Propping through related party transactions

Abstract

Based on a sample of Chinese listed firms from 1998 through 2002, this paper documents that listed firms prop up earnings by using abnormal related sales to their controlling owners. Such related sales propping is more prevalent among state-owned firms and in regions with weaker economic institutions. We also find that these abnormal related sales are not entirely accrual-based but can be cash-based as well, and they serve as a substitute rather than complement to accruals management for meeting earnings targets. Since these abnormal related sales can be cash-based, there is significant cash transfer via related lending from listed firms back to controlling owners after the propping. However, no cash transfer via related lending is found to be associated with accruals earnings management.

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Notes

  1. 1.

    We use “propping” to describe the scenario whereby a controlling owner uses its own resources to manage the listed affiliate’s earnings. This is different from accruals management in which the controlling owner or another affiliated firm is not involved in the listed firm’s earnings management.

  2. 2.

    For example, it was reported (Security Market Week, April 6, 2002) that Tianjin Nankai Guard, a company listed on the Shenzhen Stock Exchange, was facing operating difficulties. This firm reported enormous profits in the years before 2001 with 99.9% of its sales from related party transactions. When the new accounting rule about related party transactions was implemented on December 21, 2001, any such sales with a mark-up of more than 20% above book value could no longer be counted towards profits in the income statement. This new standard has significantly undermined Tianjin Nankai Guard’s accounting performance since 2001.

  3. 3.

    According to Article 157 of China’s Company Law, if a listed company sustains losses for three consecutive years, it will be temporarily delisted by the China Securities Regulatory Commission (CSRC) and subjected to ‘particular transfer’ (the stock can only be traded in the stock exchange on Fridays) and other transfer constraints. If it sustains losses for two consecutive years, it will have ‘ST’ (special treatment) prefixed to its name as a warning.

  4. 4.

    There is plenty of anecdotal evidence of related sales propping. For example, Tianjin Nankai Guard Co., Ltd. reported a significant drop in earnings in 2003 (decrease by 100.41%, from a RMB 70 million net profit in 2002 to a RMB 289,000 loss in 2003). In 2002 (2001), the listed firm sold almost RMB 88 (RMB 272) million worth of products to its parent company’s subsidiary, which amounted to 92% (84%) of total sales. The drop in profit in 2003 was due to two factors. First, the listed firm had to recognize bad debt of RMB 57 million as its parent’s subsidiary experienced financial difficulty and failed to pay back the accounts receivable. Second, the sales to the same related party decreased to less than RMB 127,000.

  5. 5.

    In 2003 the CSRC promulgated Notice No. 56 urging listed companies to reduce related lending to their affiliated firms. In 2005, CSRC issued an opinion requiring listed firms and their parent companies to eliminate related lending by the end of 2006.

  6. 6.

    Although no firm was penalized by securities regulators for excessive related lending during our sample period, three firms (company ID 600698, 000766, and 000925) were scrutinized for not providing adequate disclosure about their related lending.

  7. 7.

    Information on the bank lending rates provided by the National Bureau of Statistics of China can be found in http://www.stats.gov.cn/tjsj/ndsj/yb2004-c/indexch.htm.

  8. 8.

    Similarly, when the sample is partitioned into firms in good and bad regions based on the Market Development Index median, the good regions have fewer firms that have related sales (62%) while the bad regions have more (67%).

  9. 9.

    The summary statistics of abnormal related party transactions with the largest shareholders (not reported) show that central government firms tend to have higher abnormal related sales (mean = 0.01) than local (mean = 0) and nonstate firms (mean = −0.01), while they have a similar level of abnormal related lending as local government firms. For the subsample of firms with an incentive to manage earnings, both central and local government firms have positive abnormal related sales (mean = 0.04 and 0.01, respectively) while nonstate firms have an average of −0.02 abnormal related sales. Moreover, local government firms tend to have higher abnormal related lending than central government and nonstate firms in this subsample.

  10. 10.

    We conduct the following robustness check: (1) randomly select two 2% ROE intervals from a feasible ROE range in our sample, i.e., [−25%, 25%], set INCENTIVE equal to one for firm-years whose ROEs fall in these intervals and zero otherwise; (2) regress abnormal related sales on the pseudo-INCENTIVE. We repeat the above steps 1,000 times and find that the coefficient on pseudo-INCENTIVE is significant at the 10% level in only approximately 10% of the regressions. This suggests that the model in Table 3 is well specified and its results are unlikely to be spurious.

