Corruption, anti-corruption, and inter-county income disparity in China

The Social Science Journal 48 (2011) 435–448

Contents lists available at ScienceDirect

The Social Science Journal journal homepage: www.elsevier.com/locate/soscij

Corruption, anti-corruption, and inter-county income disparity in China Yiping Wu a , Jiangnan Zhu b,∗ a b

Department of Industrial Economics, Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou 310018, China 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 government 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

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

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 consensus 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, geography, culture, and institutions. Scholars studying Chinese regional income disparity have examined many economic factors, such as availability of resources, human capital, geographical locations, preferential policies, and

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

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.

1. Introduction

0362-3319/$ – see front matter. Published by Elsevier Inc on behalf of Western Social Science Association. doi:10.1016/j.soscij.2011.05.001

436

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

globalization, which are no doubt crucial to local economic development (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-corruption endeavors or efforts to combat corruption also plays a very important role influencing regional income disparities in China. Corruption, commonly defined as using government power for private gain, is often seen as a “comprehensive reflection of the legal, economic, cultural and political institutions” of a region.3 Recent research has shown that corruption mainly sands the wheels of economic development, as it deters business investment, distorts the allocation of human capital, and increases social instability (e.g., Aidt, 2009). We therefore expect localities with stronger anti-corruption measures should result in a better environment for economic growth and hence higher income level, since control of corruption will limit the detrimental effects of corruption. Control of corruption is also one of the six institutional indicators of governance, a factor which has been shown to 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 institutionalized. Party disciplinary punishment of corrupt officials could be substituted criminal penalty. Routine supervision strategy is often times mixed with anti-corruption campaigns, which are characterized by short periods of intensified enforcement, harsher punishment, and rhetoric calling for public attention of anti-corruption, and in the end claims of success evidenced by a large number of cadres arrested and convicted (Manion, 2004; Wedeman, 2005). Due to the authoritarian nature of the regime, anticorruption in China is also influenced by central and local leaders in terms of timing, frequency, and intensity of the campaigns, as well as investigation and punishment of corruption. We call this campaign-based and leaderdirected efforts “Chinese-style anti-corruption.” Compared with institutionalized anti-corruption which is based more on routine supervision and legal framework rather than leadership, the strength of Chinese-style anti-corruption is more likely to vary in different time periods and in localities under different leaderships. Because of this, the effectiveness of Chinese-style anti-corruption is often questioned by scholars. Therefore, it is worthwhile to study whether Chinese-style anti-corruption measures also benefit economic development and local income. Findings from the case of China could shed light on other developing countries plagued by corruption, such as Vietnam, that have similar regimes and anti-corruption systems.4 It also helps to answer some skeptics’ question whether fighting corruption is worth the bother (Gray & Kaufmann, 1998).

3 Svensson (2005, p. 20). This article also offers a discussion of the definition of corruption. 4 For instance in East Asia, anti-corruption in several countries is very institutionalized and mainly works in a legal framework, such as Singapore, Japan, and South Korea. Vietnam has a very similar anticorruption system to China, which combines both legal system and party discipline (see Tian, 2009).

Chinese local governments, at various administrative levels, have been actively involved in economic reform in recent decades (Zhou, 2007). This has also given local officials many opportunities, incentives, and advantages to seek rent and solicit bribes throughout the entire reform period. Various international indices indicate that corruption in China is very serious compared to many other countries in the world (Guo & Hu, 2003). Existing research has extensively explained changes in corruption in 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 economic development and inter-regional disparity have not been fully examined in current research. As most research has focused on the national level and problems of the Chinese anti-corruption system, it has overlooked the fact that government anti-corruption varies at local level due to the leadership factors in Chinese-style anti-corruption, and different anti-corruption endeavors could lead to different institutional environments for local economic development. In this research, we attempt to fill in this gap. We use county-level audit data as indicators of the scale of government anti-corruption efforts. We find counties with a higher scale of anti-corruption efforts tend to have higher income levels, measured by county-level per capita GDP. We choose to focus on county-level governments because counties are important for China both politically and economically, though existing research has not paid enough attention to them. The county is actually China’s oldest administrative unit and has been employed by the central government since the Qin Dynasty. For centuries, county-level governments have helped the central authorities maintain their basic control at the grassroots level. In contemporary China, the county is the lowest level of government administration positioned between the formal party-state hierarchy and the grassroots authorities (Luo & Chen, 2008). Counties control an overwhelming majority of national land and population and also contribute more than half of the national GDP.5 More importantly, counties have the de facto right of local land disposal, which gives them enormous real power (Zhang, 2009). They are also arguably the most active local economies, competing with each other fiercely. In the following, we first review the literature on the effects of corruption and anti-corruption on economic development and income in order to draw hypotheses. Here, constrained by data availability, we focus on the correlation between local variances of anti-corruption and county level average income. We leave the overtime effects of anti-corruption on inter-regional income disparity for future research and only make some conjectures in the last section. Thus, we form a cross-county dataset including the major income determinants for the year 2003. We use three indicators to measure different aspects of anti-

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

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

corruption so as to double check the robustness of our findings, since measures of corruption and anti-corruption are often controversial. The first indicator is the number of audit personnel or auditors in each county-level audit bureau. The second indicator is the number of projects audited by local audit bureaus each year. These two indicators can be seen as the input of anti-corruption. The third indicator is “illegal funds” (weiji jin’e) uncovered by local audit bureaus, which can be seen as the output of anticorruption. Illegal funds mainly refer to money obtained by officials through bribery, embezzlement, graft, and misuse of public assets in violation of government policies, party disciplines and laws. For these three indicators, we use data for the year 2002 to decrease the endogeneity between income and the control of corruption.6 Our eventual objective is to study how anti-corruption endeavors influence inter-county disparity. To quantify the contributions of anti-corruption along with other variables to inter-county income disparity, we adopt a newly developed Shapley value decomposition technique (Kolenikov & Shorrocks, 2005; Shorrocks, 1999; Wan, 2002, 2004, 2007). The Shapley value decomposition result indicates that anticorruption plays a significant role explaining inter-county income differences in China.7 The last section summarizes our major findings and discusses theoretical and policy implications, as well as some of our conjectures about future research. 2. Corruption, anti-corruption, and local economic development in China Corruption, or fubai in Chinese, can refer to “any form of improper behavior by either a state official or a member of the Chinese Communist Party (CCP)” in China.8 It ranges from economic crimes such as graft, bribery, and misappropriation of public property, to official malfeasance less relevant to monetary gains, such as shirking and torture, to individual misbehavior deviating from standards of official morality, such as having mistresses (e.g., Gong, 1994; He, 2000; Wedeman, 2004). This research is mainly interested in the economic consequences of corruption and anti-corruption, therefore we focus on those economically based improper behaviors. To be consistent with our indicators of anti-corruption, our definition of corruption is also legally based. In general, we operationally define corrup-

6 The selection of 2003 data is mainly based on data availability. Complete data at the county level is very hard to obtain. For anti-corruption measurement, we have data from 2001 to 2003. It should be noted the earliest available data of illegal funds in Chinese Audit Yearbooks is from 2001. But for other relevant data, we only have complete data from 2003. Given that 2002 is the year when the new Hu and Wen administration started, it is better to use anti-corruption data from 2002 as the lagged data to regress AGDP from 2003 so as to avoid impact of different leadership on economy and anti-corruption. 7 It should be noted that our research is interested in inter-county income disparity, which is inherently different from income inequality between persons, though the two are related. For our research, we take counties as our research subjects. Therefore, we need to first of all find out county level average income, which is influenced by local economic growth. 8 Wedeman (2004, p. 896).

