corruption, corruption, and inter-county income disparity in china

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The Social Science Journal 48 (2011) 435–448 Contents lists available at ScienceDirect The Social Science Journal j ourna l ho me pag e: www.elsevier.com/locate/soscij Corruption, anti-corruption, and inter-county income disparity in China Yiping Wu a , Jiangnan Zhu b,a Department of Industrial Economics, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou 310018, China b Department of Political Science, University of Nevada, Reno, 1664 North Virginia St., Reno, NV 89557-0302, USA a r t i c l e i n f o Article history: Received 4 December 2010 Received in revised form 6 March 2011 Accepted 10 May 2011 Available online 3 August 2011 a b s t r a c t The rapid economic growth in China has been connected with a large income gap across regions. While most existing research has focused on economic factors to explain the problem, we argue that local government’s anti-corruption endeavors also play a very significant role in influencing local income levels. Recent research shows that corruption undermines economic growth and generates poverty, we therefore hypothesize that gov- ernment anti-corruption measures should increase local income levels. Using county-level data and Ordinary Least Square (OLS) estimates, we find counties with higher degree of anti-corruption tend to have higher income measured by county-level per capita GDP. We also employ a recently developed Shapley value decomposition technique to quantify the contributions of each variable. We find that anti-corruption plays a large role in explaining inter-county income disparity in China. Published by Elsevier Inc on behalf of Western Social Science Association. “Inflation plus income inequality, and corruption are sufficient to influence social stability and even the regime consolidation.” Wen Jiabao, Premier of the People’s Republic of China, March 14th, 2010, on the Press Conference of the National People’s Congress 1. Introduction Along with the rapid economic growth, there has been a large and increasing regional income disparity in China. The per capita GDP of Central China was about 65% of East China at the beginning of the economic reform in 1980. This ratio, however, had dropped almost linearly to 49% by 2002. Similarly, the per capita GDP ratio between West and East China had declined from 53% to 39% in two decades (Wang & Fan, 2004). The income gap is even larger at provincial level. In 1983, Shanghai, as the richest provincial unit in Corresponding author. Tel.: +1 775 682 7759; fax: +1 775 784 1473. E-mail addresses: wyp [email protected] (Y. Wu), [email protected] (J. Zhu). China, had the highest real per capita GDP of Y3,245, while Guizhou, being the poorest, had a real per capita GDP of only Y259. 1 Thus the income in Shanghai was about 12.53 times of Guizhou. Although the income gap between the two places shrank to 12.18 times by 1990, it has expanded since then. Income in Shanghai had increased to about 14.5 times of Guizhou in 1995 and to 15.05 times in 2000. 2 Why is there such a large regional income disparity in China? Acemoglu (2008) has summarized from the general con- sensus that cross-country income differences are mainly related to physical and human capital, and technology. The cross-country differences in these major factors result from various fundamental causes, including luck, geogra- phy, culture, and institutions. Scholars studying Chinese regional income disparity have examined many eco- nomic factors, such as availability of resources, human capital, geographical locations, preferential policies, and 1 The GDP per capita of both provinces take the year of 1978 as the base year. 2 Data of income is collected from Chinese Statistical Yearbooks. 0362-3319/$ see front matter. Published by Elsevier Inc on behalf of Western Social Science Association. doi:10.1016/j.soscij.2011.05.001

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Page 1: Corruption, Corruption, And Inter-county Income Disparity in China

Journal Identification = SOCSCI Article Identification = 894 Date: September 14, 2011 Time: 5:32 pm

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The Social Science Journal 48 (2011) 435–448

Contents lists available at ScienceDirect

The Social Science Journal

j ourna l ho me pag e: www.elsev ier .com/ locate /sosc i j

orruption, anti-corruption, and inter-county income disparity inhina

iping Wua, Jiangnan Zhub,∗

Department of Industrial Economics, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou 310018, ChinaDepartment of Political Science, University of Nevada, Reno, 1664 North Virginia St., Reno, NV 89557-0302, USA

r t i c l e i n f o

rticle history:eceived 4 December 2010eceived in revised form 6 March 2011ccepted 10 May 2011vailable online 3 August 2011

a b s t r a c t

The rapid economic growth in China has been connected with a large income gap acrossregions. While most existing research has focused on economic factors to explain theproblem, we argue that local government’s anti-corruption endeavors also play a verysignificant role in influencing local income levels. Recent research shows that corruptionundermines economic growth and generates poverty, we therefore hypothesize that gov-ernment anti-corruption measures should increase local income levels. Using county-level

data and Ordinary Least Square (OLS) estimates, we find counties with higher degree ofanti-corruption tend to have higher income measured by county-level per capita GDP. Wealso employ a recently developed Shapley value decomposition technique to quantify thecontributions of each variable. We find that anti-corruption plays a large role in explaininginter-county income disparity in China.

blished

Pu

“Inflation plus income inequality, and corruption aresufficient to influence social stability and even theregime consolidation.”Wen Jiabao, Premier of the People’s Republic of China,March 14th, 2010, on the Press Conference of theNational People’s Congress

. Introduction

Along with the rapid economic growth, there has been large and increasing regional income disparity in China.he per capita GDP of Central China was about 65% of Easthina at the beginning of the economic reform in 1980. Thisatio, however, had dropped almost linearly to 49% by 2002.imilarly, the per capita GDP ratio between West and East

hina had declined from 53% to 39% in two decades (Wang

Fan, 2004). The income gap is even larger at provincialevel. In 1983, Shanghai, as the richest provincial unit in

∗ Corresponding author. Tel.: +1 775 682 7759; fax: +1 775 784 1473.E-mail addresses: wyp [email protected] (Y. Wu), [email protected]

J. Zhu).

362-3319/$ – see front matter. Published by Elsevier Inc on behalf of Western Sooi:10.1016/j.soscij.2011.05.001

by Elsevier Inc on behalf of Western Social Science Association.

China, had the highest real per capita GDP of Y3,245, whileGuizhou, being the poorest, had a real per capita GDP ofonly Y259.1 Thus the income in Shanghai was about 12.53times of Guizhou. Although the income gap between thetwo places shrank to 12.18 times by 1990, it has expandedsince then. Income in Shanghai had increased to about 14.5times of Guizhou in 1995 and to 15.05 times in 2000.2

Why is there such a large regional income disparity inChina?

Acemoglu (2008) has summarized from the general con-sensus that cross-country income differences are mainlyrelated to physical and human capital, and technology.The cross-country differences in these major factors resultfrom various fundamental causes, including luck, geogra-phy, culture, and institutions. Scholars studying Chinese

regional income disparity have examined many eco-nomic factors, such as availability of resources, humancapital, geographical locations, preferential policies, and

1 The GDP per capita of both provinces take the year of 1978 as the baseyear.

2 Data of income is collected from Chinese Statistical Yearbooks.

cial Science Association.

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436 Y. Wu, J. Zhu / The Social S

globalization, which are no doubt crucial to local economicdevelopment (Démurger, Sachs, & Hu, 2002; Fu & Wu,2006; Fujita & Hu, 2001; Jian, Sachs, & Warner, 1996; Wan,Lu, & Chen, 2007; Zhang & Zhang, 2003).

In this article we argue that government anti-corruptionendeavors or efforts to combat corruption also plays a veryimportant role influencing regional income disparities inChina. Corruption, commonly defined as using governmentpower for private gain, is often seen as a “comprehen-sive reflection of the legal, economic, cultural and politicalinstitutions” of a region.3 Recent research has shown thatcorruption mainly sands the wheels of economic devel-opment, as it deters business investment, distorts theallocation of human capital, and increases social instabil-ity (e.g., Aidt, 2009). We therefore expect localities withstronger anti-corruption measures should result in a bet-ter environment for economic growth and hence higherincome level, since control of corruption will limit the detri-mental effects of corruption.

