Discussion

In this paper I investigate how firms with criminal executives and criminal employees are associated with financial reporting outcomes, across different research designs, different control variables (for example firm fixed effects and governance variables), and different accounting outcome variables (for example discretionary accruals when the firm issues new finance (which indeed is the main analysis), the propensity to meet or beat earnings benchmarks, and earnings persistence). Albeit the results are generally consistent across all these various estimations – firms with criminal executives and criminal employees are associated with proxies of earnings

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Table C.14: Criminal executives, criminal employees, and earnings persistence

(1) (2) (3) (4) ROAi,t+1 ROAi,t+1 ROAi,t+1 ROAi,t+1

ROAi,t 0.6056*** 0.6297*** 0.5846*** 0.6326*** (37.07) (31.93) (36.50) (19.34) CrimEXEC / CrimEMPL 1/1i,t 0.0021 0.0015 0.0015 (0.91) (0.77) (0.42) 1/1i,t*ROA,t -0.0859*** -0.0408** -0.0888*** (-4.79) (-2.17) (-3.02)

1/0i,t 0.0007 0.0000 Base level

(0.24) (0.00)

1/0i,t*ROAi,t 0.0029 0.0480* Base level

(0.10) (1.78)

0/1,t 0.0007 Base level -0.0000

(0.39) (-0.00) 0/1i,t*ROAi,t -0.0452*** Base level -0.0480*

(-3.04) (-1.78) 0/0i,t Base level -0.0007 -0.0007

(-0.39) (-0.24) 0/0i,t*ROAi,t Base level 0.0452*** -0.0029

(3.04) (-0.10) Intercept 0.0092** 0.0087** 0.0093*** 0.0093* (2.47) (2.15) (2.89) (1.71) Industry FE YES YES YES YES

Year FE YES YES YES YES

N 39,553 39,553 39,553 39,553 Adjust R. sq. 0.3970 0.3980 0.3980 0.3980 This table shows standard earnings persistence regressions, and how earnings persistence differs by the composition of criminal executives and criminal employees. CrimEXEC indicates that the majority of executives have a criminal record. CrimEMPL indicates that the workforce is relatively criminal, and takes the value one when the percentage of employees with a criminal record is above the within-year median. 1/1 is an indicator taking the value one if CrimEXEC=1 and CrimEMPL=1. 1/0 is an indicator taking the value one if CrimEXEC=1 and CrimEMPL=0. 0/1 is an indicator taking the value one if CrimEXEC=0 and CrimEMPL=1. 0/0 is an indicator taking the value one if CrimEXEC=0 and CrimEMPL=0. Standard errors are clustered by firm and year (Gow et al. 2010). t statistics in parentheses. ***, **, * Represent significance levels at 0.01, 0.05, and 0.10, respectively (two-tailed test). All continuous variables are winsorized at the 1 and 99 percent level.

management and earnings quality – I acknowledge that the statistical significance differs across estimations, and that I find no results when I explore the propensity to meet or beat the zero earnings benchmark. However, the collective evidence provided in this paper taken into consideration, on balance the results suggest that firms with criminal executives and criminal employees are associated with adverse financial reporting outcomes.

5.2 Limitations

Despite the consistency of the findings across various analyses, I caution the reader to interpret this study carefully. I recognize that studying individuals’ underlying cognitive processes and traits using observable characteristics is challenging. The results are based on a

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Table C.15: Criminal executives, criminal employees, and earnings persistence

(1) (2) (3) (4) ROAi,t+1 ROAi,t+1 ROAi,t+1 ROAi,t+1

OPCFi,t 0.5788*** 0.5939*** 0.5632*** 0.6160*** (34.66) (31.66) (32.14) (20.00) OPACCi,t 0.5391*** 0.5534*** 0.5232*** 0.5653*** (36.03) (32.11) (33.68) (21.48) CrimEXEC / CrimEMPL 1/1i,t -0.0002 0.0000 -0.0004 (-0.08) (0.00) (-0.10)

