1. Voluntary Disclosure of Auditor’s Attestation on Internal Control: Evidence from China Fang Lou Qian Sun Rong Yang Hongqi Yuan

2. Motivation Many studies on IC disclosure – Market reactions to the disclosure of IC weaknesses (Mammersley et al. 2008; Zhang 2007) – Impact of IC disclosure on the cost of capital (Ashbaugh- Skaife et al. 2007; Beneish et al. 2008) – Risk factors associated with presence of IC problems (Ge and Mcvay 2005; Doyle et al. 2007) Not many on the incentives for IC disclosure – Mandatory vs. voluntary IC disclosure – Two exceptions: Bronson et al. (2006) and Deumes and Knechel (2008) address voluntary IC disclosure – Identification problem

3. Our Contributions We examine not only why firms would choose voluntarily to disclose their IC self-assessment reports, but also why some of these firms prefer, in addition, to have an auditor’s attestation on these reports – We use the voluntary disclosure data from SSE during 2006-2010 – The uniqueness of the data – We put forward the equity capital refinancing hypothesis – We can address the identification problem to a certain extent

4. Theoretical Framework Healy and Palepu (2001) – The main purpose for voluntary disclosure of IC reports is to reduce information asymmetry and agency costs – Similarly, we argue having an auditor’s attestation on the IC report can make it more convincing, and thus further reduce information asymmetry and agency costs – Costs and benefits of IC disclosure and the associated auditor’s attestation

5. The determinants for IC disclosure Bronson et al. (2006) and Deumes and Knechel (2008): – Size – Leverage – Institutional Ownership – Growth – ROE – Alternative governance mechanism

6. Our Hypotheses H1a: Ceteris paribus, firms controlled by the government are more likely to issue IC self-assessment reports and to attain an auditor’s attestation. H1b: Ceteris paribus, firms controlled by the central government are more likely to issue IC self-assessment reports and to attain an auditor’s attestation than those controlled by local governments. H2: Ceteris paribus, firms having more mutual fund shareholdings are more likely to disclose self-assessment on IC and to have an auditor’s attestation.

7. Our Hypotheses H3a: Ceteris paribus, firms having equity-refinancing intentions are more likely to issue IC self -assessment reports and to have an auditor’s attestation on the IC report. H3b: When planning to have equity-refinancing in the market, non-government-controlled firms are more likely to issue IC self-assessment reports and to have an auditor’s attestation on the IC report than government-controlled firms.

8. Research Design Since there are a few possible choices for a firm’s disclosure decision over time, we use three pooled Logit regression models to test our hypotheses regarding Chinese firms’ voluntary disclosure of IC self-assessment reports and attaining an auditor’s attestation. The baseline model is specified as follow: Logit (ICVD it ) = a 0 + α 1 SEO it + α 2 FUND it +α 3 GOV it + α 4 CENTRAL* GOV it + a 5 SEO it * GOV it + a 6 SIZE it + α 7 GROW it + α 8 ROE it + α 9 LEV it + α 10 INDIR it + α 11 CLIST it + α 12 BIG4 it + α 13 INV it + α 14 AGE it + α 15 IndMBR + YRDummies + ε it (1)

9. Research Design In Model 1, we set ICVD it to 1 if firm i discloses its IC self-assessment report at year t no matter whether there is an auditor’s attestation attained or not, and zero otherwise. This is to examine the likelihood to disclose IC self-assessment versus not to disclose. In Model 2, we first exclude all firms that do not disclose an IC self-assessment report in year t, then set ICVD it to 1 if firm i attains an auditor’s attestation to its IC self-assessment report, and zero otherwise. This is to examine the likelihood to attain an auditor’s attestation among disclosers.

10. Research Design In Model 3, we define a Multinomial Logit variable, MLogit (ICVD it ). Instead of being binary, it is categorized into three groups: ICVD0 represents firms with no IC self-assessment report at all, which serves as the benchmark; ICVD1 represents firms with an IC self-assessment report only; and ICVD2 represents firms with both an IC self-assessment report and an auditor’s attestation. Since ICVD0 is used as the benchmark, we replace ICVD with ICVD1 and set it equal to 1 if firm i is in the category of ICVD1 in year t, and zero otherwise. Similarly, we replace ICVD with ICVD2 and set it equal to 1 if firm i is in the category of ICVD2 in year t, and zero otherwise.

