the ACFIC, the Chinese Academy of Social Sciences, the
United Front Work Department of the Central Committee of the CCP, and the State
Administration for Industry & Commerce (SAIC). A full description of the survey
appears on the USC website (http://www.usc.cuhk.edu.hk/DCS/DCS31-16.aspx). In
this research, we use the publicly available data from the surveys conducted in 1997,
2000, 2002, 2004, and 2006. The sample ratio ranges from 0.16 to 6.37 %, and the
sample sizes are 1,946 firms in 1997, 3,073 firms in 2000, 3,258 firms in 2002, 1,613
firms in 2004, and 3,837 firms in 2006. The majority of private firms are randomly
sampled in the five surveys with a proportionate stratified sampling procedure, which
takes the 31 provincial units in China as the strata. According to the USC website, the
data from 1997, 2000, and 2002 also include a small portion of tracking data from
previous surveys. For example, the 1997 survey tracked 268 firms surveyed in
1995, the 2000 survey tracked 168 of the 268 firms tracked in 1997, and the 2002
survey tracked 839 firms surveyed in 2000. The 2004 and 2006 surveys also
include some data obtained from the fixed observation points of the SAIC. For
example, in 2006, 1536 firms were surveyed in this way (the website does not
provide the exact number for 2004). Therefore, most of the data are collected by
probability sampling. However, the total sample is not purely representative,
because a small portion comes from tracking data and the SAIC’s fixed
observation points. The data also suffer from missing values, which we discuss
subsequently. Despite these drawback, in general the survey is informative and, to
the best of our knowledge, the only one of its kind available for the public use.
Although extant research has published fruitful findings based these surveys, the
data are limited, and thus our findings are suggestive rather than conclusive.
Table 2 below provides the number of firms classified by industries.
Missing values in this data set deserve attention. Together, the 5-year survey
collected corruption-relevant data from 6622 of the 11,617 firms effectively
sampled. To address the concern that missing corruption data may cause selection
bias, we follow Svensson’s [31] technique and check whether the groups of
respondents and nonrespondents differ on observables. In total, 2,400 of the
2,571 firms not responding to the main questions on yingchou and tanapi also
declined to answer other sensitive questions (e.g., about employees and profit),
while the remaining 171 firms specifically declined to answer only the main
questions on yingchou and tanpai. In Table 3, we report a set of regressions using
observable firm characteristics, such as number of employees and profits as
dependent variables. In the first set of regressions, the regressor is a dummy
variable that takes the value of 1 if a firm has missing data on corruption. In the
second set of regressions, the regressor is a dummy variable that takes the value of
Who pays more “tributes” to the government?
327
Table 2 Number of firms classified by industries in the sample
Industry
Number of firms in
the whole sample (%
in the whole sample)
Number of firms reported
corruption-related information
(% in the sector sample, % in
the subsample of firms with
corruption information)
Number of firms reported both
corruption-related information
and sales revenues (% in the sec
tor sample, % in the subsample of
firms with corruption information)
Agriculture
702 (6.04 %)
428 (60.97 %, 6.46 %)
364 (51.85 %, 6.26 %)
Mining
198 (1.7 %)
109 (55.05 %, 1.65 %)
94 (47.47 %, 1.62 %)
Manufacturing
5,034 (43.33 %)
2,832 (56.26 %, 42.77 %)
2,587 (51.39 %, 44.47 %)
Energy
123 (1.06 %)
78 (63.14 %, 1.18 %)
74 (60.16 %, 1.27 %)
Construction
824 (7.09 %)
477 (57.89 %, 7.2 %)
422 (51.21 %, 7.25 %)
Transportation
328 (2.82 %)
216 (65.85 %, 3.26 %)
180 (54.88 %, 3.09 %)
Social services
1,524 (13.12 %)
849 (55.71 %, 12.82 %)
673 (44.16 %, 11.57 %)
Restaurant &
Retail
1,968 (16.94 %)
1,124 (57.11 %, 16.97 %)
994 (50.51 %, 17.08 %)
Real estate
369 (3.18 %)
176 (47.7 %, 2.66 %)
153 (41.46 %, 2.63 %)
Science &
Technology
258 (2.22 %)
157 (60.85 %, 2.37 %)
132 (51.16 %, 2.23 %)
CEHS
289 (2.49 %)
176 (60.9 %, 2.66 %)
145 (50.17 %, 2.49 %)
Sample Size
11,617
6,622
5,818
1 if a firm only has missing data on corruption. The results in Table 3 show that
the coefficients of both dummy variables are not statistically significant. As is
evident, the former group of firms (2,571, reported in column 1) and the latter
group of firms (171, reported in column 2) do not differ significantly in observables (in the number of employees and profits) from the group of corruptionreporting firms. The regressions suggest that at least for employment size and
profits, the sample of 6,622 firms is still representative of the whole sample.
