Zombie lending, financial reporting opacity and contagion

ZOMBIE LENDING, FINANCIAL REPORTING OPACITY AND CONTAGION YUPENG LIN (Bachelor of Economics (Hons.), Sun Yat-sen University) A THESIS SUBMITTED FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF FINANCE NATIONAL UNIVERSITY OF SINGAPORE 2014 Declaration I hereby declare that the thesis is my original work and it has been written by me in its entirety. I have duly acknowledged all the sources of information which have been used in the thesis. This thesis has also not been submitted for any degree in any university previously. _____________ Lin Yupeng 23rd May 2014 I Acknowledgements It is my pleasure to express the deepest appreciation to those who has helped me with this thesis. I would like to thank my parents and wife for their unconditional love and supports throughout the whole PhD journey. I owe sincere gratitude to my most respected supervisors, Anand Srinivasan and Oliver Zhen Li for their patience, encouragement and illuminating guidance. Through the period of the writing of this thesis, they have spent much time on each of my drafts and offered me many valuable suggestions. I am grateful to other two members of my dissertation committee: Sumit Agarwal and Wang Qi for generously sharing me with their knowledge and time. Without their help, this thesis could not have been completed. I would also like to thank Dan Dhaliwal, Shivaram Rajgopal, David Hirshleifer, Jeong Bon Kim, Zhaoyan Gu, Woody Wu, Cheng Qiang, Zhang Huai, Bin Ke, Hugh H. Kim (discussant); seminar participants at the National University of Singapore, City University of Hong Kong, Chinese University of Hong Kong, Hitotsubashi University, Singapore Management University, Nanyang Technology University, conference participants at the 2013 FMA Annual Meeting. All errors are mine. II Table of Contents Summary . IV List of Tables . V Chapter 1. Introduction Chapter 2. Institutional background . Chapter 3. Hypothesis development . 11 3.1. Zombie lending, earnings manipulation, and the evolution of reporting opacity . 11 3.2. Government intervention and evergreen lending to zombie firms 12 3.3. Contagion of reporting opacity 14 3.4. Bank capital adequacy and bank lending . 16 Chapter 4. Data and variable definitions . 19 4.1 Data collection 19 4.2. Variable definition 19 4.2.1. Zombie firm 19 4.2.2 Earning manipulation . 20 4.2.3. Financial reporting opacity . 22 Chapter 5. Empirical results . 24 5.1. Summary statistics 24 5.2. Multivariate results . 25 5.2.1. Zombie lending and earnings manipulation. . 25 5.2.2 Zombie lending and the evolution of financial reporting opacity . 28 5.2.3. Zombie lending, earnings manipulations and political motivations. . 30 5.2.4. Endogeneity: Selection model 31 5.2.5. Endogeneity: Capital injection, zombie, and financial reporting opacity . 32 5.2.6 Alternative measures of reporting opacity 34 5.2.7 Alternative measures of zombie 35 5.3. Financial reporting opacity and contagion effect 36 5.3.1. Cost for non-zombie firms to provide high-quality financial statement: Contagion effect and product market structure . 37 5.3.2. Benefits for non-zombie firms of providing a high-quality financial statement: Contagion effect and hidden losses in banks. 38 5.4. Under-capitalized banks and zombie lending . 40 Chapter 6. Conclusion . 43 References . 45 Appendix A: Definitions of variables . 