11. Research Design The dependent variable, Logit(ICVD it ), measures the likelihood of firm i having ICVD1 and ICVD2 in year t. Hence, there are two simultaneously estimated equations in the Multinomial Logit regression model: one examines whether a firm discloses its IC self- assessment report only without attaining an auditor’s attestation the other examines whether a firm has both a self-assessment disclosure and an auditor’s attestation

12. Table 1 Sample Distribution Panel A: Frequency of the whole sample Year Firms with no disclosure Firms with disclosure on self-assessment, but no auditor’s attestation Firms with both self-assessment reports and auditors’ reportsTotal number of observations 2006 7481037795 2007 6488129785 2008 473161159793 2009 468168175811 2010 459160191810 Total 2,7965076913,994 Panel B: Distribution of firms with IC weaknesses during 2009 and 2010 Year (1) Number of firms with IC weakness (2) (1) As a percentage of total number of firms (3) Firms with IC weakness and self- assessment disclosure, but no auditor’s attestation (4) (3) As a percent of total number of firms that disclose both self- assessment and auditor’s attestation (5) Number of firms with IC weakness, but that disclose both self- assessment and auditor’s attestation (6) (5) As a percent of total number of firms that disclose both self- assessment and auditor’s attestation 2009536.7274.1273.93 2010 100 12.41 25 15.52 12 6.06 Total or Average 153 9.57 32 9.82 19 5.00

13. Variable NameDescription ICVD = ICVD in Model 1 is set 1 if a firm discloses the IC self-assessment report, and zero otherwise. In Model 2 all non-disclosures are deleted. Then ICVD is set to 1 if a firm attains an auditor’s attestation on the IC self-assessment report, and zero otherwise. ICVD in Model 3 is categorized into three groups: ICVD0 represents firms with no IC self-assessment report at all, which serves as the benchmark; ICVD1 represents firms with only an IC self-assessment report; and ICVD2 represents firms with both an IC self-assessment report and the auditor’s attestation. Since ICVD0 is used as the benchmark, we replace ICVD with ICVD1 and set it equal to 1 if the firm is in the category of ICVD1, and zero otherwise. Similarly, we replace ICVD with ICVD2 and set it equal to 1 if the firm is in the category of ICVD2. SEO= A dummy variable, which is equal to 1 if a firm makes seasoned equity offerings in the next fiscal year, and zero otherwise. FUND= The number of tradable shares held by mutual-fund institutions among the ten largest shareholders, divided by total tradable shares at the end of year t. GOV= A dummy variable, which is equal to 1 if the largest shareholder is the government (including both central government and local governments), and zero otherwise. CENTRAL= A dummy variable, which is equal to 1 if the largest shareholder is the central government, and zero otherwise. SIZE= The natural logarithm of total assets at the end of year t. GROW= The change in operating revenue from year t-1 to year t divided by operating revenue at year t-1. ROE= Return on equity for year t. LEV= Total liability divided by total assets. INDIR= The ratio of the number of independent outside directors to total number of directors. CLIST= A dummy variable, which is equal to 1 if a firm’s shares are cross-listed in the US in addition to SSE, and zero otherwise. BIG 4= A dummy variable, which is equal to 1 if a firm’s auditor is one of the big four accounting firms, and zero otherwise. INV= Book value of inventory divided by total assets. AGE= A firm’s age is measured as the number of years since it was listed on the SSE. IndustryMBR= The median of market-to-book ratio for each industry. Table 2 Description of Variables

14. MeanQ1MEDIANQ3Std. Dev. SEO 0.1060000.308 FUND 0.03000.0060.0440.047 GOV 0.6680110.471 CENTRAL 0.1720000.377 SIZE a 3,2311,3622,7096,4743,666 GROW 0.338-0.0170.1390.3263.047 ROE 0.0660.0250.0720.1282.607 LEV 0.6020.3970.5410.6721.161 INDIR 0.3590.333 0.3750.050 CLIST 0.0130000.111 BIG4 0.0700000.255 INV 0.1770.0620.1380.2370.161 AGE 9.34969134.023 IndustryMBR 3.2271.9583.1144.3301.356 Table 3 Descriptive Statistics for Independent Variables Panel A: Descriptive statistics for full sample during the period of 2006-10 Panel B: Descriptive statistics of test variables for firms with IC weakness during the period of 2009-10 Firms with IC weakness (n=153) Mean Median Firms with IC weakness, but which disclose self-assessment (no auditor’s attestation) (n=321) Mean Median Firms with IC weakness, but which disclose both self-assessment and auditor’s attestation (n=19) Mean Median SEO 0.07200.05900.1570 FUND 0.0320.0040.0530.0210.0510.018 GOV 0.65310.66710.7891 CENTRAL 0.18300.29400.4210 a SIZE in Table 2 (both panels A and B) is the total assets (in million RMB) at year-end.