  11. 11.

    We exclude the tested firms’ ΔROS in calculating ΔIND_ROS. However, the results remain unchanged if we include the tested firms’ ΔROS in the calculation.

  12. 12.

    An industry-adjusted related party transaction is the gross figure minus the industry mean figure of the same year based on the CSRC industry classification.

References

  1. Aharony, J., Lee, J., & Wong, T. J. (2000). Financial packaging of IPO firms in China. Journal of Accounting Research, 38(1), 103–126.

    Article  Google Scholar 

  2. Bai, C.-E., Liu, Q., & Song, F. M. (2005). Bad news is good news: Propping and tunnelling evidence from China. http://www.hiebs.hku.hk/working_paper_updates/pdf/wp1094.pdf.

  3. Ball, R., Kothari, S. P., & Robin, A. (2000). Corrigendum: The effect of international institutional factors on properties of accounting earnings. Journal of Accounting & Economics, 29(1), 1–51.

    Article  Google Scholar 

  4. Bertrand, M., Mehta, P., & Mullainathan, S. (2002). Ferreting out tunneling: An application to Indian business groups. The Quarterly Journal of Economics, 117(1), 121–148.

    Article  Google Scholar 

  5. Bhattacharya, U., Daouk, H., & Welker, M. (2003). The world price of earnings opacity. The Accounting Review, 78(3), 641–678.

    Article  Google Scholar 

  6. Bushman, R. M., Piotroski, J. D., & Smith, A. J. (2004). What determines corporate transparency? Journal of Accounting Research, 42(2), 207–252.

    Article  Google Scholar 

  7. Chen, C. J. P., Su, X., & Wu, X. (2007). Market competitiveness and Big 5 pricing: Evidence from China’s binary market. The International Journal of Accounting, 42(1), 1–24.

    Article  Google Scholar 

  8. Chen, C. J. P., Xu, X., & Zhao, R. (2000a). An emerging market’s reaction to initial modified audit opinions: Evidence from the Shanghai Stock Exchange. Contemporary Accounting Research, 17(3), 429–455.

    Article  Google Scholar 

  9. Chen, C. W. K., & Yuan, H. Q. (2004). Earnings management and resource allocation: Evidence from China’s accounting-based regulation of rights issues. The Accounting Review, 79(3), 645–665.

    Article  Google Scholar 

  10. Chen, J. (1998). A study of the abnormal increase in accounts receivable. In Collection of accounting research on listed companies. Shanghai University of Finance & Economics (in Chinese).

  11. Chen, X. Y., Xiao, X., & Guo, X. Y. (2000b). Rights issue qualifications and earnings manipulation of listed companies. Economic Research (in Chinese), 30–36.

  12. Cheung, Y.-L., Rau, P. R., & Stouraitis, A. (2006). Tunneling, propping and expropriation: Evidence from connected party transactions in Hong Kong. Journal of Financial Economics, 82(2), 287–322.

    Article  Google Scholar 

  13. Dechow, P. M., Sloan, R. G., & Sweeney, A. P. (1995). Detecting earnings management. Accounting Review, 70(2), 193–225.

    Google Scholar 

  14. DeFond, M., Hung, M., & Trezevant, R. (2007). Investor protection and the information content of annual earnings announcements: International evidence. Journal of Accounting and Economics, 43(1), 37–67.

    Article  Google Scholar 

  15. DeFond, M., Wong, T. J., & Li, S. (1999). The impact of improved auditor independence on audit market concentration in China. Journal of Accounting & Economics, 28(3), 269–306.

    Article  Google Scholar 

  16. Demurger, S., et al. (2002). Geography, economic policy and regional development in China. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=286672.

  17. Fan, G., & Wang, X. L. (2003). NERI index of marketization of China’s Provinces. Economic Science Press.

  18. Fan, J. P. H., & Wong, T. J. (2002). Corporate ownership structure and the informativeness of accounting earnings in East Asia. Journal of Accounting and Economics, 33(3), 401–425.

    Article  Google Scholar 

  19. Fan, J. P. H., Wong, T. J., & Zhang, T. (2007). Organizational structure as a decentralization device: Evidence from corporate pyramids. The Chinese University of Hong Kong and the City University of Hong Kong.