50 Appendix B: The validity of modified Jones’ model using Japanese data. 52 III Summary Using a novel dataset of listed firms in Japan, we find that bank lending to zombie (insolvent) borrowers induces these borrowers to manipulate earnings, resulting in more opaque financial reporting. Such an effect is more pronounced when the lending is from borrowers‘ main banks or for longer term loans, suggesting a complicity of informed banks in earnings manipulations. Further evidence shows a stronger nexus between zombie lending and earnings manipulation during election years, consistent with the political motivation argument. We overcome the endogeneity concern using a natural experiment arising from capital injections into banks instituted by the Japanese Government in the late 90‘s and find a consistent result. Further, we examine the industry spillover (contagion) effect of such accounting manipulation and find that profitable firms adopt more opaque reporting when the industry is dominated by zombie firms. Finally, we find that banks with greater incentives to inflate capital ratios lend more to zombie firms. Overall, our results suggest that keeping insolvent borrowers afloat deteriorates the information environment of both zombie firms and their profitable industry peers. IV List of Tables Table 1: Summary statistics . 54 Table 2: New lending, zombie firms and earnings manipulations. 55 Table 3: The zombie lending and reporting opacity 57 Table 4: Zombie lending, earnings manipulations and political motivations. 59 Table 5: The zombie lending and reporting opacity: Treatment approach 60 Table 6: The capital injection 63 Table 7: Alternative measures of reporting opacity 65 Table 8: The contagion effect . 67 Table 9: The contagion effect and product market structure . 68 Table 10: The contagion effect and hidden losses in banking system 70 Table 11: Zombie density, hidden losses and lax screening of banks . 71 Table 12: Under-capitalized bank and zombie lending . 72 V List of Figures Figure 1: Prevalence of zombie firms in Japan 74 Figure 2: Industrial zombie density and employment growth 74 Figure 3: Industrial zombie density and capital investment . 75 Figure 4: Industrial zombie density and industrial ROA . 75 Figure 5: Zombie lending and abnormal total accrual 76 VI Chapter 1. Introduction Banks exert substantial influence on the capital allocation and risk sharing of client firms (Gerschenkron, 1962; Hellwig, 1991). By reallocating resources from less productive firms to more productive firms, banks can effectively contribute to the growth of productivity in an economy (Merton, 1993; Caballero and Hammour, 1994, 1999). Nevertheless, empirical evidence from many studies shows that banks may choose to undertake evergreen lending, lending to fundamentally insolvent borrowers, to avoid regulatory scrutiny and hope that the non-performing borrowers can turn around and repay the loans (Boot, Greenbaum, and Thakor, 1993; Dewatripont and Maskin, 1995; Caballero, Hoshi, and Kashyap, 2008; Hoshi and Kashyap, 2010). Such evergreen lending was particularly significant in Japan during the 1990s. In the Japanese context, Caballero, Hoshi, and Kashyap (2008) coined the term ―zombie lending‖ to denote that such evergreen lending maintains firms that should be liquidated as going concerns. These firms are essentially dead but are kept alive with support from banks. While the extant literature focuses on the economy-wide real effects of zombie lending (Caballero and Hammour, 1994, 1999; Caballero, Hoshi, and Kashyap, 2008), few studies have looked at the impact of such lending on the accounting environment (Akerlof and Romer, 1993). Our paper examines this important but unstudied issue. The backdrop for zombie lending in Japan was the stagnating economy of the 1990s and a crash in the real estate market. The Japanese government strongly encouraged banks to support troubled and potentially insolvent firms to keep the unemployment rate low (Tett and Ibison, 2001; Tett, 2003). Thus, banks were essentially forced to comply with governmental policies in this regard. At the same time, Japanese banks also had to comply with the international standards governing minimum levels of capital (the Basel capital standards).1 How can a bank lend to insolvent borrowers and at the same time maintain sufficient capital consistent with the Basel