437

tion as the use of public power for private gain in violation of state laws, government policies and regulations, and party disciplines. Existing studies are in agreement that corruption in China has grown in frequency, scale, and complexity during the post-Mao era. Government officials have also pointed out that corruption in contemporary China is “worse than at any other period since New China was founded in 1949. It has spread into the party, government, administration and every part of society, including politics, economy, ideology and culture.”9 The forms of corruption have changed over time and across industries and regions based on the loopholes and profit opportunities created by reform policies. They range from profiteering activities in the 1980s, illegal privatization of public assets in the 1990s to frequent corruption in the real estate and financial industries today (Gong, 1994, 1997; He, 2000; Sun, 2004; Zhu, 2012). Currently between 30,000 and 50,000 cases involving bribery, embezzlement, and misuse of public assets are investigated by the procuratorates annually. More importantly, more cases containing high stakes (i.e., amounts above Y100,000) and senior officials (i.e., officials above county level) are uncovered every year (Guo, 2008; Manion, 2004; Wedeman, 2004). Although some early research argues for the “lubricant effect” of corruption (e.g., Huntington, 1968; Leff, 1964) on economic development, recent research has increasingly shown that corruption undermines economic growth.10 On the one hand, corruption lowers private investment, thereby lowers economic growth (Mauro, 1995). From a microeconomic perspective, corruption can stop the establishment of new firms, prop up inefficient firms, and distort the allocation of entrepreneurial skills, productive technology and capital. These most important factors for economic growth will be allocated away from their socially most productive uses. This consequently results in a decline in productivity and living standards (Murphy, Shleifer, &

9 Perry (1999, p. 313). She quoted in White, G. (1996). Corruption and market reform in China. IDS Bulletin, 27(2), 41. 10 According to early research, in the context of pervasive and cumbersome regulations, corruption may actually improve development and efficiency. Corruption could be a welcome lubricant easing the path to modernization in a society where traditional norms are still powerful and rigid (Huntington, 1968, p. 69). Game theoretical models indicate that in the second-best world where preexisting policy induces distortions, additional distortions in the forms such as black-marketeering and smuggling may indeed improve welfare even when some extra cost have to be spent in such activities (Bardhan, 1997; Beck & Maher, 1986; Lien, 1986). In other words, corruption corrects or circumvents various sorts of preexisting government failures (Aidt, 2009). Corruption is also seen as “speed money” or “tips for bureaucrats,” which reduces delay in moving files in government offices and saves the opportunity cost of time for individual clients (Lui, 1985). In Chinese central-local hierarchy, bribes are sometimes regarded as the incentive bonuses for public officials to comply with the central government’s policies (Shirk, 1993). But later Critics argue that the distortions are actually part of the built-in corrupt practices of a patron–client network, though not exogenous to the system. The efficiency generated by corruption is at most the second best, and in many cases inefficiency may result from corruption (Rose-Ackerman, 1975, 1978, 1999). As for speed money, scholars have found that corrupt officials may, instead of speeding up, actually cause administrative delays in order to attract more bribes (Myrdal, 1968).

438

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

Vishny, 1991, 1993).11 On the other hand, officials’ incentives to seek rent might increase the number of public projects undertaken in a locality and subsequently increase the share of public investment in GDP. However, the average productivity of the public investment could be very inefficient because corruption can enlarge the size and complexity of the projects and inflate budgetary constraints (Tanzi & Davoodi, 1997). This will result in “a possible reduction in other public spending, such as operation and maintenance, education, and health,”12 which will 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 distribution 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 low public investment in human capital for the vast majority and social pressures for redistribution, both of which may lower economic growth (Easterly, 2007). Therefore, corruption is seen by most economists as playing a critical role 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 affect economic development and investment everywhere. The long-term coexistence of high GDP growth rate and relatively high levels of corruption in several East Asian countries, especially China, has led a group of scholars to look for factors countervailing the negative effects of corruption. For instance, larger countries have been found to have more advantages than smaller countries in shielding the cost of corruption. In order to gain unrestricted access to the large internal market and a large pool of labor in large countries, foreign investors are more likely to accept corruption as a way of doing business there (Rock & Bonnett, 2004). Domestic politics, such as power distribution within the patron–client network, also affects the efficiency of corruption. Centralized corruption is also thought to be more efficient than decentralized corruption (Bardhan, 1997; Blanchard & Shleifer, 2001; Khan, 1996; Khan & Jomo, 2000; Kang, 2002; Shleifer & Vishny, 1993). However, most of this research only shows that corruption could be less harmful under certain conditions, instead of being harmless. It is also possible that China and other East Asian countries could have developed even faster economically if less corruption had been present. Empirical studies have actually revealed that the intensification 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 farmers and entrepreneurs’ choices of technology and allocation of talent in those places, which consequently result in decline in productivity and living standards (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 working paper series, 192). 14 You and Khagram (2005, p. 140).

impacts to Chinese economic development, although the average annual GDP growth rate remains around 8% (Chen, Li, & Yin, 2008; Yang & Zhao, 2004). At the national level, it is estimated that corruption has caused an average economic loss between Y987.5 billion and Y1,257 billion annually since the second half of the 1990s. This number accounts for 13.2–16.8% of annual GDP (Hu & Guo, 2001). At the local level, prevalent corruption will lead to low income. For instance, when government positions were bought and sold widely in Heilongjiang province, limited resources and capital were all gathered in the hands of a few local leaders through the long chain of buying and selling offices. “A lot of potential market investments were transferred to cultivate administrative promotions.”15 This caused an extremely unequal distribution of social wealth, the near collapse of local economy, and plummeting average income of local residents. In recent years, many scholars argue that the “grey income,” often times earned through corruption, owned by a small proportion of privileged people, such as government officials and some higher-income groups, has greatly enlarged the unequal distribution of income among urban residents (Chen & Li, 2010; Wang, 2010).16 Moreover, public power is also utilized to monopolize certain profitable industries or lift the entry bar to favor particular companies, which increases inequality of both income and profit-making opportunities. But most important, inequality and low income caused by corruption is very likely to ignite public resentment and social unrest, which will further erode the economy.17 Mo (2001) shows empirically that the most important channel through which corruption affects economic growth is political instability. Indeed, many clashes between Chinese villagers and local officials in recent decades have been caused by local cadres’ distortion of “popular central policies (such as economic development) into harmful local policies (tu zhengce) that justify wasted investment and unauthorized extraction.”18 The arbitrary collection of various extra-budgetary fees by local governments from peasants has been one of the main sources of peasant protests and petitions (Li & O’brien, 2008; O’Brien & Li, 2005), while the extra-budgetary fund is a major source spreading local corruption (Chen, Hillman, & Gu, 2002). Premier Wen Jiabao expressed the central government’s concern by remarking, “Inflation plus income inequality, and corruption are sufficient to influence social stability and even the regime consolidation.” This reflects the important political implication of corruption and inequality for the Chinese party-state. In general, we believe corruption, especially serious corruption, can adversely affect local economic development and consequently lower the income level of a locality by

15

Zhu (2008, p. 576). “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 negative impact on per capita GDP growth during 1965–1985, after controlling for other variables suggested by the standard growth model” (see Zhuang et al., 2010, p. 10). 18 O’Brien and Li (2005 p. 241). 16

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

deterring private investment, distorting the allocation of physical and human capital, preserving social inequality, and generating political instability. Therefore, government anti-corruption endeavors become important in shifting local income up from low equilibrium. Combating corruption will limit the detrimental effects of corruption on economic growth. Control of corruption is a critical institutional dimension in forming good governance identified by the World Bank, along with accountability, rule of law, political stability, bureaucratic capability, property rights protection and contract enforcement (i.e., “Governance Matters” by Kaufmann et al. (1999)). Governance is “the manner in which power is exercised in the management of a country’s economic and social resources for development” (World Bank, 1992).19 The six institutional dimensions “are mutually reinforcing aspects of growth-enhancing institutions.” A strong control of corruption can reinforce the other five institutions; and a weak control of corruption will undermine other institutions. Cross-country studies have shown that societies that fail to establish these formal institutions effectively tend to have very high market transaction costs and “would be unable to control the ‘grabbing hand of the state,’ and, consequently, to support private initiatives, market exchanges and investments, and economic development.”20 In contrast, good public governance, whether subjectively or objectively measured, has been rigorously examined and found to help promote economic prosperity and social cohesion, reduce poverty, and deepen confidence in government and public administrations (Hall & Jones, 1999; Khan, 2007; Tarschys, 2001). Research on developing countries finds that the differences in policy implementation and quality of governance across localities can significantly influence international investors’ expectation of the policy credibility of a government and consequently affect their investment decisions to a locality (Oman & Arndt, 2007). As discussed in the introduction, anti-corruption efforts in China are still being institutionalized. There are some routine anti-corruption measures, including audit on the departure of government officials and State-Owned Enterprises (SOE) managers. However, more often the routine “police patrols” are mixed with campaign-based anticorruption strategies. As Wedeman (2005) points out, “given that enforcement resources are costly, infinite, and subject to decreasing marginal returns,” a resourceconstrained regime is unlikely to be able to afford to “deploy sufficient resources to tightly monitor and control corruption.” In this respect, “campaign-style enforcement is the poor man’s alternative to effective policing.”21 Compared with institutionalized control of corruption, anti-corruption campaigns are periodic crackdowns, which are launched by government leaders more arbitrarily in terms of timing, intensity, and targeting officials, areas, and types of corruption. Anti-corruption campaigns are used by political elites to stop inflation and even to purge fac-

