The evolution of corruption and development in transitional economies: Evidence from China

Economic Modelling xxx (xxxx) xxx

Contents lists available at ScienceDirect

Economic Modelling journal homepage: www.journals.elsevier.com/economic-modelling

The evolution of corruption and development in transitional economies: Evidence from China☆ Kenneth S. Chan a, b, Vinh Q.T. Dang c, Tingting Li d, * a

Chu Hai College of Higher Education, Hong Kong McMaster University, Canada Nanjing University of Finance and Economics, China d International School of Business & Finance, Sun Yat-sen University, Zhuhai, China b c

A R T I C L E I N F O

A B S T R A C T

JEL classification: H11 D73 K42

It is widely accepted in the literature, that the level of corruption is negatively and robustly related to economic development. However, skeptics argue that for transitional economies, this relationship may not hold. Economic reform loosens up the control of local officials and can increase corruption; Corruption and per capita income can be positively related. Using panel provincial data of China from 1995 to 2014 on prosecuted cases of corruption, we discover that during the early phase of China’s economic reform (during Zhu Rongji and Hu-Wen administrations), a positive short-run relationship is indeed observed. But, there is a robust negative long-run cointegration relationship between corruption and per capita income. The development of the market economy improves private wage and income in the long-run. The relatively inefficient and low returns to ordinary corruption cannot compete with rising market returns, which lead to dwindling corruption. However, the share of major corruption cases is increasing over time to be able to compete with rising market wages.

Keywords: Corruption Development China

1. Introduction In the empirical literature, there is a robust negative relation between the level of economic development and the extent of corruption (Trisman, 2000, 2007). One explanation for this relation is that economic development itself creates a demand for good institutions (Paldam and Gundlach, 2008). With higher average income, citizens pursue higher quality of living, including a less corrupt and more democratic society, etc.1 Meanwhile, with economic development, a larger and more efficient market would reduce the monopoly power and services provided by corrupted officials. As the market economy matures, Bai et al. (2017) argued that the increase in the mobility of firms could decrease corruption at the local level. Using microdata on around 10,000 firms from Vietnam, Bai et al. (2017) uncovered the mechanism by which

competition among regional corrupted officials to retain productive mobile firms in their respective regions reduces bribes. Another explanation, coming from somewhat a different perspective, suggests the causation should be in the opposite direction to the above: Better and less corrupted institutions enhance development. Mauro (1995) found that corruption in the institutions lowers investments, thereby lowers economic growth. Corruption can be detrimental for econnomic growth as corruption will negtively affect labour supply (Cooray and Dzhumashev, 2018). Xu and Yano (2017) found evidence that anti-corruption effort in China has a positive effect on the financing and investing in innovation. Acemoglu et al. (2001), explained that, as good institutions set the rules and incentives for the economy, they must be the key determinant for economic development. These two explanations need not preclude one another. They can form a pair of mutually causative mechanisms,

☆

We are indebted to Editor Sushanta Mallick, the reviewers and the seminar participants for their insightful comments, with the usual disclaimers. This paper was presented at the Conference “Recent Advances in International Trade, Finance and Business” at Chu Hai College of Higher Education in Hong Kong, December, 2018; at the China Meeting of the Econometric Society, Shanghai, June 2018; at the Economic Workshop, Lingnan Univ. College, SYSU, November 2018; and seminars at Jian University, Sun Yat-sen University, and the University of Science and Technology of China. Chan’s research was supported by the Hong Kong RGC-IDS project, UGC/IDS13/16, gratefully acknowledged. Li’s research was funded by the National Natural Science Foundation of China (Project No. 71903206). * Corresponding author. E-mail address: [email protected] (T. Li). 1 In the empirical literature, a common practical proxy for the level of economic development in a country is its national income. But, economic development should go beyond just a higher income. It is a broad concept that should include advances in the civil society and social values, which would also lower corruption. https://doi.org/10.1016/j.econmod.2019.09.001 Received 7 March 2019; Received in revised form 2 September 2019; Accepted 2 September 2019 Available online xxxx 0264-9993/© 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Chan, K.S. et al., The evolution of corruption and development in transitional economies: Evidence from China, Economic Modelling, https://doi.org/10.1016/j.econmod.2019.09.001

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

second reform was in the first term of the administration under President Hu Jintao and Premier Wen Jiabao, from 2003 to 2008. In the first economic reform, Zhu Rongji privatized many China’s state-owned enterprises, gradually pushing the economy in a more market oriented direction. He tackled many thorny problems left over from Deng Xiaoping’s half-reformed economy. Zhu’s effort was generally regard as instrumental in preparing the foundation of the unprecedented economic growth in China in subsequent years. In Zhu’s reform, a major devolution of state power to the local level and to the private sectors took place. He also carried out important banking reforms and tax reforms. Despite the successful reforms under Prime Zhu Rongji, rural farmers were largely neglected. Zhu’s attempts to appease urban areas widened the gap between the urban rich and the rural poor. Reforms in the agriculture policies and welfare of the farmers was left to the next generation of leaders, President Hu Jintao and Prime Wen Jiabao. Hu-Wen’s nation-wide agriculture reform from 2003 was a part of the transition in China, during which the authority of the central government was further shared with the local government to improve efficiency. Agriculture policy reform also devolved state power to the local governments. The livelihood of the farmers were improved from lowering agriculture related taxes; Income inequality was somewhat reduced. In the opinion of the public, the agriculture reforms under Hu-Wen administration was regarded as fairly successful. Under Hu-Wen administration, the devolution of state authority was reasonably orderly and more or less within the control of the central government. Perhaps for this reason, the aforementioned positive relationship between income and corruption was milder under Hu-Wen administration than under Zhu Yongji administration. The number of registered cases of corruption from each province is used here as the base measure of corruption (see the justification in Dong and Torgler (2013)). This measurement of corruption has raised some concerns because the registered cases of corruption may be reflecting the intensity of government enforcement instead of corruption itself (Wedeman, 2012). To address this problem, we will use the government expenditure in law-and-order as a proxy for the intensity of government enforcement. We will add this proxy as control in all the regressions (see Section 4; also Dong and Torgler (2013)). Another challenge is the quality of the data set. To address this challenge, the data we employed is carefully cross-checked with opinion survey and found consistency. In Section 2, we will aggregate our provincial registered corruption cases data to the national level, and compare that with the corruption perception index, provided only at the national level, by the reputable Transparency International Organization. The two data series are highly correlated. This lends credibility to the registered corruption case data set currently used. This cross-check of data quality is an important contribution of this paper. This paper finds that the index for the registered corruption cases exhibits a downward trend over a long period of time (see Table 1 later). The downward trend could be explained by another measure in this paper, that the ratio of the returns-from-corruption to urban-wage also follows a declining trend. This suggests that wages and income from the private market in China must be offering better alternatives in a fast growing economy than returns from corruption activities. Moreover, we find that the composition of corruption cases is also changing. Although the number of corruption cases is declining, we find that the share of severe cases has been rising. This is likely because only more lucrative dealings can survive the competition with the rising market wages. These findings offer further insights to the literature on the negative relationship between development and the extent of corruption. Detailed classification of registered corruption cases allows us to investigate this mechanism in depth, which aggregated measure such as Transparency International’s corruption perception index cannot offer. The added measures on the composition of corruption is another important contribution of this paper. The rest of the paper is organized as follows: Section 2 describes the data. Section 3 offers descriptive statistics and some preliminary analysis.

reinforcing each other. In short, the literature seems to suggest that there should be a long-run negative co-movement between economic prosperity and less corruption. However, skeptics argue that, for transitional economies, this negative relationship may not hold (Basu and Li, 2000; Paldam and Svendsen, 2000). They claim that per capita income and corruption could be positively related during the transition to a market economy. Corruption is intimately related to the discretionary authority of the government. Economic reforms involves a devolution of power from the center of authority to the local authorities. During the reform, local authorities would have the discretionary power in which the central government could not easily monitor and punish the offending local officers. This creates a vacuum and an opportunity for the local authorities. Profits from private firm arising from the upsurge in economic growth in the reform process provides the surplus funds and the temptation for corruption.2 A temporary relaxation in the monitoring of local bureaucrats is necessary at the initial stage of the economic reform. This new discretionary power of local bureaucrats led to an increase in corruption that provides them with an incentive to carry out reform. Without this incentive, local bureaucrats would have resisted reform of the existing system from which they had benefited (for example, layoffs of redundant bureaucrats). Since political reform usually lags economic reform, corruption can increase in the short run. Eventually as the market economy matures and the monitoring by the central government improves, more competitions are created from the private sector, from the productive mobile firms, and from local governments. All of these would weaken the power of the local bureaucrats and their corruption practices (Basu and Li, 2000). But there is no doubt that in initiating a reform, there must be a political will to make improvements in the economy and to control corruption. In doing so, the central government has to tolerate short term increase in corruption of local officials. High level of corruption can be highly correlated with high level production, while trengthening the control of corruption in a certain extent will have a negative impact on production (Coppier and Michetti, 2006). In contrast, growth will be effectively promoted if anti-corruption policies can successfully induce a structural change (Lim, 2019). Nonetheless, the economy will eventually grow and corruption will eventually come down. Other relevant issues related to institutions are found in Cooray et al. (2017a, 2017b). In an interesting paper by Dong and Torgler (2013), they found a positive relationship between economic development and the extent of corruption for the case of China. In light of the above discussion, could Dong-Torgler’s finding be only temporary? Because of these concerns, it is paramount to investigate in depth the long-run evolving time-path of corruption and economic development of an important transitional economy, China. In this paper, several estimators are applied to a panel of 26 Chinese provinces spanning from 1995 to 2014. We avoid the endogeneity of explanatory variables in our estimation by using system GMM estimator, with both 1-step and 2-step variants. We find a robust negative relationship between economic development and the level of corruption in a dynamic process. However, during some periods of the reforms (Premier Zhu Rongji’s administration and the first term of President Hu Jintao’s administration of 1998–2005), a positive relationship is found from the period dummies; This finding confirms the results from Dong and Torgler (2013). We further affirm that there is a long-run (co-integrating) relationship between corruption and per capita income at the province level, from the panel co-integration regression for 1995–2014. There were two important economic reforms in China. The first economic reform was under Premier Zhu Yongji from 1998 to 2003. The

2

Privatization in China involves the transfer of property rights from the state to the market. The Chinese government sold state properties to the market often below their market values. The existence of large windfall profits creates strong incentives for officials to demand bribes from the recipients of state assets (see Wedeman, 2012). 2

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

reflect perception bias of the subjects in the surveys. We aggregate province-level variable Case/pe to national level, National_Case/pe,4 and compare it to CPI data for China, which is only available at the country level. The time path of both series are reported in Fig. 1.5 Note that higher CPI scores indicate lower perceived corruption. The correlation coefficient between the two series, CPI and National_Case/pe, is
0.8806, suggesting that they are highly correlated.6 The above exploration lends confidence to our variable Case/pe; it appears to capture the level of corruption reasonably well. The measurement of corruption we use here should be in line with mainstream research based on CPI. The amount of involved money can capture the severity of corruption. To take into account of this aspect, we calculate GDP share of the actual amount of corruption money recovered from the registered cases and call it CorShare. This variable contains information on both the average recovered amount and the number of corruption cases. To measures the relative returns of corruption, we take the ratio of the average actual amount of corrupted money recovered from registered cases to urban wage. This variable (CorReturn) tells us the return of corruption versus the opportunity foregone from working in the private urban market economy.7 CorReturn can reveal the evolutionary path of corruption under a growing market economy. In the procuratorial yearbook of China, corruption cases involving more than 50,000 RMB are classified as major cases (Mcases).8 There is also information on the number of high-ranking (at or above county level) government officials involved in corruption. Using these series, we calculate four additional variables that qualitatively measure the severity of corruption: Mcase/pe is the number of major corruption cases over employment in the public sector; Mcase/Case is the ratio of major cases to the total corruption cases; Kco/pe is the number of high ranking government officials in the registered corruption cases divided by employment in the public sector; Kco/Case captures the share of high-ranking

Table 1 Descriptive OLS regressions of key variables (X) on a linear time trend (Year). Dependent Variable (X)

Coefficient and tvalue of Year

No. of Obs.

