The Impact of China's Anti-Corruption Campaign

Home , Bai Enpei, Corruption in China, Ling Jihua, Wang Qishan, Zhou Yongkang

THE IMPACT OF CHINA’S ANTI-CORRUPTION CAMPAIGN HAVE “TIGERS” HARBORED MORE “FLIES”?

A Thesis submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Master of Public Policy in Public Policy

Jingyu Gao, B.A.

Washington, DC April 1, 2017

ii THE IMPACT OF CHINA’S ANTI-CORRUPTION CAMPAIGN HAVE “TIGERS” HARBORED MORE “FLIES”?

Jingyu Gao, B.A.

Thesis Advisor: Andreas Kern, Ph.D.

ABSTRACT

At the end of 2012, the Chinese government launched a nationwide anti-corruption campaign with unprecedented efforts. Led by President Xi Jinping and the party whip Wang Qishan, this campaign has shocked the media and many outside observers. It has targeted officials on all levels of government, even including some top-ranking officials, known as “Tigers,” such as Bo Xilai,

Ling Jihua, Zhou Yongkang and Bai Enpei. These observations raise questions about the underlying motives for this surge in corruption scandals. I hypothesize that provinces where the prosecuted “Tigers” once headed local governments have investigated more officials than other provinces. Using province panel data covering all 31 provinces/municipal cities over a period from

2010 to 2016, I found that provinces, including Shanxi, Sichuan, Chongqing, Liaoning and Yunnan, have investigated much more officials than other provinces after controlling for several factors.

These five provinces were once governed by Bo Xilai, Ling Jihua, Zhou Yongkang and Bai

Enpei. This relationship between “Tigers” and corruption investigations across provinces is statistically significant and robust across different models pointing to the importance of local political networks in the context of corruption in China.

iii I would like to express my sincere gratitude to my thesis advisor Dr. Andreas Kern, who has always been patient and supportive to me, particularly during the times when I felt overwhelmed by the complexities of my paper’s topic.

I would also like to acknowledge with appreciation the McCourt School of Public Policy and the Georgetown University where I have spent two full years being consistently encouraged to explore my academic interests.

In addition, I would like to give a special mention for one McCourt alumnus and my dear friend Mr. Zhengyuan Bo, who provided inspiration for my thesis and has been a role model as a gentleman with utmost honor for the country and people he deeply loves.

Last but not least, thanks to my parents, Mr. Zhili Gao and Mrs. Qiuling Zhang, who have contributed all they can to provide unconditional support for my study in the U.S.

iv TABLE OF CONTENTS

Chapter I. Introduction ...... 1

Chapter II. Literature Review ...... 4

Chapter III. Conceptual Framework ...... 8

Chapter IV. Empirical Analysis ...... 10

Chapter V. Findings and Discussion...... 17

Chapter VI. Conclusions, Limitations and Future Research ...... 21

Appendix ...... 24

References ...... 30

v LIST OF TABLES AND FIGURES

Table A Number of Investigated Officials by Year in China’s Provinces ...... 12

Table B Description of Variables and Data Sources ...... 24

Table C Descriptive Statistics of Control Variables ...... 25

Table D Results Comparison between "Allies" and "Tigers" Dummies ...... 26

Table E Results Comparison between "Tigers" with/out Jiangxi ...... 27

Table F Different Models Estimates Using "Tigers" with Jiangxi ...... 28

Table G Different Models Estimates Using "Tigers" without Jiangxi ...... 29

Figure A Illustration of "Tiger" and "Allies" Provinces ...... 14

vi Chapter I. INTRODUCTION

For observers of Chinese politics, the four years since the end of 2012 have witnessed an unprecedented anti-corruption campaign that has targeted all levels of government. Between 2013 and 2015, the government anti-corruption agency, the People’s Procuratorate (PP, 人民检察院), investigated over 160,000 officials for duty crimes 1 . In 2013, compared to 2012 when the campaign was unleashed, the number of investigated officials increased by 8.4 %, the fastest growth in the past five years.2 Even more stunning, some Party leaders, notably Bo Xilai and Zhou

Yongkang, who had served so long in top positions, were prosecuted publicly. In the past, such high-profile prosecutions would have been unthinkable.

At the same time, China's performance measured by the major international corruption indices has not substantially improved. In 2015, the country ranked the 83rd out of 168 evaluated countries on Transparency International’s Corruption Perception Index (CPI). Compared to 2013, its CPI score remained almost unchanged 3 . Its percentile ranking in the World Bank’s Control of

Corruption of the Worldwide Governance Indicators (WGI) has also stayed around 50th, with minimal improvement since 2013.4

In reaction, the Chinese government harshly criticized the indices’ objectivity and usefulness, arguing that they had not taken into account the efforts made by the government to counter corruption.5 Such a contrast between the current administration's official voice and international

1 In China’s criminal code, duty crime consists of many sub-categories that are usually considered as corrupt criminal behaviors, such as bribery, embezzlement, and abuse of power. 2 Author’s calculation based on Supreme People’s Procuratorate of P.R.C. Annual Reports from 2010 to 2016. 3 Transparency International. http://www.transparency.org/country#CHN (accessed December 12, 2016) 4 World Bank Institute. http://info.worldbank.org/governance/wgi/#reports (accessed December 12, 2016) 5 Tatlow DK and Smale A, “China Loses Ground in Transparency International Report on Corruption,” The New York Times, December 3, 2014, http://www.nytimes.com/2014/12/04/world/asia/china-loses-ground-in-transparency-international-report-on-corruption.html?_r=0 (accessed March 5, 2017)

1 observers’ assessments raises a challenging yet important question: what is the real impact of Xi's anti-corruption campaign so far? This paper will try to answer this question.