Table 3 Comparison of firms
reporting and not reporting corruption (i.e., yingchou and tanpai) data
Coefficient estimates from ordinary least squares regressions appear in the second and third columns (standard errors are in parentheses, and p-values are in
brackets)
Dependent variable
[No. observations]
Firms missing
corruption data
Firms only missing
corruption data
Employees [11,168]
5.822
[9.321]
(0.532)
−4.526
[10.033]
(0.652)
Profit [9,511]
17.044
[16.015]
(0.287)
4.97
[17.186]
(0.772)
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J. Zhu, Y. Wu
Appendix B
Table 4 Overall corruption distribution across sectors
Rank
1
1996
Restaurant &
retail
STD
1999
STD
2001
STD
2003
Science &
technology
STD
2005
-0.900 Social services
-0.731
-1.223 Social services
-0.792 Agriculture
-0.707 Social services
-0.797
3 Mining
-0.410 Transportation
Restaurant &
-0.673
retail
-0.657 CEHS
-0.723 Social services
-0.629
4 Manufacturing
-0.347
-0.549 Energy
-0.437
-0.611 Energy
-0.517
5 Real estate
Restaurant &
-0.331
retail
-0.319 Transportation
-0.427 Manufacturing
Science &
-0.405
technology
-0.325
6 CEHS
-0.308 Construction
-0.239 Manufacturing
-0.369 Construction
-0.299 Transportation
-0.289
2 Social services
Science &
technology
Restaurant &
retail
-0.973 CEHS
STD
-1.475 Energy
Restaurant &
retail
-0.917
-0.751
7 Construction
0.218 Agriculture
-0.159 CEHS
-0.289 Transportation
-0.115 Manufacturing
0.025
8 Energy
0.293 CEHS
-0.134 Construction
-0.158 Mining
-0.086 Mining
0.067
0.462 Manufacturing
Science &
-0.110
technology
Science &
9
technology
0.247 Energy
0.442 Agriculture
0.123
10 Transportation
1.005 Mining
1.732 Mining
0.862 Real estate
1.195 Construction
0.477
11 Agriculture
2.116 Real estate
2.144 Real estate
2.667 Agriculture
2.372 Real estate
2.738
1. In each year, sectors are ranked according to their level of corruption from low to high
2. Cells shaded gray mean that corruption of that sector is above the average of that year
3. STD = standardized corruption level for a sector
4. To calculate STD, we use the within-year standard score, or z score of corruption, which we derive
from the following equation. We call this transformed variable CORRUPTSTDit CORRUPTSTDit ¼
it −U t
CORRUPT it −Z it ¼ CORRUPT
, where Ut is the mean value of total corruption across all the sectors in a
σt
given year and σt is the standard deviation of the total corruption in a given year across all the sectors.
Essentially, the mean of the total corruption for a given year is subtracted from the observed sectoral total
corruption, CORRUPTit, and divided by the standard deviation of total corruption for a given year, producing a
standardized total sectoral corruption with a mean of 0 and a standard deviation of 1. In this way, we can
compare sectoral corruption across years. Any sectors whose standardized CORRUPTit or CORRUPTSTDit is
above 0 have corruption levels above the average in a given year and can be regarded as having a relatively high
level of corruption. When CORRUPTSTDit is way above 0, for example, more than one standard deviation, it
means that the corruption level is very high. We apply the same process to BRIBEit and EXTORTit and generate
BRIBESTDit and EXTORTSTDit
5. Tables of distribution of sectoral bribery measured by yingchou costs and government extortion measured
by tanpai costs are available on request from the authors
Who pays more “tributes” to the government?
329
Appendix C
Table 5 Ordinary least squares regressions of average sectoral sales revenues on tributes
Independent variable
Overall corruption
Yinchou costs
Tanpai costs
Constant
−4.064a (1.324)
−4.807a (1.375)
−2.464b (1.216)
a
a
0.709 (0.181)
Ln (sales revenues)
0.37b (0.175)
0.82 (0.186)
Industry
Control
Control
Control
Year
Control
Control
Control
R2
0.677
0.682
0.641
Standard errors are in parentheses
a
Coefficient is statistically significant at the 1 % level
b
Coefficient is statistically significant at the 5 % level
Appendix D
Table 6 Sectoral overall corruption measured by ratios
Rank
1996
1 Agriculture
2
Science &
technology
STD
1999
STD
2001
-1.32
Energy
-0.88 Construction
- 0.902
Manufacturing
-0.87 Manufacturing
STD
2003
-0.538 Energy
STD
2005
STD
-0.633 Energy
-1.239
-0.623 Construction
-1.033
-0.482 Agriculture
-0.552 Agriculture
-0.953
-0.5 Construction
Restaurant &
3
retail
-0.665 Construction
-0.664 Mining
4 Real estate
-0.635 Agriculture
-0.564
Science &
technology