19 20 21

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

439

tional rivals (Quade, 2007; Shih, 2008a,b). Therefore, the campaign-based and leader-directed Chinese-style anticorruption tends to fight corruption with varied strength during different time periods and across localities under different central and local leaders. It is unlikely to eliminate corruption entirely and inevitably new waves of corruption will erupt again soon after a campaign is over. Nevertheless, to some degree the campaigns have been successful in preventing corruption from spiraling beyond the tipping point into a crisis, through a periodic increase of investigation rates and deterrence of some risk-averse officials (Manion, 2004; Wedeman, 2005). Hence, regardless of the causes and objectives, we argue that campaign-based anti-corruption in China is still able to limit some of the adverse effects of corruption, especially in the short-run and in places with larger anti-corruption endeavors. The CCP has been considering anti-corruption as a precondition to sustain healthy economic development. The investigation results of the anti-corruption agencies have been deemed, first of all, as a government achievement in combating corruption. During a press conference in 2010, the Vice-Party Secretary of the Central Discipline Inspection Commission (CDIC), Gan Yisheng said, “Those who believe that the investigation of corruption adversely affects local economic development are biased and wrong. Investigation of disciplinary and legal violations can create a good political environment benefiting and protecting economic growth. . .. In particular, anti-corruption ensures the local government’s compliance with central orders, and maintains economic order. . . . Investigation of corruption in economic sectors also purifies the development environment and improves market order. . . Finally, anti-corruption in other sectors can cleanse the social atmosphere, generating a virtuous environment for economic development in the long run.”22 Gan Yisheng cited as an example that during the crackdown of the infamous Yuanhua smuggling case in the end of the 1990s, some people worried that the economy of Xiamen would revert to the conditions from 10 years ago. However, during the first year after the investigation, the customs revenue of Xiamen increased greatly. Since the second year, the local GDP and fiscal revenue has continued to grow annually. The current GDP of Xiamen has doubled and the fiscal revenue has tripled compared to 1999.23 In sum, based on existing empirical, regional, and cross-country studies, we believe that serious corruption lowers local economic development and thereby exacerbates regional income disparity in China. Crosscountry studies also show that control of corruption can effectively improve governance quality, which greatly ben-

22 “CDIC: The view looking at investigation of corruption adversely affecting economic growth is biased and wrong” (Zhong jiwei: chaban anjian yingxiang jingji fazhan de guandian pianmian cuowu), www.xinhuanet.com from Zhongxinwang, January 7, 2010, http://news.xinhuanet.com/legal/2010-01/07/content 12770164.htm. 23 Ibid.

440

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

efits economic growth. We therefore hypothesize that stronger anti-corruption efforts launched by local governments could limit the negative impact of corruption. Anti-corruption differences across local governments also contribute to the growing regional income disparity. In particular, 2.1. Null hypothesis Anti-corruption has nothing to do with the income disparities across Chinese counties. Some other variables determine the differences. Hypothesis 1. The larger the scale of anti-corruption efforts in Chinese counties the higher average income will be locally. 3. Data, methods, and statistical results To test the preceding hypothesis, we use county-level data for the years 2002–2003 collected from the China County Statistical Yearbook 2004 and Chinese Audit Yearbook 2003–2004. We form a cross-section dataset composed of up to 1,777 county level units, including most county-level cities, counties, and ethnic minority autonomous counties in all the provincial units (including centrally administered municipalities and ethnic autonomous regions) in Mainland China. Our dependent variable is the local residents’ average income in each county for the year 2003. Ideally, this should be measured by the disposable income of local residents. However, this data is not available in county-level statistical yearbooks. We approximate local income by per capita GDP of each county in 2003. Our major independent variable is county level anticorruption efforts, measured by three indicators collected from 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 audit bureau. Audit institutions are one of the major supervisory agencies aiming at promoting government integrity. They exercise supervision mainly over various levels of local government, state banking institutions, state enterprises and undertakings through auditing their revenues and public finance expenditures. Besides public finance audits, other primary audit categories executed in recent years include monetary, enterprise, economic accountability, resources and environmental, and foreign-related audits. Audit institutions are primarily accountable to their corresponding people’s governments.24 The first two indicators measure anti-corruption from an input perspective. It is reasonable to assume that local governments vigorously attacking corruption would hire

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

more auditors and tend to audit more projects than those taking a lax approach. A larger supervisory team and larger scale of audit might also have a stronger deterrent effect on corruption. It should be noted that most audit institutions are understaffed. Auditing usually targets key areas, organizations, and funds, and based on a higher level government’s authorization. A subset of the audited projects will be revealed as problematic and require correction. The most serious ones might be referred to legal procedures for further investigation and punishment. As with any economic productivity, high input does not necessarily generate high output. Problems such as shirking and concealing during auditing might also blunt the supervision of corruption. Therefore, we also measure the output of anti-corruption by the amount of illegal funds (weiji jin’e) – bribes, graft, embezzlement, and public funds that are misused – uncovered by local audit bureau in each county. Larger sums of uncovered illegal funds are regarded as an indicator of better performance of anti-corruption in a county. In general, a larger number of auditors, audited projects and a larger sum of uncovered illegal funds are hypothesized to be correlated with higher local income. We want to point out that, first, all the indicators primarily gauge the supervision of corruption, while a complete measure of anti-corruption should also include other stages, such as punishment of corruption. However, they are the only available data at county-level. But also, supervision is a very important stage, stopping and deterring some corruption, saving some of the cost of corruption. Second, to limit the endogeneity between control of corruption and income level, we choose lagged anticorruption based on the data of 2002 (i.e., Auditor02, AudPrjt02, and IllFund02) to explain the income level across counties in 2003. While cross-country studies have robustly shown that governance, including control of corruption, can determine income level, scholars do not deny that there is mutual causality, where income also affects governance (e.g., Chong & Calderón, 2000; Gundlach & Paldam, 2008; Kaufmann & Kraay, 2002). Researchers use some “deep” historical determinants of institutions as instrumental variables of governance, such as settler mortality in the 18th and 19th centuries (Acemoglu, Johnson, & Robinson, 2001) and colonial origins (Hall & Jones, 1999). They unanimously find that the effect of governance on economic development is strong and larger in IV estimations than in OLS estimations (Kaufmann & Kraay, 2002). In this research, we do not want to deny the possibility of mutual causality either. However, we are mainly interested in the effects of anti-corruption on county income. Unfortunately, we could not find a good instrumental variable for anti-corruption, which is only related to government uncovered corruption and unrelated to per capita GDP. Hence, we measure anti-corruption by lagged data. Since it is natural to expect the positive impact of anti-corruption to emerge a little behind, Income of year 2003 should influence corruption and anti-corruption only in years following 2003. Nevertheless, we are aware this is only a second best solution and does not completely get rid of the simultaneous causality problem.