15. SEO FUNDGOV CEN- TRALSIZEGROWROELEVINDIRCLISTBIG4INVAGE SEO FUND0.10 GOV-0.010.01 CENTRAL0.020.010.32 SIZE0.050.170.290.22 GROW-0.0030.03-0.002-0.020.02 ROE-0.010.020.010.030.010.003 LEV-0.001-0.05-0.07-0.02-0.15-0.010.001 INDIR0.0020.03-0.040.030.060.003-0.01-0.001 CLIST-0.01-0.040.080.180.29-0.0040.001-0.010.02 BIG40.002-0.020.130.150.450.010.005-0.020.090.34 INV-0.010.03-0.08-0.060.020.09-0.001-0.010.04-0.06-0.10 AGE-0.07-0.06-0.01-0.11-0.05-0.010.0020.070.005-0.05-0.080.06 IndustryMBR-0.040.13-0.03-0.01-0.040.03-0.003-0.010.02-0.01-0.030.010.09 Table 4 Pearson Correlation Matrix This presents the Pearson correlation coefficients between all independent variables defined in Table 2. The bold number represents that two-tailed p-value is significant at the 10% level or better.

16. Table 5 Multinomial Regression Results for 2006-2010 Separate logistic RegressionsMultinomial logistic regressions (Model 3) Model 1 Discloser versus non-disclosers Model 2 Self-assessment report only versus Self-assessment report with auditor’s attestation (1) Self-assessment report only versus non- disclosers (ICVD 1 ) (2) Self-assessment report with auditor’s attestation versus non-disclosers (ICVD 2 ) (3) Diff Between (1) & (2) Chi-squared SEO0.75***0.540.281.05*** (3.40)(1.52)(0.90)(4.29)5.33** FUND4.63***5.12***1.805.94*** (3.84)(2.67)(1.12)(4.29)5.58** GOV0.220.47*-0.050.42** (1.45)(1.91)(-0.24)(2.10)3.54* GOV*CENTRL0.41**0.93***-0.220.69*** (2.25)(3.60)(-0.85)(3.53)11.69*** GOV*SEO-0.35-0.500.14-0.66** (-1.28)(-1.16)(0.36)(-2.19)3.85** SIZE0.55***-0.030.62***0.53*** (8.57)(-0.36)(7.29)(7.17) GROW0.010.08-0.160.01 (0.26)(0.88)(-1.64)(0.74) ROE-0.010.14-0.01 (-0.55)(0.72)(-0.48)(-0.44) LEV-1.49***0.37-1.72***-1.32*** (-4.27)(0.68)(-3.52)(-3.33) INDIR0.711.230.011.44 (0.61)(0.78)(0.01)(1.00) CLIST1.36**0.371.42**1.75** (2.11)(0.47)(1.97)2.43) BIG40.19-1.95***1.01***-0.75** (0.82)(-5.14)(3.41)(-2.38) INV0.90**-0.060.86*0.90** (2.53)(-0.09)(1.82)(2.01) AGE-0.07***-0.11***-0.01-0.11*** (-4.21)(-4.20)(-0.77)(-5.48) Industry MBR0.030.08-0.330.04 (0.37)(0.68)(-0.31)(0.80) # of Obs.3,9941,1983,994 Pseudo R 2 23.26%17.67%21.99%