My central hypothesis is that the on-going anti-corruption campaign’s impact varies across provinces, and is dependent on factors such as the political influence of investigated top officials, in Chinese political language known as “Tigers (大老虎)”. More specifically, I hypothesize that where these “Tigers” worked and have built strong local networks of corruption matters to how the anti-corruption campaign affects investigations locally.

Using province panel data, I use a random effects model to estimate the provincial differences.

My key findings suggest that in provinces where “Tigers” once headed local governments, 36-43 percent more local officials ( known as “Flies”, 苍蝇) have been investigated than in other provinces.

These results are robust across different model specifications. Thus, my findings provide statistical support for the view that the political corruption networks in the “Tiger” provinces are indeed collapsing. With their previous bosses being publicly prosecuted and the top-down anti-corruption campaign being implemented through the unprecedented effort of Xi’s team, local officials who were promoted by bribery to fallen “Tigers” have also been exposed and investigated.

My analysis provides empirical evidence for a prevailing argument: that "tiger" provinces are indeed experiencing a "collapsing corruption," a term used by the Chinese media to describe the ripple effect of local corruption investigations after "Tigers" have fallen.

Given the non-transparent process of corruption investigations in China, it is extremely challenging to directly observe the underlying factors that contribute to the number and distribution of investigated cases. However, past studies and newly available data may help to answer my paper’s central question. As Xi’s anti-corruption continues to shake different levels of

2 governments, it is a good natural experiment to observe how the fall of “Tigers” may have led to political earthquakes among the “Flies.”

3 Chapter II. LITERATURE REVIEW

Existing literature has broadly discussed the effectiveness of China’s Anti-Corruption

Agencies (ACA). For example, Quah (2015) concluded that ACAs in Taiwan and Singapore have been more efficient in deterring corruption than ACAs in China, Japan, and Philippines, mainly due to the government’s political will in supporting impartial, independent agencies. However, the motives of such enforcement may not always have been solely a desire to fight corruption. They may also have been mixed with other objectives, such as political purge or ensuring internal solidarity (Broadburst and Wang, 2014).

Focusing on China's ACAs, scholars have found that the investigation of officials’ suspected corruption crimes is largely determined by the Central Commission for Discipline Inspection

(CCDI, 中央纪律检查委员会 ) and its local branches, the party organs responsible for the ethics of party members who hold public offices (Guo, 2014). By interviewing local procurators who probed government corruption cases, Li and Deng (2015) found that most investigations are first reviewed by local party leaders, who then decide whether corrupt officials’ cases should be transferred to the PP or local court for further investigation. However, due to budget constraints and legal loopholes, such probes are subject to transactions between local procuratorates and other government entities involved in the cases, leaving the prosecutions selective and unsupervised.

Various researchers have given special attention to the underlying logic of Xi’s anti-corruption campaign. One group holds the view that the campaign is nothing more than a political purge of his rivals within the Party who oppose his reform plans. This view has earned considerable popularity from the Western media and Chinese dissidents overseas. For example, Murong Xuecun, a liberal Chinese writer argues in an article published in the New York Times that before 2015, few corrupt “Tigers” had been found in Fujian and Zhejiang provinces where President Xi once

4 worked as a provincial governor and party secretary (NYT, 2015). Meanwhile, more “Tigers” were prosecuted in Sichuan, Jiangxi, and Shanxi provinces than others, and these provinces were considered as being controlled by the purged “Tigers” who had opposed Xi (Andrew, 2017).

However, other scholars reject the political purge hypothesis and argue that Xi's campaign is mere sincerer than many believe. For example, Cheng Li at the Brookings Institution argues that although Xi’s anti-corruption campaign did help Xi consolidate his power by “purging” high- ranking officials, the view that the campaign is primarily driven by factional politics is “inaccurate and misleading” (Li and Mcelveen, 2014) because some prosecuted officials are believed to be in the same political faction as Xi. And in a recent and compelling empirical analysis, Lu and

Lorentzen (2016) conclude that the crackdown is "primarily a sincere effort to cut down the widespread corruption" to help the government build a meritocratic system. These authors find little evidence that Xi and his team only purge their opponents and protect their allies.

Previous studies also examined the relationship between local corruption and promotion within the Chinese bureaucratic system, shedding some light on how the fall of high-profile

“Tigers” shakes local politics. These studies hypothesize that, to get promoted, one needs to maintain close personal relations with his/her superior officials by paying bribes (Nava and Liu,

2008). Moreover, if the top officials who head the local governments are involved in buying and selling offices, the officials below are usually "compelled to follow suit" (Zhu, 2008). Otherwise, their likelihood to be promoted could be negatively affected (Zhang, 2008).

However, other scholars hold a more optimistic view of the nested relation between corruption and promotion. For example, based on survey research, Ma and Tang (2015) concluded that merit- based and guanxi-oriented 6 selection mechanisms are interconnected and both influence how

6 In Chinese, guanxi refers to personal relations. Many previous studies examined the interaction between guanxi and other factors, such as official’s education, that contribute to the likelihood of promotion.

5 higher ranking officials pick candidates to be promoted, rather than completely relying on personal connections as many believe. In addition, Jia and Kudamatsu (2015) found that among China's provincial leaders, relationships with the top politicians and their performances as provincial officials are two complementing factors contributing to their promotion.

Nonetheless, observers continue to believe that maintaining personal connections with top politicians by corruption, no matter the form, is still an important step to being promoted. Therefore, it could be true that on their way to the top, fallen “Tigers” created large, corrupt political networks across different levels of local governments.

