rent seeking and corporate finance: evidence from corruption cases joseph p.h. fan* oliver m. rui*...
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Rent Seeking and Corporate Finance: Evidence from
Corruption Cases
Joseph P.H. Fan*
Oliver M. Rui*
Mengxin Zhao**
*Chinese University of Hong Kong
**Bentley College
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Corporate Financing Patterns in Emerging Markets
Companies in emerging markets rely on debt much more than equity to finance their investment
Moreover, they rely on short-term debt, even when they engage in long-term investment
Banks, not capital markets, are the primary sources of funds for firms in developing countries
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Cross country pattern of corporate leverage (Fan, Titman, Twite, 2006)
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Cross country pattern of corporate debt maturity (Fan, Titman, Twite, 2006)
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Research Questions in This Study
To investigate the impact of political rent seeking on corporate financing behaviors in China
Research questions How do rent seeking and corruption affect capital
structure, debt maturity, and long-term debt financing of the Chinese firms?
In retrospect, whether political connections provide the firms financing advantages?
Are the financing advantages of the politically connected firms reflected in their stock prices?
Does corruption affect capital allocation efficiency?
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Why China?Pervasive corruption
One of the world’s highly corrupted countries According to China’s official record, during 1997-2002,
there are totally 861917 corruption cases under investigation 842760 corruption cases concluded 846150 people punished by communist laws, of which
137711 expelled from the communist party Among the punished communist party members,
28996 county (县) level2422 intermediate(厅 ,局) level98 provincial (省 , 部) level or above
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Corruption in Asian Economies(Source: Transparency International: mean Corruption
Perception Index 1992-2000)
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Economy
ChinaTaiwanHong KongIndonesiaJapanKorea (South)MalaysiaPhilippinesSingaporeThailandUnited StateUnited Kingdom
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Why China?Dominant role of bank debt in corporate
finance Equity markets small Banks as the primary external sources of funds
Big-four commercial banks dominate (accounting 63 percent of loans outstanding and 62 percent of deposits in 2001)
10 national and 90 regional commercial banks, 3 policy banks, 3000 urban and 42000 rural credit cooperatives
Limited foreign bank activities The banking system is largely state run, suffering from high
NPL problem and scandals Given the state control of banks and the highly corrupted
system in China, connections with government bureaucrats are likely important in influencing bank loan decisions
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Empirical Design
Identifying 23 high (provincial) level government officer corruption cases during 1995-2003 Tracking publicly listed companies in China that
are bribers or are connected with the corrupted bureaucrats
Track changes in financial leverage and debt maturities of the event (bribing or connected) firms from 3 years before to 3 years after the corruption event
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Empirical Design Compared with the prior studies, our single-
economy time-serial empirical design provides advantages Focusing on a specific institutional factor – rent