Description

Case/pe


0.0002*** (17.92)

468

CorShare


0.004*** (12.29)

449

CorReturn


0.620*** (6.90)
0.974*** (10.45)

425

Kco/pe


0.013 (1.18)

483

MCase/Case

0.007*** (3.71)

402

Kco/Case

0.004*** (8.86)

432

Ln(Case)


0.0334*** (6.05) 0.0216*** (5.11)
0.0159** (2.40) 0.0096* (1.91)

468

Number of registered corruption cases adjusted by the total number of government employees GDP share of corruption money (actually) recovered from registered cases. The relative returns of a corruption case to urban market wage Number of major registered corruption cases adjusted by the total number of government employees Number of registered corrupted high rank officials adjusted by the total number of government employees. Number of major registered corruption cases adjusted by the total number of registered corruption cases. Number of corrupted high rank officials out of all the registered corruption cases. The logarithm of the number of registered corruption cases The logarithm of the total number of government employees The logarithm of the number of major registered corruption cases The logarithm of the number of registered corrupted high rank officials The logarithm of the total number of registered corrupted officials The logarithm of real per capita GDP at province level

MCase/pe

Ln(pe) Ln(MCase) Ln(Kco)

Ln(Co) Ln(Income)


0.0025 (0.37) 0.102*** (223.23)

443

518 441 481

403 520

4 We aggregate the denominator and the numerator of Case/pe separately and then divide to get the National_Case/pe. 5 The CPI time series before 2012 was a ranking index that compares across countries over time. The methodology from Transparency International had been changed in 2012.The post-2012 CPI series compares only with the country’s own past. Hence, the pre- and the post-2012 series, though sharing some common features, are not exactly identical. Our data series ends in 2014 because of the change to a new regime under President Xi. The impact of the new methodology is on the three end points. Note that before 2012, the scale for CPI score is from 0 to 10, but the scale is changed to 0 to 100 from 2012. The post2012 data are therefore divided by 10. 6 For the whole sampling period (1995–2014), the correlation coefficient between CPI and National_Case_pe is very high at
0.8806. The correlation coefficient between CPI and National_Case_pe before the change in the sampling method in 2012 is
0.8582, while the correlation after 2012 (only 3 points) is
0.5699. These high correlation coefficients suggest that CPI and National_Case_ pe are highly negatively correlated in spite of the change in the CPI methodology.To further test the significance of the change in CPI methodology between the two series, we run an OLS of CPI with respect to National_Case/pe, with a time dummy for the year 2012–2014, and a cross-product term (National_Case/ pe)*(Time_Dummy). The Time_Dummy captures the change in methodology. The coefficient for National_Case/pe is
0.030, the t-statistic is significant at
6.47 with a p-value of 0; The coefficient of the Time_Dummy is þ0.18, with an insignificant t-statistic of þ0.22 and a p-value of 0.825. The coefficient of the cross-product term is negative and insignificant at
0.004, with t ¼ 0.07 and a pvalue of 0.942. Without the Time_Dummy and the cross-product term, the coefficient of the National_Case/pe is
0.032, with a significant t-statistic ¼
7.88, and a p-value of 0. The insignificance of the Time_Dummy shows that the change in the CPI methodology has no statistically significant impact. The two variables are negatively correlated and statistically significant. 7 Technically, a parameter that converts the same unit of labor required in corruption activities to equivalent unit of work for the private labor market is needed for this index. Here, we assume that this parameter is constant throughout the sample period and hence can be ignored. 8 Before 1998, the threshold amount of money was 10,000RMB.

Note: *p < 0.10, **p < 0.05, ***p < 0.01.

Section 4 presents the empirical framework and estimation results. Section 5 summarizes with a few concluding remarks. 2. Data sources and measures of corruption A panel comprising 26 provinces over the period of 1995–2014 is constructed in this paper.3 The data for income and other control variables are collected from various publications from China’s National Bureau of Statistics: (1) China Statistical Yearbook (both of national level and provincial level, 1996–2015), (2) China Labor Statistical Yearbook (1996–2015), and (3) China Compendium of Statistics (1949–2008). The data for corruption are collected from Procuratorial Yearbook of China (1996–2015) published by the Supreme People’s Procuratorate.

2.1. Measures of corruption We let (nominal) variable Case be equal to the number of registered corruption cases and (real) variable Case/pe be equal to the number of registered cases divided by the employment in the public sector in each province. A commonly used measure of corruption in the literature is corruption perception index (CPI) constructed by Transparency International. The CPI also factors in judgment and experience of the subjects in the surveys, which are valuable, because under-reporting of corruption may be substantial. But it also has its own shortcomings, in which it may

3 Because of missing data for income distribution or corruption cases, the provinces, Jilin, Hainan, Tibet, and Ningxia, they are eliminated from the sample.

3

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Fig. 1. CPI score and ratio of corruption cases to public-sector employment. Data source: National_Case/pe is calculated by the authors, based on the data drawn from annual Procuratorial Yearbook of China and China Statistical Yearbook; Corruption Perception Index (CPI) is obtained from Transparency International.

corrupted officials over the total number of corrupted officials.9 The time series of Mcase/Case and Kco/Case will tell us about the changing share of severe corruption cases over time. Among these measures of corruption, data on Case/pe have more observations and are well documented. Hence, in the regressions in Section 4, we will focus on discussing empirical results involving this variable. But results with other measures are also presented in robustness checks. Another related variable is local government’s expenditure for public. Although it is not directly related to corruption per se, it does represent the government’s effort and ability to fight corruption. Therefore we will include GDP share of expenditure for public security, Anticorrup, to proxy anticorruption effort by the government.10 The ratio of expenditure for public security to total government expenditure, Anticor_Pubex, is also used as a robustness check.

3. Descriptive statistics and preliminary investigation Please refer to Table A1 for the summary Statistics and Table A2 for the correlation coefficient in Appendix A. As part of the preliminary analysis, each variable is regressed on a linear time trend (Year). The results are presented in Table 1. Apart from Kco and Kco/pe, the nominal variables on the absolute level of corruption (Case, Mcase) and those on the real level of corruption (Case/pe, CorShare, CorReturn, Mcase/pe, Kco/ pe) are decreasing over time (Fig. A1).11 This observation is also consistent with Transparency International’s CPI that indicates corruption in China has been decreasing. The composition indices of severe corruption cases (Mcase/Case, Kco/Case), however, are increasing over time, indicating that a change in the composition of corruption has taken place. The relative share of severe corruption cases has gone up. More importantly, from Table 1, the relative return of corruption to urban market wage (CorReturn) in China is declining over the year. With per capita real come rising, wages from the private market must be offering better alternative returns in a fast growing economy than those from corruption activities. This is evident from the aforementioned declining corruption indices. Moreover, the composition of corruption cases (Mcase/Case) is changing. The share of the severe cases of the total registered cases of corruption has been rising. This is because only more lucrative (or severe) corruption cases can compete with the rising market wages and market activities. Since high ranking government officials have more authority, they are well positioned in corruption than low-ranking ones. As the market develops, both high- and low-ranking officials would move out of corruption activities into market activities, but less so for the high-ranking ones because they have more political power and can get more lucrative gains from corruption activities (i.e., Kco/Case rises). Section 4 below will offer more vigorous tests of these conjectures.

9 Note that, because of the short time series of variable co, we use longer time series Case here to replace co. This assumes that the number of corruption cases should be proportional to the number of corrupted officials, co, and this proportion is constant on the average. The regression reported in Table 1 affirms that. 10 From 1995 to 2006, the data are from an item entitled ‘expenditure for public security agency, procuratorial agency and court of justice’. This item was under the budgetary expenditure of central and local governments. However, from 2007 to 2014, the title of this item was changed to ‘expenditure for public security’. The name change reflected that the statistical standard had been slightly changed. We do not think this slight change of standard affects the data series, because the values around the break point (2005, 2006, 2007 and 2008) do not change much.Note that the items in the public security expenditure are diverse, not just on prosecuting corruption alone. We used this because there is no further breakdown of items available from the public security expenditure data. We have to assume here, that the share of public security expenditure on anticorruption is roughly constant. The empirical results, using this expenditure for public security as control, seem reasonable, perhaps, lending support to this assumption.

11 For the Kco/pe variable in Table 1, the coefficient of Year is insignificant. To cross-check, we scatter plot the time series at the aggregated national level of Kco/pe in Fig. A1 inAppendix A. We notice that Kco/pe is noisy and downward trended.

4

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

To explore the data further, we plot national per capita GDP (1980 ¼ 100) together with National_Case/pe in Fig. 1. They appear to be negatively correlated. This correlation is confirmed by bi-variate vector auto regression in Appendix B. The direction of causality runs from per capita GDP to National_Case/pe. In Fig. 1, National_Case/pe shows a downward trend over the sample period. But there is large fluctuation in the beginning of the sample period. The series exhibits sudden surge from 1998 to 2002, suggesting that the relationship between income and corruption could be positive in that period. This is worthy of a closer examination. In Fig. 2, we plot real per-capita GDP and Case/pe using province-level data. The scatter plot confirms our conjecture. If data from the full sample (1995–2014) are used, as in Figure (2a), Case/pe is negatively correlated with per capita GDP. Inspired by Dong and Torgler (2013), in Figure (2b) we restrict the time period to 1998–2007, which covers important reforms instituted by Premier Zhu Rongji’s administration (1998–2002); here the slope of the fitted line, while still negative, is much less steep. When the period under consideration is further shrunk to 1998–2002, as in Figure (2c), the slope of fitted line becomes weakly positive. To sum up, Fig. 2 suggests that although the relationship between income and corruption in the long-run is negative, it can be positive during the reform period in the short run. This is consistent with Dong and Torgler (2013)’s findings. 4. Empirical framework and results In this section, we start off by offering panel OLS and fixed effect (FE) regressions as the baseline model in sub-section 4.1. We will next introduce system GMM to deal with the endogeneity and instrument issues in sub-section 4.2. Finally, since we are interested in long-run relationship, pool mean group estimator is used for panel autoregressive distributive lag model (ARDL) in sub-section 4.3. These three different estimators also help check the robustness of key findings. 4.1. An exploratory baseline model The relationship between corruption (Case/pe) and per capita GDP (Income) and other control variables is specified as in Eq. (1): Case=pei;t ¼ β0 þ β1 *Incomei;t þ β2 *Anticorrupi;t þ β3 *Edui;t þβ4 *Ginii;t þ β5 *Resourcei;t þ β6 *Tradei;t þ β7 *Wagei;t þ εi;t

(1)

where subscript i denotes province and subscript t denotes time, and ε is the stochastic disturbance term. The GDP share of expenditure for public security by local government, Anticorrup, can proxy anticorruption ability or effort from the government (Dong and Torgler, 2013). The enrollment rate of secondary school, Edu, correlates with the incidence of corruption (Glaeser and Saks, 2006). The improvement in education can help the public to identify, prevent and monitor corrupt practice. More importantly, education improves political institutions (Glaeser and Saks, 2006). Another possible determinant of corruption is income inequality, here measured by Gini coefficient. Several empirical studies found that higher income inequality is associated with more corruption (You and Khagram, 2005; Glaeser and Saks, 2006; Dong and Torgler, 2013) because the poor is less capable of monitoring or opposing corruption when the rich is more inclined to bribery and abuse of power. Resource abundance is also an important determinant of corruption. Because of government’s vested power in allocating resources, resource rents contribute to high incidence of corruption. Leite and Weidmann (2002) and Dong and Torgler (2013) found empirical evidence indicating that the resource-richer regions suffer from more corruption. Following Dong and Torgler (2013), we use the ratio of employees in the mining and quarrying sector to total employment to measure resource abundance (Resource). We also control for trade openness, Trade. A more open economy has

Fig. 2. Province-level per capita GDP and ratio of corruption cases to publicsector employment. Data source: Case/pe is calculated by the authors, based on the data drawn from Procuratorial Yearbook of China and National Bureau of Statistics (NBS); per capita GDP is converted to real value by the authors.