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

Third, we are also aware that illegal funds discovered by auditing result not only from the intensity of anticorruption activity, but also actual corruption, and even random chance. But we argue the variances of illegal funds mainly reflect different anti-corruption efforts across counties. The previous citation of Gan Yisheng shows that the government looks at uncovered corruption primarily as an achievement of anti-corruption. Government officials in charge of anti-corruption also tend to interpret the regional differences of revealed corruption as variations of anticorruption. Several of our interviewees who worked in local audit institutions and procuratorates said that uncovered corruption mainly reflects anti-corruption, because a large amount of corruption goes undetected everywhere.25 Wei Jianxing, the former First Secretary of the CDIC commented in 1995, a few months after the exposure of Chen Xitong’s case in Beijing municipality, “I must point out that the investigation and handling of cases involving very big sums is uneven. There are provinces and localities with nothing to show for several years—they have handled no such cases. That they have handled none does not mean there are no cases involving very big sums [in these provinces and localities]. For many years, Beijing had no cases involving big sums, but this by no means signifies that all was well in Beijing, that the work was being done well. Rather, the problems were being covered up. Beijing is not an isolated case, there are many such examples. . .If a large locality or large department has no such cases for a long time, it is hard to believe that it has done such a good job. In some ministries, the discipline inspection group has had no cases for many years. I, for one, do not believe there are no problems [in these ministries]. It has to be that the problems are being concealed or that they have been discovered but not investigated. Every locality, every workplace should handle several influential cases.”26 We have also tested the correlation between the total amount of illegal funds uncovered in a province (where Sichuan and Chongqing are combined into one unit) in 2002 (PROVIllFund02) and one of our control variables, an indicator of provincial legal institution, the length of time needed to enforce a contract.27 PROVIllFund02 gauges the anti-corruption effort of each province in 2002. As for the indicator of contract enforcement, a shorter time length indicates greater bureaucratic efficiency and

25 Interviews were conducted through telephones and emails in November 2010. For the interviewees’ sake, we cannot reveal their names and their affiliation details. Interviewee No. 1 works in the Shaanxi Higher Procuratorate. Interviewee No. 2 previously worked in Henan Provincial Audit Bureau. Interviewee No. 3 works in a local procuratorate in Zhejiang province. All of them hold the opinion that uncovered corruption reflects anti-corruption more than corruption. But there are a couple other interviewees think that uncovered corruption reflects neither anti-corruption nor corruption, since the problem is too complicated and caused by so many reasons. 26 Manion, 2004 (p. 162). 27 We also attempted to test the correlation between the other two indicators of anti-corruption and contract enforcement. However, there is so much missing data of these indicators at provincial level.

441

stronger enforcement of contracts. We find the two variables are closely correlated at −0.448. This shows higher uncovered illegal funds are correlated with more efficient bureaucracy and better contract enforcement. This is consistent with the literature of governance that control of corruption and contract enforcement mutually reinforce each other. It shows our assumption that uncovered illegal funds mainly reflect anti-corruption endeavors is valid. Fourth, county-level anti-corruption efforts could also be affected by differences between provinces. Thus we add in provincial dummies to control the provincial differences in fighting corruption. In addition, we also use standardized illegal funds as an alternative measure of anti-corruption to double check the findings based on the absolute measure. We use the within-province standard score, or Z score of illegal funds, which we derive by the following equation. Although this is equivalent to adding province dummies, we derive the standard score instead to maintain a higher degree of freedom. We call this transformed variable IllFundSTD02ij . IllFundSTD02ij = IllFund02ij − Zij =

IllFund02ij − Uj j

In the above equation, subscript i is county, while j is province. IllFund02ij is the absolute amount of illegal funds in a given county-province observation, while Uj is the mean number of uncovered illegal funds across all the counties in a given province. Finally,  j is the standard deviation of the illegal funds in a given province across all the counties. Essentially, the mean of the illegal funds for a given province is subtracted from the observed county illegal funds and divided by the standard deviation of the illegal funds for a given province, producing a standardized uncovered illegal fund with a mean of zero and a standard deviation of 1. The standardized measure also minimizes the noises of the differences in county-level corruption on uncovered illegal funds. We can assume the level of corruption across counties within a given province is similar, since they have roughly the same corruption opportunities. Hence, the transformed variable measures how much each county’s disclosed corruption departs from the provincial norm in anti-corruption. Because the standardized illegal fund takes into account provincial differences, it is comparable across provinces.28 In addition to the independent variables, we employ a series of control variables that also determine income level according to previous research and mainly the recent research of Wan et al. (2007) regarding the effects of globalization on inter-regional income disparity in China.29 Generally, control variables are chosen based on human capital 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 “Three Represents” across years. 29 See Wan et al. (2007). We also refer to this research in terms of applying Shapley value decomposition techniques to qualify the contribution of corruption other variables, as this research also studies inter-province income inequality in China and their method has generated some very valuable findings.

442

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

In particular, we include the following variables, most of which are taken from 2003 values. Capital input (K) is regarded as one of the most important factors for economic growth by economists. Based on the availability for county-level data, we measure capital input by average investment for basic construction, which is the total basic construction investment divided by county population. Higher capital input should generate higher income. It should be noted that basic construction investment is public investment conducted by the government instead of private investment. Government support (fisexp) is another source of economic growth, which leads to higher per capita income, as argued by Ma and Yu (2001). We measure government support by per capita fiscal expenditure. Higher fiscal expenditure to support local economy should lead to higher local income. Fiscal dependency (fisdep) is measured by the ratio of fiscal dependents to total population in each county. Fiscal dependents mainly include officials, cadres, employees, and staff hired by government and various public organizations such as schools and hospitals.30 The ratio of fiscal dependents 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 is expected to be related with low income levels. Urbanization (urban). Differences in urbanization between counties could also affect per capita income and thus income disparities. A higher degree of urbanization often means a relatively mature market economy, a higher degree of industrialization, and more job opportunities (e.g., Tiffen, 2003; Zhou, 2009). It is expected to be positively related with per capita income. Urbanization is gauged by the proportion of nonagricultural population of each county. Labor (dep) and Education (edu). These two variables measure labor and human capital, which are also important for economic growth and income differences. In particular, labor is the dependency ratio which is used as a proxy of labor input. This variable is calculated as such: dependency = ((total population − employment) × 100%)/employment. Education is measured by the ratio of number of students enrolled to secondary school to total population. The higher this ratio is, the higher the level of human capital in a county. Contract enforcement (conenforce) is an institutional dimension of governance. It mainly measures the quality of legal institutions of a society. In the World Bank report Do Business in China 2008, one indicator gauging contract enforcement in Chinese provinces is the time needed to enforce a contract. This is the number of days from the time a plaintiff files a lawsuit, the court coming to a verdict, to finally the plaintiff receiving the money owed by the defendant. Apparently, the fewer days spent in this

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

Table 1 Summary of relevant variables. Obs ln AGDP ln Auditor02 ln AudPrjt02 ln IllFund02 IllFundSTD02 ln K ln fisexp ln fisdep ln urban ln dep ln edu ln conenforce

1,661 1,777 1,510 1,770 1,770 1,661 1,661 1,661 1,661 1,661 1,661 30

Mean

Min

Max

Std dev

8.598 2.866 3.464 7.160 −5.65e−06 6.483 6.444 1.125 2.686 4.695 1.849 5.737

4.510 1.386 0.693 1.176 −1.301 2.278 2.947 0.322 0.358 1.308 0.049 4.718

11.148 4.727 8.109 11.179 8.836 10.462 9.199 3.637 4.580 7.233 2.664 6.292

0.697 0.431 0.701 1.415 0.992 1.024 0.565 0.355 0.656 0.455 0.281 0.315

AGDP, per capita GDP; Auditor02, number of audit personnel of each county (auditors) in 2002; AudPrjt02, number of the audited projects in each county in 2002; IllFund02, amount of the illegal funds uncovered by a county audit bureau in 2002; IllFundSTD02, standardized uncovered illegal fund in 2002; K, capital input; fisexp, government support through fiscal expenditure; fisdep, fiscal dependency; urban, urbanization; dep, labor input measured by the dependency ratio; edu, education; conenforce, contract enforcement.

process, the more efficient the province’s legal institutions. Unfortunately, publication of the report only began in 2005 and the earliest available data for this indicator that we have is from 2006. Additionally, this indicator is only collected at the provincial level. However, it is reasonable for us to assume that the legal institutions of a province would not change radically from 2003 to 2006 due to institutional inertia (Hannan & Freeman, 1984). We can also roughly approximate the quality of legal institution in counties by their corresponding provincial indicator, since county-level courts are led by courts at the prefectural and provincial levels. It should also be noted that conenforce is highly correlated with the geographical locations of provinces. Eastern coastal provinces tend to have better legal institutions than the rest of China. Due to this high multicollinearity, we do not include a geographical dummy variable in our model. Table 1 summarizes the basic statistics of all the relevant variables. We adopt a double log model, based on production theory, to test the hypothesis. Zhang and Zhang (2003) employed a double log model estimating the effects of globalization on regional disparity in China. It has the advantage of reducing heteroskedasticity, a common problem for cross-sectional data. The basic regression model is presented 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 measures of anti-corruption. We estimate the basic model in Eq. (1) based on different indicators of anti-corruption with and without provincial dummy variables, respectively, by Ordinary Least Square (OLS) regression. All together there are eight models. The results are reported in Table 2.