Control of corruption is also one of the six institutionalindicators of governance, a factor which has been shownto be crucial for economic growth in cross-country studies(e.g., Kaufmann, Kraay, & Zoido-Lobatón, 1999). However,in China, control of corruption is still being institution-alized. Party disciplinary punishment of corrupt officialscould be substituted criminal penalty. Routine supervi-sion strategy is often times mixed with anti-corruptioncampaigns, which are characterized by short periods ofintensified enforcement, harsher punishment, and rhetoriccalling for public attention of anti-corruption, and in theend claims of success evidenced by a large number ofcadres arrested and convicted (Manion, 2004; Wedeman,2005). Due to the authoritarian nature of the regime, anti-corruption in China is also influenced by central and localleaders in terms of timing, frequency, and intensity ofthe campaigns, as well as investigation and punishmentof corruption. We call this campaign-based and leader-directed efforts “Chinese-style anti-corruption.” Comparedwith institutionalized anti-corruption which is based moreon routine supervision and legal framework rather thanleadership, the strength of Chinese-style anti-corruption ismore likely to vary in different time periods and in localitiesunder different leaderships. Because of this, the effective-ness of Chinese-style anti-corruption is often questionedby scholars. Therefore, it is worthwhile to study whetherChinese-style anti-corruption measures also benefit eco-nomic development and local income. Findings from thecase of China could shed light on other developing coun-tries plagued by corruption, such as Vietnam, that havesimilar regimes and anti-corruption systems.4 It also helps

to answer some skeptics’ question whether fighting cor-ruption is worth the bother (Gray & Kaufmann, 1998).

3 Svensson (2005, p. 20). This article also offers a discussion of the def-inition of corruption.

4 For instance in East Asia, anti-corruption in several countries isvery institutionalized and mainly works in a legal framework, such asSingapore, Japan, and South Korea. Vietnam has a very similar anti-corruption system to China, which combines both legal system and partydiscipline (see Tian, 2009).

urnal 48 (2011) 435–448

Chinese local governments, at various administrativelevels, have been actively involved in economic reformin recent decades (Zhou, 2007). This has also given localofficials many opportunities, incentives, and advantagesto seek rent and solicit bribes throughout the entirereform period. Various international indices indicate thatcorruption in China is very serious compared to manyother countries in the world (Guo & Hu, 2003). Existingresearch has extensively explained changes in corruptionin China (e.g., Gong, 2002; Guo, 2008; Hao & Johnston,1995; Manion, 2004; Sun, 2004; Wedeman, 2004; Wu,2005). However, the effects of anti-corruption on economicdevelopment and inter-regional disparity have not beenfully examined in current research. As most research hasfocused on the national level and problems of the Chineseanti-corruption system, it has overlooked the fact that gov-ernment anti-corruption varies at local level due to theleadership factors in Chinese-style anti-corruption, and dif-ferent anti-corruption endeavors could lead to differentinstitutional environments for local economic develop-ment. In this research, we attempt to fill in this gap. We usecounty-level audit data as indicators of the scale of gov-ernment anti-corruption efforts. We find counties with ahigher scale of anti-corruption efforts tend to have higherincome levels, measured by county-level per capita GDP.

We choose to focus on county-level governmentsbecause counties are important for China both politicallyand economically, though existing research has not paidenough attention to them. The county is actually China’soldest administrative unit and has been employed by thecentral government since the Qin Dynasty. For centuries,county-level governments have helped the central author-ities maintain their basic control at the grassroots level.In contemporary China, the county is the lowest level ofgovernment administration positioned between the formalparty-state hierarchy and the grassroots authorities (Luo &Chen, 2008). Counties control an overwhelming majorityof national land and population and also contribute morethan half of the national GDP.5 More importantly, countieshave the de facto right of local land disposal, which givesthem enormous real power (Zhang, 2009). They are alsoarguably the most active local economies, competing witheach other fiercely.

In the following, we first review the literature on theeffects of corruption and anti-corruption on economicdevelopment and income in order to draw hypotheses.Here, constrained by data availability, we focus on the cor-relation between local variances of anti-corruption andcounty level average income. We leave the overtime effectsof anti-corruption on inter-regional income disparity forfuture research and only make some conjectures in the

last section. Thus, we form a cross-county dataset includ-ing the major income determinants for the year 2003. Weuse three indicators to measure different aspects of anti-

5 Up to 2003, 94% of the national territory and 70.9% of the national pop-ulation were under the control of counties. In 2003, the overall nationallevel GDP was Y0.645 billion, accounting for 55.15% of national GDP.Data source: Chinese County Economic Network (Zhongguo Xianyu Jingji-wang): http://www.china-county.org.

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growth will be allocated away from their socially mostproductive uses. This consequently results in a declinein productivity and living standards (Murphy, Shleifer, &

9 Perry (1999, p. 313). She quoted in White, G. (1996). Corruption andmarket reform in China. IDS Bulletin, 27(2), 41.

Y. Wu, J. Zhu / The Social S

orruption so as to double check the robustness of ourndings, since measures of corruption and anti-corruptionre often controversial. The first indicator is the numberf audit personnel or auditors in each county-level auditureau. The second indicator is the number of projectsudited by local audit bureaus each year. These two indica-ors can be seen as the input of anti-corruption. The thirdndicator is “illegal funds” (weiji jin’e) uncovered by localudit bureaus, which can be seen as the output of anti-orruption. Illegal funds mainly refer to money obtainedy officials through bribery, embezzlement, graft, and mis-se of public assets in violation of government policies,arty disciplines and laws. For these three indicators, wese data for the year 2002 to decrease the endogeneityetween income and the control of corruption.6 Our even-ual objective is to study how anti-corruption endeavorsnfluence inter-county disparity. To quantify the contri-utions of anti-corruption along with other variables to

nter-county income disparity, we adopt a newly devel-ped Shapley value decomposition technique (Kolenikov &horrocks, 2005; Shorrocks, 1999; Wan, 2002, 2004, 2007).he Shapley value decomposition result indicates that anti-orruption plays a significant role explaining inter-countyncome differences in China.7 The last section summarizesur major findings and discusses theoretical and policymplications, as well as some of our conjectures aboututure research.

. Corruption, anti-corruption, and local economicevelopment in China

Corruption, or fubai in Chinese, can refer to “any formf improper behavior by either a state official or a mem-er of the Chinese Communist Party (CCP)” in China.8 Itanges from economic crimes such as graft, bribery, andisappropriation of public property, to official malfeasance

ess relevant to monetary gains, such as shirking and tor-ure, to individual misbehavior deviating from standardsf official morality, such as having mistresses (e.g., Gong,994; He, 2000; Wedeman, 2004). This research is mainly

nterested in the economic consequences of corruption and

nti-corruption, therefore we focus on those economicallyased improper behaviors. To be consistent with our indi-ators of anti-corruption, our definition of corruption is alsoegally based. In general, we operationally define corrup-

6 The selection of 2003 data is mainly based on data availability. Com-lete data at the county level is very hard to obtain. For anti-corruptioneasurement, we have data from 2001 to 2003. It should be noted the

arliest available data of illegal funds in Chinese Audit Yearbooks is from001. But for other relevant data, we only have complete data from 2003.iven that 2002 is the year when the new Hu and Wen administrationtarted, it is better to use anti-corruption data from 2002 as the lagged datao regress AGDP from 2003 so as to avoid impact of different leadershipn economy and anti-corruption.7 It should be noted that our research is interested in inter-county

ncome disparity, which is inherently different from income inequalityetween persons, though the two are related. For our research, we takeounties as our research subjects. Therefore, we need to first of all findut county level average income, which is influenced by local economicrowth.8 Wedeman (2004, p. 896).

urnal 48 (2011) 435–448 437

tion as the use of public power for private gain in violationof state laws, government policies and regulations, andparty disciplines.

