1. 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

2. 2 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

3. 3 Cross country pattern of corporate leverage (Fan, Titman, Twite, 2006)

4. 4 Cross country pattern of corporate debt maturity (Fan, Titman, Twite, 2006)

5. 5 What explains the cross country corporate financing patterns? R 2 LeverageDebt maturity Country+Industry+Firm0.320.23 Industry+Firm0.240.09 Firm0.210.07  Where a firm is located has a greater effect on its capital structure and debt maturity than its industry affiliation.  Country factors and firm factors are both important

6. 6 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?

7. 7 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） level  2422 intermediate （厅, 局） level  98 provincial （省, 部） level or above

8. 8 Corruption in Asian Economies (Source: Transparency International: mean Corruption Perception Index 1992-2000)

9. 9 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

10. 10 Debt-Equity Mix in China Only 10 to 20% of the total fund raised from equity markets. Equity financing Debt financing

11. 11 International Comparison of Stock Market Capitalization (end of 2003)

12. 12 Importance: Market Cap./GDP MarketMC/GDP Hong Kong449% Malaysia166% Singapore166% UK137% New York+ NASDAQ130% Japan70% South Korea57% Germany45% China(Gross MC)36% China(Floating MC)11%

13. 13 Long-term loans of Chinese listed companies in 2000 Bank Average Loan Size (RMB) Average Maturity (years) No. of loans All35,501,9124.152881 Big-four commercial banks 31,053,0013.882279 Other commercial banks 37,331,9443.33123 往来单位31,151,7584.72331 Policy banks106,647,6056.3498 Foreign gov’t154,237,09316.1729 Foreign banks23,476,1018.4221

14. 14 Long-term loans of Chinese listed companies - Average Size and Maturity in 2000

15. 15 Long-term loans of Chinese SOEs and Private companies in 2000 Companies Average Loan Size (RMB) Average Maturity (years) No. of loans No. of firms All35,501,9124.152881588 State- owned 36,598,4324.192584514 Private27,639,7033.7629774

16. 16 Long-term loans of Chinese SOE and Private companies - Average Size and Maturity in 2000

17. 17 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

18. 18 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

19. 19 A Corrupted Bureaucrat and His Allies

20. 20 The Corruption List

21. 21 The sample grouping  The event firms  43 bribing firms  Firms that have engaged in bribing the bureaucrats in the corruption cases  42 connected firms  Connected firms are those whose senior managers, directors, or large shareholders have prior job affiliation or family relationship with the corrupted bureaucrats  Benchmark firms  308 unconnected (non-event) firms  Firms that are neither bribers nor connected with the corrupted bureaucrats, but are located in the bureaucrats ’ jurisdictions  Matching firms  Firms with similar size with the event firms, geographically located outside the corrupted bureaucrats ’ jurisdictions  Three matching firms and one matching firm

22. 22 Data sources  Corruption cases  Excerpts of Discipline Cases of the Communist Party of China (1921-2001)  Villains of the Communist Party of China (2002-2003)  Public disclosures by the Central Commission for Discipline Inspection of the Communist Party of China  Connections with publicly traded companies  Corporate prospectuses, annual reports  Financial and stock return data  China Stock Market and Accounting Research (CSMAR) Database

23. 23 Measures of firm leverage and maturity  Key variables  Debt over assets  Long-term debt over total debt  Long-term debt over assets  Short-term debt over assets  Robust checks  Including trade credit (account payable) as a potential financing source  Sales as an alternative scaling factor  Contemporaneous versus lagged scaling factors

24. 24 Figure 1.1 Mean Total Debt/Assets (The event firms and the non-event firms)

25. 25 Figure 1.2 Mean Total Debt/Assets (The connected firms and the non-event firms)

26. 26 Figure 2.1 Mean Long Term Debt/Total Debt (The event firms and the non-event firms)

27. 27 Figure 2.2 Mean Long Term Debt/Total Debt (The connected firms and the non-event firms)

28. 28 Figure 3.1 Mean Long Term Debt/Assets (The event firms and the non-event firms)

29. 29 Figure 3.2 Mean Long Term Debt/Assets (The connected firms and the non-event firms)

30. 30 Table 2 Financing Policies and Other Firm Characteristics around the Corruption Events (Mean)

31. 31 Table 2 Financing Policies and Other Firm Characteristics around the Corruption Events (Median)

32. 32 Table 3 (Panel A) Differences in the Change in Financing Variables around the Corruption Events between the Event Firms and Control Firms

33. 33 Table 3 (Panel B) Differences in the Change in Financing Variables around the Corruption Events between the Event Firms and Control Firms

34. 34 Table 4 Fixed-Effect Regression: Event firms and non-event firms

35. 35 Table 4 Fixed-Effect Regression: Event firms and matching firms

36. 36 Table 5 Fixed-effect Regression: Connected Firms and Non-event firms

37. 37 Table 5 Fixed-effect Regression: Connected Firms and matching firms

38. 38 Event Study  Cumulative abnormal return (CAR)  Cumulating daily abnormal return (AR) over various event windows ranging from (-60, +60)  AR estimated from the market model, using value weighted market index  The event day (day 0) is the day of initial public disclosure of corruption

39. 39 Figure 4 Mean Cumulative Abnormal Returns around Corruption Events

40. 40 Table 6 Changes in Leverage and Stock Market Reactions around the Corruption Events

41. 41 Table 7 Regressions of Long-term Changes in Performance on Changes in Financial Policy around the Corruption Scandals (Panel A Full Sample)

42. 42 Table 7 Regressions of Long-term Changes in Performance on Changes in Financial Policy around the Corruption Scandals ( Panel B Sub-sample Excluding the Bribing Firms)

43. 43 Robustness Check  Redefine event firm  Different degree of punishment  Survival of the firms  Different event windows  Different scaling factors  Bribing firms and connected firms  Different time period  Different regions

44. 44 So what?  Bribe paying and connected firms get better capital access before the scandal,  Disfavored firms get worse capital access  The connected get financing Good to have the evidence; But does it imply distorted capital allocation?  Not so soon

45. 45 Unclear whether the bribe paying and connected are better or worse firms?  Three arguments Endogeneity argument  Given a non-transparent capital allocation system, more capable firms can afford the bribes, are connected  Randomness argument  Given a non-transparent capital allocation system, everyone pays bribe, whoever caught is a random event  Purely social injustice argument  Less capable gets ahead via immoral practices

46. 46 Implications Getting rid of corruption is always good in the long run, but, Endogeneity argument  In the short run, exposing scandals punish good firms  Randomness argument  Exposing scandals = expected happenstance with no implications on firm performance  Purely social injustice argument  Exposing scandals is good in the short and long run, help good firms

47. 47 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

48. 48 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)

49. 49 Table 8 Mean and Median Differences in Performance between the Event Firms and the Non-event Firms before and after the Corruption Scandals (Panel B Differences Between the Connected Firms and the Non-event Firms)

50. 50 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.

51. 51 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