5

OLS estimation

Income Anticorrup Edu

Fixed-effect estimation

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)


0.0007*** (6.73e-05)
0.0017*** (0.00014)
3.34e-05 (3.86e-05)


0.0009*** (7.04e-05)
0.0012*** (0.00017) 3.99e-05 (3.94e-05)
0.0067*** (0.00115)


0.0007*** (6.74e-05)
0.0017*** (0.00014)
4.71e-05 (3.81e-05)


0.0007*** (8.69e-05)
0.0017*** (0.00014)
3.47e-05 (3.94e-05)


0.0007*** (7.11e-05)
0.0017*** (0.00014)
3.23e-05 (3.87e-05)


0.0008*** (8.63e-05)
0.0011*** (0.00017) 3.12e-05 (3.92e-05)
0.0075*** (0.00124) 0.0002*** (4.45e-05)
2.43e-06 (1.73e-06) 0.00214*** (0.00074) 0.0114*** (0.00095)


0.0014*** (0.00012)
0.00062** (0.00027)
1.91e-05 (3.98e-05)


0.0013*** (0.00012)
0.00051* (0.00027) 3.17e-05 (4.16e-05)
0.0054*** (0.00147)


0.0013*** (0.00012)
0.00052* (0.00027)
1.36e-05 (3.83e-05)


0.0014*** (0.00012)
0.0005* (0.00027)
2.80e-05 (3.92e-05)


0.0014*** (0.00012)
0.00061** (0.00028)
1.96e-05 (3.98e-05)

0.0157*** (0.00085)

0.0163*** (0.00085)

0.0140*** (0.00086)

0.0158*** (0.00083)


0.00059 (0.00086) 0.0162*** (0.00114)


0.0012*** (0.00012)
0.00037 (0.00026)
8.80e-06 (4.04e-05)
0.00149 (0.00156) 0.0005*** (8.79e-05)
1.38e-05*** (3.42e-06)
0.00037 (0.00085) 0.0147*** (0.00111)

468 26 0.460

468 26 0.483

468 26 0.497

468 26 0.521

468 26 0.501

468 26 0.483

468 26 0.539

Gini Resource

0.0002*** (4.36e-05)
2.78e-07 (1.69e-06)

Trade Wage Constant

0.0111*** (0.00059)

0.0139*** (0.00075)

0.0105*** (0.00060)

0.0110*** (0.00068)

0.00035 (0.00074) 0.0108*** (0.00087)

N Number of province adj. R2

468 26 0.397

468 26 0.437

468 26 0.417

468 26 0.396

468 26 0.396

0.0005*** (8.10e-05)
1.42e-05*** (3.45e-06)

K.S. Chan et al.

Table 2 Pooled OLS and fixed-effect regressions; dependent variable ¼ Case/pe.

Notes: The dependent variable is Case/pe. Standard errors in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01.

6

Table 2a Pooled OLS and fixed-effect regressions; dependent variable ¼ alternative corruption measure (Z). Z¼

Income Anticorrup Edu Gini Resource

Wage Constant N Number of province adj. R2

Fixed-effect estimation

CorrShare

CorReturn

Mcase/pe

Kco/pe

Mcase/Case

Kco/Case

CorrShare

CorReturn

Mcase/pe

Kco/pe

MCase/Case

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)


0.026*** (0.00281) 0.0105* (0.00610)
0.00229* (0.00131)
0.146*** (0.0414)
0.00275* (0.00156) 0.00013** (5.75e-05)
0.0404* (0.0221) 0.361*** (0.0294)


2.285*** (0.793) 0.783 (1.644) 0.251 (0.357)
58.53*** (11.10)
1.693*** (0.415) 0.0566*** (0.0157)
20.06*** (6.067) 71.74*** (7.928)


2.974*** (0.791)
3.003* (1.532)
0.395 (0.382)
99.63*** (11.96) 0.404 (0.412) 0.00751 (0.0178) 12.57** (6.197) 72.80*** (8.619)

0.157 (0.0976)
0.119 (0.193) 0.0337 (0.0469)
3.118** (1.515)
0.00359 (0.0531) 0.00282 (0.00205) 0.617 (0.784) 1.288 (1.095)

0.0919*** (0.0149) 0.0758*** (0.0285)
0.020*** (0.00692)
1.321*** (0.211)
0.0158** (0.00721) 0.00034 (0.00032) 0.0965 (0.121) 0.285* (0.159)

0.0222*** (0.00352) 0.0413*** (0.00687)
0.00212 (0.00163)
0.121** (0.0518)
0.00346* (0.00180) 0.00031*** (7.18e-05)
0.0297 (0.0298)
0.0550 (0.0393)


0.025*** (0.00449)
0.0163 (0.0107)
0.00036 (0.00159) 0.0517 (0.0596) 0.00536* (0.00319)
0.0004*** (0.00013)
0.0266 (0.0307) 0.284*** (0.0410)


3.397*** (1.207)
4.709* (2.775) 1.320*** (0.411)
17.60 (15.55)
0.634 (0.830)
0.188*** (0.0331)
4.326 (7.787) 53.54*** (10.59)


2.192* (1.160)
4.150* (2.439) 0.452 (0.441)
90.02*** (15.59) 3.027*** (0.817)
0.110*** (0.0368)
10.04 (8.352) 82.56*** (11.03)


0.176* (0.0987)
0.484** (0.228) 0.167*** (0.0386) 2.564* (1.388)
0.0130 (0.0763)
0.00611 (0.00384)
0.462 (0.809) 2.750*** (1.005)

0.205*** (0.0232)
0.0413 (0.0503)
0.00825 (0.00845)
1.946*** (0.301) 0.0370** (0.0159) 0.0010 (0.00067)
0.128 (0.163)
0.193 (0.217)

0.0260*** (0.00324)
0.0105 (0.00761) 0.0043*** (0.00121)
0.00795 (0.0442)
0.00254 (0.00244) 0.00043*** (0.00012) 0.00580 (0.0252)
0.165*** (0.0319)

445 26 0.244

423 26 0.185

441 26 0.331

481 26 0.048

400 26 0.342

432 26 0.377

445 26 0.221

423 26 0.168

441 26 0.329

481 26 0.028

400 26 0.212

432 26 0.294

Notes: The dependent variable is Z. Standard errors in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01.

Kco/Case

Economic Modelling xxx (xxxx) xxx

Trade

OLS estimation

K.S. Chan et al.

Table 3 System GMM regressions with Case/pe used as corruption measure; Anticorrup and Wage are exogenous variables. 1-step

Case/pe

t-1

Income Anticorrup Edu

2-step

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.549*** (0.0433)
0.0003*** (9.74e-05)
0.0005*** (0.00012)
5.66e-05 (4.50e-05)

0.576*** (0.0483)
0.00028** (0.0001)
0.0006*** (0.00016)
0.0002** (4.25e-05) 0.00286 (0.00187)

0.474*** (0.0499)
0.00031** (0.00013)
0.0005*** (0.00017)
2.90e-05 (5.66e-05)

0.583*** (0.0275)
0.0004*** (0.00010)
0.0004*** (0.00013)
7.20e-06 (4.22e-05)

0.544*** (0.0420)
0.0004*** (0.00010)
0.0005*** (0.00012)
6.48e-05 (4.34e-05)

0.522*** (0.0546)
0.00037** (0.00014)
0.0006*** (0.00022)
4.98e-05 (5.81e-05) 0.00261 (0.00255) 0.0003*** (0.00010) 5.17e-06 (3.04e-06) 0.00169 (0.00110) 0.00213 (0.0019)

0.538*** (0.0591)
0.00033** (0.00012)
0.0005*** (0.00015)
6.68e-05 (6.32e-05)

0.575*** (0.0549)
0.00028** (0.00012)
0.0006*** (0.00018)
0.0001** (4.97e-05) 0.00269 (0.00226)

0.468*** (0.0528)
0.00029* (0.00015)
0.00056** (0.00022)
2.40e-05 (7.18e-05)

0.582*** (0.0336)
0.00037** (0.00014)
0.0004*** (0.00013)
6.87e-06 (4.28e-05)

0.533*** (0.0582)
0.00037** (0.00014)
0.0005*** (0.00014)
7.37e-05 (6.10e-05)

0.0051*** (0.0014)

0.00395** (0.0015)

0.00436** (0.0016)

0.0045*** (0.0012)

0.00071 (0.00068) 0.0047*** (0.0013)

0.511*** (0.0678)
0.00037* (0.00020)
0.00053** (0.00024)
5.38e-05 (6.79e-05) 0.00290 (0.00284) 0.00037** (0.00015) 4.96e-06 (6.10e-06) 0.00174 (0.00206) 0.00186 (0.0031)

419 23.69 [0.823]
3.853 [0.00012]
0.819 [0.413]

419 25.54 [0.0608]
3.579 [0.00035]
0.768 [0.443]

419 25.78 [0.215]
3.679 [0.00024]
0.668 [0.504]

419 25.55 [0.224]
3.527 [0.00042]
0.968 [0.333]

419 25.83 [0.213]
3.793 [0.00015]
0.658 [0.511]

419 25.62 [0.0597]
3.586 [0.00034]
0.791 [0.429]

419 23.69 [0.823]
3.416 [0.00064]
0.887 [0.375]

Gini 7

Resource

0.00033** (0.00014)

Trade

5.08e-06 (3.07e-06)

Wage Constant

0.0049*** (0.0011)

0.0039*** (0.0013)

0.0045*** (0.0014)

0.0044*** (0.0009)

0.00076* (0.00042) 0.0044*** (0.0010)

N Hansen p-value AR (1) p-value AR (2) p-value

419 25.54 [0.0608]
3.712 [0.00021]
0.748 [0.455]

419 25.78 [0.215]
3.775 [0.00016]
0.679 [0.497]

419 25.55 [0.224]
3.645 [0.00027]
0.937 [0.349]

419 25.83 [0.213]
3.849 [0.00012]
0.662 [0.508]

419 25.62 [0.0597]
3.725 [0.00020]
0.777 [0.437]

0.00036** (0.00015) 5.43e-06 (3.53e-06)

Note: The regressions are adjusted for small sample and the standard errors are robust. Lag range (2–6) is used as instruments. Robust Standard errors in parentheses in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Economic Modelling xxx (xxxx) xxx

K.S. Chan et al.