Existing studies are in agreement that corruption inChina has grown in frequency, scale, and complexity duringthe post-Mao era. Government officials have also pointedout that corruption in contemporary China is “worse thanat any other period since New China was founded in 1949. Ithas spread into the party, government, administration andevery part of society, including politics, economy, ideologyand culture.”9 The forms of corruption have changed overtime and across industries and regions based on the loop-holes and profit opportunities created by reform policies.They range from profiteering activities in the 1980s, ille-gal privatization of public assets in the 1990s to frequentcorruption in the real estate and financial industries today(Gong, 1994, 1997; He, 2000; Sun, 2004; Zhu, 2012). Cur-rently between 30,000 and 50,000 cases involving bribery,embezzlement, and misuse of public assets are investi-gated by the procuratorates annually. More importantly,more cases containing high stakes (i.e., amounts aboveY100,000) and senior officials (i.e., officials above countylevel) are uncovered every year (Guo, 2008; Manion, 2004;Wedeman, 2004).

Although some early research argues for the “lubricanteffect” of corruption (e.g., Huntington, 1968; Leff, 1964) oneconomic development, recent research has increasinglyshown that corruption undermines economic growth.10

On the one hand, corruption lowers private investment,thereby lowers economic growth (Mauro, 1995). From amicroeconomic perspective, corruption can stop the estab-lishment of new firms, prop up inefficient firms, and distortthe allocation of entrepreneurial skills, productive technol-ogy and capital. These most important factors for economic

10 According to early research, in the context of pervasive and cum-bersome regulations, corruption may actually improve development andefficiency. Corruption could be a welcome lubricant easing the path tomodernization in a society where traditional norms are still powerful andrigid (Huntington, 1968, p. 69). Game theoretical models indicate that inthe second-best world where preexisting policy induces distortions, addi-tional distortions in the forms such as black-marketeering and smugglingmay indeed improve welfare even when some extra cost have to be spentin such activities (Bardhan, 1997; Beck & Maher, 1986; Lien, 1986). Inother words, corruption corrects or circumvents various sorts of preex-isting government failures (Aidt, 2009). Corruption is also seen as “speedmoney” or “tips for bureaucrats,” which reduces delay in moving filesin government offices and saves the opportunity cost of time for indi-vidual clients (Lui, 1985). In Chinese central-local hierarchy, bribes aresometimes regarded as the incentive bonuses for public officials to com-ply with the central government’s policies (Shirk, 1993). But later Criticsargue that the distortions are actually part of the built-in corrupt prac-tices of a patron–client network, though not exogenous to the system.The efficiency generated by corruption is at most the second best, andin many cases inefficiency may result from corruption (Rose-Ackerman,1975, 1978, 1999). As for speed money, scholars have found that corruptofficials may, instead of speeding up, actually cause administrative delaysin order to attract more bribes (Myrdal, 1968).

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Vishny, 1991, 1993).11 On the other hand, officials’ incen-tives to seek rent might increase the number of publicprojects undertaken in a locality and subsequently increasethe share of public investment in GDP. However, the aver-age productivity of the public investment could be veryinefficient because corruption can enlarge the size andcomplexity of the projects and inflate budgetary con-straints (Tanzi & Davoodi, 1997). This will result in “apossible reduction in other public spending, such as oper-ation and maintenance, education, and health,”12 whichwill impede long-term economic development. In addition,corruption not only generates income inequality but also“tends to preserve or widen existing income inequalities”(Johnston, 1989).13 It perpetuates “an unequal distribu-tion of asset ownership and unequal access to education,”minimizes the “progressiveness of the tax,” and lowers“the effectiveness of social spending” (e.g., Gupta, Davoodi,& Alonso-Terme, 2002; Li, Xu, & Zou, 2000; World Bank,2000).14 Widening income inequality can give rise to lowpublic investment in human capital for the vast majorityand social pressures for redistribution, both of which maylower economic growth (Easterly, 2007). Therefore, cor-ruption is seen by most economists as playing a criticalrole in causing low income and poverty traps (Andvig &Moene, 1990; Aidt, 2009; Blackburn, Bose, & Haque, 2006;Blackburn & Sarmah, 2008).

However, corruption does not seem to equally affecteconomic development and investment everywhere. Thelong-term coexistence of high GDP growth rate and rel-atively high levels of corruption in several East Asiancountries, especially China, has led a group of scholars tolook for factors countervailing the negative effects of cor-ruption. For instance, larger countries have been found tohave more advantages than smaller countries in shieldingthe cost of corruption. In order to gain unrestricted accessto the large internal market and a large pool of labor in largecountries, foreign investors are more likely to accept cor-ruption as a way of doing business there (Rock & Bonnett,2004). Domestic politics, such as power distribution withinthe patron–client network, also affects the efficiency of cor-ruption. Centralized corruption is also thought to be moreefficient than decentralized corruption (Bardhan, 1997;Blanchard & Shleifer, 2001; Khan, 1996; Khan & Jomo, 2000;Kang, 2002; Shleifer & Vishny, 1993). However, most of thisresearch only shows that corruption could be less harm-ful under certain conditions, instead of being harmless. Itis also possible that China and other East Asian countriescould have developed even faster economically if less cor-

ruption had been present.

Empirical studies have actually revealed that the inten-sification of corruption in China has brought negative

11 This argument is supported by cases in sub-Saharan African countries,Peru, Indonesia, and south Indian state. Corruption does distort farmersand entrepreneurs’ choices of technology and allocation of talent in thoseplaces, which consequently result in decline in productivity and livingstandards (Bates, 1981; De Soto, 1989; Fisman, 2001; Wade, 1982).

12 Tanzi and Davoodi (1997, p. 2).13 Zhuang, Dios, and Lagman-Martin (2010, p. 14) (ADB economics work-

ing paper series, 192).14 You and Khagram (2005, p. 140).

urnal 48 (2011) 435–448

impacts to Chinese economic development, although theaverage annual GDP growth rate remains around 8% (Chen,Li, & Yin, 2008; Yang & Zhao, 2004). At the national level, it isestimated that corruption has caused an average economicloss between Y987.5 billion and Y1,257 billion annuallysince the second half of the 1990s. This number accountsfor 13.2–16.8% of annual GDP (Hu & Guo, 2001). At thelocal level, prevalent corruption will lead to low income.For instance, when government positions were bought andsold widely in Heilongjiang province, limited resources andcapital were all gathered in the hands of a few local lead-ers through the long chain of buying and selling offices.“A lot of potential market investments were transferredto cultivate administrative promotions.”15 This caused anextremely unequal distribution of social wealth, the nearcollapse of local economy, and plummeting average incomeof local residents. In recent years, many scholars argue thatthe “grey income,” often times earned through corruption,owned by a small proportion of privileged people, such asgovernment officials and some higher-income groups, hasgreatly enlarged the unequal distribution of income amongurban residents (Chen & Li, 2010; Wang, 2010).16 More-over, public power is also utilized to monopolize certainprofitable industries or lift the entry bar to favor particularcompanies, which increases inequality of both income andprofit-making opportunities.

But most important, inequality and low income causedby corruption is very likely to ignite public resentment andsocial unrest, which will further erode the economy.17 Mo(2001) shows empirically that the most important chan-nel through which corruption affects economic growth ispolitical instability. Indeed, many clashes between Chi-nese villagers and local officials in recent decades havebeen caused by local cadres’ distortion of “popular cen-tral policies (such as economic development) into harmfullocal policies (tu zhengce) that justify wasted investmentand unauthorized extraction.”18 The arbitrary collection ofvarious extra-budgetary fees by local governments frompeasants has been one of the main sources of peasantprotests and petitions (Li & O’brien, 2008; O’Brien & Li,2005), while the extra-budgetary fund is a major sourcespreading local corruption (Chen, Hillman, & Gu, 2002).Premier Wen Jiabao expressed the central government’sconcern by remarking, “Inflation plus income inequality,and corruption are sufficient to influence social stabil-ity and even the regime consolidation.” This reflects theimportant political implication of corruption and inequal-ity for the Chinese party-state.