-0.424 Manufacturing
-0.529 Manufacturing
-0.778
5 Manufacturing
-0.499
Restaurant &
retail
-0.481
Restaurant &
retail
-0.381 Real estate
-0.466 Mining
-0.665
Restaurant &
-0.328
retail
-0.448 CEHS
6 Construction
7 Mining
-0.342 Transportation
0.17 Mining
8 Social services
0.297 Real estate
9 CEHS
Science &
0.463
technology
10 Energy
11 Transportation
1.638 CEHS
1.795 Social services
-0.466 Energy
-0.334 CEHS
-0.223 Transportation
1.022 Agriculture
1.535 Social services
1.924 Real estate
-0.28 CEHS
-0.269 Social services
Science &
-0.16
technology
-0.37 Social services
-0.123 Real estate
0.18
0.473
0.5
0.044 Transportation
0.605
0.462 Transportation
Restaurant &
1.069
retail
1.046
2.899 Mining
Science &
2.629
technology
1.864
1. In each year, sectors are ranked according to their level of corruption from low to high
2. Cells shaded gray mean that corruption of that sector is above the average of that year
3. STD = standardized corruption level for a sector
4. Tables of distribution of sectoral bribery measured by yingchou costs and government extortion measured
by tanpai costs are available on request from the authors
5.516 (0.587)
Agriculture
Manufacturing
Construction
9
10
11
1.876 (−1.041)
2.062 (−0.957)
2.55 (−0.739)
2.521 (−0.752)
2.899 (−0.583)
0
0
0
1
1
3
3
3
4
2
2
3.799 (1.731)
Construction
Manufacturing
Agriculture
Restaurant & retail
Energy
Real estate
Transportation
Social services
CEHS
Government extortion
1
3
4
4
3
1
1.287 (−1.425) 0
1.453 (−1.217) 0
1.686 (−0.923) 1
1.931 (−0.616) 1
4
4
2
4
0.864 (−0.508) 0
0.968 (−0.451) 1
2.632 (0.471)
2.858 (0.596)
2.872 (0.603)
6.374 (2.543)
AVE EXTORT Frequency
above yearly
average
Energy
Manufacturing
Construction
CEHS
1
0
0.429 (−0.749) 0
0.61 (−0.649)
0.59 (−0.66)
0.688 (−0.606) 1
Science & technology 0.718 (−0.589) 1
Agriculture
Restaurant & retail
Social services
Mining
Transportation
Real estate
Sector
Frequency
above yearly
average
2.093 (−0.413) 2
2.614 (0.243)
2.644 (0.28)
2.887 (0.586)
2.969 (0.688)
Science & technology 3.269 (1.066)
Mining
AVE BRIBE
Average ratios are calculated by taking the average of the annual ratios. Standard deviations are in parentheses
Restaurant & retail
Energy
7
8
3.656 (−0.245)
Science & technology 3.987 (−0.096)
Transportation
4
5.519 (0.589)
6.656 (1.097)
CEHS
Social services
3
8.989 (2.141)
5
Mining
2
Bribery
AVE CORRUPT Frequency above Sector
yearly average
6
Real estate
1
Sector
No. Overall corruption
Table 7 Average, frequency, and levels of sectoral corruption measured by ratios
Appendix E
330
J. Zhu, Y. Wu
Restaurant & retail
(18.769)
Manufacturing (18.679)
4
5
Social service (9.335)
23.415
10
11
Average tributes
b
15.683
Social services (7.344)
CEHS (9.027)
Restaurant & retail
(9.112)
Transportation (10.466)
Energy (11.028)
Agriculture (12.233)
Construction (13.611)
Manufacturing (14.427)
Science & tech (19.737)
Absolute tributes are in parentheses
Sectors are ranked from high to low levels of corruption
Energy (11.788)
9
a
Mining (17.448)
CEHS (16.481)
8
Transportation (18.357)
Construction (18.826)
3
Science & tech (18.05)
Agriculture (45.333)
2
7
Real estate (64.497)b
1
6
Real estate (40.266)
East China
No.
Mining (25.299)
Non-east China
Overall corruption
Ranka
Table 8 Regional distribution of sectoral corruption
Appendix F
14.926
Social services (8.533)
Energy (8.275)
Mining (8.569)
Transportation (10.948)
Restaurant & retail
(10.979)
CEHS (11.105)
Science & tech (12.358)
Construction (12.539)
Manufacturing (13.013)
Agriculture (33.87)
Real estate (36)
East China
Bribery
10.457
Social services (4.063)
Restaurant & retail
(6.07)
CEHS (6.655)
Transportation (6.681)
Agriculture (8.098)
Manufacturing (9.914)
Construction (10.387)
Mining (12.633)
Science & tech (14.857)
Energy (16.214)
Real estate (18.911)
Non-east China
7.809
Social services (2.953)
Energy (4.343)
CEHS (4.728)
Science & tech (5.247)
Manufacturing (6.228)
Agriculture (6.333)
Construction (6.726)
Restaurant & retail
(7.008)
Mining (7.157)
Transportation (8.066)
Real estate (27.108)
East China
Government extortion
6.160
CEHS (2.335)
Social services (2.441)
Energy (2.785)
Restaurant & retail (3.205)
Transportation (3.752)
Construction (4.124)
Manufacturing (4.277)
Agriculture (4.719)
Science & tech (5.837)
Mining (12.954)
Real estate (21.326)
Non-east China
Who pays more “tributes” to the government?
331
332
J. Zhu, Y. Wu
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