seeking Providing more directly link between corporate
financing decisions with rent seeking and corruption Mitigating endogeneity issues
The corruption events are likely shocks to connected firms. The connected firms are non-bribers, hence their financing policy changes upon the corruption events are likely caused by lost connections
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A Corrupted Bureaucrat and His Allies
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The Corruption ListProvince Name Position Event
Day Sentence Day Sentence Number
of firms in the
province
The Bribing Firms
The Connected Firms
The Non-event Firms
Anhui Wang Huizhong
Vice-Province Governor 20010407 20031229 Death penalty 27 1 2 24
Bank Liu Jinbao Vice-Chairman & CEO of Bank Of China (HK)
20030525 20050812 Suspended death penalty 5 5 0 0
Bank Wang Xuebin CEO of China Construction Bank 20020111 20031210 12 years of imprisonment 5 5 0 0
Beijing Chen Xitong CPC Secretary 19950426 19980731 16 years of imprisonment 11 5 3 3
Central Xu Penghang Vice chairman of national defense technology commission and national economics and trade commission
20001011 20001011 Dismissal from the service 1 1 0 0
Fujian Shi Zhaobin Vice-CPC Secretary 19990818 20010927 Dismissal from the service and CPC 34 5 3 26
Guangxi Xu Binsong Vice-Chairman of Municipality 19980523 19990827 Life imprisonment 6 0 1 5
Guangxi Chen Kejie Chairman of Municipality 20000111 20000731 Death penalty 10 2 1 7
Guangxi Liu Zhibin Vice-Chairman of Municipality 20000319 20020624 15 years of imprisonment 10 2 2 6
Guangxi Wang Qinglu Vice-Chairman of PPCC 20010222 20010222 Dismissal from the service and CPC 13 0 1 12
Guizhou Liu Changgui Vice-Province Governor 20030417 20040430 11 years of imprisonment 12 1 3 8
Guizhou Liu Fangren CPC Secretary & PC Chairman 20030422 20040629 Life imprisonment 12 1 1 10
Hainan Xin Yejiang Vice-PC Chairman 19961227 19980526 5 years of imprisonment 10 2 0 8
Hebei Jiang Dianwu Vice-PC Chairman 19971101 19981207 10 years of imprisonment 13 0 0 13
Hebei Chen Weigao CPC Secretary & PC Chairman 20000301 20030809 Dismissal from CPC 25 1 3 21
Hebei Cong Fukui Vice-Province Governor 20000627 20010518 Dismissal from the service and CPC 24 0 2 22
Hubei Li Daqiang Vice-Province Governor 20000925 20000925 Dismissal from the service and CPC 2 2 0 0
Hubei Meng QingPing
Vice-Province Governor 19980410 19991201 10 years of imprisonment 33 1 4 28
Jiangxi Hu Changqing Vice-Province Governor 19990808 20000215 Death penalty 12 0 2 10
Liaoning Mu TuoXing Vice-Province Governor 20010321 20011113 Death penalty 51 5 6 40
Xingjiang Aman.Haji Vice-Province Governor 20031015 N/A N/A (Still under investigation) 25 1 1 23
Yunnan Li Jiating Vice CPC Secretary & Province Governor 20010620 20030509 Death penalty 17 1 4 12
Zhejiang Xu Yunhong Vice-Province Governor 19990922 20001017 10 years of imprisonment 35 2 3 30
Total 393 43 42 308
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Figure 1.1 Mean Total Debt/Assets (The event firms and the non-event firms)
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Figure 2.1 Mean Long Term Debt/Total Debt
(The event firms and the non-event firms)
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Figure 3.1 Mean Long Term Debt/Assets(The event firms and the non-event
firms)
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Table 4 Fixed-Effect Regression: Event firms and non-event firms
Dependent Variables
Total Debt/Assets
(Total Debt+AP)/Assets
Long-term Debt/Total
Debt
Long-term Debt/(Total Debt+AP)
Long term Debt/Assets
Short-term Debt/Assets
AP/Assets (Short-term Debt+AP)/Assets