  20. Friedman, E., Johnson, S., & Mitton, T. (2003). Propping and tunneling. Journal of Comparative Economics, 31(4), 732–750.

    Article  Google Scholar 

  21. Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005). The economic implications of corporate financial reporting. Journal of Accounting and Economics, 40(1–3), 3.

    Article  Google Scholar 

  22. Gramlich, J. D. G., Limpaphayom, P., & Rhee, S. G. (2004). Taxes, keiretsu affiliation, and income shifting. Journal of Accounting & Economics, 37(2), 203–228.

    Article  Google Scholar 

  23. Gwartney, J., Lawson, R., & Gartzke, E. (2005). Economic freedom of the world: 2005 annual report. Vancouver, BC: Fraser Institute.

  24. Haw, I.-M., et al. (2005). Market consequences of earnings management in response to security regulations in China. Contemporary Accounting Research, 22(1), 95–140.

    Article  Google Scholar 

  25. Healy, P. M. (1985). The effect of bonus schemes on accounting decisions. Journal of Accounting & Economics, 7(1–3), 85–107.

    Article  Google Scholar 

  26. Healy, P. M., & Wahlen, J. M. (1999). A review of the earnings management literature and its implications for standard setting. Accounting Horizons, 13(4), 365–383.

    Article  Google Scholar 

  27. Jiang, Y. H., & Wei, G. (1998). An empirical study of the ROE distribution of listed companies. Shanghai University of Finance & Economics.

  28. Jones, J. (1991). Earnings management during import relief investigations. Journal of Accounting Research, 29(2), 193–228.

    Article  Google Scholar 

  29. Khanna, T., & Palepu, K. (2000). Is group affiliation profitable in emerging markets? An analysis of diversified Indian business groups. Journal of Finance, 55(2), 867–892.

    Article  Google Scholar 

  30. Khanna, T., & Yafeh, Y. (2005). Business groups and risk sharing around the world. Journal of Business, 78(1), 301–340.

    Article  Google Scholar 

  31. Khanna, T., & Yafeh, Y. (2007). Business groups in emerging markets: Paragons or parasites? Journal of Economic Literature, 45(2), 331–372.

    Article  Google Scholar 

  32. Klassen, K. J., Lang, M., & Wolfson, M. (1993). Geographic income shifting by multinational corporations in response to tax rate changes. Journal of Accounting Research, 31(Suppl.), 141–182.

    Article  Google Scholar 

  33. Leuz, C., Nanda, D., & Wysocki, P. D. (2003). Earnings management and investor protection: An international comparison. Journal of Financial Economics, 69, 505–727.

    Article  Google Scholar 

  34. Leuz, C., & Oberholzer-Gee, F. (2006). Political relationships, global financing, and corporate transparency: Evidence from Indonesia. Journal of Financial Economics, 81(2), 411–439.

    Article  Google Scholar 

  35. Li, H., Meng, L., & Zhang, J. (2006). Why do entrepreneurs enter politics? Evidence from China. Economic Inquiry, 44(3), 559–578.

    Article  Google Scholar 

  36. Li, H., & Zhou, L.-A. (2005). Political turnover and economic performance: The disciplinary role of personnel control in China. Journal of Public Economics, 89, 1743–1762.

    Article  Google Scholar 

  37. Roychowdhury, S. (2006). Earnings management through real activities manipulation. Journal of Accounting and Economics, 42(3), 335–370.

    Article  Google Scholar 

  38. Teoh, S. H., Welch, I., & Wong, T. J. (1998). Earnings management and the long-run market performance of initial public offerings. Journal of Finance, 53(6), 1935–1974.

    Article  Google Scholar 

  39. Wang, Q., Wong, T. J., & Xia, L. (2008). State ownership, institutional environment and auditor choice: Evidence from China. Journal of Accounting and Economics (forthcoming).

  40. Watts, R. L., & Zimmerman, J. L. (1986). Positive accounting theory. Prentice-Hall.

  41. Williamson, O. (1964). The economics of discretionary behavior: Managerial objectives in a theory of the firm. Englewood Cliffs, NJ: Prentice Hall.