In general, we believe corruption, especially serious cor-ruption, can adversely affect local economic developmentand consequently lower the income level of a locality by

15 Zhu (2008, p. 576).16 “Grey income” here means income that is not reported formally and

publicly to the government.17 It is found that “political instability, proxied by the frequency of coups

d’etat, political assassinations, and revolutions, had a significant and nega-tive impact on per capita GDP growth during 1965–1985, after controllingfor other variables suggested by the standard growth model” (see Zhuanget al., 2010, p. 10).

18 O’Brien and Li (2005 p. 241).

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exacerbates regional income disparity in China. Cross-country studies also show that control of corruption can

Y. Wu, J. Zhu / The Social S

eterring private investment, distorting the allocation ofhysical and human capital, preserving social inequality,nd generating political instability. Therefore, governmentnti-corruption endeavors become important in shiftingocal income up from low equilibrium. Combating cor-uption will limit the detrimental effects of corruption onconomic growth.

Control of corruption is a critical institutional dimen-ion in forming good governance identified by the Worldank, along with accountability, rule of law, political sta-ility, bureaucratic capability, property rights protectionnd contract enforcement (i.e., “Governance Matters” byaufmann et al. (1999)). Governance is “the manner inhich power is exercised in the management of a country’s

conomic and social resources for development” (Worldank, 1992).19 The six institutional dimensions “are mutu-lly reinforcing aspects of growth-enhancing institutions.”

strong control of corruption can reinforce the otherve institutions; and a weak control of corruption willndermine other institutions. Cross-country studies havehown that societies that fail to establish these formalnstitutions effectively tend to have very high market trans-ction costs and “would be unable to control the ‘grabbingand of the state,’ and, consequently, to support pri-ate initiatives, market exchanges and investments, andconomic development.”20 In contrast, good public gover-ance, whether subjectively or objectively measured, haseen rigorously examined and found to help promote eco-omic prosperity and social cohesion, reduce poverty, andeepen confidence in government and public administra-ions (Hall & Jones, 1999; Khan, 2007; Tarschys, 2001).esearch on developing countries finds that the differ-nces in policy implementation and quality of governancecross localities can significantly influence internationalnvestors’ expectation of the policy credibility of a govern-

ent and consequently affect their investment decisionso a locality (Oman & Arndt, 2007).

As discussed in the introduction, anti-corruption effortsn China are still being institutionalized. There are someoutine anti-corruption measures, including audit on theeparture of government officials and State-Owned Enter-rises (SOE) managers. However, more often the routinepolice patrols” are mixed with campaign-based anti-orruption strategies. As Wedeman (2005) points out,given that enforcement resources are costly, infinite,nd subject to decreasing marginal returns,” a resource-onstrained regime is unlikely to be able to afford todeploy sufficient resources to tightly monitor and controlorruption.” In this respect, “campaign-style enforcements the poor man’s alternative to effective policing.”21

ompared with institutionalized control of corruption,nti-corruption campaigns are periodic crackdowns, whichre launched by government leaders more arbitrarily in

erms of timing, intensity, and targeting officials, areas, andypes of corruption. Anti-corruption campaigns are usedy political elites to stop inflation and even to purge fac-

19 Zhuang et al. (2010, p. 6).20 Zhuang et al. (2010, p. 4).21 Wedeman (2005, 9. 96).

urnal 48 (2011) 435–448 439

tional rivals (Quade, 2007; Shih, 2008a,b). Therefore, thecampaign-based and leader-directed Chinese-style anti-corruption tends to fight corruption with varied strengthduring different time periods and across localities underdifferent central and local leaders. It is unlikely to eliminatecorruption entirely and inevitably new waves of corruptionwill erupt again soon after a campaign is over. Nevertheless,to some degree the campaigns have been successful in pre-venting corruption from spiraling beyond the tipping pointinto a crisis, through a periodic increase of investigationrates and deterrence of some risk-averse officials (Manion,2004; Wedeman, 2005). Hence, regardless of the causes andobjectives, we argue that campaign-based anti-corruptionin China is still able to limit some of the adverse effects ofcorruption, especially in the short-run and in places withlarger anti-corruption endeavors.

The CCP has been considering anti-corruption as aprecondition to sustain healthy economic development.The investigation results of the anti-corruption agen-cies have been deemed, first of all, as a governmentachievement in combating corruption. During a press con-ference in 2010, the Vice-Party Secretary of the CentralDiscipline Inspection Commission (CDIC), Gan Yishengsaid,

“Those who believe that the investigation of corrup-tion adversely affects local economic development arebiased and wrong. Investigation of disciplinary and legalviolations can create a good political environment bene-fiting and protecting economic growth. . .. In particular,anti-corruption ensures the local government’s compli-ance with central orders, and maintains economic order.. . . Investigation of corruption in economic sectors alsopurifies the development environment and improvesmarket order. . . Finally, anti-corruption in other sectorscan cleanse the social atmosphere, generating a virtu-ous environment for economic development in the longrun.”22

Gan Yisheng cited as an example that during the crack-down of the infamous Yuanhua smuggling case in the endof the 1990s, some people worried that the economy ofXiamen would revert to the conditions from 10 years ago.However, during the first year after the investigation, thecustoms revenue of Xiamen increased greatly. Since thesecond year, the local GDP and fiscal revenue has con-tinued to grow annually. The current GDP of Xiamen hasdoubled and the fiscal revenue has tripled compared to1999.23

In sum, based on existing empirical, regional, andcross-country studies, we believe that serious corrup-tion lowers local economic development and thereby

effectively improve governance quality, which greatly ben-

22 “CDIC: The view looking at investigation of corruption adverselyaffecting economic growth is biased and wrong” (Zhong jiwei: chabananjian yingxiang jingji fazhan de guandian pianmiancuowu), www.xinhuanet.com from Zhongxinwang, January 7, 2010,http://news.xinhuanet.com/legal/2010-01/07/content 12770164.htm.

23 Ibid.

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440 Y. Wu, J. Zhu / The Social S

efits economic growth. We therefore hypothesize thatstronger anti-corruption efforts launched by local gov-ernments could limit the negative impact of corruption.Anti-corruption differences across local governments alsocontribute to the growing regional income disparity. In par-ticular,

2.1. Null hypothesis

Anti-corruption has nothing to do with the incomedisparities across Chinese counties. Some other variablesdetermine the differences.

Hypothesis 1. The larger the scale of anti-corruptionefforts in Chinese counties the higher average income willbe locally.

3. Data, methods, and statistical results

To test the preceding hypothesis, we use county-leveldata for the years 2002–2003 collected from the ChinaCounty Statistical Yearbook 2004 and Chinese Audit Yearbook2003–2004. We form a cross-section dataset composed ofup to 1,777 county level units, including most county-levelcities, counties, and ethnic minority autonomous countiesin all the provincial units (including centrally administeredmunicipalities and ethnic autonomous regions) in Main-land China.

Our dependent variable is the local residents’ averageincome in each county for the year 2003. Ideally, this shouldbe measured by the disposable income of local residents.However, this data is not available in county-level statisti-cal yearbooks. We approximate local income by per capitaGDP of each county in 2003.

Our major independent variable is county level anti-corruption efforts, measured by three indicators collectedfrom the county audit bureaus. They are:

Number of audit personnel of each county (auditors).Number of the audited projects in each county.Amount of the illegal funds uncovered by a county auditbureau.