accord? We argue that an inflation in earnings by zombie firms facilitated banks in zombie lending, serving both the government‘s goal of increasing employment and the bank‘s capital requirements. Specifically, by manipulating its financial statements, a zombie firm can make it easier for a bank to classify it as solvent even though it is actually insolvent. We find strong evidence consistent with this notion; zombie firms have greater abnormal accruals than non-zombie firms during the year they obtain new bank loans. We further use loan maturity as a proxy for relationship lending and find that longer term loans lead to more earnings inflation than shorter terms loans. Since long-term loans are typically made by relationship lenders, it appears unlikely that the bank is unaware of the accrual manipulations, especially given the long duration of the relationship between the main banks and borrowing firms in Japan (approximately 32 years, as documented in Uchida, Udell, and Watanabe, 2008). Next, we examine the impact of political motivations on the nexus between zombie lending and earnings manipulations. We find that the earnings manipulations due to zombie lending are more pronounced in election years, consistent with the notion that the observed effect on earnings manipulations is caused by purchasing political benefits. The guideline published by Basle Committee in 1988 highlights the requirements on the core capital ratios (Part III, page 14), general provisions (Part II, page 5) and the implementation arrangement (Part IV, page 15). The details refer to http://www.bis.org/publ/bcbs04a.pdf. It is stated that ―Committee expects there to be no erosion of existing capital standards in individual member countries' banks‖. Caballero, Hoshi, and Kashyap (2008) imply that the restructuring of loans to distressed firms can help reduce the required capital needed by banks. For example, in Japan, without such restructuring, banks would be forced to classify the loans to those borrowers as ―at risk,‖ which usually would require banks to set aside 70% of the loan value as loan loss reserves. With restructuring, banks need only move the loans to the ―special attention‖ category, which require reserves of at most 15%. A second important issue that we examine is industry-level spillover effects. In particular, when zombie firms change their accounting opacity, non-zombie firms also change their accounting opacity. On the one hand, non-zombie firms can decrease their opacity so as to signal their better quality than zombie firms. On the other hand, providing high-quality financial statements can result in substantial costs to non-zombie firms. Thus, the quality of financial reporting that non-zombie firms provide is determined by the change in the trade-off between the cost and benefit of high-quality reporting in the presence of zombie rivals. First, the cost for non-zombie firms to provide high-quality financial statements increases when the industry is dominated by zombie firms. In particular, precise financial information can facilitate competitors‘ investment and thus is costly when the product market competition is severe (Gigler, 1994; Bushman and Smith, 2001; Bagnoli and Watts, 2010; Corona and Nan, 2011). From a non-zombie firm‘s perspective, high zombie density suggests that more rivals can sell the same product at a lower price and thus a non-zombie firm is placed in an unfavorable position (Caballero, Hoshi, and Kashyap, 2008). As a result, non-zombie firms can strategically choose to bias their accounting numbers so as to misguide zombie firms and protect their market share.3 We find that profitable firms are associated with a higher level of reporting opacity when their industry is dominated by zombie firms. This contagion effect, specifically, the transmission of poor reporting quality from some firms to others in the same