Using provincial-level panel data, notably the number of investigated cases and officials, another cohort of scholars has examined socio-economic indicators that affect local corruption.

They argue that the Chinese anti-corruption enforcement effort seems to be associated with local economic performance and foreign direct investment (FDI). For example, assuming the number of investigations indicates the intensity of corruption, Chen and Li (2008) test the conventional wisdom that corruption harms economic growth. And they find it does -- that provincial GDP growth is negatively associated with corruption.

In contrast, Dong and Torgler’s study (2012) shows a positive association between local income and the number of investigated corruption cases. These authors conclude that “corruption becomes more evasive when the government power is widened through increased economic activity”. Similarly, Cole and Elliott (2009) find that provinces with a greater number of investigated corruption cases are associated with higher FDI inflow as a percentage of provincial

GDP. They argue that corrupt local authorities often benefit from the bribery paid by foreign investors to ease official red tape that restricts their business. Thus, in provinces with higher FDI inflow, more corruption investigations can be observed.

6 This paper contributes to the literature on China’s anti-corruption campaign in two different ways. First, it uses the most recent data – the number of probed officials in each province from

2010 to 2015 – to analyze the variability of the impact of the Xi’s campaign on different provinces.

Previous studies primarily used data from before the current anti-corruption campaign began.

Second, by creating a dummy variable denoting whether or not fallen “Tigers” worked as top political leaders in a certain province, the paper investigates whether this fact is associated with a significant difference in the current crackdown efforts across provinces. Synthesizing these findings, I complement previous research and provide a novel explanation concerning observed provincial differences.

7 Chapter III. CONCEPTUAL FRAMEWORK

My central argument is that the impact of the anti-corruption campaign in each province depends on the political factor – the purged “Tigers” who held positions in the Politburo Standing

Committee (PSC, 中央政治局常委). These “Tigers” include Bo Xilai, Zhou Yongkang and Ling Jihua who were known to be the most influential figures being officially investigated during Xi’s anti- corruption campaign. To link this influence to the dependent variable – the number of investigated officials in each province – I build my argument on the following two observations in Chinese politics.

First, if a top politician is investigated due to corruption crimes, then his loyalists in the local governments where he previously worked will be similarly affected because they are all closely connected with each other by corrupt transactions, especially bribery to get promoted. In other words, once a “tiger” is targeted in a corruption investigation, the network of personal connections behind him would also be exposed, resulting in what the Chinese state-owned media have described as “collapsing corruption (塌方式腐败 )”. For example, the fall of Ling Jihua who worked in Shanxi province, led to waves of corruption probes of different levels of local officials across sectors in the provinces’ governments7. Similar cascading effects can be observed in the case of

Bo Xilai in Liaoning8 and Zhou Yongkang in Sichuan9.

Second, the political networks built in the process of promotion to the top involve corruption, notably bribery to buy public offices. According to a study in 2012 that examined about 2800 corruption cases, over 10% of the probed officials had conducted “organizational and personnel”

7 Wang Shu, “How Shanxi Has Handled Its Collapsing Corruption?” The Beijing News, January 27, 2016, http://www.bjnews.com.cn/news/2016/01/27/392960.html (accessed February 26, 2017) 8 Bi Ling, “Liaoning’s Shortest-term Mayor Investigated,” DW News, October 3, 20160 http://china.dwnews.com/news/2016-10-30/59778603.html (accessed February 26, 2017) 9 Caixin Special Report, “The Big Tiger Zhou Yongkang”, Caixin, July 29, 2014, http://www.caijing.com.cn/140730/ (accessed February 26, 2017)

8 corruption, which refers to selling public offices (Gong and Wu, 2012). Many fallen “Tigers” probed in the campaign, especially officials at the vice provincial or ministerial level and above, engaged in such office selling transactions when they sought promotion or promoted others (Pei,

2016, p. 82).

Based on these observations, provinces where the fallen “Tigers” headed the local governments are expected to have more officials investigated owing to the collapse of political networks previously backed by those Tigers. This hypothesis is specified as follows:

Hypothesis 1:

Provinces where the “Tigers”, who formerly headed local governments have more officials investigated than other provinces.

In addition, I test a second hypothesis that Xi and Wang have selectively protected local government officials during the campaign. More precisely:

Hypothesis 2：

Provinces where Xi and Wang headed local governments have fewer cofficials investigated than other provinces.

My conceptual framework differs from previous papers in that it includes these political factors that may affect the anti-corruption campaign locally. Most relevant existing empirical papers have analyzed the crackdown from an economic perspective, looking at factors such as

GDP and FDI. However, as many qualitative studies have suggested, the anti-corruption investigations in China are subject to the CCP’s political influence.

9 Chapter IV. EMPIRICAL ANALYSIS

Owing to the nature of my panel dataset and research design, where the central independent variable is a time-invariant dichotomous variable, a GLS random effects (RE) model is the most appropriate tool for addressing the inter-temporal differences within each province. By including time dummies, RE models can also control overall trends throughout time to avoid over-fit that may result in misleading results. The model is specified as:

푌푖,푡+1 = α + 훽1푋푖,푡 + ∑ 훽푗푍푗푖,푡 + 푌푒푎푟 + 휀푖,푡

My dependent variable Yi,t is the number of investigated government officials reported by province i for year t, as a measurement of corruption. Key independent variables Xi,t are dummy variables that record in which provinces Xi, his campaign allies and the prosecuted “Tigers” used to serve in the top local political positions.