Table 3a System GMM regressions with Case/pe used as corruption measure; All explanatory variables (including Anticorrup and Wage) are endogenous variables. 1-step

Case/pe

t-1

Income Anticorrup Edu

2-step

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.531*** (0.0403)
0.00029** (0.00013)
0.00094* (0.00048)
2.74e-05 (5.46e-05)

0.572*** (0.0507)
0.00015 (0.00016)
0.0017** (0.00069)
0.0001** (4.57e-05) 0.00579** (0.00216)

0.466*** (0.0465)
0.00030* (0.00015)
0.00079 (0.00055)
1.92e-05 (5.76e-05)

0.558*** (0.0279)
0.00031** (0.00012)
0.0009** (0.00041) 6.27e-06 (4.56e-05)

0.511*** (0.0454)
0.0004*** (0.00012)
0.00084* (0.00042)
4.54e-05 (4.90e-05)

0.507*** (0.0580)
0.00028 (0.00017)
0.0013** (0.00053)
4.54e-05 (5.60e-05) 0.00456* (0.00240) 0.00031*** (9.75e-05) 5.00e-06 (3.43e-06) 0.00066 (0.00159) 0.00246 (0.00218)

0.519*** (0.0523)
0.0003** (0.00014)
0.00089 (0.00053)
4.03e-05 (7.34e-05)

0.572*** (0.0847)
0.00011 (0.00021)
0.0017** (0.00070)
0.0001* (5.79e-05) 0.00559** (0.00257)

0.460*** (0.0541)
0.00030 (0.00020)
0.00075 (0.00064)
1.75e-05 (7.87e-05)

0.553*** (0.0460)
0.00028* (0.00016)
0.00099* (0.00049) 1.90e-05 (5.07e-05)

0.483*** (0.0781)
0.00034** (0.00014)
0.00086* (0.00042)
4.70e-05 (5.04e-05)

0.0051*** (0.00130)

0.00248 (0.00205)

0.00467** (0.00184)

0.0043*** (0.00128)

0.00099 (0.00159) 0.0045*** (0.00128)

0.500*** (0.0731)
0.00033 (0.00026)
0.00105 (0.00064)
5.29e-05 (7.43e-05) 0.00410 (0.00298) 0.00029* (0.00016) 5.01e-06 (6.25e-06) 0.00217 (0.00311) 0.00127 (0.00329)

419 26 23.22 0.989
3.727 0.000194
0.731 0.465

419 26 25.64 0.221
3.566 0.000362
0.745 0.457

419 26 24.97 0.248
3.553 0.000380
0.582 0.560

419 26 25.44 0.229
3.540 0.000400
0.929 0.353

419 26 25.80 0.214
3.633 0.000281
0.649 0.516

419 26 25.75 0.216
3.595 0.000324
0.792 0.428

419 26 23.22 0.989
3.366 0.000762
0.822 0.411

Gini Resource 8

0.00030* (0.00016)

Trade

3.86e-06 (3.30e-06)

Wage Constant

0.0049*** (0.00118)

0.00271 (0.00162)

0.0047*** (0.00141)

0.0045*** (0.000963)

0.00071 (0.00117) 0.0048*** (0.00114)

N Number of province Hansen J Statistics p-value AR (1) p-value AR (2) p-value

419 26 25.64 0.221
3.751 0.000176
0.712 0.477

419 26 24.97 0.248
3.524 0.000425
0.576 0.564

419 26 25.44 0.229
3.728 0.000193
0.883 0.377

419 26 25.80 0.214
3.832 0.000127
0.648 0.517

419 26 25.75 0.216
3.672 0.000241
0.805 0.421

0.00032* (0.00018) 4.01e-06 (3.47e-06)

Note: The regressions are adjusted for small sample and the standard errors are robust. Lag range (2–6) is used as instruments; Robust Standard errors in parentheses in parentheses * p < 0.10, **p < 0.05, ***p < 0.01. Economic Modelling xxx (xxxx) xxx

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 4 System GMM regressions with other corruption measures. Z¼

1-step CorrShare

Zt-1 Income Anticorrup Edu Gini Resource Trade Wage Constant N Number of province Hansen J statistic p-value Number of IVs AR(1) p-value AR(2) p-value

2-step CorReturn

Mcase/ pe

Kco/pe

Mcase/ Case

Kco/Case

CorrShare

CorReturn

Mcase/ pe

Kco/pe

Mcase/ Case

Kco/Case

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

0.105 (0.0773)
0.031*** (0.00703) 0.00948 (0.00822)
0.00323 (0.00376)
0.0627 (0.0813) 0.00102 (0.00358) 0.00042** (0.00018)
0.0406* (0.0212) 0.361*** (0.0586)


0.0141 (0.0909)
5.055*** (1.653) 0.527 (2.555)
0.0201 (0.782)
50.69 (30.19)
1.519 (1.060) 0.109* (0.0536)
17.76** (7.056) 89.53*** (17.22)

0.679*** (0.0628) 0.571 (0.661)
0.821 (1.875)
0.750* (0.423) 7.919 (24.91) 1.841*** (0.494) 0.0627 (0.0439) 3.997 (8.336)
4.906 (10.29)

0.663*** (0.0585) 0.0761 (0.135)
0.431* (0.212)
0.0696 (0.0971) 6.413** (2.963) 0.170* (0.0854) 0.0116** (0.00503)
1.032 (0.960)
0.863 (1.597)

0.367** (0.152) 0.0937*** (0.0222) 0.129* (0.0663)
0.0209 (0.0152)
1.512** (0.712)
0.00273 (0.0152) 0.00047 (0.00104) 0.00537 (0.170) 0.180 (0.338)

0.649*** (0.0995) 0.00814 (0.00524) 0.0164 (0.00999)
0.00033 (0.00303)
0.0949 (0.103)
0.00447 (0.00385) 3.73e-05 (0.00012)
0.0191 (0.0407) 0.0122 (0.0430)

0.120 (0.133)
0.029*** (0.00926) 0.0134 (0.0132)
0.00192 (0.00422)
0.121 (0.166) 0.00078 (0.00496) 0.00037* (0.0002)
0.0409 (0.0313) 0.351*** (0.110)

0.0209 (0.0630)
3.596* (2.015)
0.00868 (4.104)
0.204 (0.642)
40.44 (33.14) 0.221 (2.108) 0.109** (0.0414)
28.68 (28.69) 85.13*** (15.81)

0.687# (0.0618) 1.155 (1.025)
1.433 (2.459)
0.598 (0.476) 17.30 (31.17) 2.248* (1.170) 0.0644 (0.0498)
3.757 (12.80)
6.078 (15.23)

0.681*** (0.0850) 0.0130 (0.217)
0.234 (0.458)
0.0873 (0.120) 5.039 (3.411) 0.0507 (0.224) 0.00885 (0.00569)
0.178 (3.416)
0.698 (2.198)

0.337 (0.225) 0.0827* (0.0409) 0.174 (0.115)
0.0132 (0.0194)
1.751 (1.400)
0.00761 (0.0484) 0.00061 (0.00198)
0.182 (0.959) 0.483 (0.866)

0.636*** (0.114) 0.00932 (0.00741) 0.0118 (0.0154)
0.00031 (0.00439)
0.0622 (0.123)
0.00382 (0.00428) 6.75e-05 (0.00013)
0.0176 (0.0484)
0.00822 (0.0634)

415 26

389 26

403 26

450 26

342 26

378 26

415 26

389 26

403 26

450 26

342 26

378 26

23.44

14.13

19.31

21.06

21.64

22.09

23.44

14.13

19.31

21.06

21.64

22.09

0.833 40

0.996 40

0.949 40

0.910 40

0.894 40

0.880 40

0.833 40

0.996 40

0.949 40

0.910 40

0.894 40

0.880 40


2.416 0.0157 1.805 0.0711


2.226 0.0260 1.537 0.124


2.344 0.0191
0.363 0.717


1.522 0.128 0.766 0.444


3.625 0.000289 0.520 0.603


2.542 0.0110 1.320 0.187


2.024 0.0430 1.489 0.137


1.835 0.0664 1.225 0.221


2.184 0.0290
0.368 0.713


1.458 0.145 0.730 0.466


1.857 0.0633 0.177 0.860


2.487 0.0129 1.299 0.194

Note: The dependent variable is Z. The regressions are adjusted for small sample and the standard errors are robust. Lag range (2–6) is the lags of endogenous variables used as instruments. Variables used as instruments. All variables are treated as endogenous except for Anticorrup and Wage. Robust standard errors in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

columns; this is puzzling as it is contrary to our expectation. However, the impact of resource abundance (Resource) is highly positive in each regression, as expected. In addition, the impacts of trade openness (Trade) is negative, and the impact of relative wage (Wage) are mixed, with various levels of significance. In sum, Tables 2 and 2a support the conventional wisdom that an increase in per capita real GDP (Income) lowers the incidence of corruption (Case/pe, CorShare). Table 2a extends the investigation to other variables. The relative return of corruption activities over urban market wage (CorReturn) falls with higher income. Moreover, other measures of the severity of corruption (Mcase/pe, Kco/pe) also decrease with higher income. The composition indices of severe corruption (Mcase/Case, Kco/ Case) rise with higher income, suggesting only lucrative corruption remains strong in response to challenges from the rising market wages and income.

access to a larger global market and faces greater competition. Hence, there are fewer opportunities for corrupted officers. Ades and Di Tella (1999), Treisman (2000), Gerring and Thacker (2005), and Dong and Torgler (2013) all found negative relation between trade openness and corruption. Another control variable for corruption is the wage of civil servants measured relative to that of ordinary citizens, Wage. Higher relative wage implies higher opportunity cost for corrupted officials, who would suffer a greater loss if they are caught. Van Rijckeghem and Weder (2001) found some evidence supporting this claim in developing countries. Tables A1 and A2 in Appendix A present the summary statistics, definitions and correlation coefficients of these variables. Results from pooled OLS and fixed effects estimation in Table 2 are similar. The coefficient of Income is negative and highly significant in each column, indicating the amount of corruption would decline if the per capita real GDP increases; this is consistent with the literature. The coefficient of Anticorrup is negative and significant in all columns except in Column (12). Since the coefficient of Edu is insignificant, the impact of education is unclear.12 This result is also in line with the correlation coefficients listed in Table A2 in Appendix A. For the rest of the control variables, the estimated coefficient of Gini is negative and significant in several

4.2. Panel data model To capture some dynamics in the corruption and mitigate potential endogeneity of the explanatory variables, system Generalized Method of Moments (GMM) estimator (Arellano and Bover, 1995) is applied to Eq. (2). The first lag of the dependent variable is added to the right-hand-side of the baseline model as follow:

12 We redo the Pooled OLS and FE regressions in Tables 2 and 2a with the additional control variables, Year-specific dummies, and the Zhu- and HuDummies, under Fixed Effect regressions and Random Effect regressions, available upon request. The variable Year-specific dummies, the Zhu- and HuDummies and the Income variable are significant, the Anticorrup turned insignificant. It seems that corruption is related more to the year-specific effects than to anticorruption measures.