industry, is more pronounced in concentrated industries and industries with fewer growth opportunities. This is consistent with the contagion effect of opacity due to oligopolistic competition (Bagnoli and Watts, 2010). The banks‘ subsidization of zombie firms places non-zombie firms in an unfavorable competitive position and crowds out effective investment by non-zombie firms (Caballero, Hoshi, and Kashyap, 2008). Panel B: Second step Variables ZOMBIE ZOMBIE× LN (NEW CREDIT) LN (NEW CREDIT) Abnormal Total Accrual -0.005** 0.003*** -0.008** (0.002) (0.001) (0.003) -0.008*** (0.003) 0.003** (0.001) 0.002*** (0.000) ZOMBIE× NEW CREDIT(MAINBANK) 0.011** (0.004) 0.013*** (0.001) NEW CREDIT(MAINBANK) ZOMBIE×NEWCREDIT(OTHERBANK) (0.003) -0.002** (0.001) (0.003) NEW CREDIT(OTHERBANK) ZOMBIE×NEW CREDIT(LONG) 0.019** (0.008) 0.011*** (0.001) NEW CREDIT(LONG) ZOMBIE× NEW CREDIT(SHORT) NEW CREDIT(SHORT) Hazard Lambda -0.007*** (0.002) -0.015 (0.018) -0.014 (0.023) -0.012 (0.013) -0.017 (0.020) 0.010*** (0.003) 0.017*** (0.001) -0.016 (0.023) Panel C: Second step Variables ZOMBIE ZOMBIE× LN (NEW CREDIT) LN (NEW CREDIT) Abnormal Working Capital Accrual 0.002 0.002 0.000 (0.002) (0.002) (0.002) 0.000 (0.002) 0.001** (0.000) 0.000*** (0.000) ZOMBIE× NEW CREDIT(MAINBANK) 0.002 (0.004) 0.004*** (0.001) NEW CREDIT(MAINBANK) ZOMBIE×NEWCREDIT(OTHERBANK) 0.002 (0.002) 0.000 (0.001) NEW CREDIT(OTHERBANK) ZOMBIE×NEW CREDIT(LONG) 0.007** (0.003) 0.003*** (0.001) NEW CREDIT(LONG) ZOMBIE× NEW CREDIT(SHORT) NEW CREDIT(SHORT) Hazard Lambda 0.001 (0.002) 0.004 (0.019) 0.005 (0.019) 61 0.004 (0.019) 0.004 (0.019) 0.002 (0.003) 0.006*** (0.001) 0.004 (0.020) Panel D: Second step Variables ZOMBIE ZOMBIE× LN (NEW CREDIT) LN (NEW CREDIT) 0.002* (0.001) 0.001*** (0.000) -0.000** (0.000) 0.003** (0.001) ZOMBIE× NEW CREDIT(MAINBANK) Reporting opacity 0.003*** 0.002* (0.001) (0.001) 0.004* (0.002) -0.001 (0.001) NEW CREDIT(MAINBANK) ZOMBIE×NEWCREDIT(OTHERBANK) 0.001 (0.003) -0.002** (0.001) NEW CREDIT(OTHERBANK) ZOMBIE×NEW CREDIT(LONG) 0.004** (0.002) -0.002*** (0.001) NEW CREDIT(LONG) ZOMBIE× NEW CREDIT(SHORT) NEW CREDIT(SHORT) Hazard Lambda 0.004*** (0.001) -0.011 (0.013) -0.013 (0.014) Panel E: Second step Variables ZOMBIE -0.012 (0.013) OPACITY 0.023*** (0.004) EMPLOYEE_ASSET SIZE -0.002*** (0.000) -0.003*** (0.000) 0.009*** (0.001) 0.003** (0.001) -0.014*** (0.003) 0.024*** (0.006) 0.060*** (0.002) -0.010*** (0.002) YES 20674 TURN GROWTH BOOKLEV CFO ROA CONSTANT Hazard Lambda Industry Effects Observations 62 -0.012 (0.013) -0.001 (0.001) -0.001 (0.000) -0.012 (0.013) Table 6: The capital injection This table reports the results of how capital injection affects the reporting quality of zombie firms. The dependent variable is defined as the average of absolute abnormal accruals during 1998 to 2000 minus the average of absolute abnormal accruals during 1995 to 1997. Similarly, the independent variables are defined as the change in mean value during 1998 to 2000 relative to the mean value during 1995 to 1997. The sample period is from 1995 to 2000. The capital injection exposure is defined as a firm's borrowing from recapitalized banks divided by the total borrowing. Large capital injection exposure equals to when the capital injection exposure is larger than the sample median value and otherwise. The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. Panel A Variables ZOMBIE ZOMBIE × LARGE INJECTION EXPOSURE ∆OPACITY -0.010*** (0.004) ∆OPACITY -0.009** (0.004) 0.012*** (0.004) 0.011** (0.005) ∆TURN ∆GROWTH ∆BOOKLEV ∆CFO ∆ROA CONSTANT Industry Effects Observations Adj R-squared 0.018** (0.008) 0.017** (0.008) -0.003 (0.002) -0.020*** (0.006) -0.012* (0.006) 0.004 (0.007) 0.011 (0.012) 0.044** (0.017) 0.033 (0.036) 0.003* (0.002) NO 1589 0.013 -0.003 (0.003) -0.023*** (0.006) -0.011* (0.006) 0.004 (0.007) 0.011 (0.012) 0.049*** (0.018) 0.031 (0.037) 0.003* (0.002) Yes 