To address province-specific factors, I include a vector Zji,t of additional control variables. It denotes the control variable j for province i in year t. These variables include the staff size of local government, provincial GDP, and provincial spending on public security. I also include variables such as distance from Beijing, the number of state-owned enterprises and local representatives appointed to the National People’s Congress (NPC, 全国人民代表大会). The overall trends over time are captured by Year dummy variables. α is the constant term and εi,t represents the error term.

Using RE as the base model, I use the logarithm form of the dependent variable to alleviate the distortion from outliers. Also, on the assumption that the impact of Xi’s campaign should be

10 10 lagged, I test a lagged regression indicated by Yi,t+1 . In the following paragraphs, I outline my dependent variable and related control variables.

Dependent Variable

My dependent variable is the number of investigated government officials (stata code: investigations) under the criminal name “duty crimes (职务犯罪)” in each province per year. Duty crimes consist of bribery, embezzlement, abuse of power, and other misconduct that is considered as corruption in China. Each spring, the PP of each province publishes the number of such crimes in the province in reporting the anti-corruption measures enforced by the government in the previous year.

The data on duty crimes are drawn either from the Procuratorate Yearbook of China (中国检

察年鉴) or from official reports of the provincial governments (各省、直辖市人民检察院报告

)11. Usually, high-profile corruption cases are transferred to the provincial procuratorate only after they are reviewed and approved by local party leaders. Currently, these data are the only publicly available data that directly relate to provincial-level anti-corruption efforts. Also, data are missing for some provinces, such as Xinjiang and Ningxia, in some years.

Table A summarizes the available data for the dependent variable. It shows that the average number of investigated officials in each province has grown from 2010 to 2015, with a sharper increase between 2012 and 2013 when the anti-corruption campaign was officially launched. On average, in 2013, provinces investigated over 200 more officials than in 2012 while annual increase for other years remains fewer than 100. Also, the number of investigated officials in each

10 Many researchers have warned against using the lagged RE regressions due to increased standard errors. In fact, I found little difference between the results of the lagged and contemporaneous regressions using the RE model (see Appendix Table F). Therefore, using lagged regression does not seem to be worrying here. 11 Data for 2010 to 2012 are drawn from the Yearbook. Other data are extracted from the government report of each individual province/municipal city.

11 province has shown greater variability, which is indicated by larger standard errors (SD). In particular, standard deviation of investigated officials jumped by almost 100 in 2014 as compared to 2013.

Table A. Number of Investigated Officials by Year in China’s Provinces Year Observationsa Mean Standard Deviation Min Max 2010 30 1391 866 212 4063 2011 30 1458 884 36 4180 2012 31 1474 943 36 4177 2013 30 1676 989 39 4157 2014 31 1753 1080 84 4523 2015 31 1737 1048 84 4297 Source: Provincial Procuratorate Reports and Procuratorate Yearbook a: Full sample size is 31, including all provinces and municipal cities. In 2010, Tibet is missing. In 2011 and 2013, Ningxia is missing.

Synthesizing these observations reveals that the crackdown effort has been increasingly strengthened and its impact has been different across provinces.

Independent Variables

My core independent variable is a dummy that indicates the presence of political influence in

Xi’s anti-corruption campaign. As Chinese politics observers and media sources suggest, provinces where Xi and his allies in this campaign previously worked as leaders seem to have fewer corrupt officials investigated by the procuratorate (NYT, 2015). In contrast, other observers of Chinese politics suggest that provinces where Xi’s purged “Tigers”, notably Bo Xilai, Ling

Jihua and Zhou Yongkang, once worked have had more officials investigated. To test whether this argument holds, I construct two dummy variables to be used in different models.

First, the variable “Tigers” denotes the career history of “Tigers” who have been officially investigated during the present campaign. Key figures are Bo Xilai, Ling Jihua and Zhou

12 Yongkang. All of them served as party or government leaders in local and provincial offices in

Sichuan, Chongqing, Liaoning, and Shanxi.

I also consider two other “Tigers”: Bai Enpei and Zeng Qinghong. Bai was investigated in

2014 because he had worked in Yunnan province as the provincial party secretary-general and was believed to be closely connected with Zhou’s corruption case. Zeng has not yet been probed or questioned publicly, but he is seen by many Chinese political observers as the leader of a strong political coalition opposing Xi’s anti-corruption campaign called the “Jiangxi Gang,” after his hometown Jiangxi (Wedeman, 2017). However, Zeng’s relation with Xi is not as clear as that of other Tigers. Many news sources that exposed Zeng’s deteriorating relationship with Xi and his allies are not official.

Also, scholars such as Cheng Li have rejected the view that Zeng has been a target of Xi’s campaign (Li and Mcelveen 2014). I coded all provinces – Sichuan, Chongqing, Liaoning, Shanxi,

Yunnan and Jiangxi – as 1 for the “Tigers” variable to test whether these provinces had investigated more officials compared to others. In addition, to address the question of whether Zeng Qinghong has had any impact on the results of Xi’s campaign, I created an additional dummy variable “Tigers

(without Jiangxi)” that excludes Jiangxi where he used to control, while keeping the other five provinces in “Tigers”.

Second, the dummy variable “Allies” denotes the political careers12 of Xi and his biggest ally

Wang Qishan. Wang is the commander-in-chief of Xi’s anti-corruption campaign and holds both a position in the PSC and the CCDI. in charge of Party discipline. He is considered the party whip.

12 I limited the time of political career to the period after 2000. Only local positions that these politicians held after 2000 are considered. For example, Bai Enpei worked in Qinghai and Inner Mongolia as local party leader in 1990s and in Yunnan since 2001. In his case, only Yunnan is considered and coded as “1” in the “Tigers” variable, excluding Qinghai and Inner Mongolia. The reason for this is based on my assumption that the anti-corruption campaign focues more on recently built political networks than those from a long time ago.