17. Separate logistic RegressionsMultinomial logistic regressions (Model 3) Model 1 Discloser versus non-disclosers Model 2 Self-assessment report only versus Self-assessment report with auditor’s attestation (1) Self-assessment report only versus non-disclosers (ICVD 1 ) (2) Self-assessment report with auditor’s attestation versus non-disclosers (ICVD 2 ) (3) Diff Between (1) & (2) Chi-square SEO0.410.390.110.68** (1.47)(0.86)(0.29)(2.09)1.73 FUND3.61**6.47***0.166.26*** (2.22)(2.77)(0.08)(3.32)8.23*** GOV0.210.42-0.120.43* (1.15)(1.58)(-0.05)(1.89)2.78* GOV*CENTRL0.301.03***-0.290.67*** (1.38)(3.64)(-0.97)(2.83)11.16** GOV*SEO-0.24-0.580.21-0.62 (-0.67)(-1.06)(0.44)(-1.49)2.42 SIZE0.67***-0.050.71***0.65*** (8.25)(-0.50)(7.33)(7.14) GROW-0.11*0.11-0.21-0.08 (-1.76)(0.99)(-0.44)(-1.59) ROE-0.010.57-0.220.02 (-0.24)(1.43)(-0.65)(0.48) LEV-1.80***0.82-2.13***-1.43*** (-4.06)(1.33)(-3.90)(-2.81) INDIR0.570.510.331.02 (0.42)(0.31)(0.22)(0.62) CLIST16.02***0.3414.92***15.27*** (28.46)(0.46)(23.86)(24.06) BIG40.35-1.77***0.93**-0.71 (1.02)(-4.11)(2.65)(-1.62) INV0.98**-0.151.00*0.95* (2.24)(-0.22)(1.88)(1.74) AGE-0.07***-0.12***-0.02-0.13*** (-3.61)(-4.31)(-0.83)(-5.42) Industry MBR-0.030.11-0.070.01 (-0.34)(0.91)(-0.71)(0.11) # of Obs.1,6216941,621 Pseudo R 2 17.28%11.96%15.47% Table 6 Multinomial Regressions Results for 2009-2010

18. Separate logistic RegressionsMultinomial logistic regressions (Model 3) Model 1 Discloser versus non-disclosers Model 2 Self-assessment report only versus Self-assessment report with auditor’s attestation (1) Self-assessment report only versus non-disclosers (ICVD 1 ) (2) Self-assessment report with auditor’s attestation versus non-disclosers (ICVD 2 ) (3) Diff Between (1) & (2) Chi-square SEO-14.86***0.07-16.72***-14.54*** (-15.82)(0.08)(-14.62)(10.77)2.68 FUND10.97**-4.1214.73***11.40 (2.21)(-0.49)(2.86)(1.60)0.31 GOV-0.811.07-0.850.10 (-1.35)(0.71)(-1.47)(0.08)0.58 GOV*CENTRL0.771.160.681.39 (1.17)(0.93)(1.00)(1.57)0.61 GOV*SEO14.91*** -0.3615.31*** (12.32) (-0.24)(10.61)128.26*** SIZE1.01***0.160.87***0.97** (3.23)(0.35)(2.64)(2.57) GROW-0.67*0.31-0.82*-0.98** (-1.65)(0.52)(-1.87)(-2.08) ROE1.27-2.532.271.80 (0.67)(-0.61)(0.87)(0.45) LEV-0.252.68-0.67-1.78 (-0.18)(0.64)(-0.46)(-0.66) INDIR-5.14-2.94-2.620.28 (-1.02)(-0.29)(-0.51)(0.03) CLIST13.58***-0.0115.66***13.27*** (7.82)(-0.01)(8.23)(4.65) BIG41.15-17.56***0.24-16.51*** (1.06)(-9.27)(0.02)(-7.80) INV1.11-0.730.662.02 (0.81)(-0.17)(0.48)(0.74) AGE-0.22***-0.39***-0.12-0.45*** (-2.91)(-2.99)(-1.62)(-3.61) Industry MBR0.17-0.20-0.11-0.34 (0.69)(-0.33)(-0.34)(-0.68) # of Obs.15351153 Pseudo R 2 30.50%33.91%32.20% Table 7 Multinomial Regressions Results for Firms with IC Weaknesses (2009-10)

19. Robustness Check Use SEO scale in place of SEO dummy Poor credibility as an incentive to have an audit attestation on IC report – Previous penalty by CSRC or SSE – Non-clean auditor opinion Lead-lag relationship between Fund and Disclosure

20. Conclusion We hypothesize and find supporting evidence that a firm with the government, especially the central government, as its largest shareholder, with more mutual fund shareholdings, and with an equity refinancing plan in the near future, is more likely to disclose an IC self-assessment report and to attain an auditor’s attestation on the report. We have also addressed the identification issue to a certain extent.