Case=pei;t ¼ β0 þ β1 *Case=pei;t
1 þ β2 *Incomei;t þ β3 *Anticorrupi;t þ β4 *Edui;t þβ5 *Ginii;t þ β6 *Resourcei;t þ β7 *Tradei;t þ β8 *Wagei;t þ εi;t (2) Explanatory variables in Eq. (2) other than Anticorrup and Wage are treated as endogenous variables. Anticorrup is exogenous because the 9

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 5 SGMM regressions when the time dummy of the Zhu administration and its cross-product term are included. Lag (2–6)

Lag (2–5)

Lag (2–4)

Lag (2–3)

1-step

2-step

1-step

2-step

1-step

2-step

1-step

2-step

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.477*** (0.0854)
0.00061*** (0.000196) 0.00143** (0.00053)
0.0116*** (0.00412)
0.00063*** (0.000223)
0.00019** (7.47e-05) (0.00122) 0.00479** (0.00232)

0.446*** (0.122)
0.00062** (0.000248) 0.00154* (0.00087)
0.0124* (0.00687)
0.00071* (0.000401)
0.00023** (0.000104) (0.00259) 0.00631 (0.00516)

0.444*** (0.0914)
0.00074*** (0.000215) 0.00175*** (0.00062)
0.0143*** (0.00483)
0.00064** (0.00024)
0.00022** (8.42e-05) (0.00127) 0.00602** (0.00247)

0.416*** (0.118)
0.00077** (0.00028) 0.00172** (0.00074)
0.0141** (0.00575)
0.00055* (0.000292)
0.00021** (8.73e-05) (0.00150) 0.00745** (0.00340)

0.463*** (0.0907)
0.00087*** (0.00024) 0.00172** (0.000712)
0.0144** (0.00550)
0.00079*** (0.000283)
0.00020** (8.92e-05) (0.00128) 0.00704** (0.00272)

0.403*** (0.114)
0.00098*** (0.00028) 0.00173** (0.00078)
0.0144** (0.00598)
0.00080** (0.00037)
0.00019* (0.00011) (0.00158) 0.00936*** (0.00332)

0.498*** (0.170)
0.00091*** (0.000281) 0.00498* (0.00263)
0.0396* (0.0203)
0.00011 (0.000521)
0.00027** (0.00012) (0.00202) 0.00708** (0.00306)

0.446** (0.213)
0.00076*** (0.000257) 0.00511*** (0.00154)
0.0401*** (0.0119) 0.00010 (0.00036)
0.000234 (0.00014) (0.00202) 0.00617* (0.00326)

N Number of province Hansen J statistic p-value Number of IVs AR(1) p-value AR(2) p-value

419 26 21.92 0.823 40
3.305 0.00095
0.538 0.591

419 26 21.92 0.823 40
2.997 0.00273
0.626 0.531

419 26 19.87 0.650 34
3.143 0.00167
0.429 0.668

419 26 19.87 0.650 34
2.865 0.00417
0.365 0.715

419 26 21.42 0.208 28
3.208 0.00134
0.142 0.887

419 26 21.42 0.208 28
2.781 0.00542
0.109 0.913

419 26 12.81 0.306 22
2.269 0.0232
0.401 0.688

419 26 12.81 0.306 22
2.135 0.0327
0.647 0.518

F-statistic of Joint test for (β2 þ β3) p-value

5.86 0.0082

3.49 0.046

6.76 0.0045

4.36 0.0237

7.04 0.0038

6.31 0.0060

5.35 0.0116

26 0.0065

Case/pe

t-1

Income Income*Dummy_Zhu Dummy_Zhu Anticorrup Edu

Constant

Note: The dependent variable is Case/pe. The regressions are adjusted for small sample and the standard error estimates are robust. Control variables (Gini, Resource, Trade, Wage) are included in the regressions but not reported. All variables are treated as endogenous except for Anticorrup and Wage. Lag range (a ‒ b) is the lags of endogenous variables used as instruments. Robust standard errors in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

budgets for regional public security agencies, procuratorial agencies, court and judicial agencies are made directly by the central government. Wage can be regarded as exogenous because labor compensation of civil servants in different provinces is set by the central government based on regional living costs. This assumption of exogeneity reduces the number of instruments in the GMM estimations. There is always the possibility of reverse causality that because of more corruption (more Case/pe) may lead the government to increase Anticorrup and Wage. To take care of this possibility, we examine another case in which Anticorrup and Wage are treated as endogenous variables and they are instrumented in the GMM regressions. This result is presented in Table 3a. However, doing this would increase the number of instruments. Roodman (2009a, b) recommended that the number of province should be small relative to the number of instrument. We can reduce the number of lags to 2–3 to make the number of instruments less than the number of provinces (see Table 5). We also use the ‘Collapse’ command in Stata to reduce the number of instruments by combining them into smaller sets (Roodman, 2009a, b). The new results, assuming Anticorrup and Wage are endogenous, are almost the same as those assuming Anticorrup and Wage as exogenous. Perhaps, the Anticorrup and Wage variables are statistically close to being exogenous. Because of this robustness, in subsequent tables, we only report one version, the exogenous Anticorrup and Wage version, to cut down the number of tables in the paper.13

The results of one- and two-steps estimation variants are reported in Table 3.14 The 2nd to 6th lags are used as instruments. In addition, the regressions are estimated in the collapsed form to relieve the problem of too many instruments. Statistical significance of the estimated coefficients is based on robust standard errors that are also adjusted for small sample size. The results of system GMM estimation in Table 3 are in line with the results of the pooled OLS and FE estimations in Table 2. Our system GMM estimations are reliable according to several diagnostic tests. The p-value of Hansen test is greater than 0.1, confirming the regressions are clear of over-identification problem. Arellano-Bond test, AR (2), shows that the regressions do not suffer from the problem of the second-order serial correlation either. Summing up, the results of GMM estimation in Tables 3 and 3a support the conventional wisdom that an increase in per capita real GDP lowers the incidence and the relative return of corruption (Case/pe, CorShare, CorReturn). However, other measures of the severity of corruption (Mcase/pe, Kco/pe) are insignificantly related to Income. This suggests that the severity of corruption could be fairly constant, but definitely not positively related to Income. It does not contradict the results from OLS and FE regressions. Severe corruption (Mcase/Case, Kco/ Case)15 rises with rising Income as shown in Table 4. 4.2.1. Introducing transitional reform under premier Zhu Rongji This sub-section turns our attention to Dong and Torgler (2013)’s finding, that there is a positive impact of income on corruption during reform periods. As shown in our preliminary analysis in Section 3, the effect of income on corruption were different during the earlier periods,

13 In all subsequent tables, the results from the endogenous assumption version are available from the authors upon request. 14 In practice, the results from one-step and two-step estimations are not the same. Although the two-step estimation is more efficient, the benefits of using the optimal weighting matrix may not outweigh the extra cost of estimation in two-steps regression (see Stata menu). For the present paper, the one-step estimation here serves as a robustness check of the two-step regressions.

15 In the Kco/Case and Kco/pe regressions, we consider Anticorrup endogenous as senior officials may be able to influence Anticorrup. Treating Anticorrup as endogenous improves the estimation slightly, with no major changes in the results.

10

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 5a Additional SGMM regressions when the time dummy of the Zhu administration and its cross-product term are included. Z¼

1-step

2-step

1-step

2-step

CorShare

CorShare

CorReturn

CorReturn

(1)

(2)

(3)

(4)

0.0855 (0.0733)
0.0384*** (0.00692) 0.0372** (0.0146)
0.303** (0.114) 0.0141* (0.00797)
0.00537 (0.00454)
0.0305 (0.0209) 0.442*** (0.0627)

0.252 (0.153)
0.0292*** (0.00950) 0.0439*** (0.0143)
0.350*** (0.106) 0.0188 (0.0131)
0.00509 (0.00506)
0.0312 (0.0292) 0.337** (0.123)


0.0813 (0.152)
6.181** (2.220) 18.21** (8.005)
140.5** (61.46) 4.898* (2.703)
0.539 (0.989)
9.665 (6.387) 99.11*** (21.83)


0.0770 (0.130)
2.469 (2.493) 15.86** (6.245)
120.1** (47.05) 0.262 (5.420)
0.687 (0.916)
52.53 (40.05) 100.9*** (25.90)

N Number of province Hansen J statistic p-value Number of IVs AR(1) p-value AR(2) p-value

415 26 18.44 0.935 40
2.54 0.011 1.91 0.057

415 26 18.44 0.935 40
2.11 0.035 1.71 0.088

389 26 13.64 0.993 40
2.88 0.004 2.05 0.041

389 26 13.64 0.993 40
1.76 0.079 0.87 0.382

F-statistic of Joint test for (β2 þ β3) p-value

29 0.0000

16.18 0.0000

4.30 0.0248

3.22 0.0568

Z

t-1

Income Income*Dummy_Zhu Dummy_Zhu Anticorrup Edu Wage Constant

Note: Control variables (Gini, Resource, Trade, Wage) are included in the regressions but not reported. The regressions are adjusted for small sample and the standard error estimates are robust. All variables are treated as endogenous except for Anticorrup and Wage. The lags 2–6 of endogenous variables used as instruments. Robust standard errors in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

corruption was less under the Zhu Rongji’s administration. The results of lag (2–5), lag (2–4) or lag (2–3) are similar. In general, results in Table 5 confirm our speculation with instrument variables reduced about the positive relationship between corruption and economic development

which overlapped with the period of economic reforms in China. The most notable reforms then were the state-own enterprise reforms under Premier Zhu Rongji’s administration (1998–2002). We therefore introduce a period dummy variable and an interactive term of income and

Case=pe ¼ β0 þ β1 *Case=pe þ β2 *Incomei;t þ β3 *Income*Dummy Zhui;t þ β4 *Dummy Zhui;t i;t i;t
1 þβ5 *Anticorrupi;t þ β6 *Edui;t þ β7 *Ginii;t þ β8 *Resourcei;t þ β9 *Tradei;t þ β10 *Wagei;t þ εi;t

(3)

during reform periods. In the transition from a planned economy to a market economy, the central government loosens up control. This gives local officials an opportunity to abuse their power for personal gain. The ratio of anticorruption expenditure to total public expenditure (Anticor_Pubex) is used as the measure of anticorruption to do the robustness test as shown in Table C1. Table 5a presents results from other measures of corruption (CorShare and CorReturn).

period dummy into the regression specification as follow: Here, Dummy_Zhu equals to 1 during the period of 1998–2002, and 0 otherwise. The estimation results of Eq. (3) are reported in Table 5, under different GMM variants as well as different sets of instrument variables. Under the Windmeijer’s small-sample correction procedure, the two-step robust variant is more efficient than the one-step robust variant. Take the regression in Column (2) of Table 5 for illustration. The coefficient of Income itself is β2 ¼
0.000624 and the coefficient of the interactive term (Income* Dummy_Zhu) is β3 ¼ 0.00154, both are significant. Hence the total impact of per capita real income on corruption case is positive (¼β2 þ β3 ¼
0.000624 þ 0.00154 ¼ þ0.000916, when Dummy_Zhu ¼ 1). The values of β2 þ β3 are positive in all the regressions in Table 5, consistent with the finding in Dong and Torgler (2013). The coefficient of Dummy_Zhu is negative and significant, suggesting that