1589 0.022 INJECTION -0.003** (0.002) -0.004** (0.002) CAPITAL INJECTION EXPOSURE ∆SIZE ∆OPACITY -0.013** (0.006) CAPITAL ZOMBIE × CAPITAL INJECTION EXPOSURE LARGE CAPITAL EXPOSURE ∆OPACITY -0.014** (0.006) -0.021*** (0.006) -0.012* (0.006) 0.004 (0.007) 0.012 (0.012) 0.045** (0.017) 0.031 (0.036) 0.003*** (0.001) NO 1589 0.015 63 -0.023*** (0.006) -0.011* (0.006) 0.004 (0.007) 0.012 (0.012) 0.049*** (0.018) 0.029 (0.037) 0.003*** (0.001) Yes 1589 0.025 Panel B Variables ZOMBIE ∆OPACITY -0.019** (0.009) ZOMBIE × LARGE CAPITAL INJECTION EXPOSURE 0.011** (0.005) ZOMBIE × CAPITAL INJECTION EXPOSURE LARGE CAPITAL INJECTION EXPOSURE ∆OPACITY -0.021** (0.010) 0.017* (0.009) -0.005** (0.002) CAPITAL INJECTION EXPOSURE ZOMBIE × BANK EXPOSURE TO ZOMBIE 0.055 (0.053) 0.018 (0.014) -0.022*** (0.006) -0.011 (0.006) 0.004 (0.007) 0.010 (0.012) 0.048*** (0.018) 0.027 (0.037) 0.001 (0.002) Yes 1589 0.026 BANK EXPOSURE TO ZOMBIE ∆SIZE ∆TURN ∆GROWTH ∆BOOKLEV ∆CFO ∆ROA CONSTANT Industry Effects Observations Adj R-squared 64 -0.005 (0.004) 0.046 (0.055) 0.020 (0.018) -0.022*** (0.006) -0.010 (0.007) 0.004 (0.007) 0.010 (0.012) 0.049*** (0.018) 0.028 (0.037) 0.002 (0.002) Yes 1589 0.023 Table 7: Alternative measures of reporting opacity The dependent variable in the first column is the stock return. The dependent variable in second column is the net income in year t scaled by total market value in year t-1. Other control variables are specified in equations (12) and (13). The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. (1) RET ROA 1.531*** (0.155) ROA ×ZOMBIE 0.427 (0.399) ROA ×ZOMBIE× NEW CREDIT -1.175* (0.592) ZOMBIE×NEW CREDIT 0.015 (0.023) ROA ×NEW CREDIT -0.017 (0.130) ZOMBIE -0.015 (0.013) NEW CREDIT -0.008 (0.007) (2) XI -0.004 (0.015) RET -0.010 (0.034) D -0.036 (0.035) RET× D -0.192 (0.162) RET ×ZOMBIE 0.018 (0.020) D ×ZOMBIE 0.002 (0.018) RET × D ×ZOMBIE 0.047 (0.039) RET ×NEW CREDIT -0.006 (0.005) D ×NEW CREDIT -0.024** (0.011) RET × D ×NEW CREDIT -0.082* (0.046) RET × D ×NEW CREDIT × ZOMBIE -0.083* (0.044) RET ×NEW CREDIT ×ZOMBIE -0.025 (0.032) D × NEW CREDIT × ZOMBIE 0.005 (0.019) 65 CONTROLS YES YES Industry × Year Effects Industry YES NO NO YES Year Effects Observations NO YES 13806 0.534 13695 0.061 Adj R-squared 66 Table 8: The contagion effect This table reports the contagion effect resulting from a high zombie density. The dependent variable is reporting opacity. Zombie desity1 is defined as the percentage of zombie in the industry in a given year. Zombie desity2 is defined as the capital weighted percentage of zombie in the industry in a given year. The sample period is from 1990 to 2000. In Panel B, we quantify the economic significance of contagion effect for zombie-dominated industries. The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. Panel A Variables NON-ZOMBIE (1) -0.005*** (0.001) 0.013** (0.005) NON-ZOMBIE × ZOMBIE DENSITY1 (2) -0.004*** (0.001) NON-ZOMBIE × ZOMBIE DENSITY2 SIZE 0.011** (0.005) -0.002*** (0.000) -0.004*** (0.000) 0.006*** (0.001) 0.005*** (0.001) -0.009*** (0.003) 0.013** (0.006) 0.069*** (0.002) Yes 20674 0.096 -0.002*** (0.000) -0.004*** (0.000) 0.006*** (0.001) 0.005*** (0.001) -0.009*** (0.003) 0.013** (0.006) 0.069*** (0.002) Yes 20674 0.096 TURN GROWTH BOOKLEV CFO ROA CONSTANT Industry × Year Effects Observations Adj R-squared Panel B Wholesale Construction Real estate Service (Raw 13% 12% 16% 18% The increase in reporting opacity : Comparing to the case of zero zombie density (∆%) 4% 4% 5% 6% Average zombie percentage) density 67 Table 9: The contagion effect and product market structure The dependent variable is reporting opacity. Zombie desity1 is defined as the percentage of zombie in the industry in a given year. Zombie desity2 is defined as the capital weighted percentage of zombie in the industry in a given year. Concentrated industry is defined as if the HHI index is