Audit institutions are one of the major supervisoryagencies aiming at promoting government integrity. Theyexercise supervision mainly over various levels of local gov-ernment, state banking institutions, state enterprises andundertakings through auditing their revenues and publicfinance expenditures. Besides public finance audits, otherprimary audit categories executed in recent years includemonetary, enterprise, economic accountability, resourcesand environmental, and foreign-related audits. Audit insti-tutions are primarily accountable to their corresponding

people’s governments.24

The first two indicators measure anti-corruption froman input perspective. It is reasonable to assume that localgovernments vigorously attacking corruption would hire

24 National Audit Office of the PRC, http://www.cnao.gov.cn/main/AboutUs ArtID 1082.htm.

urnal 48 (2011) 435–448

more auditors and tend to audit more projects than thosetaking a lax approach. A larger supervisory team and largerscale of audit might also have a stronger deterrent effecton corruption. It should be noted that most audit institu-tions are understaffed. Auditing usually targets key areas,organizations, and funds, and based on a higher level gov-ernment’s authorization. A subset of the audited projectswill be revealed as problematic and require correction. Themost serious ones might be referred to legal procedures forfurther investigation and punishment.

As with any economic productivity, high input does notnecessarily generate high output. Problems such as shirk-ing and concealing during auditing might also blunt thesupervision of corruption. Therefore, we also measure theoutput of anti-corruption by the amount of illegal funds(weiji jin’e) – bribes, graft, embezzlement, and public fundsthat are misused – uncovered by local audit bureau in eachcounty. Larger sums of uncovered illegal funds are regardedas an indicator of better performance of anti-corruptionin a county. In general, a larger number of auditors,audited projects and a larger sum of uncovered illegalfunds are hypothesized to be correlated with higher localincome.

We want to point out that, first, all the indicatorsprimarily gauge the supervision of corruption, while acomplete measure of anti-corruption should also includeother stages, such as punishment of corruption. How-ever, they are the only available data at county-level. Butalso, supervision is a very important stage, stopping anddeterring some corruption, saving some of the cost ofcorruption.

Second, to limit the endogeneity between control ofcorruption and income level, we choose lagged anti-corruption based on the data of 2002 (i.e., Auditor02,AudPrjt02, and IllFund02) to explain the income levelacross counties in 2003. While cross-country studies haverobustly shown that governance, including control of cor-ruption, can determine income level, scholars do not denythat there is mutual causality, where income also affectsgovernance (e.g., Chong & Calderón, 2000; Gundlach &Paldam, 2008; Kaufmann & Kraay, 2002). Researchers usesome “deep” historical determinants of institutions asinstrumental variables of governance, such as settler mor-tality in the 18th and 19th centuries (Acemoglu, Johnson, &Robinson, 2001) and colonial origins (Hall & Jones, 1999).They unanimously find that the effect of governance oneconomic development is strong and larger in IV estima-tions than in OLS estimations (Kaufmann & Kraay, 2002).In this research, we do not want to deny the possibility ofmutual causality either. However, we are mainly interestedin the effects of anti-corruption on county income. Unfor-tunately, we could not find a good instrumental variablefor anti-corruption, which is only related to governmentuncovered corruption and unrelated to per capita GDP.Hence, we measure anti-corruption by lagged data. Since itis natural to expect the positive impact of anti-corruptionto emerge a little behind, Income of year 2003 should influ-

ence corruption and anti-corruption only in years following2003. Nevertheless, we are aware this is only a second bestsolution and does not completely get rid of the simultane-ous causality problem.
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Y. Wu, J. Zhu / The Social S

Third, we are also aware that illegal funds discoveredy auditing result not only from the intensity of anti-orruption activity, but also actual corruption, and evenandom chance. But we argue the variances of illegal fundsainly reflect different anti-corruption efforts across coun-

ies. The previous citation of Gan Yisheng shows that theovernment looks at uncovered corruption primarily as anchievement of anti-corruption. Government officials inharge of anti-corruption also tend to interpret the regionalifferences of revealed corruption as variations of anti-orruption. Several of our interviewees who worked in localudit institutions and procuratorates said that uncoveredorruption mainly reflects anti-corruption, because a largemount of corruption goes undetected everywhere.25 Weiianxing, the former First Secretary of the CDIC commentedn 1995, a few months after the exposure of Chen Xitong’sase in Beijing municipality,

“I must point out that the investigation and handlingof cases involving very big sums is uneven. There areprovinces and localities with nothing to show for sev-eral years—they have handled no such cases. That theyhave handled none does not mean there are no casesinvolving very big sums [in these provinces and locali-ties]. For many years, Beijing had no cases involving bigsums, but this by no means signifies that all was wellin Beijing, that the work was being done well. Rather,the problems were being covered up. Beijing is not anisolated case, there are many such examples. . .If a largelocality or large department has no such cases for a longtime, it is hard to believe that it has done such a goodjob. In some ministries, the discipline inspection grouphas had no cases for many years. I, for one, do not believethere are no problems [in these ministries]. It has tobe that the problems are being concealed or that theyhave been discovered but not investigated. Every local-ity, every workplace should handle several influentialcases.”26

We have also tested the correlation between the totalmount of illegal funds uncovered in a province (whereichuan and Chongqing are combined into one unit) in002 (PROVIllFund02) and one of our control variables,n indicator of provincial legal institution, the lengthf time needed to enforce a contract.27 PROVIllFund02

auges the anti-corruption effort of each province in 2002.s for the indicator of contract enforcement, a shorter

ime length indicates greater bureaucratic efficiency and

25 Interviews were conducted through telephones and emails in Novem-er 2010. For the interviewees’ sake, we cannot reveal their names andheir affiliation details. Interviewee No. 1 works in the Shaanxi Higherrocuratorate. Interviewee No. 2 previously worked in Henan Provincialudit Bureau. Interviewee No. 3 works in a local procuratorate in Zhejiangrovince. All of them hold the opinion that uncovered corruption reflectsnti-corruption more than corruption. But there are a couple other inter-iewees think that uncovered corruption reflects neither anti-corruptionor corruption, since the problem is too complicated and caused by soany reasons.

26 Manion, 2004 (p. 162).27 We also attempted to test the correlation between the other two indi-ators of anti-corruption and contract enforcement. However, there is souch missing data of these indicators at provincial level.

urnal 48 (2011) 435–448 441

stronger enforcement of contracts. We find the two vari-ables are closely correlated at −0.448. This shows higheruncovered illegal funds are correlated with more efficientbureaucracy and better contract enforcement. This is con-sistent with the literature of governance that control ofcorruption and contract enforcement mutually reinforceeach other. It shows our assumption that uncovered illegalfunds mainly reflect anti-corruption endeavors is valid.

Fourth, county-level anti-corruption efforts could alsobe affected by differences between provinces. Thus we addin provincial dummies to control the provincial differencesin fighting corruption. In addition, we also use standardizedillegal funds as an alternative measure of anti-corruptionto double check the findings based on the absolute mea-sure. We use the within-province standard score, or Zscore of illegal funds, which we derive by the followingequation. Although this is equivalent to adding provincedummies, we derive the standard score instead to main-tain a higher degree of freedom. We call this transformedvariable IllFundSTD02ij.