4.2.2. The full range of the transitional period To find out the full range of the transitional period where there is an unusual positive relationship between corruption and economic development, we introduce an alternative dummy variable (Dummy_Period) into the model to replace the Dummy_Zhu as shown in Eq. (4):

11

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 6 Test for the upper bound of the critical transitional period. 1998–2002

1998–2003

1998–2004

(1)

(2)

(3)

(4)

(5)

(6)

0.446*** (3.67)
0.00062** (-2.51) 0.00154* (1.77)
0.0124* (-1.81)
0.00071* (-1.77)
0.00023** (-2.18) 0.00631 (0.00516)

0.465*** (4.55)
0.00068** (-2.45) 0.00130** (2.06)
0.0106** (-2.12)
0.00060 (-1.61)
0.00020* (-1.77) 0.00505 (0.00358)

0.429*** (3.78)
0.00077** (-2.44) 0.00120** (2.22)
0.00988** (-2.26)
0.00072 (-1.65)
0.00023* (-1.82) 0.00832 (0.00582)

0.463*** (5.25)
0.00071** (-2.33) 0.00079** (2.19)
0.00672** (-2.25)
0.00069* (-1.79)
0.00016 (-1.43) 0.00564 (0.00380)

0.419*** (4.18)
0.00086*** (-3.05) 0.00083** (2.28)
0.00725** (-2.42)
0.00092* (-1.87)
0.00012 (-1.19) 0.00837 (0.00538)

0.435*** (5.29)
0.00081** (-2.73) 0.00069** (2.53)
0.00606** (-2.65)
0.00073* (-2.02)
0.00009 (-0.95) 0.00631* (0.00362)

N Number of province Hansen J statistic p-value Number of IVs AR(1) p-value AR(2) p-value

419 26 21.92 0.823 40
3.00 0.003
0.63 0.531

419 26 22.93 0.780 40
3.28 0.001
0.95 0.342

419 26 21.63 0.835 40
2.96 0.003
0.97 0.332

419 26 23.26 0.765 40
3.32 0.001
0.59 0.555

419 26 23.74 0.742 40
3.08 0.002
0.36 0.721

419 26 23.38 0.759 40
3.30 0.001
0.67 0.504

β2 þ β 3 F-statistic of Joint test for (β2 þ β3) p-value

0.000916 3.49 0.0460

0.000625 3.43 0.0483

0.000435 3.25 0.0557

0.000078 3.12 0.0619


0.00003 4.95 0.0154


0.000114 4.31 0.0247

Case/pet-1 Income Income*Dummy_ Period Dummy_Period Anticorrup Edu Constant

1998–2005

1998–2006

1998–2007

Note: The dependent variable is Case/pe. Control variables (Gini, Resource, Trade, Wage) are included in the regressions but not reported. Two-step variant of system GMM estimator is applied. The regressions are adjusted for small sample and the standard error estimates are robust. All variables are treated as endogenous except for Anticorrup and Wage. Lags (2–6) of endogenous variables are used as instruments. Robust standard errors in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Case=pe ¼ β0 þ β1 *Case=pe þ β2 *Incomei;t þ β3 *Income*Dummy Periodi;t þ β4 *Dummy Periodi;t i;t i;t
1 þβ5 *Anticorrupi;t þ β6 *Edui;t þ β7 *Ginii;t þ β8 *Resourcei;t þ β9 *Tradei;t þ β10 *Wagei;t þ εi;t

(4)

benchmark. As the lower bound moves backward from 1998 to 1995, both the sign and the statistical significance of the total effect of income on corruption (β2þβ3) are decreasing as the lower bound moves away from 1998. The value of (β2þβ3) immediately switches to negative in Column (3). Hence, the lower threshold of the transitional period is the year 1998. In conclusion, the entire transitional period should be 1998–2005, which includes the whole period (5 years) under the administration of Premier Zhu Rongji and part of the first term (3 years) under the administration of President Hu Jintao and Premier Wen Jiabao. After 2005, the impact of income on corruption reverts to negative, as it was before 1998. Another important reform was agriculture policy, which occurred in the first term of the administration of President Hu Jintao and Premier Wen Jiabao. It started with the experiment of agriculture policy reform in Anhui in 2000. The policy reform then spread out to the rest of the country in 2003. The agriculture reform inevitably brought in an increasing amount of corruption, such as jerry-built projects and embezzlement of social security fund. The GMM estimations in this section offer some idea on the magnitude of the short-run positive relationship and the long-run negative relationship between corruption and per capita income. The positive short-run impact of per capita income is shown in Table 6, from the ??2þ??3 row. The positive impact of per capita income gradually decreases from the maximum of þ0.000916 in 1998–2002, to 0 in

We first test for the upper threshold of the transitional period. Starting with the period under Zhu Rongji (1998–2002), each year beyond 2002 is sequentially included into the Dummy_Period variable until the sign of (β2þβ3) switches from positive to negative. The switching year is the upper threshold of the transitional period. The reported regressions in Table 6 are based on two-step system GMM estimation with the lag range for instruments set at (2–6).16 With the upper bound moving forward from 2002 to 2007, the total effect of income on corruption, (β2þβ3), is decreasing from Column (1) to Column (6).17 From Column (1) to (4), the total effect of income on corruption, when Dummy_Period equals to 1, is positive, but from Column (5) to (6), the sign switches to negative. Therefore, the upper threshold of the transitional period must be 2005. In Table 7, we test for the lower threshold of the transitional period starting from the period 1998–2005 (Column (4) of Table 6) as

16

The estimation results with regression results based on the two-step system GMM estimation with lag range for instrument variables to be (2–5) and/or (2–4) are similar to the regressions in Table 5. These additional results are available upon request. 17 The regression results with upper bound of the critical transitional period set to be larger than 2007 are similar to that listed in Columns (5) and (6), i.e., the total effect of income on corruption is negative. They are not reported but available upon request. 12

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 7 Test for the lower bound of critical transitional period. 1995–2005

1996–2005

1997–2005

1998–2005

(1)

(2)

(3)

(4)

0.517*** (7.02)
0.00028 (-0.37) 0.00017 (0.14)
0.00112 (-0.11)
0.00042 (-0.88)
0.00011 (-0.35) 0.00104 (0.00873)

0.517*** (7.02)
0.00028 (-0.37) 0.00017 (0.14)
0.00112 (-0.11)
0.00042 (-0.88)
0.00011 (-0.35) 0.00104 (0.00873)

0.490*** (5.35)
0.00061* (-1.93) 0.00051* (1.72)
0.00431* (-1.80)
0.00071* (-1.97)
0.00015 (-1.57) 0.00462 (0.00392)

0.463*** (5.25)
0.00071** (-2.33) 0.00079** (2.19)
0.00672** (-2.25)
0.00069* (-1.79)
0.00016 (-1.43) 0.00564 (0.00380)

N Number of province Hansen J statistic p-value Number of IVs AR(1) p-value AR(2) p-value

419 26 23.09 0.772 40
3.45 0.001
0.76 0.445

419 26 23.09 0.772 40
3.45 0.001
0.76 0.445

419 26 23.14 0.770 40
3.40 0.001 0.26 0.791

419 26 23.26 0.765 40
3.32 0.001
0.59 0.555

β2 þ β 3 F-statistic of Joint test for (β2 þ β3) p-value


0.000116 0.18 0.8405


0.000115 0.18 0.8405


0.000104 2.41 0.1106

0.000078 3.12 0.0619

Case/pet-1 Income Income*Dummy_Period Dummy_Period Anticorrup Edu Constant

Note: The dependent variable is Case/pe. Control variables (Gini, Resource, Trade, Wage) are included in the regressions but not reported. Two-step variant of system GMM estimator is applied. The regressions are adjusted for small sample and the standard error estimates are robust. All variables are treated as endogenous except for Anticorrup and Wage. Lags (2–6) of endogenous variables are used as instruments. Robust standard errors in parentheses. *p < 0.10, **p < 0.05, ***p < 0.01.

Table 8 PMG estimation. Dependent variable: Ln (Case/pe). (1)

(2)

(3)

(4)

(5)

(6)


0.546*** (0.0517)


0.531*** (0.0569)


0.605*** (0.0618)


0.472*** (0.0463)


0.550*** (0.0556)


0.590*** (0.0589)


0.410*** (0.0292)


0.338*** (0.0490)
0.279*** (0.106)


0.349*** (0.0333)


0.215*** (0.0474) 0.156 (0.116)


0.564*** (0.0328)


0.543*** (0.0401)


0.776*** (0.127)


0.791*** (0.136)
0.00922 (0.0136)

419

419

Adjustment coefficient

Long-run coefficients: ln(Income) ln(Anticorrup) ln(Anticor_Pubex) edu N

419

419


0.0400*** (0.0135)


0.0944*** (0.0181)

419

419

Note: The dependent variable is Δln(Case/pe). Standard errors in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01.

distributive lag (ARDL) model:

2005–2006. The maximum positive impact is about three times the (absolute) magnitude of the long-run negative impact from per capita income (around – 0.00033, from Table 3). From Table 6, the positive relationship is also more pronounce under the Zhu Yongji Administration than under the period (in Column (1)) when the Hu-Wen administration also included (in Column (4)) as suggested in the introduction.

ΔCase=pe ¼ ϕi ðCase=pe
θ0;i
θi X’i;t Þ þ i;t i;t
1 þ

q
1 X

δ*i;j ΔXi;t
j þ ηi þ εi;t

p
1 X j¼1

λ*i;j ΔCase=pe

i;t
j

(5)

j¼0

4.3. Determinants of corruption in the long run

where,18

The above GMM regressions provide robust evidence that the negative relationship between corruption and income would turn positive during the short reform period. We are also interested in how the corruption-development nexus evolves over a long period of time. In this sub-section, we will study what factors determine the level of corruption in the long-run by applying three alternative dynamic panel errorcorrection estimators of Pooled Mean Group (PMG), Mean Group (MG) and Dynamic Fixed Effect (DFE) to the following auto-regressive

Xi;t ¼ ðIncomei;t ; Anticorrupi;t ; Edui;t Þ

18 The variables (Gini, Resource, Trade, Wage) are not included in the set of control variables, Xi,t, because PMG estimator requires a large degree of freedom.