  42. Yuan, H. Q. (1998). Disclosure of related party transactions in interim report: Facts and thoughts. Kuai Ji Yan Jiu (Accounting Studies, in Chinese), 4, 1–6.

    Google Scholar 

Download references

Acknowledgements

This paper, previously titled “Earnings Management and Tunneling through Related Party Transactions: Evidence from Chinese Corporate Groups”, has benefited from discussions with seminar participants at the Chinese University of Hong Kong, the Hong Kong University of Science and Technology, Nanyang Technological University of Singapore, Shanghai University of Finance and Economics, the University of Hong Kong, the 2003 European Finance Association Conference, the 2004 Asian Finance Association Meeting, the 2004 American Accounting Association Annual Meeting, and the 2005–2006 Global Issues in Accounting Conference at University of North Carolina—Chapel Hill. We especially appreciate comments from Kee-Hong Bae, Robert Bushman, Kalok Chan, Kevin Chen, Joseph Fan, Chul Park, Joseph Piostroski, Gordon Richardson, Woody Wu, and Tianyu Zhang. Any errors are our own. We thank Tianyu Zhang for providing the pyramid data. We also acknowledge the financial support of the Direct Allocation Grant at the Chinese University of Hong Kong and Singapore Ministry of Education Academic Research Fund Tier 1, Grant number of project SUG 1/04.

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Correspondence to Ming Jian.

Appendices

Appendix 1

Correlation matrix

This table presents the correlation matrix for all variables used in Tables 3 through 7. The variables include: Abnormal RPS, which is the residual term from the regression of related sales on SIZE, LEVERAGE, MARKET-TO-BOOK, and industry dummies (see Appendix 2 for details); Abnormal RPL, which is the residual term from the regression of net related lending (excluding related party accounts receivables and related party accounts payable) on SIZE, LEVERAGE, MARKET-TO-BOOK, and industry dummies (see Appendix 2 for details); Operating profit from abnormal RPS, which equals abnormal RPS multiplied by the firm’s average operating profit margin, divided by the firm’s total equity at year-end; discretionary related receivable, which is the residual term of the annual regression model in which related party accounts receivable is regressed on related sales; discretionary total accruals from the cross-sectional modified Jones Model in Dechow et al. (1995); the firm’s change in ROS (ΔROS), which is the ROS of the current year minus the ROS of the prior year; ΔIND_ROS, which is the change in industry average ROS (excluding the tested firm’s change in ROS) based on the industry classifications in Table 1 ; NEGATIVE SHOCK, which equals one when ΔIND_ROS is negative and zero otherwise; ΔRPS, which is the change in abnormal related sales; INCENTIVE, which equals one when the firm’s ROE is at the margin of qualifying for rights issue (9–11% for protected industries; 10–12% for other industries before 2001; 6–8% afterwards), when the firm’s ROE is just above zero (0–2%), or when the firm is going to seek a rights issue in the following year and zero otherwise; PROP RPS, which equals one if the listed firm has propping incentives (INCENTIVE equals one) and abnormal related sales are greater than zero in the current or previous year and zero otherwise; PROP acc, which equals one if the listed firm has propping incentives (INCENTIVE equals one) and discretionary accruals are greater than zero in the current or previous year and zero otherwise; LOCAL, which equals one when the firm’s ultimate shareholder is the local government and zero otherwise; CENTRAL, which equals one when the firm’s ultimate shareholder is the central government and zero otherwise; BIG 10, which equals one when the firm’s auditor is one of the 10 largest accounting firms based on client assets in China and zero otherwise; LAYERS, which represents the number of layers between the listed firm and its ultimate shareholder; Market Development Index, which measures the progress of institutional transformation in China’s 30 provinces (excluding Tibet due to lack of data) and identifies the differences in institutions and economic policies among various regions (Fan and Wang 2003); Deregulation index, which measures the degree of deregulation in these different provinces (Demurger et al. 2002); Unemployment, which is the unemployment rate in different provinces in China; Fiscal Surplus, which is the fiscal surplus divided by provincial GDP for the province in which the firm is located; LEVERAGE, which is total debt over total assets at the year-end; SIZE, which is the natural logarithm of total assets at the year-end; MARKET-TO-BOOK, which is market value divided by book value of total equity at the year-end; Previous year’s ROS, which is the return on sales of the firm one year before the related party transaction occurs; Rights issue dummy, which equals one if the listed firm has a rights issue in the year and zero otherwise; and Change in Debt, which is the change in total debt of the listed firm in the current year, divided by the total sales of the firm over the year.