13 Without Wang’s support, the campaign machine would fail to work because the CCDI must review and approve every important corruption investigation, and is the only agency with power to push forward a probe. Xi once worked as a top leader in Fujian and Zhejiang provinces, and Shanghai municipal city, and is also believed to have formed strong ties to his previous colleagues in Shaanxi, where he was a county leader (Li, 2016). Wang Qishan was the provincial party secretary-general of Guangdong and Hainan provinces, and the municipal mayor 13 of Beijing 14. I coded these provinces as 1 for the “Allies” dummy to test whether these provinces had investigated fewer officials than others. Figure 1 shows how the “Tigers” and “allies” dummy variables are coded.

Figure A. Illustration of “Tiger” and “Allies” Provinces Source: the author

13 The mayor of Beijing, Shanghai, Chongqing and Tianjin municipal city government holds the same administrative level position as the provincial governor. 14 Xinhua, “Wang Qishan’s Biography”, http://news.xinhuanet.com/rwk/2013-02/07/c_124334702.htm (accessed December 12, 2016)

14 Additional Control Variables

I also include variables to control the impact of the total population of government officials on the dependent variable. Government staff in my models is defined as the annual number of public servants working in each province/municipal city government. It is highly possible that a province has more officials investigated because it has a larger number of government officials. Therefore, the number of officials is controlled in my models. Data for this variable are drawn from China’s

National Bureau of Statistics (NBS).

Provincial gross domestic production (GDP) may also be correlated with the anti-corruption crackdown because better GDP performance indicates more economic activity. Thus, local authorities may have more opportunities to get involved in rent-seeking behaviors (Dong and

Torgler, 2012). Also, as a measure of local government performance, provincial GDP may affect the possibility of provincial officials’ promotion to higher positions (Li, 2015).

I also include provincial spending on public security as a proxy for spending on the provincial procuratoate. Anti-corruption enforcement by provincial procuratorates is heavily dependent on budgets (Li and Deng, 2015; Ma, 2007). If the procuratorate budget is limited, enforcement will also be limited, even though the local government wants to crackdown on corruption.

Another control variable is the number of state-owned enterprises in a province, defined as the number of locally registered state-owned enterprises in each province. It is commonly believed that corruption largely comes from the connections between local state-owned firms and governments.

I also control for foreign direct investment (FDI) because previous literature (Dong and

Torgler, 2012) suggests a potentially significant association between FDI inflow and the number of investigated officials in a province.

15 The last two controls in my models are the distance from the provincial capital to Beijing and the number of provincial representatives in the NPC. These two variables are considered proxies for the political closeness of provincial government with the central government, which may affect local corruption investigations.

My analysis uses relevant data for the period 2010 to 2015, drawn from various sources because no single database stores all the data for all my variables. The units of analysis are China’s

31 provinces15. The sample size is 186 (31 provinces times 5 years). Specific data sources and descriptive statistics can be found in the Appendix (see Table A and Table B).

15 This total includes Beijing, Chongqing, Shanghai, and Tianjin, four municipal cities (直辖市) directly administered by the central government, enjoying the same administrative status at the 27 provinces (省/自治区).

16 Chapter V. FINDINGS AND DISCUSSION

Key Findings

The regression results support my first hypothesis that during Xi’s campaign provinces where

“Tigers” worked as top officials are predicted to have more local officials investigated than other provinces. On average, there have been 36-43% more officials investigated in these “tiger” provinces, as compared to the rest of the provinces. This finding consistently withstands multiple models, providing relatively strong evidence for the view of “collapsing corruption” in Sichuan,

Chongqing, Liaoning, Shanxi and Jiangxi16.

This section discusses the results of each of my independent variables in detail. Primary focus is my dummy variables “Tigers” and “Allies,” but I also elaborate on the impact of other controls.

A simple OLS model estimates a significant association between the dummy independent variable and the number of investigations by province (see Appendix Table D). Appendix Table

D indicates that “Tiger” provinces have more officials investigated, while provinces where Xi and

Wang worked have fewer. However, despite their statistical significance, the magnitude of the results can be biased because the OLS model does not address province-specific effects and overall trends through time. To improve accuracy, I ran random effects models that factor in these effects.

As for the “Tigers” variable, its coefficient is positive and statistically significant, at the 5% significance level (see Appendix Table D). Holding others factors equal, provinces including

Sichuan, Liaoning, Shanxi, Yunnan, Jiangxi and Chongqing Municipal City are predicted to have

36.33% more local officials being investigated than other provinces and municipal cities.

Appendix Table D and Table F indicate that, if Jiangxi, the place where Zeng Qinghong worked,

16 Due to the conflicting views about Zeng’s relation with Xi, I tested two models that include/exclude Jiangxi as a “tiger” province where Zeng is believed to have strong local coalition.

17 is excluded from the list, the result remains almost unchanged. In this scenario, holding other variables constant, there have been 34.86% more investigated officials in Sichuan, Liaoning,

Shanxi, Yunnan and Chongqing than other provinces. Moreover, changing any of the above provinces coded as 1 to 0 would render the results insignificant. That means the local impact of the anti-corruption campaign has been distinct in Sichuan, Liaoning, Shanxi, Yunnan, Chongqing and Jiangxi. All these findings offer relatively strong empirical evidence to supporting the argument that “Tiger” provinces have been more affected by the anti-corruption campaign than other provinces.