13

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table 8a Panel error-correction estimators. Dependent variable: Alternative measures of corruption. (1)

(2)

PMG

DFE

Δln(CorShare)

(3)

(4)

PMG

DFE

(5)

Δln(CorReturn)

(6)

(7) DFE

DFE

PMG

Δln(Mcase/pe)

Δln(Kco/pe)

(8)

(9)

DFE

DFE

Δln(Mcase/Case)

Δln(Kco/Case)

Adjustment coefficient
0.850*** (0.1011) Long-run coefficients: ln(Income)
0.927*** (0.0753) ln(Anticorrup) 0.210 (0.201) Edu
0.0207 (0.0280)


0.744*** (0.0522)


0.864*** (0.1235)


0.810*** (0.0543)


0.370*** (0.0386)


0.492*** (0.0781)


0.390*** (0.0433)


0.451*** (0.0453)


0.558*** (0.0486)


0.628*** (0.121)
0.485* (0.254) 0.0630 (0.0396)


0.311*** (0.0800)
0.747*** (0.175) 0.0296 (0.0246)


0.208* (0.114)
0.679*** (0.234) 0.0454 (0.0366)

0.271** (0.128)
0.768*** (0.265)
0.0188 (0.0444)


0.242*** (0.0808) 0.242 (0.203)
0.00189 (0.0245)


0.0469 (0.101)
0.0604 (0.237) 0.0553 (0.0359)

0.666*** (0.113)
0.700*** (0.232)
0.0535 (0.0377)

0.273*** (0.0729) 0.245 (0.170) 0.0352 (0.0254)

N

414

388

388

402

449

449

341

378

414

Note: Hausman’s tests indicate that PMG is superior to MG; DFE is also superior to MG. There is a toss-up between PMG and DFE, therefore the MG estimation results are not reported. The PMG estimation results for ΔMcase/pe, ΔMcase/Case, ΔKco/Case are not available as the Hessian has become unstable or asymmetric. Standard errors in parentheses; *p < 0.10, **p < 0.05, ***p < 0.01.

ϕi ¼
ð1


p X j¼1

q P

λi;j Þ; θi ¼


δi;j

j¼0

ð1


q P

p P

λi;j Þ

¼


δi;j

j¼0

ϕi

; λ*i;j ¼


p X m¼jþ1

λi;m ; δ*i;j ¼


q X

exploratory OLS and FE regressions in Tables 2 and 2a, again observed in the GMM estimations in Tables 3 and 3a, as well as the PMG estimations in Tables 8 and 8a. This finding is also in agreement with the descriptive regressions in Table 1, Section 3. The explanation for this result could be as follows: It takes efforts for the corrupted officials to seek bribes. As the market economy matures and becomes more efficient, the value of marginal product of efforts in the free market would rise above the value of marginal gain of efforts from bribe seeking. When this happens, corruption must go down. Only lucrative bribes remains.

δi;m

m¼jþ1

j¼1

Above, ϕi is the adjustment coefficient. There is no stable long-run relationship among the variables if ϕi  0. The variables would converge toward a stable long-run equilibrium if ϕi < 0. The vector θi represents the coefficients of the explanatory variables at the long-run equilibrium. Note that the transitional period dummy variables in the GMM regressions are not included into Xi,t vector in Eq. (5). Apart from the technical difficulty in implementation, these variables have no long-run effects, because they occur only for the short reform period. They are just transitory shocks. In applying PMG estimator, we focus on the basic case of ARDL (p ¼ 1, q ¼ 1), as in most of the PMG estimation in the literature. In Table 8, all the adjustment coefficients are negative and significant, indicating that there is a long-run stable relationship (co-integration) between corruption and all the significant specified long-run variables in that column (Pesaran and Smith, 1995). The coefficient of ln(Income) is negative and significant in all regressions, indicating the negative relationship between ln(Income) and ln(Case/pe) is valid in the long run. The coefficient of Edu is significantly negative in the long-run under various specifications, contrary to the insignificant effect in the short-run under the GMM estimation. From Table 8, when per capita income increases by one percent, the corruption cases per capita would be reduced by 0.215 percent to 0.543 percent. In Table 8a, other corruption indicators are tested. As for the regressions for the variables (CorShare, CorReturn, Mcase/pe, Mcase/Case, Kco/pe, Kco/Case), PMG estimation sometimes is not very stable due to the number of observations. We therefore try MG and DFE estimators and preform the Hausman test for our selection. The selected choices are reported in Table 8a. Summing up, the PMG estimators in Tables 8 and 8a support the conventional wisdom that an increase in per capita real GDP lowers the incidence and the relative return of corruption (Case/pe, CorShare, CorReturn). Measure of the severity of corruption (Mcase/pe) is negative related to Income and statistically significant at the 10 percent level. The severity index (Kco/pe) produces inconclusive results. The composition of severe corruption indices (Mcase/Case, Kco/Case) increase with rising Income. A noteworthy and robust finding in this Section is, as per capita income rises, while corruption cases per capita goes down, the share of severe corruption cases goes up. This is observed in the simple

5. Conclusions This paper investigates the relationship between corruption and economic development from Chinese provincial panel data on prosecuted corruption cases. An important challenge of this research is the validity of our concept and measurement of corruption. To address the challenge, we check how well our measurement is in line with the Corruption Perception Index from Transparency International Inc., which is the widely accepted notion of corruption, however defined. The high correlation between them lends confidence to the validity of the persecuted corruption cases indices. This paper finds significant support for the conventional negative long-run relationship between corruption and economic development. Another important contribution in this paper is the construction of several corruption indicators to detect the constantly evolving composition and structure of corruption over a long time horizon. We break down the registered corruption index into different indicators. The number of registered corruption cases,19 adjusted for the size of the government employees, and the GDP share of recovered money from registered corruption (variables Case/pe, CorShare) clearly show a negative relationship with real per capita GDP. We also construct two indicators that reflect the severity of corruption (variables Mcase/pe, Kco/pe), one by the number of major/severe corruption cases, and the other by the number of corrupted high ranking officials, both adjusted by total government employees. By and large, these two indicators show a negative relationship with per capita GDP.20 The relative return to corruption (variable CorReturn) decreases with the rise of per capita GDP. With the development of the market economy, which offers an attractive

19

The number of registered corruption cases (Case) and the number of corrupted officials (Co) are highly correlated (Corr.Coef. ¼ 0.9559, R2 ¼ 0.914). We use Case here because the variable Case has a much larger number of observations than co. 20 The GMM tests for both Mcase/pe and Kco/pe variables are insignificantly related to Income. The PMG and DFE tests for the Kco/pe variable is also inconclusive. However, all other estimators for both variables show a negative significant relationship with per capita GDP. 14

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

should be from 1998 to 2005, which is the entire period (5 years) of Premier Zhu Rongji’s administration and part of the first term (3 year) of President Hu Jintao and Premier Wen Jiabao’s administration. Several tough and deep reforms had taken place, such as state-owned enterprises (SOE) reform, housing reform and government institutional reform and agricultural taxation system reform. With the deepening of reform and the improvement of the economy, the incidence of corruption should go down in the long-run. We have demonstrated that this long-run negative relationship indeed exists based on dynamic panel error-correction estimation. Our system GMM results also show that in the short-run, massive corruption tends to occur in the provinces with abundant mineral and mining resources. By and large, government efforts and expenditure on anti-corruption do suppress the incidence of corruption. The improvement in education also plays a role in reducing the incidence corruption in the long-run. In general, the corruption in China is no different from those in the other countries. The incidence of corruption will decrease with the continuing development of China’s economy, as suggested by the conventional wisdom. And, during the critical transition period, high level of corruption, intense institutional reforms and rapid economic development coevolve.

alternative to ordinary corruption activities, the decline in the relative return to corruption is not surprising. This decline in relative returns to corruption might be why the incidence of corruption cases is negatively related to real per capita GDP. With economic development, the composition of severe corruption is also changing. The share of severe corruption and the share of high ranking registered corrupted officials are captured by two indices (Mcase/Case, Kco/Case). They are positively correlated with development, suggesting that only the lucrative corruption can remain competitive with the rising market wages and income.21 The above results are robust against different estimation methods and models. The time path on the incidence of corruption is also interesting. This paper finds that the negative relationship between corruption and development can reverse itself to positive during the transitional period, supporting the finding of Dong and Torgler (2013). The driving force behind the worsening corruption along with increasing per capita GDP is the structural change during the period of transition. As the central government loosens up the control of economy in the early stage of reforms, officials have new opportunities to engage in corruption. The intensification of corruption is the inevitable price which China has to pay for shifting from a planned economy to a market economy. Another novel finding of the present paper is, this transitional period

Appendix A

Table A1 Summary statistics. Variable

Definition

Obs

Mean

Std. Dev.

Min

Max

Time period

Unit

468 0.0032672 0.0015365 0.000746 0.0113446 [1995–2014] case/ The ratio of corruption cases over the total number of public person employees (working in the public management and social organization) CorShare The total amount of money recovered from registered corruption 449 0.0443141 0.0422238 0.003093 0.382324 [1995–2014] % cases/GDP CorReturn The amount of money recovered from registered corruption cases/ 425 11.74572 10.7656 1.265013 100.764 [1995–2014] NA (number of registered corruption cases  urban wage) 402 0.5602675 0.2165713 0.1711899 1.611785 [1995–2014] ratio Mcase/Case Number of major registered corruption cases (those cause severe damage to the economy)/total number of registered corruption case Mcase/pe Number of major registered corruption cases over the total number 443 18.22683 12.48246 4.692557 102.6351 [1995–2014] case/ of government employees person Kco/co Number of corrupted high ranking (or above average rank) 385 0.0751449 0.049926 0.0151515 0.3307985 [1995–2014] ratio officials/total number of corrupted officials Kco/pe Number of corrupted high ranking (or above average rank) 483 2.237464 1.362315 0.4543764 13.23746 [1995–2014] ratio officials/(total number of public employees  10000) Income Per capita real GDP (base year is 1980) 520 5694.778 5306.745 664.7 33466.8 [1995–2014] Yuan Anticorrup The ratio of expenditure for Public Security to GDP 520 0.9897115 0.4015573 0.27 2.63 [1995–2014] % Anticor_Pubex The ratio of expenditure for Public Security to government 520 6.284769 1.225074 3.48 10.79 [1995–2014] % expenditure The enrollment rate of secondary school 520 6.731225 1.438672 2.5818 11.9814 [1995–2014] % Edu Gini index 520 0.3836871 0.0602762 0.2322653 0.5032386 [1995–2014] % Gini The ratio of employees in the mining and quarrying sector to total 520 1.032815 1.23243 0.0046248 9.662368 [1995–2014] % Resource employment Trade The ratio of the sum of import and export to GDP 520 32.43806 41.75974 3.207362 205.1279 [1995–2014] % Wage The ratio of the average wage of government employee to the 520 1.073856 0.0855133 0.9088069 1.571672 [1995–2014] provincial average wage The 26 provinces considered in the regressions are: Beijing, Tianjin, Hebei, Shanxi, Nei Mongol, Liaoning, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Henan, Hubei, Hunan, Guangdong, Guangxi, Chongqing, Sichuan, Guizhou, Yunnan, Shaanxi, Gansu, Qinghai, and Xinjiang. Case/pe

Notes: Refer to Section 2 for more details of the definition of variables.

21

It takes the time and efforts of the corrupted officials to seek bribes. To focus ideas, let the marginal gain of the corrupted officials diminishes with more timeand-efforts spent on seeking bribes. The time-and-efforts on collecting bribes could also be spent on the alternative labor market for the equivalent amount of market wage. From the downward slopping marginal gain curve, as market wage rises, less time-and-effort would be spent on seeking bribes, and more on alternative productive labor market. Hence, less lucrative bribes disappear, only the more lucrative bribes remain. 15

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table A2 Coefficient of correlation (whole panel)

Case/pe Income Anticorrup Anticor_Pubex Edu Gini Resource Trade Wage

Case/pe

Income

Anticorrup

Anticor_Pubex

Edu

Gini

Resource

Trade

Wage

1.0000
0.3721
0.5053 0.0233
0.1497
0.3213 0.2587
0.2032
0.0787

1.0000 0.0665
0.0664
0.1971
0.3146
0.2022 0.6182 0.2705

1.0000
0.1165 0.1966 0.5466
0.1061
0.0630
0.0474

1.0000 0.0653
0.0808
0.1781 0.3955 0.3290

1.0000 0.3391 0.0553
0.1351
0.0445

1.0000
0.0661
0.4297 0.0346

1.0000
0.2654
0.2751

1.0000 0.2277

1.0000

Fig. A1. Corrupted high rank officials adjusted by the total number of government employees (Kco/pe).1 Data source: Kco/pe is calculated by authors, based on the data drawn from annual Procuratorial Yearbook of China and China Statistical Yearbook.

