reveals that these variables have non-linear relationships with the proxies for tax avoidance. Moreover, potential non-linearity is indicated by the Ramsey RESET test, using powers of the fitted values of the dependent variables. The null of no omitted variables is rejected in our tax avoidance models. A linear regression may increase bias in the estimation of treatment effects when there are even moderately nonlinear relations between the dependent and independent variables. This problem is exacerbated when there are significant differences in means and variances in the independent variables between treated and control groups (see Rubin 1979; Heckman et al. 1998; Rubin and Thomas 2000; and Rubin 2001). The aforementioned conditions appear in our analyses, (1) client size has a non-linear relationship with the tax avoidance proxies and (2) client size is different between specialist and non-specialist auditors. The specialist variable may be capturing the effect of size non-linearity due to the correlation between the specialist choice and client size. Matching on client characteristics may mitigate the non-linearity problem as it aims to balance characteristics between treated and control groups (e.g., average size is the same for specialist and non-specialist auditors) and to eliminate the correlation between the treatment variable and other variables (e.g., after matching the specialist variable and client size are uncorrelated). Finally, regressions may also suffer from misspecification in the form of omitted variables. The omitted variables could be either transformations of the variables currently in the model, such as omitting the square of client size or other unobservable variables. If the omitted variables are correlated with the matching variables the matching process may mitigate this problem; however, this is an inherently difficult problem to address. Another potential way to mitigate the effect of correlated omitted variables is including client fixed
reveals that these variables have non-linear relationships with the proxies for tax avoidance. Moreover, potential non-linearity is indicated by the Ramsey RESET test, using powers of the fitted values of the dependent variables. The null of no omitted variables is rejected in our tax avoidance models. A linear regression may increase bias in the estimation of treatment effects when there are even moderately nonlinear relations between the dependent and independent variables. This problem is exacerbated when there are significant differences in means and variances in the independent variables between treated and control groups (see Rubin 1979; Heckman et al. 1998; Rubin and Thomas 2000; and Rubin 2001). The aforementioned conditions appear in our analyses, (1) client size has a non-linear relationship with the tax avoidance proxies and (2) client size is different between specialist and non-specialist auditors. The specialist variable may be capturing the effect of size non-linearity due to the correlation between the specialist choice and client size. Matching on client characteristics may mitigate the non-linearity problem as it aims to balance characteristics between treated and control groups (e.g., average size is the same for specialist and non-specialist auditors) and to eliminate the correlation between the treatment variable and other variables (e.g., after matching the specialist variable and client size are uncorrelated). Finally, regressions may also suffer from misspecification in the form of omitted variables. The omitted variables could be either transformations of the variables currently in the model, such as omitting the square of client size or other unobservable variables. If the omitted variables are correlated with the matching variables the matching process may mitigate this problem; however, this is an inherently difficult problem to address. Another potential way to mitigate the effect of correlated omitted variables is including client fixed
Meza-BMS-Sept-Revised-auditors-industry-specialization-a-reliable-indicator-of-tax-avoidance.docx