Thus, my findings provide statistical support for the view that the political corruption networks in the “Tiger” provinces are indeed collapsing. With their previous bosses being publicly prosecuted and the top-down anti-corruption campaign being implemented through the unprecedented effort of Xi’s team, local officials who were promoted by bribery to fallen “Tigers” have also been exposed and investigated. A typical example is Shanxi, the province that used to be controlled by Ling Jihua. After his fall in 2014, it was reported that over 300 provincial-level government officials were investigated and these positions remained vacant to be filled by candidates from other provinces until 201617. Many more officials were probed in prefecture- and county-level governments in Shanxi. In addition, in 2016, the exposure of NPC election fraud in

Liaoning, where Bo Xilai had governed before, led to massive investigations involving all parties who bribed to enter key local public offices18. Previous studies that observed public office selling

17 Wang Shu, “How Shanxi Has Handled Its Collapsing Corruption?” The Beijing News, January 27, 2016, http://www.bjnews.com.cn/news/2016/01/27/392960.html (accessed February 26, 2017) 18 Michael Forsythe. “An Unlikely Crime in One-Party China: Election Fraud,” The New York Times, September 14, 2016, https://www.nytimes.com/2016/09/15/world/asia/china-npc-election-fraud-liaoning.html?_r=0 (accessed February 27, 2017)

18 behaviors among local Chinese officials also provide micro-level explanations to confirm this finding (Zhu, 2009; Zhang, 2008; Nava and Liu, 2008).

Appendix Table D also shows that my RE model only provides weaker evidence for hypothesis 2 where provinces where Xi and Wang once worked has fewer officials investigated.

Although the sign of the coefficient of the dummy variable “Allies” is negative and substantial, it is not statistically significant. That means provinces where Xi and his campaign ally Wang Qishan held top political positions do not seem to have fewer local officials investigated than others, which is in line with Lu and Lorentzen’s argument that Xi and Wang have not protected their personal connections built in local offices.

Among the control variables in Table D, local government staff size is positively associated with the number of investigated officials, and its coefficient has strong statistical significance.

Overall, every 10 thousand more employees in the local government are predicted to result in 0.3 to 1 percent more investigated officials, other factors held constant. It makes intuitive sense that more officials are expected to be found simply due to the larger population base.

GDP performance (in logarithm terms) also shows a positive connection with my dependent variable. Specifically, a 1% increase in provincial GDP is associated with about a 1.1% to 1.9% increase in the number of investigated local officials. Coefficients on this variable are statistically significant in all models (see Appendix Table F). This result is consistent with previous literature

(Dong and Torgler, 2012; Cole and Elliott, 2009), suggesting that corruption is more rampant in more economically prosperous provinces due to more transactions between local businesses and government officials than in other places.

Interestingly, the variable spending on public security is negatively associated with the dependent variable, indicating that in provinces with more spending on public security, fewer

19 officials are reported to be investigated by the local procuratorate. Overall, every 1% increase of this government expenditure is associated with 0.6% to 1% decrease in the number of the probed local officials. This statistical relation is consistently strong in all of my models (see Appendix

Table D, E and F). A possible explanation is that spending on public security may have a greater deterrence effect on local corrupt behaviors. I leave an in-depth analysis of this possibility to future research.

20 Chapter VI. CONCLUSIONS, LIMITATIONS AND FUTURE RESEARCH

Many previous studies have tried to understand the CCP’s crackdown on rampant corruption.

But there has never been a better experiment than Xi’s anti-corruption campaign for scholars and journalists to unpack the complexities of the Party’s fight against graft. Some hold the view that

Xi’s crackdown is solely a political tool to purge his opponents, while there is also evidence suggesting the opposite, that the campaign is sincere and tries to save the regime by targeting worms within the CCP.

Using a panel of all 31 provinces/municipal cities in the period between 2010 to 2015, my findings suggest that provinces that had been headed by political “Tigers” – Zhou Yongkang, Ling

Jihua, Bo Xilai, Bai Enpei – have been affected by Xi’s campaign far more than other provinces/municipal cities. During the campaign, after controlling other factors also believed to influence provincial corruption investigations, more local officials were publicly investigated by the PP in the “tiger” provinces than other provinces. This result adds evidence to the prevailing argument that some provinces, including Shanxi, Sichuan, Yunnan, Chongqing and probably

Jiangxi, are experiencing “collapsing corruption.”

In contrast, my analysis found little evidence that the crackdown has investigated fewer officials in provinces where the campaign commanders-in-chief – Xi and Wang – once held local party or government positions. This finding casts doubt on the assertion of some Chinese politics observers who believe the crackdown is looser in Xi and Wang’s previous political domains. In other words, my research is in line with Li and Lu’s finding that the campaign appears to be sincere about its objectives of punishing both “Tigers” and the “Flies” following them.

21 Nevertheless, my analysis has a number of limitations. First, a long debated issue, is the quality of the data published by the NBS that I used as primary sources for my control variables.

For example, provincial GDP values are believed to be over-reported; and skepticism about

China’s official statistics seems to be justified by the recent exposure of Liaoning’s fabrication of its GDP growth numbers19. If other provinces have the same problems, then this paper’s statistical inferences could be compromised.

Second, the data for the dependent variable – the number of investigated officials – are published by the local procuratorates. But due to the political sensitivity of the anti-corruption issues that might trigger chaos in Chinese public opinion, these numbers could be underreported.

Also, procuratorate reports are carefully reviewed by the CCDI before they are released. Thus, final numbers reported could be a result of internal discussion within the local government rather than a reflection of the real situation.