0.203*** (0.015) 0.332*** (0.031) −0.366*** (0.045) 0.235*** (0.024) −0.176*** (0.031) 0.483*** (0.047) −0.270*** (0.042) 5.732*** (0.362) No 167.06 (0.000) 0.429 1,770 0.185*** (0.014) 0.278*** (0.031) −0.183*** (0.045) 0.287*** (0.024) −0.183*** (0.029) 0.327*** (0.048) −0.279 (0.194) 6.567*** (0.948) Yes 67.44 (0.000) 0.568 1,770 0.214*** (0.016) 0.339*** (0.034) −0.420*** (0.050) 0.257*** (0.026) −0.168*** (0.035) 0.520*** (0.051) −0.328*** (0.046) 6.056*** (0.402) No 136.95 (0.000) 0.419 1,510 Standard errors are in square brackets. ** Coefficient is statistically significant at 5% level. *** Coefficient is statistically significant at 1% level.

0.212*** (0.015) 0.365*** (0.032) −0.388*** (0.045) 0.221*** (0.025) −0.169*** (0.000) 0.475*** (0.047) −0.269*** v 5.312*** (0.381) No 165.67 (0.000) 0.426 1,777 0.189*** (0.014) 0.300*** (0.031) −0.255*** (0.045) 0.281*** (0.024) −0.182*** (0.029) 0.325*** (0.047) −0.439** (0.196) 7.023*** (0.946) Yes 67.92 (0.000) 0.569 1,777

0.186*** (0.015) 0.297*** (0.033) −0.287*** (0.050) 0.290*** (0.026) −0.183*** (0.000) 0.304*** (0.033) −0.544*** (0.095) 7.919*** (0.575) Yes 57.03 (0.000) 0.565 1,510

0.301*** (0.032) 0.205*** (0.033)

Model 1 Dependent variable

Table 2 OLS estimation results.

ln AGDP ln Auditor02 ln AudPrjt02 ln IllFund02 IllFundSTD02 ln K ln fisexp ln fisdep ln urban ln dep ln edu ln conenforce Constant Province F-test (p-value) Adj R2 Obs.

Model 2

Model 3

0.083*** (0.021)

Model 4

0.134*** (0.020)

Model 5

0.060*** (0.009)

Model 6

0.095*** (0.010)

Model 7

0.059*** (0.011) 0.185*** (0.014) 0.273*** (0.031) −0.260*** (0.045) 0.292*** (0.024) −0.183*** (0.029) 0.345*** (0.047) −0.286 (0.195) 7.113*** (0.949) Yes 66.55 (0.000) 0.565 1,770

Model 8

0.047*** (0.013) 0.205*** (0.015) 0.322*** (0.032) −0.433*** (0.046) 0.261*** (0.025) −0.186*** (0.031) 0.565*** (0.047) −0.351*** (0.042) 6.818*** (0.358) No 149.17 (0.000) 0.401 1,770

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

443

Table 2 reveals several robust and striking patterns. All the coefficients are statistically significantly different from 0 at levels of 1% or 5% except ˛8 in Models 5 and 7. Most importantly, anti-corruption exerts a relatively strong and positive impact on county income. From an input perspective, Models 1–4 show that a 100% increase of anti-corruption, whether measured by Auditor02 or AudPrjt02, will increase AGDP by 8.3–30.1%, all else being equal. And Auditor02 has a larger impact of increasing AGDP for more than 20.5%. Thus on average inputting 100% more in anti-corruption could increase AGDP by 18.075%. Models 5–8 test the hypothesis from an output perspective of anti-corruption. Models 5 and 6 show that a 100% increase of IllFund02 will increase AGDP by 6–9.5%. Models 7 and 8 illustrate that, all else being equal, a one-unit increase of IllFundSTD02 will generate a roughly 0.047–0.059 increase of ln AGDP, which equals to an increase of AGDP by 4.8–6.1%.31 All the findings are robust 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 nearly 15%. Therefore, Hypothesis 1 is accepted with confidence. Besides the independent variable that we are most interested in, all control variables are also statistically significant in most models and perform as predicted. Based on the slightly different coefficients of the control variables in the eight models, we summarize the findings as follows: Capital input, government support through fiscal expenditure, urbanization, and education all have strong and positive effects on income level. A 100% increase of K, fisexp, urbanization, and edu will, respectively, lead to around 20%, 30%, at least 22.1% and 30.4% increase in AGDP. In contrast, large government is inefficient for economic development and less labor input also decreases local income. A 100% increase of fisdep or dep will decrease AGDP by at least 18.3% each. Finally, contract enforcement is very important for the local economy. A 100% increase of the number of days spent to enforce a contract could decrease AGDP for 26.9–54.4%. To quantify the contributions of every variable, especially anti-corruption, to inter-county income disparity, we apply the Shapley value decomposition technique. The Shapley value framework is first raised by Shorrocks (1999) based on the cooperative game theory. The Shapley value decomposition technique was developed by Wan (2002) and recently used by Kolenikov and Shorrocks (2005) and Wan (2004). This method requires that all the decomposition variables’ values be positive and that most estimated coefficients be statistically significant. Thus, this method is only applicable to estimates based on an absolute measure of anti-corruption, because some of the standardized anti-corruption is negative. Additionally, many provincial dummies are not statistically significant. We therefore employ the Shapley value decomposition technique to

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

444

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

Table 3 Contribution of the residual and explanatory variables. Anti-corruption

Auditor02

AudPrjt02

IllFund02

Total disparity Contribution of Independent variables Contribution of residual Proportion explaineda = 100 × (1 − |residual|/total)

0.383 0.263 0.12 68.67

0.384 0.262 0.122 68.23

0.384 0.265 0.119 69.01

a

A negative contribution of residual implies that variables not considered are equalizing forces (Wan et al., 2007).

Models 2, 4 and 6. According to the results of the three models and the basic model in Eq. (1), the income level variable 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 the equation when relative measures of inequality are used (Wan et al., 2007). The contribution of residual term ε is equal to I(AGDP) − I(AGDP).32 The Gini coefficient is selected as a measure to decompose income disparity, since it is the most common index measuring inequality.33 The Chinese county-level Gini coefficient in 2003 was 0.384. This is much higher than the United States and Japan. For United States, its state-level Gini coefficient in 2003 was only 0.131, while the county/city-level Gini coefficient of Japan was even lower, only 0.102. We also find countylevel 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 variables to inter-county income disparity are tabulated in Table 3. We can explain up to 69.01% of total Gini, which indicates decomposition exercise is successful. Because residual contribution implies the proportion of income disparity that is not explained by the model, we only focus on the proportion that is explained. According to Table 4, the two most influential factors on per capita county income across different measures of anti-corruption are capital input (K) and fiscal expenditure (fisexp). Together, these two factors contribute up to 50% 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 contributes 10.65–12.83% to total income disparity. When using AudPrjt02 as the indicator, anti-corruption makes

32 I denotes income inequality index. AGDP and AGDP denote original income 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 explanation is low. We therefore report the best decomposition results. 34 We calculate the Gini indices of China, the United State, and Japan based provincial level per capita GDP data of 2003 collected from the China Statistical Yearbook 2004, American Statistical Yearbook 2004 and Japan Statistical Yearbook 2004. Counties in Japan are comparable to provinces in China.

