Note(s): Presented in Table 8 are the results for the ITVC analysis. Presented in Column 1, 2, and 3 is the impact of each control variable on the coefficients of accounting quality variables (i.e. Big 1, Earning Predict CF, and Abnormal Accruals), which is the product of the partial correlation between that control variable and the dependent variable (i.e. Discretionary Bonus Weight) and the partial correlation between that control variable and one of the accounting quality variables. Big4 and Earning Predict CF have negative coefficients in regressions of Discretionary Bonus Weight, and a control variable with a positive (negative) impact factor will make the coefficients more (less) negative. Abnormal Accruals has a positive coefficient in regressions of Discretionary Bonus Weight, and a control variable with a negative (positive) impact factor in the regression will make the coefficient more (less) positive. ITCV is the lowest product of the partial correlation between the dependent variable and the unobserved confounding variable and the partial correlation between the accounting quality variable and the confounding variable that makes the coefficient statistically insignificant. Ratio of ITCV to largest impact of control variables indicates how impactful an unobserved confounding variable needs to be to make the coefficients of accounting quality variables insignificant. For example, the impact of an unobserved confounding variable needs to be at least 4.02 times larger than the most impactful control variable to make the coefficient of Big4 insignificant