Finally, the construction of the “Tigers” and “Allies” dummy variables are arguably subject to debate. Although supported by some previous studies and media reports, determining which provinces are “Tigers” is somewhat subjective. While many agree that Shanxi was controlled by

Ling Jihua and Chongqing by Bo Xilai, designations of other “tiger” provinces such as Jiangxi, which is believed to be controlled by Zeng Qinghong, are controversial because, unlike other

“Tigers” who were officially targeted during the campaign, there is little official confirmation, but only outsiders’ speculations.

Given the overwhelming complexities of China’s domestic politics, this paper might have obscured many nuances that are specific to each individual province, such as the local political

19 Tom Hancock, “China Province Admits Falsifying Fiscal Data,” Financial Times, January 18, 2017, https://www.ft.com/content/b25d1b32-dd37-11e6-9d7c-be108f1c1dce (accessed February 27, 2017)

22 power structure and its relation to the central government. For this reason, my analysis only provides a general picture of provincial differences in the impact of the anti-corruption campaign.

Future research on corruption politics in China can delve deeper into the network of local political power that determines how in particular provinces procuratorates handle corruption convictions. My findings suggest that Shanxi, Liaoning, Sichuan, Chongqing, Jiangxi or Yunnan can be a good start point for such province-specific analysis. As these provinces seem to be in the middle of “collapsing corruption”, the local political landscape has never been more intriguing to outsiders. It is also a good time to examine further how the fall of “Tigers” triggered political earthquakes among “Flies”.

Nevertheless, the viability of arguments for and against the recent corruption cases in China, will critically depend on the performance of newly appointed candidates, who were selected to fill the vacant positions left by the “Tigers” and “Flies.” Any future evidence suggesting that the fallen corrupt officials are replaced by more honest and well-trained technocrats has the potential to challenge the conventional belief: a one-party ruled authoritarian regime is unlikely to be able to discipline itself from within. Thus, it may silence critical voices on Xi’s corruption crackdown.

23 APPENDIX

Table B. Description of Variables and Data Sources

Variables Sources Description Unit Corruption Annual Reports of Provincial The number of investigated government Person investigation Procuratorate; China officials with "duty crimes" that include Procuratorial Yearbook corruption

Allies Political career resume of Xi Fujian, Zhejiang, Shanghai, Shaanxi, None Jinping and Wang Qishan, Guangdong, Hainan and Beijing available on Xinhua and Chinese government website Tigers Political career resume of Bo Sichuan, Chongqing, Liaoning, Shanxi, None Xilai, Zhou Yongkang, Ling Yunnan and Jiangxi. Jihua, Bai Enpei and Zeng Qinghong, available on Xinhua and Chinese government web Provincial GDP National Bureau of Statistics Annual provincial GDP RMB/100 million Government staff size National Bureau of Statistics Total number of staff working for the Per 10 government or its affiliation thousand

Spending on public National Bureau of Statistics Total annual spending on the public RMB/100 security security million State-owned business National Bureau of Statistics The number of reported registered state- Per firm owned firms Distance from Google Earth Distance between Beijing and the Kilometer Beijing provincial capital or municipal cities Local representatives Official Website of National The number of People's Representatives Person in NPC People's Congress who attended the 11th and 12th NPC from each province or municipal cities FDI National Bureau of Statistics Value of annual foreign direct investment RMB/100 million

24 Table C. Descriptive Statistics of Control Variables Variable Observations Mean Std. Dev. Min Max Government Staff Size 186 49.69 27.48 8.30 112.76 Total GDP 186 19228.48 15270.59 507.46 72812.55 Log of Total GDP 186 9.50 0.99 6.23 11.20 Spending on Public Security (SPS) 186 198.94 123.94 31.49 834.54 Log of SPS 186 5.12 0.62 3.45 6.73 State-owned Businesses (SOB) 186 601.13 274.60 25.00 1258.00 Log of SOB 186 6.19 0.81 3.22 7.14 Distance from Beijing 186 1162.06 665.84 0.00 2555.26 Local Representatives in NPC 186 85.60 43.86 19.00 181.00 Total FDI 186 6918.47 10061.16 36.15 48728.17 Log of Total FDI 186 7.92 1.49 3.59 10.79

25 Table D. Results Comparison between “Allies” and “Tigers” Dummies Dependent Variable: Log of Number of Investigated Officials Coefficients (Robust Standard Errors) OLS GLS RE OLS GLS RE Dummies Allies -0.2138** -0.4189 (0.0974) (0.2673) Tigers (with Jiangxi) 0.4258*** 0.3633*** (0.0629) (0.1268) Controls Government staff size 0.0068** 0.0124*** 0.0035 0.0100*** (0.0028) (0.0046) (0.0027) (0.0047) Ln(total GDP) 1.7146*** 1.1115*** 1.9128*** 1.2404** (0.1790) (0.3652) (0.1695) (0.3403) Ln(spending on public security) -1.1005*** -0.4583** -1.1192*** -0.6344*** (0.1437) (0.2141) (0.1382) (0.2166) Ln(state-owned business) 0.1118 0.0624 0.0025 0.0645 (0.0950) (0.1861) (0.0835) (0.1761) Distance from Beijing 0.0003*** 0.0002 0.0002*** 0.0001 (0.0001) (0.0001) (0.0001) (0.0001) Local representatives in NPC -0.0008 -0.0034 0.0013 -0.0002 (0.0021) (0.0045) (0.0013) (0.0034) Ln(FDI total) -0.3444*** -0.1359 -0.4595*** -0.2458*** (0.0538) (0.0876) (0.6824) (0.0764) Year dummy No Yes No Yes Province dummy No Yes No Yes Number of observations 183 153 183 153 R-squared 84.31% 83.41% 87.08% 86.47% *** 99% significant, ** 95% significant, * 90% significant