IllFundSTD02ij = IllFund02ij − Zij = IllFund02ij − Uj

�j

In the above equation, subscript i is county, while jis province. IllFund02ij is the absolute amount of illegalfunds in a given county-province observation, while Uj isthe mean number of uncovered illegal funds across all thecounties in a given province. Finally, �j is the standarddeviation of the illegal funds in a given province across allthe counties. Essentially, the mean of the illegal funds fora given province is subtracted from the observed countyillegal funds and divided by the standard deviation of theillegal funds for a given province, producing a standardizeduncovered illegal fund with a mean of zero and a standarddeviation of 1. The standardized measure also minimizesthe noises of the differences in county-level corruption onuncovered illegal funds. We can assume the level of cor-ruption across counties within a given province is similar,since they have roughly the same corruption opportunities.Hence, the transformed variable measures how much eachcounty’s disclosed corruption departs from the provincialnorm in anti-corruption. Because the standardized illegalfund takes into account provincial differences, it is compa-rable across provinces.28

In addition to the independent variables, we employa series of control variables that also determine incomelevel according to previous research and mainly the recentresearch of Wan et al. (2007) regarding the effects of glob-

alization on inter-regional income disparity in China.29

Generally, control variables are chosen based on humancapital theory and production theory (Wan et al., 2007).

28 As for the method of standardization, we refer to Shih’s (2008b, p.6) research of measuring provincial newspaper articles count of “ThreeRepresents” across years.

29 See Wan et al. (2007). We also refer to this research in terms of apply-ing Shapley value decomposition techniques to qualify the contributionof corruption other variables, as this research also studies inter-provinceincome inequality in China and their method has generated some veryvaluable findings.

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cience Journal 48 (2011) 435–448

Table 1Summary of relevant variables.

Obs Mean Min Max Std dev

ln AGDP 1,661 8.598 4.510 11.148 0.697ln Auditor02 1,777 2.866 1.386 4.727 0.431ln AudPrjt02 1,510 3.464 0.693 8.109 0.701ln IllFund02 1,770 7.160 1.176 11.179 1.415IllFundSTD02 1,770 −5.65e−06 −1.301 8.836 0.992ln K 1,661 6.483 2.278 10.462 1.024ln fisexp 1,661 6.444 2.947 9.199 0.565ln fisdep 1,661 1.125 0.322 3.637 0.355ln urban 1,661 2.686 0.358 4.580 0.656ln dep 1,661 4.695 1.308 7.233 0.455ln edu 1,661 1.849 0.049 2.664 0.281ln conenforce 30 5.737 4.718 6.292 0.315

AGDP, per capita GDP; Auditor02, number of audit personnel of eachcounty (auditors) in 2002; AudPrjt02, number of the audited projects ineach county in 2002; IllFund02, amount of the illegal funds uncoveredby a county audit bureau in 2002; IllFundSTD02, standardized uncov-ered illegal fund in 2002; K, capital input; fisexp, government support

442 Y. Wu, J. Zhu / The Social S

In particular, we include the following variables, most ofwhich are taken from 2003 values.

Capital input (K) is regarded as one of the most impor-tant factors for economic growth by economists. Based onthe availability for county-level data, we measure capitalinput by average investment for basic construction, whichis the total basic construction investment divided by countypopulation. Higher capital input should generate higherincome. It should be noted that basic construction invest-ment is public investment conducted by the governmentinstead of private investment.

Government support (fisexp) is another source of eco-nomic growth, which leads to higher per capita income,as argued by Ma and Yu (2001). We measure govern-ment support by per capita fiscal expenditure. Higher fiscalexpenditure to support local economy should lead to higherlocal income.

Fiscal dependency (fisdep) is measured by the ratio offiscal dependents to total population in each county. Fis-cal dependents mainly include officials, cadres, employees,and staff hired by government and various public organi-zations such as schools and hospitals.30 The ratio of fiscaldependents to total population reflects government size,with a larger ratio indicating a larger government (Zhang,2008). Large government is usually considered inefficient,both administratively and economically, and therefore isexpected to be related with low income levels.

Urbanization (urban). Differences in urbanizationbetween counties could also affect per capita income andthus income disparities. A higher degree of urbanizationoften means a relatively mature market economy, a higherdegree of industrialization, and more job opportunities(e.g., Tiffen, 2003; Zhou, 2009). It is expected to be pos-itively related with per capita income. Urbanization isgauged by the proportion of nonagricultural population ofeach county.

Labor (dep) and Education (edu). These two vari-ables measure labor and human capital, which arealso important for economic growth and income dif-ferences. In particular, labor is the dependency ratiowhich is used as a proxy of labor input. This vari-able is calculated as such: dependency = ((total popu-lation − employment) × 100%)/employment. Education ismeasured by the ratio of number of students enrolled tosecondary school to total population. The higher this ratiois, the higher the level of human capital in a county.

Contract enforcement (conenforce) is an institutionaldimension of governance. It mainly measures the qualityof legal institutions of a society. In the World Bank reportDo Business in China 2008, one indicator gauging contractenforcement in Chinese provinces is the time needed toenforce a contract. This is the number of days from the

time a plaintiff files a lawsuit, the court coming to a ver-dict, to finally the plaintiff receiving the money owed bythe defendant. Apparently, the fewer days spent in this

30 “Zhongguo Caizheng Gongyang Xianzhuang: 5300 Wan ren zai chicaizhengfan?” (The status quo of Chinese fiscal dependency: 53 millionpeople are fed by fiscal budget?), Touzizhebao [Investors Post], posted onhttp://finance.jrj.com.cn/2009/03/0619073760102-1.shtml (06.03.09).

through fiscal expenditure; fisdep, fiscal dependency; urban, urbaniza-tion; dep, labor input measured by the dependency ratio; edu, education;conenforce, contract enforcement.

process, the more efficient the province’s legal institu-tions. Unfortunately, publication of the report only beganin 2005 and the earliest available data for this indicatorthat we have is from 2006. Additionally, this indicator isonly collected at the provincial level. However, it is rea-sonable for us to assume that the legal institutions of aprovince would not change radically from 2003 to 2006due to institutional inertia (Hannan & Freeman, 1984). Wecan also roughly approximate the quality of legal institu-tion in counties by their corresponding provincial indicator,since county-level courts are led by courts at the prefecturaland provincial levels. It should also be noted that conen-force is highly correlated with the geographical locationsof provinces. Eastern coastal provinces tend to have bet-ter legal institutions than the rest of China. Due to this highmulticollinearity, we do not include a geographical dummyvariable in our model.

Table 1 summarizes the basic statistics of all the rel-evant variables. We adopt a double log model, based onproduction theory, to test the hypothesis. Zhang and Zhang(2003) employed a double log model estimating the effectsof globalization on regional disparity in China. It has theadvantage of reducing heteroskedasticity, a common prob-lem for cross-sectional data. The basic regression model ispresented in Eq. (1).

ln AGDPi = ˛0 + ˛1 ln Anti-corruption02i + ˛2 ln Ki

+˛3 ln fisexpi + ˛4 ln fisdepi + ˛5 ln urbani

+˛6 ln depi + ˛7 ln edui + ˛8 ln conenforce

+εi i = 1, 2, . . . , 1, 777 (1)

Anti-corruption02 in Eq. (1) refers to different measuresof anti-corruption. We estimate the basic model in Eq. (1)

based on different indicators of anti-corruption with andwithout provincial dummy variables, respectively, by Ordi-nary Least Square (OLS) regression. All together there areeight models. The results are reported in Table 2.
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Y. Wu, J. Zhu / The Social Science JoTa

ble

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urnal 48 (2011) 435–448 443

Table 2 reveals several robust and striking patterns.All the coefficients are statistically significantly differentfrom 0 at levels of 1% or 5% except ˛8 in Models 5 and7. Most importantly, anti-corruption exerts a relativelystrong and positive impact on county income. From aninput perspective, Models 1–4 show that a 100% increase ofanti-corruption, whether measured by Auditor02 or Aud-Prjt02, will increase AGDP by 8.3–30.1%, all else beingequal. And Auditor02 has a larger impact of increasingAGDP for more than 20.5%. Thus on average inputting100% more in anti-corruption could increase AGDP by18.075%. Models 5–8 test the hypothesis from an out-put perspective of anti-corruption. Models 5 and 6 showthat a 100% increase of IllFund02 will increase AGDPby 6–9.5%. Models 7 and 8 illustrate that, all else beingequal, a one-unit increase of IllFundSTD02 will generatea roughly 0.047–0.059 increase of ln AGDP, which equalsto an increase of AGDP by 4.8–6.1%.31 All the findings arerobust at the 0.01 level across all the eight models in Table 2.Combining the results of Models 1–6, on average, a 100%increase of anti-corruption can improve AGDP by nearly15%. Therefore, Hypothesis 1 is accepted with confidence.