The numbers shown in the table are the Pearson correlation coefficients. If the absolute value of the coefficient is higher than 0.04, it is significant at the 1% level.

  Abnormal RPS Abnormal RPL Operating profit from abnormal RPS Discretionary related receivable Discretionary total accruals ΔROS ΔIND_ ROS NEGATIVE SHOCK ΔRPS INCEN TIVE PROP RPS PROP acc LOCAL
Abnormal RPL 0.12 1            
Operating profit from abnormal RPS 0.07 0.02 1           
Discretionary related receivable 0.06 0.03 0.02 1          
Discretionary total accruals 0.02 −0.01 0.00 −0.01 1         
ΔROS 0.01 0.02 0.03 0.01 0.02 1        
ΔIND_ROS 0.01 0.01 0.00 0.00 0.01 0.09 1       
NEGATIVE SHOCK 0.00 −0.03 −0.01 0.00 0.00 0.02 0.64 1      
ΔRPS 0.40 0.02 0.07 0.10 0.00 0.02 0.02 0.03 1     
INCENTIVE 0.04 −0.01 −0.01 0.01 0.05 0.57 0.01 0.00 0.02 1    
PROP RPS 0.67 0.08 0.04 0.01 0.02 0.06 0.03 0.00 0.03 0.12 1   
PROP acc 0.04 0.00 0.01 0.02 0.10 0.08 −0.02 −0.04 −0.01 0.28 0.07 1  
LOCAL 0.02 0.06 0.01 −0.01 −0.01 0.03 0.05 0.00 0.01 0.02 0.03 −0.03 1
CENTRAL 0.06 0.03 −0.01 0.01 0.01 0.04 −0.01 0.00 0.00 0.04 0.05 0.02 −0.55
BIG 10 −0.03 0.01 −0.01 −0.01 0.00 0.01 0.01 0.02 0.00 0.01 −0.04 −0.06 −0.07
LAYERS 0.06 0.01 0.01 0.00 0.01 0.03 −0.03 −0.01 0.01 0.03 0.05 0.02 −0.22
Market Development Index −0.05 0.00 0.02 0.00 −0.01 0.01 −0.01 0.04 −0.01 −0.03 −0.07 −0.09 −0.05
Deregulation Index −0.08 0.00 0.03 0.00 0.00 0.00 −0.01 0.05 −0.01 −0.05 −0.10 −0.10 −0.06
Unemployment 0.02 0.00 −0.01 0.00 0.00 −0.02 −0.12 −0.17 −0.01 −0.04 0.03 −0.02 0.04
Fiscal Surplus 0.01 0.02 0.01 0.00 −0.02 0.03 0.01 0.04 0.01 0.01 −0.01 −0.06 −0.06
LEVERAGE 0.00 −0.19 0.02 0.00 0.02 −0.25 −0.07 0.02 −0.01 −0.32 0.01 −0.08 −0.04
SIZE 0.00 0.21 −0.03 −0.01 −0.04 0.16 0.01 −0.08 −0.01 0.20 0.02 0.01 0.07
MARKET-TO-BOOK 0.00 −0.10 0.04 0.01 0.02 0.01 0.07 0.06 0.01 −0.14 −0.02 −0.07 −0.13
Previous year’s ROS 0.02 −0.02 −0.01 −0.01 0.01 −0.16 −0.03 −0.02 0.02 0.06 0.04 0.07 0.01
Rights Issue Dummy 0.01 0.00 −0.01 0.01 0.03 0.03 0.05 0.06 0.03 0.14 0.00 0.14 0.01
Change in debt −0.03 −0.13 −0.04 0.03 0.04 −0.15 −0.05 0.00 −0.01 0.03 0.02 0.13 −0.04
  CENTRAL BIG 10 LAYERS Market Development Index Deregulation Index Unemployment Fiscal Surplus LEVERAGE SIZE MARKET TO-BOOK Previous year’s ROS Rights Issue Dummy
BIG 10 0.05 1           
LAYERS 0.31 0.06 1          
Market Development Index 0.00 0.20 0.07 1         
Deregulation Index 0.00 0.20 0.08 0.84 1        
Unemployment −0.10 0.05 −0.03 −0.08 −0.12 1       
Fiscal Surplus 0.05 0.11 0.03 0.74 0.42 −0.05 1      
LEVERAGE −0.13 0.01 0.02 0.01 0.09 0.04 −0.06 1     
SIZE 0.13 0.15 0.06 0.12 0.11 0.04 0.16 −0.44 1    
MARKET-TO-BOOK −0.04 0.04 0.08 0.03 0.05 −0.11 0.01 0.17 −0.44 1   
Previous year’s ROS 0.01 0.03 0.02 −0.02 −0.02 −0.02 −0.01 −0.03 0.03 −0.08 1  
Rights Issue Dummy 0.01 −0.04 −0.03 −0.06 −0.08 −0.11 −0.01 −0.13 0.01 −0.06 0.03 1
Change in debt −0.05 −0.03 −0.01 −0.07 −0.05 0.01 −0.10 0.35 −0.04 −0.09 0.09 0.01