1/1i,t*OPCFi,t -0.0584*** -0.0277 -0.0805**

(-2.98) (-1.45) (-2.41)

1/1i,t*OPACCi,t -0.0441

**

-0.0139 -0.0560*

(-2.40) (-0.83) (-1.80)

1/0i,t 0.0002 0.0004 Base level

(0.07) (0.13)

1/0i,t*OPCFi,t 0.0221 0.0528** Base level

(0.79) (2.07)

1/0i,t*OPACCi,t 0.0119 0.0421* Base level

(0.51) (1.70)

0/1i,t -0.0002 Base level -0.0004

(-0.12) (-0.13) 0/1i,t*OPCFi,t -0.0307** Base level -0.0528**

(-2.53) (-2.07) 0/1i,t*OPACCi,t -0.0301** Base level -0.0421*

(-2.47) (-1.70) 0/0i,t Base level 0.0002 -0.0002

(0.12) (-0.07) 0/0i,t*OPCFi,t Base level 0.0307** -0.0221

(2.53) (-0.79) 0/0i,t*OPACCi,t Base level 0.0301** -0.0119

(2.47) (-0.51) Intercept 0.0160*** 0.0160*** 0.0158*** 0.0162***

(4.29) (3.97) (4.69) (3.43) Industry FE YES YES YES YES

Year FE YES YES YES YES

N 39,553 39,553 39,553 39,553 Adjust R. sq. 0.3932 0.3939 0.3939 0.3939 This table shows standard earnings persistence regressions, and how earnings persistence differs by the composition of criminal executives and criminal employees. In this regression table, current earnings are separated into comprehensive operating cash flows (OPCF) and comprehensive operating accruals (OPACC), respectively. CrimEXEC indicates that the majority of executives have a criminal record. CrimEMPL indicates that the workforce is relatively criminal, and takes the value one when the percentage of employees with a criminal record is above the within-year median. 1/1 is an indicator taking the value one if CrimEXEC=1 and CrimEMPL=1. 1/0 is an indicator taking the value one if CrimEXEC=1 and CrimEMPL=0. 0/1 is an indicator taking the value one if CrimEXEC=0 and CrimEMPL=1. 0/0 is an indicator taking the value one if CrimEXEC=0 and CrimEMPL=0. Standard errors are clustered by firm and year (Gow et al. 2010). t statistics in parentheses. ***, **, * Represent significance levels at 0.01, 0.05, and 0.10, respectively (two-tailed test). All continuous variables are winsorized at the 1 and 99 percent level.

belief that the presence of criminal record is an observable outcome of a certain personal trait. Additionally, the criminal registers cover only Danish citizens and foreigners with a Danish address and hence (1) persons with a criminal record from a country not Denmark and (2) foreigners not residing in Denmark, working in Danish companies are not covered by the sample. Based on employer-employee data provided by Statistics Denmark, I find that 98% of

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executives are Danish citizens, and 93% of employees are Danish citizens, and hence the limitation seems of minor importance, and bias against the findings of this paper. Also, the criminal registers cover crimes dating back to 1980, and hence there is a risk that individuals are classified as non-criminal, albeit they have been convicted prior to 1980. This is particularly pertinent to the early years of the firm-year observations used in this dataset.

Further, my conclusions are subject to the standard caveat of whether discretionary accruals during events where a firm issues new finance actually capture earnings management. Several prominent researchers have raised concerns with accrual estimation models (see e.g. Ball 2013). However, I exploit recent academic advancements in accrual estimation techniques enhancing my ability to distinguish normal from discretionary accruals (Godsell et al. 2017). Further, the insights from Figure C.2 (that firms with criminal executives and criminal employees have income-increasing accruals when the firm issues new finance but not in the preceding and following years) corroborate my interpretation that the measure of discretionary accruals actually captures earnings management. Further, the findings outside discretionary accruals – that these firms are more likely to meet or beat last year’s earnings benchmark, and have lower earnings persistence – conform to an overall story that these firms are more likely to manage earnings.