Intercept -0.442*** -0.544*** -0.785*** -0.734*** -0.432*** -0.026 -0.108* -0.082 (-2.46) (-2.87) (-2.70) (-2.99) (-4.48) (-0.18) (-1.84) (-0.49) Corrupt 0.015 0.016 0.042 0.019 0.003 0.013 0.001 0.014 (0.97) (0.97) (1.36) (0.70) (0.33) (1.14) (0.20) (1.01) Post 0.037 -0.283 -1.073*** -0.664*** -0.233* 0.376* -0.372*** -0.170 (0.13) (-0.88) (-3.01) (-2.26) (-1.85) (1.66) (-3.54) (-0.55) Corrupt*Post -0.082*** -0.093*** -0.043* -0.029* -0.034*** -0.053*** -0.005 -0.041 (-3.13) (-3.10) (-1.84) (-1.66) (-3.07) (-2.50) (-0.49) (-1.33) Lass 0.033*** 0.042*** 0.040*** 0.037*** 0.022*** 0.009 0.009*** 0.018*** (3.67) (4.48) (2.75) (3.02) (4.54) (1.22) (3.13) (2.23) Lass*Post 0.005 0.022 0.051*** 0.030*** 0.011* -0.011 0.020*** 0.019 (0.32) (1.40) (2.91) (2.11) (1.82) (-1.00) (3.83) (1.27) Tangible 0.029 0.028 0.529*** 0.442*** 0.121*** -0.089*** -0.057*** -0.147*** (0.75) (0.70) (6.04) (5.30) (4.26) (-3.16) (-3.49) (-4.22) Tangible*Post -0.019 -0.041 -0.050 -0.010 0.035 -0.116*** -0.027 -0.174*** (-0.30) (-0.59) (-0.52) (-0.12) (1.08) (-2.58) (-1.06) (-2.71) Sales Growth 0.003 0.001 0.004 0.008* 0.002 0.001 -0.002* -0.002 (0.36) (0.05) (0.85) (1.68) (0.77) (0.06) (-1.95) (-0.37) Sales Growth *Post
0.002 (0.22)
0.007 (0.90)
0.004 (0.88)
-0.002 (-0.34)
0.002 (0.75)
-0.001 (-0.25)
0.005*** (3.81)
0.003 (0.60)
Profit -0.791*** -0.870*** 0.166 -0.019 -0.117*** -0.658*** -0.083*** -0.741*** (-5.71) (-5.65) (0.93) (-0.12) (-2.21) (-4.97) (-2.82) (-5.39) Profit*Post 0.776*** 0.859*** -0.166 -0.015 0.112*** 0.650*** 0.086*** 0.737*** (5.05) (5.56) (-0.93) (-0.10) (2.11) (4.89) (2.91) (5.34) N 1933 1933 1917 1917 1933 1933 1933 1933 Adj R-squared
21.05% 24.13% 22.44% 22.07% 20.27% 19.41% 17.47% 17.38%
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Table 4 Fixed-Effect Regression: Event firms and matching firms
Dependent Variables
Total Debt/Assets
(Total Debt+AP)/Assets
Long-term Debt/Total
Debt
Long-term Debt/(Total Debt+AP)
Long term Debt/Assets
Short-term Debt/Assets
AP/Assets (Short-term Debt+AP)/Assets
Intercept -0.137 -0.262 -1.178*** -0.658*** -0.392*** 0.319 -0.117 0.202 (-0.63) (-1.07) (-3.02) (-2.35) (-4.42) (1.58) (-1.38) (0.89) Corrupt 0.028* 0.026 0.062* 0.050* 0.027*** 0.001 -0.002 -0.001 (1.75) (1.34) (1.83) (1.77) (3.61) (0.07) (-0.33) (-0.08) Post -0.270 -0.713 0.243 0.101 0.082 -0.280 -0.628*** -0.950** (-0.65) (-1.56) (0.40) (0.21) (0.47) (-0.85) (-2.62) (-2.10) Corrupt*Post -0.076*** -0.059 -0.119*** -0.115*** -0.053*** -0.002 -0.006 -0.020 (-2.17) (-1.64) (-2.50) (-2.86) (-4.05) (-0.06) (-0.28) (-0.36) Lass 0.019* 0.030*** 0.060*** 0.032*** 0.019*** -0.003 0.010*** 0.007 (1.80) (2.46) (3.11) (2.40) (4.63) (-0.30) (2.41) (0.62) Lass*Post 0.017 0.038* -0.014 -0.005 -0.004 0.018 0.032*** 0.055*** (0.84) (1.74) (-0.47) (-0.22) (-0.54) (1.10) (2.61) (2.45) Tangible -0.031 -0.095 0.452*** 0.430*** 0.083*** -0.129*** -0.065*** -0.194*** (-0.62) (-1.57) (3.96) (4.13) (2.85) (-3.22) (-2.69) (-3.56) Tangible*Post 0.070 0.067 0.102 0.031 0.094*** -0.104 -0.058 -0.290** (0.68) (0.56) (0.67) (0.23) (2.12) (-1.29) (-1.06) (-2.06) Sales Growth 0.051* 0.053* -0.031 -0.018 0.012 0.037 0.004 0.041 (1.83) (1.74) (-0.54) (-1.40) (0.74) (1.63) (0.34) (1.53) Sales Growth *Post
0.049 (0.62)
0.054 (0.75)
0.131* (1.68)
0.108* (1.68)
0.029 (1.18)
-0.049 (-0.97)
0.082 (1.36)
0.089 (0.83)
Profit -1.596*** -1.748*** 0.805*** 0.452** -0.081 -1.514*** -0.157*** -1.671*** (-9.30) (-8.00) (2.94) (2.01) (-1.43) (-8.99) (-2.20) (-7.75) Profit*Post 1.279*** 1.616*** -0.959*** -0.717** -0.036 1.287*** 0.474*** 2.029*** (4.55) (4.84) (-2.39) (-2.03) (-0.34) (4.94) (3.90) (4.98) N 792 792 792 792 792 792 792 792 Adj R-squared
21.79% 23.09% 15.18% 14.33% 15.73% 25.38% 12.74% 19.43%