26 Table E. Results Comparison between “Tigers” with/out Jiangxi Dependent Variable: Log of Number of Investigated Officials Coefficients (Robust Standard Errors) OLS GLS RE OLS GLS RE Dummies Tigers (with Jiangxi) 0.4258*** 0.3633*** (0.0629) (0.1268) Tigers (without Jiangxi) 0.4315*** 0.3486** (0.0720) (0.1458) Controls Government staff size 0.0035 0.0100*** 0.0043 0.0104** (0.0027) (0.0047) (0.0026) (0.0046) Ln(total GDP) 1.9128*** 1.2404** 1.9306*** 1.2209*** (0.1695) (0.3403) (0.1729) (0.3414) Ln(spending on public security) -1.1192*** -0.6344*** -1.1764*** -0.6415*** (0.1382) (0.2166) (0.1379) (0.2169) Ln(state-owned business) 0.0025 0.0645 0.0037 0.0691 (0.0835) (0.1761) (0.0841) (0.1785) Distance from Beijing 0.0002*** 0.0001 0.0002*** 0.0002 (0.0001) (0.0001) (0.0001) (0.0001) Local representatives in NPC 0.0013 -0.0002 0.0009 -0.0003 (0.0013) (0.0034) (0.0014) (0.0034) Ln(FDI total) -0.4595*** -0.2458*** -0.4478*** -0.2325*** (0.6824) (0.0764) (0.0569) (0.0747) Year dummy No Yes No Yes Province dummy No Yes No Yes Number of observations 183 153 183 153 R-squared 87.08% 86.47% 86.69% 85.82% *** 99% significant, ** 95% significant, * 90% significant

27 Table F. Different Models Estimates Using “Tigers” with Jiangxi Dependent Variable: Log of Number of Investigated Officials Coefficients (Robust Standard Errors)

Model (1) Model (2) Model (3) Model (4) Model (5)

Dummies Tigers (with Jiangxi) 0.426*** 0.355*** 0.362*** 0.363*** 0.384*** (0.0629) (0.123) (0.117) (0.127) (0.119)

Controls Government staff size 0.00348 0.0124** 0.00998* 0.0100** 0.0102** (0.00269) (0.00576) (0.00577) (0.00466) (0.00441)

Ln(GDP) 1.913*** 1.106*** 1.298*** 1.240*** 1.127*** (0.169) (0.236) (0.270) (0.340) (0.214)

Ln(spending on public security) -1.119*** -0.590*** -1.026*** -0.634*** -0.463*** (0.138) (0.188) (0.201) (0.217) (0.178)

Ln(state-owned firms) 0.00250 0.191 0.204** 0.0645

(0.0835) (0.146) (0.103) (0.176)

Ln(FDI) -0.460*** -0.245*** -0.276*** -0.246*** -0.238*** (0.0583) (0.0791) (0.0777) (0.0764) (0.0789)

Distance from Beijing 0.000202*** 0.000109 0.000162 0.000145

(5.38e-05) (0.000132) (0.000103) (0.000120)

Local representatives in NPC 0.00125 -0.00187 0.00141 -0.000155

(0.00135) (0.00323) (0.00295) (0.00336)

Constant -2.355*** -0.324 -0.0294 -0.553 0.146

(0.682) (1.348) (1.475) (1.567) (1.350)

OLS Yes

GLS RE Yes Yes Yes Yes

Year dummy Yes Yes Yes

Province dummy Yes Yes Yes Yes First lag of dependent Yes Yes

Observations 183 183 183 153 153 R-squared 0.871 0.831 0.863 0.865 0.847 Number of provinces 31 31 31 31 *** 99% significant, ** 95% significant, * 90% significant

28 Table G. Different Models Estimates Using “Tigers” without Jiangxi Dependent Variable: Log of Number of Investigated Officials Coefficients (Robust Standard Errors)

Model (1) Model (2) Model (3) Model (4) Model (5)

Dummies Tigers (without Jiangxi) 0.431*** 0.344** 0.359*** 0.349** 0.367*** (0.0720) (0.140) (0.135) (0.146) (0.135)

Controls Government staff size 0.00428 0.0130** 0.0108* 0.0104** 0.0105** (0.00261) (0.00580) (0.00586) (0.00464) (0.00448)

Ln(GDP) 1.931*** 1.099*** 1.270*** 1.221*** 1.104*** (0.173) (0.237) (0.271) (0.341) (0.213)

Ln(spending on public security) -1.176*** -0.600*** -1.042*** -0.641*** -0.466*** (0.138) (0.184) (0.197) (0.217) (0.178)

Ln(state-owned firms) 0.00370 0.193 0.213** 0.0691

(0.0841) (0.149) (0.104) (0.178)

Ln(FDI) -0.448*** -0.234*** -0.260*** -0.233*** -0.224*** (0.0569) (0.0779) (0.0758) (0.0747) (0.0777)

Distance from Beijing 0.000219*** 0.000116 0.000168 0.000150

(5.25e-05) (0.000133) (0.000104) (0.000122)

Local representatives in NPC 0.000864 -0.00219 0.00120 -0.000265

(0.00135) (0.00324) (0.00300) (0.00339)

Constant -2.345*** -0.294 0.112 -0.470 0.264

(0.677) (1.358) (1.516) (1.576) (1.357)

OLS Yes

GLS RE Yes Yes Yes Yes

Year dummy Yes Yes Yes

Province dummy Yes Yes Yes Yes First lag of dependent Yes Yes

Observations 183 183 183 153 153 R-squared 0.867 0.824 0.857 0.858 0.838 Number of provinces 31 31 31 31 *** 99% significant, ** 95% significant, * 90% significant

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