large than the sample median, and otherwise. The sample period is from 1990 to 2000. The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. Panel A: Product Market Concentration Variables NON-ZOMBIE -0.002 (0.002) NON-ZOMBIE × ZOMBIE DENSITY1 0.005 (0.015) NON-ZOMBIE × ZOMBIE DENSITY2 SIZE TURN GROWTH BOOKLEV CFO ROA CONSTANT Industry × Year Effects Observations Adj R-squared -0.002*** (0.000) -0.004* (0.002) 0.007*** (0.002) -0.002 (0.005) -0.013*** (0.004) 0.015 (0.017) 0.070*** (0.006) Yes 11685 0.057 68 -0.002** (0.001) Concentrated Industry -0.009*** -0.008*** (0.002) (0.002) 0.020*** (0.005) 0.014 (0.008) -0.002*** (0.000) -0.004* (0.002) 0.007*** (0.002) -0.002 (0.005) -0.013*** (0.003) 0.015 (0.017) 0.070*** (0.006) Yes 11685 0.057 -0.002*** (0.000) -0.004*** (0.001) 0.005*** (0.001) 0.012*** (0.004) -0.005 (0.012) 0.010 (0.019) 0.070*** (0.006) Yes 8989 0.147 0.015*** (0.004) -0.002*** (0.000) -0.004*** (0.001) 0.005*** (0.001) 0.012*** (0.004) -0.005 (0.012) 0.010 (0.019) 0.069*** (0.006) Yes 8989 0.147 Panel B: Industrial Sales Growth. Variables NON-ZOMBIE NON-ZOMBIE DENSITY1 × NON-ZOMBIE DENSITY2 × SIZE TURN GROWTH BOOKLEV CFO ROA CONSTANT Industry × Year Effects Observations Adj R-squared Low Growth -0.008*** -0.006*** (0.001) (0.001) High Growth -0.002 -0.003** (0.002) (0.001) 0.029*** (0.008) -0.006 (0.008) ZOMBIE ZOMBIE -0.002*** (0.000) -0.005*** (0.001) 0.004** (0.002) 0.005 (0.004) -0.001 (0.007) 0.016 (0.015) 0.069*** (0.004) Yes 12225 0.087 69 0.026*** (0.005) -0.002*** (0.000) -0.005*** (0.001) 0.004** (0.002) 0.005 (0.004) -0.001 (0.007) 0.016 (0.015) 0.068*** (0.004) Yes 12225 0.087 -0.002*** (0.000) -0.002 (0.001) 0.007*** (0.002) 0.005 (0.006) -0.020** (0.009) 0.010 (0.017) 0.070*** (0.005) Yes 8449 0.104 -0.003 (0.004) -0.002*** (0.000) -0.002 (0.001) 0.007*** (0.002) 0.005 (0.005) -0.020** (0.009) 0.010 (0.017) 0.070*** (0.005) Yes 8449 0.104 Table 10: The contagion effect and hidden losses in banking system The dependent variable is reporting opacity. Zombie desity1 is defined as the percentage of zombie in the industry in a given year. Zombie density2 is defined as the capital weighted percentage of zombie in the industry in a given year. The sample period is from 1990 to 2000. The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. Small exposure to hidden losses Variables NON-ZOMBIE -0.000 (0.002) NON-ZOMBIE DENSITY1 × NON-ZOMBIE DENSITY2 × -0.001 (0.002) ZOMBIE -0.009 (0.016) SIZE TURN GROWTH BOOKLEV CFO ROA CONSTANT Industry × Year Effects Observations Adj R-squared Large exposure to hidden losses -0.006*** -0.005*** (0.001) (0.001) 0.016** (0.007) ZOMBIE -0.002*** (0.000) -0.004** (0.002) 0.004** (0.002) 0.005 (0.004) -0.009 (0.008) 0.036* (0.018) -0.002*** (0.000) Yes 8296 0.086 70 -0.005 (0.013) -0.002*** (0.000) -0.004** (0.002) 0.004** (0.002) 0.005 (0.004) -0.009 (0.008) 0.036* (0.018) -0.002*** (0.000) Yes 8296 0.086 -0.003*** (0.001) -0.004** (0.002) 0.006*** (0.001) 0.008 (0.005) -0.009 (0.009) -0.004 (0.022) -0.003*** (0.001) Yes 8285 0.113 0.014*** (0.004) -0.003*** (0.001) -0.004** (0.002) 0.006*** (0.001) 0.007 (0.005) -0.009 (0.009) -0.004 (0.022) -0.003*** (0.001) Yes 8285 0.113 Table 11: Zombie density, hidden losses and lax screening of banks This table reports the average cost of debt for non-zombie. The dependent variable is the average cost of debt, which is defined as the interest expenditure divided by total debt. High zombie industry is an industry with higher than median level of capital weighted zombie density in a given year. The sample period is from 1990 to 2000. The standard errors are clustered by two-digit industry code, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions. Variables NON-ZOMBIE NON-ZOMBIE × OPACITY OPACITY SIZE GROWTH BOOKLEV TURN ZSCORE CONSTANT Firm effect Observations Adj R-squared Low zombie industry 0.004 (0.004) 0.207* (0.110) -0.196 (0.123) -0.023** (0.010) 0.014*** (0.004) -0.007 (0.016) 0.006 (0.009) 0.008*** (0.001) 0.285** (0.117) Yes 7354 0.415 71 High zombie industry 0.008* (0.004) -0.000 (0.057) 0.044 (0.058) -0.005 (0.013) 0.014*** (0.005) -0.037* (0.019) 0.009 (0.010) 0.006 (0.004) 0.087 (0.154) Yes 5337 0.501 Table 12: Under-capitalized bank and zombie lending Panel A provides the summary statistics of bank characteristics. Panel B reports the association between banks' capital inadequacy and zombie lending. The definition of zombie follows Caballero et.al (2008). The dependent variable in Panel B is the fraction of zombie lending in total lending of a bank. The sample period is from 1990 to 2000. We report in parentheses t-statistics based on standard errors that are clustered by bank, and are robust to heteroskedasticity. ***. **. * indicate significance levels at 0.01, 0.05 and 0.10 using two-tail tests, respectively. See Appendix for all variable definitions Panel A: Bank Characteristics Variables OBS ZOMBIE LENDING 818 ROA 905 SIZE 905 LOAN TO ASSET 905 INTEREST TO ASSET 905 EQUITY TO LIABILITY 905 UNDER CAPITALIZED(after manipulation) 905 UNDER CAPITALIZED(before manipulation) 905 ZOMBIE LENDING 818 MEAN 0.275 -0.002 7.994 0.707 1.694 4.110 STD 0.217 0.007 1.499 0.909 0.651 1.739 Q1 0.130 0.000 6.812 0.660 1.370 3.280 MEDIAN 0.233 0.001 7.700 0.722 1.880 3.960 Q3 0.343 0.001 8.920 0.767 2.140 4.750 0.183 0.384 0.000 0.000 0.000 0.291 0.275 0.454 0.217 0.000 0.130 0.000 0.233 1.000 0.343 72 Panel B: Banks' Capital Inadequacy and Zombie Lending ZOMBIE ZOMBIE LENDING LENDING ROA 0.449 2.741 (1.352) (1.782) SIZE 0.031 0.105 (0.107) (0.106) NET LOAN TO ASSET 0.001 0.004 (0.003) (0.005) INTEREST TO ASSET 0.054 0.042 (0.078) (0.059) EQUITY TO LIABILITY -0.009 -0.015 (0.01) (0.012) LLP TO ASSET 4.806*** 2.322 (1.783) (2.096) NPL TO GROSS LOAN 0.017*** (0.005) UNDER CAPITALIZED(before manipulation) ZOMBIE LENDING 0.489 (1.379 0.013 (0.104) 0.002 (0.003) 0.063 (0.078) -0.014 (0.011) 4.174** (1.744 ZOMBIE LENDING 0.411 (1.361) 0.032 (0.108) 0.001 (0.003) 0.055 (0.078) -0.008 (0.011) 4.866*** (1.771) 0.043** (0.021) UNDER CAPITALIZED(after manipulation) CONSTANT Bank fixed Effects Observations Adj R-squared -0.012 0.081 (0.951) Yes 815 0.57 73 0.557 (0.716) Yes 708 0.612 0.084 (0.955) Yes 815 0.575 (0.016) 0.141 (0.926) Yes 815 0.57 Figure 1: Prevalence of zombie firms in Japan Persentage (Zombie) 0.2 0.15 0.1 %zombie %asset weighted 0.05 Figure 2: Industrial zombie density and employment growth Employment Growth 0.1 0.05 Employment Growth -0.05 0.1 0.2 0.3 0.4 -0.1 -0.15 74 0.5 y = -0.0883x - 0.0003 Figure 3: Industrial zombie density and capital investment Capital Investment 0.4 0.35 0.3 0.25 0.2 0.15 0.1 0.05 Investment y = -0.3109x + 0.1604 0.2 0.4 0.6 Figure 4: Industrial zombie density and industrial ROA ROA 0.12 0.1 0.08 R… 0.06 y = -0.0462x + 0.0392 0.04 0.02 0 0.2 0.4 75 0.6 Figure 5: Zombie lending and abnormal total accrual Zombie Lending and Abnormal Accrual 0.02 0.015 0.01 non-zombie 0.005 zombie -2 -1 -0.005 -0.01 76 [...]... 5.2.2 Zombie lending and the evolution of financial reporting opacity In this section, we establish the causal effect from bank lending to zombie firms‘ financial reporting opacity (Hypothesis 1(b)) Following Hutton, Marcus, and Tehranian (2009), we define opacity as the three-year moving average absolute 28 value of abnormal accruals (Equation 5) We use the following equation to test the impact of zombie. .. the zombie indicator and reporting opacity Results show that the coefficient on zombie is positive and significant (coef =0.002, Se=0.001), implying that zombie firms adopt more opaque financial reporting than non -zombie firms The magnitude is economically significant as it represents 6% of the median value of the opacity measure for all observations In column (2), following Caballero, Hoshi, and Kashyap... 