Appendix B. VAR analysis Vector auto-regression (VAR) model is helpful to estimate the fundamental relationship of variables without any prior constraints. The estimation results of VAR with different lags are provides in Table B1. The results of VAR (2) are listed in Columns (1) and (2). It can be seen that the first lag of national GDP (national per capita GDP, 1995 ¼ 100) has a significantly negative impact while the second lag of GDP has a significantly positive impact on the National_Case/pe, with coefficients of
0.00888 and 0.00866 respectively. Hence, the total impact of these two lags of GDP on corruption is negative. On the contrary, the lags of the National_Case/pe has insignificant impact on national GDP. The estimation results of VAR (3) presented in Columns (3) and (4) and the estimation results of VAR (4) presented in Columns (5) and (6) are similar as those of VAR (2). The VAR results reported in Table B1 confirms that the direction of the causality is only from GDP to corruption (Treisman, 2007; Gundlach and Paldam, 2009; Dong and Torgler, 2013), and that the impact of GDP on corruption is negative. The impulse response function (IRF) simulation of VAR (2) in Fig. B1 further confirms this result. Table B1 Time-series VAR. VAR (2)

National _Case/pe (-1) National _Case/pe (-2)

VAR (3)

VAR (4)

National_Case/pe

GDP

National_Case/pe

GDP

National _Case/pe

GDP

(1)

(2)

(3)

(4)

(5)

(6)

0.487*** (2.26)
0.161 (-0.88)


3.122 (-0.34)
5.854 (-0.76)

0.0879 (0.47)
0.234 (-1.34)
0.0965 (-0.70)


8.565 (-0.77) 0.339 (0.03)
8.692 (-1.07)


0.00888* (-1.91)

1.713*** (8.71)


0.00906** (-2.10)

1.659*** (6.46)


0.301 (-0.72)
0.307 (-1.54)
0.0729 (-0.36)
0.221 (-1.22)
0.0153** (-2.22)


7.288 (-0.31)
7.452 (-0.65) 0.199 (0.02)
8.422 (-0.81) 1.668*** (4.21)

National _Case/pe (-3) National _Case/pe (-4) GDP (-1)

(continued on next column)

16

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table B1 (continued ) VAR (2)

GDP (-2)

VAR (3)

VAR (4)

National_Case/pe

GDP

National_Case/pe

GDP

National _Case/pe

GDP

(1)

(2)

(3)

(4)

(5)

(6)

0.00866* (1.77)


0.712*** (-3.44)


0.00173 (-0.23) 0.0106** (2.34)


0.742 (-1.63) 0.0806 (0.30)

0.00521 (0.54) 0.00394 (0.41) 0.00582 (0.87)


1.008* (-1.83) 0.801 (1.45)
0.501 (-1.30)

18

18

17

17

16

16

GDP (-3) GDP (-4) N

Note: t statistics in parentheses, *p < 0.10, **p < 0.05, ***p < 0.01.

Fig. B1. Impulse response function.2

Appendix C

Table C1 The ratio of anticorruption expenditure to total public expenditure is used as the measure of anticorruption (Anticor_Pubex); The time dummy of the Zhu administration and its cross-product term are added. OLS

FE

System GMM lag(2 6)

Income Income*Dummy_Zhu Dummy_Zhu Anticor_Pubex

lag(2 5)

lag(2 4)

1-step

2-step

1-step

2-step

1-step

2-step

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)


0.00114*** (-11.41) 0.000914*** (4.69)
0.00755*** (-4.88)
0.00000530 (-0.09)


0.00173*** (-16.85) 0.000132 (0.80)
0.00179 (-1.38)
0.000149** (-2.46)


0.000771*** (-3.42) 0.00138** (2.50)
0.0111** (-2.58)
0.0000696 (-0.67)


0.000981* (-2.03) 0.00111** (2.35)
0.00892** (-2.35)
0.000186 (-0.95)


0.000861*** (-3.57) 0.00173** (2.63)
0.0140** (-2.75)
0.0000418 (-0.36)


0.000999*** (-3.23) 0.00164** (2.30)
0.0134** (-2.39)
0.000107 (-0.70)


0.00101*** (-3.74) 0.00189** (2.44)
0.0156** (-2.60)
0.0000764 (-0.62)


0.00123*** (-3.33) 0.00187** (2.11)
0.0154** (-2.26)
0.000156 (-1.05)

(continued on next column)

17

K.S. Chan et al.

Economic Modelling xxx (xxxx) xxx

Table C1 (continued ) OLS

FE

System GMM lag(2 6)

lag(2 5)

1-step

2-step

1-step

lag(2 4) 2-step

1-step

2-step

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)


0.00000604 (-0.14)
0.0114*** (-10.77) 0.000167*** (3.70)
0.00000370* (-1.78) 0.00317*** (3.93)


0.0000130 (-0.34)
0.000801 (-0.54) 0.000349*** (4.17)
0.0000165*** (-4.89)
0.000244 (-0.28)

_Cons

0.0138*** (12.88)

0.0195*** (18.49)


0.000127 (-1.50) 0.000640 (0.27) 0.000324*** (2.89) 0.00000459 (1.06) 0.00297** (2.49) 0.488*** (5.92) 0.00540** (2.18)


0.000154* (-1.74) 0.00207 (0.79) 0.000328** (2.41) 0.0000113 (1.10) 0.00350** (2.58) 0.460*** (3.96) 0.00672 (1.43)


0.000168* (-1.71) 0.000585 (0.24) 0.000329** (2.70) 0.00000334 (0.63) 0.00299** (2.40) 0.458*** (5.11) 0.00644** (2.49)


0.000171 (-1.45) 0.000596 (0.22) 0.000263* (2.04) 0.00000620 (0.95) 0.00281* (1.76) 0.434*** (4.46) 0.00827** (2.72)


0.000160 (-1.53) 0.00204 (0.77) 0.000339** (2.75) 0.00000789 (1.59) 0.00269* (2.03) 0.478*** (5.11) 0.00749** (2.68)


0.000157 (-1.46) 0.000686 (0.23) 0.000277* (2.04) 0.0000101 (1.48) 0.00270 (1.64) 0.400*** (3.43) 0.0106*** (2.79)

N adj. R2 Hansen p-value AR(1) p-value AR(2) p-value

468 0.447

468 0.598

419

419

419

419

419

419

20.45 [0.878]
3.374 [0.000740]
0.485 [0.627]

20.45 [0.878]
3.127 [0.00177]
0.616 [0.538]

21.49 [0.551]
3.175 [0.00150]
0.326 [0.745]

21.49 [0.551]
3.030 [0.00244]
0.219 [0.827]

19.45 [0.303]
3.148 [0.00164] 0.000901 [0.999]

19.45 [0.303]
2.739 [0.00616] 0.0841 [0.933]

Edu Gini Resource Trade Wage Caset-1

Note: The dependent variable is Case/pe. t statistics in parentheses, *p < 0.10, **p < 0.05, ***p < 0.01.

Appendix D. Supplementary data Supplementary data to this article can be found online at https://doi.org/10.1016/j.econmod.2019.09.001.

Leite, C., Weidmann, J., 2002. Does Mother Nature Corrupt? Natural Resources, Corruption and Economic Growth. Governmance, corruption, and economic performance, pp. 159–196. Lim, K., 2019. Modelling the dynamics of corruption and unemployment with heterogeneous labour. Econ. Model. 79, 98–117. Mauro, P., 1995. Corruption and growth. Q. J. Econ. 110, 681–712. Paldam, M., Gundlach, E., 2008. Two views on institutions and development: the grand transition versus the primacy of institutions. Kyklos 61, 65–100. Paldam, M., Svendsen, G., 2000. Missing Social Capital and the Transition in Eastern Europe. Department of Economics, Faculty of Business Administration, Aarhus School of Business. Pesaran, M., Smith, R., 1995. Estimating long-run relationships from dynamic heterogeneous panels. J. Econom. 68, 79–113. Roodman, D., 2009. A note on the theme of too many instruments. Oxf. Bull. Econ. Stat. 71 (1), 135–158. Roodman, D., 2009. How to Do xtabond2: an introduction to difference and system GMM in Stata. STATA J. 9, 86–136. Trisman, D., 2000. The cause of corruption: a cross-national study. J. Public Econ. 76, 399–457. Trisman, D., 2007. What have we learned about the cause of corruption form ten years of cross-national empirical research? Annu. Rev. Pol. Sci. 10, 211–244. Van Rijckeghem, C., Weder, B., 2001. Bureaucratic corruption and the rate of temptation: do wages in the civil service affect corruption, and by how much? J. Dev. Econ. 65, 307–331. Wedeman, A., 2012. Double Paradox: Rapid Growth and Rising Corruption in China. Cornell University Press, Ithaca, New York, USA. Xu, G., Yano, G., 2017. How does anti-corruption affect corporate innovation? Evidence from recent anti-corruption efforts in China. J. Comp. Econ. 45, 498–519. You, J., Khagram, S., 2005. Inequality and corruption. Am. Sociol. Rev. 70, 136–157.

References Acemoglu, D., Johnson, S., Robinson, J., 2001. The colonial origins of comparative development: an empirical investigation. Am. Econ. Rev. 91, 1369–1401. Ades, A., Di Tella, R., 1999. Rents, competition and corruption. Am. Econ. Rev. 89, 982–993. Arellano, M., Bover, O., 1995. Another look at the instrumental variables estimation of error-components models. J. Econom. 68 (1), 29–51. Bai, J., Jayachandran, S., Malesky, E., Olken, B., 2017. Firm growth and corruption: empirical evidence from Vietnam. Econ. J. 129, 982–993. Basu, S., Li, D., 2000. A Theory of the Reform of Bureaucratic Institutions. Econometric Society World Congress. Cooray, A., Dutta, N., Mallick, S., 2017. Trade openness and labor force participation in Africa: the role of political institutions. Ind. Relat.: A J. Econ. Soc. 56 (2), 319–350. Cooray, A., Dutta, N., Mallick, S., 2017. The right to be free: is media freedom good news for women’s rights? J. Inst. Econ. 13 (2), 327–355. Cooray, A., Dzhumashev, R., 2018. The effect of corruption on labour market outcomes. Econ. Model. 74, 207–218. Coppier, R., Michetti, E., 2006. Corruption vs production. A non-linear relationship. Econ. Model. 23, 622–637. Dong, B., Torgler, B., 2013. Cause of corruption: evidence from China. China Econ. Rev. 26, 152–169. Gerring, J., Thacker, S., 2005. Do neoliberal policies deter political corruption? Int. Organ. 59, 233–254. Glaeser, E., Saks, R., 2006. Corruption in America. J. Public Econ. 90, 1053–1072. Gundlach, E., Paldam, M., 2009. The transition of corruption: from poverty to honesty. Econ. Lett. 103, 146–148.

18