Besides the independent variable that we are mostinterested in, all control variables are also statistically sig-nificant in most models and perform as predicted. Basedon the slightly different coefficients of the control vari-ables in the eight models, we summarize the findings asfollows: Capital input, government support through fiscalexpenditure, urbanization, and education all have strongand positive effects on income level. A 100% increase ofK, fisexp, urbanization, and edu will, respectively, leadto around 20%, 30%, at least 22.1% and 30.4% increase inAGDP. In contrast, large government is inefficient for eco-nomic development and less labor input also decreaseslocal income. A 100% increase of fisdep or dep will decreaseAGDP by at least 18.3% each. Finally, contract enforcementis very important for the local economy. A 100% increaseof the number of days spent to enforce a contract coulddecrease AGDP for 26.9–54.4%.

To quantify the contributions of every variable, espe-cially anti-corruption, to inter-county income disparity,we apply the Shapley value decomposition technique. TheShapley value framework is first raised by Shorrocks (1999)based on the cooperative game theory. The Shapley valuedecomposition technique was developed by Wan (2002)and recently used by Kolenikov and Shorrocks (2005) andWan (2004). This method requires that all the decomposi-tion variables’ values be positive and that most estimatedcoefficients be statistically significant. Thus, this methodis only applicable to estimates based on an absolute mea-sure of anti-corruption, because some of the standardized

anti-corruption is negative. Additionally, many provincialdummies are not statistically significant. We thereforeemploy the Shapley value decomposition technique to

31 Models 1–6 are double-log models, therefore coefficients of ln Anti-corruption are the elasticity of AGDP with respect to Anti-corruption. Thusa 1% increase will lead to “coefficient %” of increase of AGDP. Models 7and 8 are semi-log models. If IllFundSTD02 increases by one, AGDP willincrease by 100 × [exp(ˇ) − 1] (%). ̌ is the coefficient of IllFundSTD02. SeeWooldridge (2002, pp. 183–184).

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444 Y. Wu, J. Zhu / The Social Science Journal 48 (2011) 435–448

Table 3Contribution of the residual and explanatory variables.

Anti-corruption Auditor02 AudPrjt02 IllFund02

Total disparity 0.383 0.384 0.384Contribution of Independent variables 0.263 0.262 0.265Contribution of residual 0.12 0.122 0.119

68.67

red are

Proportion explaineda = 100 × (1 − |residual|/total)

a A negative contribution of residual implies that variables not conside

Models 2, 4 and 6. According to the results of the threemodels and the basic model in Eq. (1), the income levelvariable AGDP is:

AGDP = exp(˛0) · exp(˛1 ln Anti-corruption02i

+˛2 ln Ki + ˛3 ln fisexpi + ˛4 ln fisdepi

+˛5 ln urbani + ˛6 ln depi + ˛7 ln edui

+˛8 ln conenforce) · exp(ε) (2)

The constant term exp(˛0) can be removed from theequation when relative measures of inequality are used(Wan et al., 2007). The contribution of residual term εis equal to I(AGDP) − I(AGDP).32 The Gini coefficient isselected as a measure to decompose income disparity, sinceit is the most common index measuring inequality.33 TheChinese county-level Gini coefficient in 2003 was 0.384.This is much higher than the United States and Japan. ForUnited States, its state-level Gini coefficient in 2003 wasonly 0.131, while the county/city-level Gini coefficient ofJapan was even lower, only 0.102. We also find county-level disparity was higher than the provincial level in China,while the provincial Gini coefficient was 0.34 that year.34

The contributions of the residual and independent vari-ables to inter-county income disparity are tabulated inTable 3. We can explain up to 69.01% of total Gini, whichindicates decomposition exercise is successful. Becauseresidual contribution implies the proportion of income dis-parity that is not explained by the model, we only focus onthe proportion that is explained.

According to Table 4, the two most influential factorson per capita county income across different measures ofanti-corruption are capital input (K) and fiscal expendi-ture (fisexp). Together, these two factors contribute up to50% of total regional disparity. Urbanization stands third,contributing 15.21–18.32% to the total regional disparity.

Anti-corruption by local government ranks fourth

when measured by Auditor02 and IllFund02, and con-tributes 10.65–12.83% to total income disparity. Whenusing AudPrjt02 as the indicator, anti-corruption makes

32 I denotes income inequality index. AGDP and AGDP denote originalincome level and estimated income level when assuming ε = 0 (Wan et al.,2007).

33 We also used other indices, such as GE. But the percentage of expla-nation is low. We therefore report the best decomposition results.

34 We calculate the Gini indices of China, the United State, and Japanbased provincial level per capita GDP data of 2003 collected from the ChinaStatistical Yearbook 2004, American Statistical Yearbook 2004 and Japan Sta-tistical Yearbook 2004. Counties in Japan are comparable to provinces inChina.

68.23 69.01

equalizing forces (Wan et al., 2007).

a smaller but still sizable contribution of 6.11% toregional disparity. To make sure the large contribution ofanti-corruption is not an inflated one, we test the corre-lation between anti-corruption and the control variables.Table 5 illustrates clearly that the correlations between thethree anti-corruption indicators and all the control vari-ables are below 0.282, which is minimal. This indicatesanti-corruption does not affect income level indirectlythrough influencing other variables. All together, we con-fidently conclude that the estimated contributions ofanti-corruption to income disparity are relatively accurate.

In addition to the above variables, education and con-tract enforcement also contribute considerably to totalregional income disparity. On average, education con-tributes 9.62% and contract enforcement contributes 7.85%to income disparity across the different measures ofanti-corruption. Labor dependents and government sizecontribute a smaller proportion to regional disparity,around 2% and 3%, respectively. This finding indicates thatcontrol of corruption plays an even more important rolethan some commonly emphasized factors for economicdevelopment, such as human capital and contract enforce-ment. County governments’ differences in anti-corruptionefforts can exert a huge influence on local income and inter-county income disparity.

4. Discussion and conclusion

This paper emphasizes the impact of anti-corruptionefforts on China’s growing regional income disparity inrecent years. Based on cross-country studies and empiricalresearch of corruption in China we induce that overall cor-ruption hinders economic development, generates povertyand instability, and leads to an unequal distribution ofincome across localities in China. Therefore, we argue gov-ernment control of corruption will reduce the detrimentaleffects of corruption. It also helps improve the qualityof governance by local governments. Good governancecan create better institutional environment for economicdevelopment and foster higher incomes. However, anti-corruption in China presently is often campaign-basedand leader-directed. It is not clear whether Chinese-styleanti-corruption can promote regional economic growthand influence income disparity. We use a county levelcross-section dataset combining data for years 2002–2003to study the question. We measure the local scale ofanti-corruption by three indicators, the number of local

auditors, the number of audited projects, and the amountof uncovered illegal funds in a county so as to examinethe robustness of our findings. To limit the endogeneitybetween control of corruption and local income level, we
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Table 4Inequality decomposition results.a

Independent variable Auditor02 Independent variable AudPrjt02 Independent variable IllFund02

Absolutecontribution

Relativecontribution (%)

Absolutecontribution

Relativecontribution (%)

Absolutecontribution

Relativecontribution (%)

Capital input 0.071 27.00 Capital input 0.076 29.01 Capital input 0.074 27.92Fiscal expenditure 0.062 23.57 Fiscal expenditure 0.058 22.14 Fiscal expenditure 0.058 21.89Urbanization 0.04 15.21 Urbanization 0.048 18.32 Urbanization 0.042 15.85Anti-corruption 0.028 10.65 Education 0.026 9.92 Anti-corruption 0.034 12.83Education 0.025 9.51 Contract enforcement 0.024 9.16 Education 0.025 9.43Contract enforcement 0.019 7.22 Anti-corruption 0.016 6.11 Contract enforcement 0.019 7.17Fiscal dependency 0.007 2.66 Fiscal dependency 0.009 3.44 Fiscal dependency 0.007 2.64Labor 0.006 2.28 Labor 0.004 1.53 Labor 0.006 2.26

a Variables are sorted on their relative contributions from the largest to the smallest.