1994; Bushman and Smith, 2001; Bagnoli and Watts, 2010; Corona and Nan, 2011; Beatty, Liao, and Yu, 2013).9 The prevalence of zombie firms in an industry will affect both the cost and the benefit of non -zombie firms providing high-quality financial statements and separating themselves from zombie firms Specifically, Caballero, Hoshi, and Kashyap (2008) suggest that non -zombie firms are often placed in an... firms‘ operating characteristics, then by definition zombie firms as well as industries dominated by zombie firms would have low profitability and low growth (Caballero, Hoshi, and Kashyap, 2008) and a greater likelihood of being associated with opaque financial reporting Therefore, we are not forcing a correlation between zombie and opaque financial reporting but are providing evidence of this correlation... government and regulator‘s purposes However, the international investors and regulators were not able to effectively evaluate the severity of the problems in both the real and financial sectors 10 Chapter 3 Hypothesis development 3.1 Zombie lending, earnings manipulation, and the evolution of reporting opacity The costs related to bank loan defaults provide incentive for banks to screen and monitor... more money to zombie firms Based on the aforementioned results on zombie lending and reporting opacity, there appears to be a transmission mechanism through which reporting opacity is transmitted from banks to bank clients and then from bank clients to their industrial peers An alternative explanation of our findings is that un-modeled variables can affect banks‘ subsidization and firms‘ opacity simultaneously... that lending from banks induces zombie borrowers to manipulate their earnings and thereby results in more opaque financial reporting We further test the impact of lending from main banks and find a consistent result that main bank lending leads to more opaque financial reporting In addition, we investigate the impact of long-term lending and short-term lending on reporting opacity Results show that longer... non -zombie firm in an industry with a high zombie density will have lower reporting quality than a non -zombie firm in an industry with a lower zombie density.13 This leads to our third hypothesis: Hypothesis 3: Non -zombie firms adopt more opaque financial reporting if their industry is dominated by zombie firms 3.4 Bank capital adequacy and bank lending 11 For example, a non -zombie firm can overstate (understate)... competitive position because their rivals (zombie firms) are subsidized by banks and can sell products at a lower price When zombie density is high, non -zombie firms face a greater challenge in protecting their market share 10 In such a circumstance, truthful reporting is costlier for non -zombie firms as high-quality financial reporting can facilitate their competitors‘ (zombie firms‘) investment decisions,... discretionary accruals of zombie and non -zombie firms around the year when they receive new credits from banks The figure shows large and positive discretionary accruals recorded by zombie firms in the year they received new lending and negative discretionary accruals before and after the event year For non -zombie firms, though we also observe an increase in discretionary accruals before and during the event . injection, zombie, and financial reporting opacity . 32 5.2.6 Alternative measures of reporting opacity 34 5.2.7 Alternative measures of zombie 35 5.3. Financial reporting opacity and contagion effect. Table 3: The zombie lending and reporting opacity 57 Table 4: Zombie lending, earnings manipulations and political motivations. 59 Table 5: The zombie lending and reporting opacity: Treatment. 3.1. Zombie lending, earnings manipulation, and the evolution of reporting opacity 11 3.2. Government intervention and evergreen lending to zombie firms 12 3.3. Contagion of reporting opacity