a smaller but still sizable contribution of 6.11% to regional disparity. To make sure the large contribution of anti-corruption is not an inflated one, we test the correlation between anti-corruption and the control variables. Table 5 illustrates clearly that the correlations between the three anti-corruption indicators and all the control variables are below 0.282, which is minimal. This indicates anti-corruption does not affect income level indirectly through influencing other variables. All together, we confidently conclude that the estimated contributions of anti-corruption to income disparity are relatively accurate. In addition to the above variables, education and contract enforcement also contribute considerably to total regional income disparity. On average, education contributes 9.62% and contract enforcement contributes 7.85% to income disparity across the different measures of anti-corruption. Labor dependents and government size contribute a smaller proportion to regional disparity, around 2% and 3%, respectively. This finding indicates that control of corruption plays an even more important role than some commonly emphasized factors for economic development, such as human capital and contract enforcement. County governments’ differences in anti-corruption efforts can exert a huge influence on local income and intercounty income disparity. 4. Discussion and conclusion This paper emphasizes the impact of anti-corruption efforts on China’s growing regional income disparity in recent years. Based on cross-country studies and empirical research of corruption in China we induce that overall corruption hinders economic development, generates poverty and instability, and leads to an unequal distribution of income across localities in China. Therefore, we argue government control of corruption will reduce the detrimental effects of corruption. It also helps improve the quality of governance by local governments. Good governance can create better institutional environment for economic development and foster higher incomes. However, anticorruption in China presently is often campaign-based and leader-directed. It is not clear whether Chinese-style anti-corruption can promote regional economic growth and influence income disparity. We use a county level cross-section dataset combining data for years 2002–2003 to study the question. We measure the local scale of anti-corruption by three indicators, the number of local auditors, the number of audited projects, and the amount of uncovered illegal funds in a county so as to examine the robustness of our findings. To limit the endogeneity between control of corruption and local income level, we

Table 4 Inequality decomposition results.a Independent variable

a

Independent variable

Absolute contribution

Relative contribution (%)

0.071 0.062 0.04 0.028 0.025 0.019 0.007 0.006

27.00 23.57 15.21 10.65 9.51 7.22 2.66 2.28

Capital input Fiscal expenditure Urbanization Education Contract enforcement Anti-corruption Fiscal dependency Labor

AudPrjt02

Independent variable

Absolute contribution

Relative contribution (%)

0.076 0.058 0.048 0.026 0.024 0.016 0.009 0.004

29.01 22.14 18.32 9.92 9.16 6.11 3.44 1.53

Capital input Fiscal expenditure Urbanization Anti-corruption Education Contract enforcement Fiscal dependency Labor

IllFund02 Absolute contribution

Relative contribution (%)

0.074 0.058 0.042 0.034 0.025 0.019 0.007 0.006

27.92 21.89 15.85 12.83 9.43 7.17 2.64 2.26

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

Table 5 Correlations of explanatory variables.

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

ln Auditor02

ln AudPrjt02

ln IllFund02

1.000 0.359 (0.000) 0.423 (0.000) 0.149 (0.000) −0.122 (0.000) −0.220 (0.000) −0.252 (0.000) 0.044 (0.066) −0.012 (0.620) 0.282 (0.000) −0.247 (0.000)

IllFundSTD02

1.000 0.260 (0.000) 0.128 (0.000) −0.066 (0.010) −0.187 (0.000) −0.215 (0.000) −0.063 (0.014) −0.090 (0.000) 0.187 (0.000) −0.144 (0.000)

1.000 0.638 (0.000) 1.000 −0.014 (0.565) 0.022 (0.359) −0.091 (0.000) 0.000 (0.996) −0.225 (0.000) −0.096 (0.000) 0.043 (0.068) 0.000 (0.987) −0.007 (0.761) 0.004 (0.865) 0.234 (0.000) 0.088 (0.000) −0.244 (0.000) 0.000 (1.000)

ln K

ln fisexp

ln fisdep

ln urban

ln dep

ln edu

ln conenforce

1.000 0.542 (0.000) 0.346 (0.000) 0.385 (0.000) 0.060 (0.011) −0.032 (0.177) 0.006 (0.797)

1.000 0.563 (0.000) 0.444 (0.000) 0.091 (0.000) −0.141 (0.000) −0.030 (0.203)

1.000 0.375 (0.000) 0.146 (0.000) −0.047 (0.000) 0.182 (0.000)

1.000 0.426 (0.000) 0.006 (0.799) −0.016 (0.498)

1.000 0.028 (0.232) 0.040 (0.090)

1.000 −0.118 (0.000)

1.000

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

Capital input Fiscal expenditure Urbanization Anti-corruption Education Contract enforcement Fiscal dependency Labor

Auditor02

p values are in brackets.

445

446

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

use lagged anti-corruption in the year of 2002 to interpret per capita GDP across Chinese counties in 2003. The results confirm our hypothesis that anti-corruption is able to exert a fairly strong and positive impact on local average income. In other words, anti-corruption campaigns are still beneficial for poverty reduction, though not effective enough in controlling corruption. Counties with higher degrees of anti-corruption tend to be richer than those with lower degrees of anti-corruption. We further quantify the contributions of anticorruption and other variables by the newly developed Shapley value decomposition technique. We find: (1) Capital input, fiscal expenditure, and urbanization are the most influential factors on local income disparity. Together these top three factors contribute more than 65% to explaining the average income gap across counties. (2) Anti-corruption also contributes a lot to total income disparity. Based on different measures, it contributes from 6% to almost 13% to regional disparity. On average, anti-corruption is the fourth most important contributors to inter-county income gap. (3) All the other variables, including education, contract enforcement, government size, and labor input contribute less than anti-corruption to regional disparity. Our empirical results find that growing regional income disparity 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 influential factor, following capital input, fiscal expenditure, and urbanization, contributing to regional income disparity. Counties with a higher scale of anti-corruption, regardless of the measure employed, have higher average income than those with lower anti-corruption scales. This suggests that worries of adverse effect of anti-corruption on economic development are not necessary. However, contributions of anti-corruption to total income disparity do vary somewhat depending on different indicators. Although the difference is partially a natural statistical outcome, intuitively it also has policy implications. The decomposition results show that in general a larger supervisory team and a higher output of anti-corruption are more effective in controlling corruption and contributes more into regional disparity than auditing more projects. This is probably because a large team of auditors has a stronger deterrent effect on corruption, while uncovering more illegal funds indicates that the auditors are committed to their anti-corruption duties. However, when choosing audit projects, Chinese audit institutions are still broadly under the direction of higher-level governments and have limited autonomy and independence from the government. This problem, plus the campaign style, makes corrupt officials behave opportunistically and decreases the effectiveness of anti-corruption. Therefore, granting greater autonomy to anti-corruption agencies is important in improving the control of corruption. If that is hard to realize in the short-run, recruiting more anti-corruption personnel and enhancing their performance would also be helpful in controlling corruption. Our results also show that at county level, the main momentum pushing economic development seems still to be public investment and government fiscal expenditures.