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Table 7 Regressions of Long-term Changes in Performance on Changes in Financial Policy around
the Corruption Scandals (Panel A Full Sample)
Change in ROS Change in Market-to-Book ratio Intercept 0.036 0.032 0.029 -0.123*** -0.109 0.163 0.038 0.393*** (0.68) (0.68) (0.55) (-3.08) (-0.79) (1.20) (0.26) (2.17) Intercept 0.080 1.650*** (1.20) (9.44) Change in total leverage 0.394*** 3.595*** (2.20) (7.08) Change in long-term debt 0.129 1.741*** (1.44) (6.89) Change in short-term debt 0.074 1.108*** (1.20) (3.93) Change in debt maturity -0.124*** -0.113*** -0.108* -0.034 -0.054 -0.062 -0.108 -0.202 (-2.12) (-2.36) (-1.86) (-0.74) (-0.35) (-0.46) (-0.66) (-0.97) Corrupt 0.049 0.505 (0.30) (1.15) Corrupt* Change in total leverage
0.398 (0.86)
1.200 (0.91)
Corrupt* Change in long-term debt
-0.042 (-0.20)
0.417 (0.71)
Corrupt* Change in short-term debt
0.268*** (2.18)
0.608 (1.09)
N 334 334 334 334 334 334 334 334 Adj R-squared 4.54% 6.34% 4.56% 5.51% 29.70% 19.83% 19.47% 12.89%
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Does rent seeking facilitate capital allocation?
Examining efficiency (performance) around the corruption scandals
The endogeneity hypothesis The event firms should outperform the non-event firms
before the scandals, and should not underperform the non-event firms after the scandals
The social injustice hypothesis Event firms should underperform non-event firms after
the scandals The randomness hypothesis
No difference in performance between event and non-even firms. Non-even firms outperform event firms after the scandals
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Table 8 Mean and Median Differences in Performance between the Event Firms and the Non-
event Firms before and after the Corruption Scandals (Panel A Differences Between the Event
Firms and the Non-event Firms)
Three years before the scandal Three years after the scandal
Mean Median Mean Median ROA 0.012
(1.06) 0.042 (1.08)
-0.021*** (-2.39)
-0.007* (-1.95)
ROE 0.024 (0.98)
0.293 (1.02)
-0.041*** (-2.26)
-0.018* (-1.74)
Operating Margin 0.042 (1.27)
0.216 (1.15)
-0.091*** (-3.06)
-0.037*** (-2.44)
ROS 0.049 (1.36)
0.168 (1.09)
-0.077*** (-2.66)
-0.014 (-1.55)
Market-to-Book 0.244*** (2.88)
0.230*** (2.43)
0.046 (0.34)
0.140 (0.16)
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Summary of findings An overall increasing trend of financial leverage, but
decreasing trend of debt maturity Significant lowered leverage, debt maturity, and the use
of long-term debt of the bribing firms and the connected firms, relative to control (unconnected or matching) firms
The results hold even if we focus on the connected firms that are not involved in the corruption cases
The weakened debt financing ability in the sample is not just due to the corruption, but also due to loss of political connections
Change in stock value and change in leverage are significantly positively related around the corruption events
We find little evidence from the sample suggesting that rent seeking facilitates capital allocation in China.
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Conclusions
Corporate financing policies are importantly affected by rent seeking and corruption activities
Connections with government bureaucrats provide firm financing advantages, in particular access to long-term bank debt
Compared with the prior studies, this paper provides more direct links between rent seeking and corporate finance, and empirical design less subject to endogeneity issues
The overall evidence is consistent with recent cross-country studies’ findings that country-level institutional factors matter to corporate financing decisions