Appendix 2

Abnormal related party transaction regressions

Independent variables Dependent variables
Related sales coefficients Related lending coefficients
Min Max # of Sig Min Max # of Sig
LEVERAGE −0.186 −0.155 5 −0.070 0.116 2
SIZE 0.019 0.028 5 −0.049 −0.013 3
MARKET-TO-BOOK −0.001 0.013 3 −0.008 0.011 2
Industry dummy Farming, Forestry, Animal Husbandry, and Fishing −0.006 0.059 0 −0.136 0.073 1
Mining 0.066 0.225 4 −0.123 0.043 0
Food & Beverage 0.019 0.076 1 −0.075 0.028 0
Textile, Apparel, Fur and Leather 0.028 0.087 3 −0.063 0.153 1
Furniture Manufacturing 0.082 0.769 2 −0.136 0.517 1
Paper and Allied Products; Printing 0.043 0.121 2 −0.071 0.156 1
Petroleum, Chemical, Plastics, and Rubber Products Manufacturing 0.056 0.108 5 −0.090 0.076 3
Electronics 0.021 0.055 0 −0.066 −0.029 0
Metal, Non-metal 0.109 0.158 5 −0.080 0.098 2
Machinery, Equipment, and Instrument Manufacturing 0.067 0.100 5 −0.053 0.026 0
Medicine and Biological Products 0.017 0.118 3 −0.031 0.075 0
Other Manufacturing −0.035 0.107 1 −0.048 0.178 1
Utilities 0.148 0.267 5 −0.099 −0.025 1
Construction −0.007 0.038 0 −0.106 0.033 0
Transportation and Warehousing −0.046 −0.001 0 −0.113 0.066 1
Information Technology −0.008 0.040 0 −0.120 −0.022 2
Wholesale and Retail Trade −0.015 0.007 0 −0.135 −0.017 4
Banking & Financial Institutions −0.098 −0.017 0 −0.106 0.000 0
Real Estate −0.011 0.042 0 −0.109 0.101 1
Public Facilities and Other Services −0.030 0.018 0 −0.073 −0.043 0
Communication and Cultural Industries −0.037 0.049 0 −0.136 0.106 0
Conglomerates −0.332 −0.221 5 0.183 0.783 4
N 816 1124   816 1122  
Adjusted R2 0.080 0.110   0.001 0.030  

This table presents the results on abnormal related party transactions. Annual regressions are run using related sales (RPS) and related lending (RPL) as dependent variables and LEVERAGE, SIZE, MARKET-TO-BOOK, and the Industry dummies (see Table 1 for SIC equivalence). SIZE is the natural logarithm of total assets at year-end; LEVERAGE is total debt over total assets at year-end, and MARKET-TO-BOOK is the market value divided by book value of total equity at year-end. The numbers reported in the table are the maximum and minimum coefficients in the five annual regressions (1998–2002) and the number of significant coefficients (10% level, two-tailed). The residuals obtained from these annual regressions are used to proxy for abnormal related sales and related lending in later analyses.

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Jian, M., Wong, T.J. Propping through related party transactions. Rev Account Stud 15, 70–105 (2010). https://doi.org/10.1007/s11142-008-9081-4

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Keywords

  • Propping
  • Related party transactions
  • Corporate governance
  • Controlling shareholders

JEL Classifications

  • G3
  • M4