However, public investment is one of the major sources of official corruption. This also makes control of corruption urgent for promoting economic development and average income in county level. Finally, our paper is only a preliminary examination of the economic consequences of anti-corruption efforts in China. We only provide a positive answer to the question whether Chinese-style anti-corruption can benefit economic development and local income. There are more specific questions worth further research, such as how long the positive effect of Chinese-style anti-corruption can hold, what different impacts campaign-based anticorruption and institutionalized-anti-corruption can generate on inter-regional disparity, and what types of corruption Chinese-style anti-corruption is more effective in controlling. To answer these questions we will need both in-depth case studies and more carefully formed panel data. But based on our current research, we may make some conjectures on different impacts of campaign-based anti-corruption and institutionalized anti-corruption on inter-regional disparity. First of all, as discussed previously, campaign-based anti-corruption is most likely to be effective in the short run, while the effects of institutionalized anti-corruption tend to hold longer. Therefore, the decrease in inter-regional disparity caused by anticorruption campaigns will not be sustained for a long period of time. Inter-regional disparity will increase again soon after an anti-corruption campaign ends. Second, effects of campaign-based anti-corruption tend to vary more greatly from region to region, since leadership matters more in this type of anti-corruption than institutionalized anti-corruption. Regions with good leaders might fight corruption more, while other places with ineffective leaders could simply ignore anti-corruption. Therefore, countries resorting to campaign-based anticorruption should have higher inter-regional disparity than those with institutionalized anti-corruption. We can actually roughly see this pattern when comparing China and the United States, if the latter is taken as an example of institutionalized anti-corruption. We find from 1990 to 2006, the interstate Gini coefficient in the United States had been very stable and quite low, only fluctuating between 0.1 and 0.15. However, the interprovincial Gini coefficient of China in the same time period had been higher and varied in a wider range between 0.25 and 0.35. The Gini coefficient was also less stable and fluctuated more frequently than the American one.35 However, more rigorous hypothesis testing is needed. Acknowledgements We thank Professor Guanghua Wan for providing the software for the Shapley value decomposition. We also thank Dr. Jie Lu, Dr. Bin Yu, Dr. Zhengxin Shi, Dr. Qi Zhang, Brandon T. Condren, and editors of the Social Science Journal for their valuable comments and suggestions to our article. 35 Data source for American state level GDP per capita and Chinese provincial GDP per capita are at http://www.bea.gov/ and http://db.cei.gov.cn/.

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

References Acemoglu, D. (2008). Introduction to modern economic growth. Princeton University Press. Acemoglu, D., Johnson, S., & Robinson, J. A. (2001). The colonial origins of comparative development: An empirical investigation. American Economic Review, 91(December (5)), 1369–1401. Aidt, T. S. (2009). Corruption, institutions, and economic development. Oxford Review of Economic Policy, 25(2), 271–291. Andvig, J. C., & Moene, K. O. (1990). How corruption may corrupt. Journal of Economic Behavior and Organization, 13(January (1)), 63–76. Bardhan, P. (1997). Corruption and development: A review of issues. Journal of Economic Literature, 35(3), 1320–1346. Bates, R. H. (1981). Markets and states in tropical Africa: The political basis of agricultural policies. Berkeley: University of California Press. Beck, P. J., & Maher, M. W. (1986). A comparison of bribery and bidding in thin markets. Economics Letters, 20(1), 1–5. Blackburn, K., Bose, N., & Haque, M. E. (2006). The incidence and persistence of corruption in economic development. Journal of Economic Dynamics and Control, 30, 2447–2467. Blackburn, K., & Sarmah, R. (2008). Corruption, development and demography. Economics of Governance, 9, 341–362. Blanchard, O., & Shleifer, A. (2001). Federalism with and without political centralization: China versus Russia (pp. 171–179). IMF staff papers 48 special issue. Chen, G., & Li, S. (2010). Chinese corruption, income distribution, and income inequality [Zhongguo de fubai, shouru fenpei he shouru chaju]. Economic Science, 2, 55–68. Chen, G., Li, S., & Yin, X. (2008). Corruption and Chinese economic growth—Empirical perspective [Fubai yu zhongguo jingjizengzhang—Shizheng zhuyi de shijiao]. Economic Society System Comparison, 3, 59–68. Chen, K., Hillman, A., & Gu, Q. (2002). Fiscal centralization and local government behavior change: From the assisting hand to the grabbing hand [Caizheng jiquan yu defang zhengfu xingwei de bianhua: Cong yuanzhu zhishou dao juequ zhishou]. China Economic Quarterly, 2(1), 111–130. Chong, A., & Calderón, C. (2000). Institutional quality and poverty measures in a cross-section of countries. Economics of Governance, 1(2), 123–135. De Soto, H. (1989). The other path: The invisible revolution in the Third World. New York: Harper & Row. Démurger, S., Sachs, J., & Hu, Y. (2002). The Relative contributions of location and preferential policies in China’s regional development [Dili weizhi yu youhui zhengce duizhongguo diqu jingji fazhan de xiangguan gongxian]. Economic Studies, 9, 14–23. Easterly, W. (2007). Inequality does cause underdevelopment: Insights from a new instrument. Journal of Development Economics, 64, 755–776. Fisman, R. (2001). Estimating the value of political connections. The American Economic Review, 91(4), 1095–1102. Fu, X., & Wu, L. (2006). Technology efficiency, capital deepening, and regional differences [Jishu xiaolv, ziben shenhua yu diqu chayi]. Economic Studies, 10, 52–61. Fujita, M., & Hu, D. (2001). Regional disparity in China 1985–1994: The effects of globalization and economic liberalization. The Annals of Regional Science, 35(1), 3–37. Gong, T. (1994). The politics of corruption in contemporary China: An analysis of policy outcomes. Westport, Conn./London: Praeger. Gong, T. (1997). Forms and characteristics of China’s corruption in the 1990s: Change with continuity. Communist and Post-Communist Studies, 30(3), 277–288. Gong, T. (2002). Dangerous collusion: Corruption as a collective venture in contemporary China. Communist and Post-Communist Studies, 35(1), 85–103. Gray, C. W., & Kaufmann, D. (1998). Corruption and development. Finance & Development, March, 7–10. Gundlach, E., & Paldam, M. (2008). The democratic transition. A study of the causality between income and the Gastil Democracy Index. Kiel working papers 1459. Kiel Institute for the World Economy. Guo, Y. (2008). Corruption in transitional China: An empirical analysis. The China Quarterly, 194(1), 349–364. Guo, Y., & Hu, A. (2003). Administrative monopoly, rent seeking, and corruption—The analysis of the mechanism of corruption in transforming economies [Xingzheng longduan, xunzu yu fubai—Zhuanxing jingji de fubai jili fenxi]. Economic Society System Comparison, 106(2), 61–69. Gupta, S., Davoodi, H., & Alonso-Terme, R. (2002). Does corruption affect income inequality and poverty? Economics of Governance, 3(1), 23–45.

447

Hall, R. E., & Jones, C. (1999). Why do some countries produce so much more output per worker than others? Quarterly Journal of Economics, 114(1), 83–116. Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149– 164. Hao, Y., & Johnston, M. (1995). China’s surge of corruption. Journal of Democracy, 6(4), 80–94. He, Z. (2000). Corruption and anti-corruption in reform China. Communist and Post-Communist Studies, 33(2), 243–270. Hu, A., & Guo, Y. (2001). Comprehensive strategies of corruption control and institutional design during transition [Zhuanxingqi fangzhi fubai de zonghe zhanlue yu zhidu sheji]. Management World, 6, 44–55. Huntington, S. P. (1968). Political order in changing societies. New Haven: Yale University Press. Jian, T., Sachs, J. D., & Warner, A. M. (1996). Trends in regional inequality in China. China Economic Review, 7(1), 1–21. Johnston, M. (1989). Corruption, inequality and change. In P. Ward (Ed.), Corruption, development and inequality. London/New York: Routledge. Kang, D. C. (2002). Crony capitalism: Corruption and development in South Korea and the Philippines. Cambridge/New York: Cambridge University Press. Kaufmann, D., & Kraay, A. (2002). Growth without governance. Paper presented at the 5th economic panel meeting. Kaufmann, D., Kraay, A., & Zoido-Lobatón, P. (1999). Governance Matters. World Bank Policy Research Department working paper no. 2196. Retrieved from: http://www.worldbank.org/wbi/governance/ pubs/govmatters.htm. Khan, M. H. (1996). The efficiency implications of corruption. Journal of International Development, 8(5), 683–696. Khan, M. H. (2007). Governance, economic growth and development since the 1960s. DESA working paper no. 54 ST/ESA/2007/DWP/54. Khan, M. H., & Jomo, K. S. (2000). Rents rent-seeking and economic development: Theory and evidence in Asia. Cambridge/New York: Cambridge University Press. Kolenikov, S., & Shorrocks, A. (2005). A decomposition analysis of regional poverty in Russia. Review of Development Economics, 9(1), 25–46. Leff, N. H. (1964). Economic development through bureaucratic corruption. American Behavioral Scientist, 8(3), 8–14. Li, H., Xu, L. C., & Zou, H. (2000). Corruption, income distribution, and growth. Economics & Politics, 12(2), 155–182. Li, L., & O’brien, K. J. (2008). Protest leadership in rural China. The China Quarterly, 193(1), 1–23. Lien, D.-H. D. (1986). A note on competitive bribery games. Economics Letters, 22(4), 337–341. Lui, F. T. (1985). An equilibrium queuing model of bribery. The Journal of Political Economy, 93(4), 760–781. Luo, D., & Chen, J. (2008). (Zhongguo xianxiang caizheng diaocha) Chinese county township fiscal investigation. Shanghai Fareast Press[[nl]]Shanghai Yuandong Chubanshe. Ma, S., & Yu, H. (2001). Fiscal transfer and convergence of regional economy [Caizheng zhuanyizhifu yu diqu jingjishoulian]. Economic Studies, 3, 26–33. Manion, M. (2004). Corruption by design: Building clean government in Mainland China and Hong Kong. Cambridge, MA: Harvard University Press. Mauro, P. (1995). Corruption and growth. The Quarterly Journal of Economics, 110(3), 681–712. Mo, P. H. (2001). Corruption and economic growth. Journal of Comparative Economics, 29(1), 66–79. Murphy, K. M., Shleifer, A., & Vishny, R. W. (1991). The allocation of talent: Implications for growth. The Quarterly Journal of Economics, 106(2), 503–530. Murphy, K. M., Shleifer, A., & Vishny, R. W. (1993). Why is rent-seeking so costly to growth? The American Economic Review, 83(2), 409– 414. Myrdal, G. (1968). Corruption as hindrance to modernization in South Asia. In A. J. Heidenheimer, M. Johnston, & V. LeVine (Eds.), Political corruption: A handbook (pp. 405–421). New Brunswick, USA/Oxford, UK: Transaction Publishers. O’Brien, K. J., & Li, L. (2005). Popular contention and its impact in rural China. Comparative Political Studies, 38(3), 235–259. Oman, C., & Arndt, C. (2007). Governance indicators. In M. Bevir (Ed.), Encyclopedia of governance. Sage Publications. Perry, E. J. (1999). Crime, corruption, and contention. In M. Goldman, & R. MacFarquhar (Eds.), The paradox of China’s post-Mao reforms. Harvard University Press.

