Logistic regression model measuring the impact of OC on the probability of receiving a M_O
| Variable | Coefficients (robust std. errors clustered per firm– Petersen (2009) | |||||
|---|---|---|---|---|---|---|
| Exp. Sign | Basic | Rob. Std. errors | Extended | Rob. Std. errors | ||
| β0 | Const | −3.325*** | (0.273) | 2.376 | (4.481) | |
| β1 | OrCi,t | + | 0.169*** | (0.013) | 0.185*** | (0.013) |
| β2 | A_ACi,t | + | 0.282 | (0.487) | ||
| β3 | B4 | + | −0.019 | (0.278) | ||
| β4 | ROA | – | −0.351** | (0.137) | ||
| β5 | SIZE | – | −0.204*** | (0.049) | ||
| β6 | LEV | + | 0.988** | (0.300) | ||
| β7 | INVREC | + | −1.155 | (0.726) | ||
| β8 | ZSCORE | + | 0.351** | (0.146) | ||
| β9 | LS | + | −0.103 | (0.221) | ||
| β10 | GDP | ? | −0.001 | (0.000) | ||
| Sample firms (balanced): N° firms: 423 N° obs.: 4,230 VIF < 5 for all variables | Basic’s model diagnostic: LLR p-value = 0.000** Pseudo R-sq. = 21.3% Aic: 1,576.28 Bic: 1,646.13 Year control: yes Industry control: yes Country control: yes | Extended’s model diagnostic: LLR p-value = 0.000*** Pseudo R-sq. = 29.1% Aic: 1,446.53 Bic: 1,592.57 Year control: yes Industry control: yes Country control: yes | ||||
| Variable | Coefficients (robust std. errors clustered per firm– Petersen (2009) | |||||
|---|---|---|---|---|---|---|
| Exp. Sign | Basic | Rob. Std. errors | Extended | Rob. Std. errors | ||
| Const | −3.325 | (0.273) | 2.376 | (4.481) | ||
| + | 0.169 | (0.013) | 0.185 | (0.013) | ||
| + | 0.282 | (0.487) | ||||
| B4 | + | −0.019 | (0.278) | |||
| ROA | – | −0.351 | (0.137) | |||
| SIZE | – | −0.204 | (0.049) | |||
| LEV | + | 0.988 | (0.300) | |||
| INVREC | + | −1.155 | (0.726) | |||
| ZSCORE | + | 0.351 | (0.146) | |||
| LS | + | −0.103 | (0.221) | |||
| GDP | ? | −0.001 | (0.000) | |||
| Sample firms (balanced): | Basic’s model diagnostic: | Extended’s model diagnostic: | ||||
Notes:
The table shows findings for the binary regression. We use the maximum likelihood estimation (MLE) that aims to find the values of coefficients that maximize the likelihood of the observed data. The dependent variable is M_O, the modified audit opinion issued by an auditor. The testing variable is OrC, proxying the measure of the organizational capital estimated as in Lev et al. (2009). The basic model only investigates the relationship between the dependent variable M_O and the test variable OrC. The extended model also includes some control variables impacting the probability that a modified audit opinion will be used. Several control variables impacting the probability that a modified audit opinion is issued are added in the model extended. To avoid heteroscedasticity and multicollinearity problems, the model in equation (3) is estimated using Petersen et al. (2009). In brackets, the standard errors are shown. Variable descriptions and measurements are provided in Table 2. ***indicates a significance of 1% and **indicates a significance of 5%