Table 5Correlations of explanatory variables.

ln Auditor02 ln AudPrjt02 ln IllFund02 IllFundSTD02 ln K ln fisexp ln fisdep ln urban ln dep ln edu ln conenforce

ln Auditor02 1.000ln AudPrjt02 0.359 (0.000) 1.000ln IllFund02 0.423 (0.000) 0.260 (0.000) 1.000IllFundSTD02 0.149 (0.000) 0.128 (0.000) 0.638 (0.000) 1.000ln K −0.122 (0.000) −0.066 (0.010) −0.014 (0.565) 0.022 (0.359) 1.000ln fisexp −0.220 (0.000) −0.187 (0.000) −0.091 (0.000) 0.000 (0.996) 0.542 (0.000) 1.000ln fisdep −0.252 (0.000) −0.215 (0.000) −0.225 (0.000) −0.096 (0.000) 0.346 (0.000) 0.563 (0.000) 1.000ln urban 0.044 (0.066) −0.063 (0.014) 0.043 (0.068) 0.000 (0.987) 0.385 (0.000) 0.444 (0.000) 0.375 (0.000) 1.000ln dep −0.012 (0.620) −0.090 (0.000) −0.007 (0.761) 0.004 (0.865) 0.060 (0.011) 0.091 (0.000) 0.146 (0.000) 0.426 (0.000) 1.000ln edu 0.282 (0.000) 0.187 (0.000) 0.234 (0.000) 0.088 (0.000) −0.032 (0.177) −0.141 (0.000) −0.047 (0.000) 0.006 (0.799) 0.028 (0.232) 1.000ln conenfoce −0.247 (0.000) −0.144 (0.000) −0.244 (0.000) 0.000 (1.000) 0.006 (0.797) −0.030 (0.203) 0.182 (0.000) −0.016 (0.498) 0.040 (0.090) −0.118 (0.000) 1.000

p values are in brackets.

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use lagged anti-corruption in the year of 2002 to interpretper capita GDP across Chinese counties in 2003. The resultsconfirm our hypothesis that anti-corruption is able to exerta fairly strong and positive impact on local average income.In other words, anti-corruption campaigns are still bene-ficial for poverty reduction, though not effective enoughin controlling corruption. Counties with higher degrees ofanti-corruption tend to be richer than those with lowerdegrees of anti-corruption.

We further quantify the contributions of anti-corruption and other variables by the newly developedShapley value decomposition technique. We find: (1)Capital input, fiscal expenditure, and urbanization arethe most influential factors on local income disparity.Together these top three factors contribute more than65% to explaining the average income gap across counties.(2) Anti-corruption also contributes a lot to total incomedisparity. Based on different measures, it contributesfrom 6% to almost 13% to regional disparity. On average,anti-corruption is the fourth most important contributorsto inter-county income gap. (3) All the other variables,including education, contract enforcement, governmentsize, and labor input contribute less than anti-corruptionto regional disparity.

Our empirical results find that growing regional incomedisparity in China is caused by a wide range of factors,rather than solely by economic policies and investment.Government control of corruption is the fourth most influ-ential factor, following capital input, fiscal expenditure,and urbanization, contributing to regional income dis-parity. Counties with a higher scale of anti-corruption,regardless of the measure employed, have higher averageincome than those with lower anti-corruption scales. Thissuggests that worries of adverse effect of anti-corruptionon economic development are not necessary. However,contributions of anti-corruption to total income dispar-ity do vary somewhat depending on different indicators.Although the difference is partially a natural statisticaloutcome, intuitively it also has policy implications. Thedecomposition results show that in general a larger super-visory team and a higher output of anti-corruption aremore effective in controlling corruption and contributesmore into regional disparity than auditing more projects.This is probably because a large team of auditors has astronger deterrent effect on corruption, while uncoveringmore illegal funds indicates that the auditors are com-mitted to their anti-corruption duties. However, whenchoosing audit projects, Chinese audit institutions are stillbroadly under the direction of higher-level governmentsand have limited autonomy and independence from thegovernment. This problem, plus the campaign style, makescorrupt officials behave opportunistically and decreasesthe effectiveness of anti-corruption. Therefore, grantinggreater autonomy to anti-corruption agencies is importantin improving the control of corruption. If that is hard torealize in the short-run, recruiting more anti-corruptionpersonnel and enhancing their performance would also be

helpful in controlling corruption.

Our results also show that at county level, the mainmomentum pushing economic development seems still tobe public investment and government fiscal expenditures.

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However, public investment is one of the major sources ofofficial corruption. This also makes control of corruptionurgent for promoting economic development and averageincome in county level.

Finally, our paper is only a preliminary examination ofthe economic consequences of anti-corruption efforts inChina. We only provide a positive answer to the ques-tion whether Chinese-style anti-corruption can benefiteconomic development and local income. There are morespecific questions worth further research, such as howlong the positive effect of Chinese-style anti-corruptioncan hold, what different impacts campaign-based anti-corruption and institutionalized-anti-corruption can gen-erate on inter-regional disparity, and what types ofcorruption Chinese-style anti-corruption is more effectivein controlling. To answer these questions we will needboth in-depth case studies and more carefully formed paneldata. But based on our current research, we may makesome conjectures on different impacts of campaign-basedanti-corruption and institutionalized anti-corruption oninter-regional disparity. First of all, as discussed previ-ously, campaign-based anti-corruption is most likely tobe effective in the short run, while the effects of institu-tionalized anti-corruption tend to hold longer. Therefore,the decrease in inter-regional disparity caused by anti-corruption campaigns will not be sustained for a longperiod of time. Inter-regional disparity will increase againsoon after an anti-corruption campaign ends. Second,effects of campaign-based anti-corruption tend to varymore greatly from region to region, since leadershipmatters more in this type of anti-corruption than insti-tutionalized anti-corruption. Regions with good leadersmight fight corruption more, while other places withineffective leaders could simply ignore anti-corruption.Therefore, countries resorting to campaign-based anti-corruption should have higher inter-regional disparity thanthose with institutionalized anti-corruption. We can actu-ally roughly see this pattern when comparing China and theUnited States, if the latter is taken as an example of insti-tutionalized anti-corruption. We find from 1990 to 2006,the interstate Gini coefficient in the United States had beenvery stable and quite low, only fluctuating between 0.1 and0.15. However, the interprovincial Gini coefficient of Chinain the same time period had been higher and varied in awider range between 0.25 and 0.35. The Gini coefficientwas also less stable and fluctuated more frequently thanthe American one.35 However, more rigorous hypothesistesting is needed.

Acknowledgements

We thank Professor Guanghua Wan for providing thesoftware for the Shapley value decomposition. We alsothank Dr. Jie Lu, Dr. Bin Yu, Dr. Zhengxin Shi, Dr. Qi Zhang,

35 Data source for American state level GDP per capita and Chi-nese provincial GDP per capita are at http://www.bea.gov/ andhttp://db.cei.gov.cn/.

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