448

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

Quade, E. A. (2007). The logic of anticorruption enforcement campaigns in contemporary China. Journal of Contemporary China, 16(February (50)), 65–77. Rock, M. T., & Bonnett, H. (2004). The comparative politics of corruption: Accounting for the East Asian paradox in empirical studies of corruption, growth and investment. World Development, 32(6), 999–1017. Rose-Ackerman, S. (1975). The economics of corruption. Journal of Public Economics, 4(2), 187–203. Rose-Ackerman, S. (1978). Corruption: A study in political economy. New York: Academic Press. Rose-Ackerman, S. (1999). Corruption and government: Causes, consequences and reform. Cambridge/New York: Cambridge University Press. Shorrocks, A. F. (1999). Decomposition procedures for distributional analysis: A unified framework based on the Shapley value. Unpublished manuscript, Department of Economics, University of Essex. Shih, V. C.-H. (2008a). Factions and finance in China: Elite conflict and inflation. New York: Cambridge University Press. Shih, V. C.-H. (2008b). “Nauseating” displays of loyalty: Monitoring the factional bargain through ideological campaigns in China. The Journal of Politics, 70(October (4)), 1–16. Shirk, S. L. (1993). The political logic of economic reform in China. Berkeley: University of California Press. Shleifer, A., & Vishny, R. W. (1993). Corruption. The Quarterly Journal of Economics, 108(August (3)), 599–617. Sun, Y. (2004). Corruption and market in contemporary China. Ithaca, NY: Cornell University Press. Svensson, J. (2005). Eight questions about corruption. The Journal of Economic Perspectives, 19(Summer (3)), 19–42. Tanzi, V., & Davoodi, H. (1997). Corruption, public investment, and growth. IMF working paper 97/139. Washington: International Monetary Fund. Tarschys, D. (2001). Wealth, values, institutions: Trends in government and governance. In OECD (Ed.), Governance in the 21st century. Paris: Organisation for Economic Co-operation and Development. Tian, H. (2009). (Yazhou fanfubai falv jizhi bijiao yanjiu) Comparative study of Asian anticorruption legal systems. Beijing: Zhongguo renmin gongan daxue. Tiffen, M. (2003). Transition in sub-Saharan Africa: Agriculture, urbanization and income growth. World Development, 31(8), 1343. Wade, R. (1982). The system of administrative and political corruption: Canal irrigation in South India. Journal of Development Studies, 18(3), 287–328. Wan, G. (2002). Regression-based inequality decomposition: Pitfalls and a solution procedure. Discussion paper, UNU-WIDER no. 2002/101. Wan, G. (2004). Accounting for income inequality in rural China: A regression-based approach. Journal of Comparative Economics, 32(2), 348–363. Wan, G., Lu, M., & Chen, Z. (2007). Globalization and regional income inequality: Empirical evidence from within China. Review of Income and Wealth, 53(March (1)), 35–59.

Wang, X. (2010). Grey income and national income distribution [Huise shouru yu guoming shouru fenpei]. Comparison, 48, 1–29. Wang, X., & Fan, G. (2004). The changing trend and causes of Chinese regional inequality [Zhongguo diqu chaju de biandong qushi he yingxiang yinsu]. Economic Studies, 1, 33–44. Wedeman, A. (2004). The intensification of corruption in China. The China Quarterly, 180(1), 895–921. Wedeman, A. (2005). Anticorruption campaigns and the intensification of corruption in China. Journal of Contemporary China, 14(42), 93–116. Wooldridge, J. M. (2002). Introductory econometrics: A modern approach (2nd edition). South-Western College Pub. (1992). Governance and development. Washington, DC: World Bank. (2000). Making transition work for everyone: Poverty and inequality in Europe and Central Asia. Washington, DC: World Bank. (2008). Do business in China. World Bank. Wu, Y. (2005). Economic transition, collective corruption and political reform—An economic analysis based on the experiences of transition in China [Jingji Zhuangui, jiti fubai yu zhengzhi gaige—Jiyu zhongguo zhuangui jingan de jingjixue fenxi]. Contemporary Economic Science, 27(2), 21–26. Yang, C., & Zhao, F. (2004). A macro-economic analysis of administrative corruption [Xingzheng fubai de hongguan jingjixue fenxi]. Economic Studies, 9, 101–109. You, J.-S., & Khagram, S. (2005). A comparative study of inequality and corruption. American Sociological Review, 70(February (1)), 136– 157. Zhang, G. (2008). The determinants of employment in public administration in China: A cross province study [Guanminbi shengjichayi yuanyin yanjiu]. Journal of Public Administration, 1, 89–111. Zhang, W. (2009). (Zhongguo Jingji Zhidu) Chinese economic institutions. Zhong Xin Press. Zhang, X., & Zhang, K. H. (2003). How does globalization affect regional inequality within a developing country? Evidence from China. Journal of Development Studies, 39(4), 47–67. Zhou, L. (2007). The study of Chinese local officials’ promotion competition model [Zhongguo defang guanyuan de jinsheng jinbiaosai moshi yanjiu]. Economic Studies, 7, 36–50. Zhou, Y. (2009). Urbanization, rural-urban income gap, and overall income inequality in China—An empirical test of the inverse U hypothesis [Chengshihua, chengxiang chaju yiji quanguo jumin zongti shouru chaju de biandong—Shouru chaju dao U xing jiashuo de shizheng jianyan]. China Economics Quarterly, 8(4), 1239–1256. Zhu, J. (2008). Why are offices for sale in China? A case study of the officeselling chain in Heilongjiang Province. Asian Survey, 48(July/August (4)), 558–579. Zhu, J. (2012). Shadows of the skyscrapers: Real estate corruption in China. Journal of Contemporary China, 21(March (74)) Zhuang, J., Dios, E. d., & Lagman-Martin, A. (2010). Governance and institutional quality and the links with economic growth and income inequality: With special reference to developing Asia. ADB economics working paper series, 193.