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
SEC_TIER, load in our models as we expect them to or, where no prediction is stated, load consistently across the different proxies. Our BTD proxy of tax avoidance increases with NOL and BTM but our ETR and CETR proxies of tax avoidance decrease with respect to these variables. Finally, EQINC and PPE generally show consistent associations with our tax avoidance and aggressiveness proxies, except for DTAX and UTBSC. Matched samples Our matched samples allow us to control for the aforementioned biases and to more appropriately assess the influence that specialists could have on their client’s level of tax avoidance. Each panel in Table 4 shows the results for three different matched samples – match on all control variables, match on all control variables except for APTS, and match only on size, industry, and year. Each panel also shows results for both of our market-share expertise variables, TAXEXPERT and OVERALLEXPERT. In Table 4, Panel A, only our ETR proxy of tax avoidance is associated with expertise using the joint audit and tax market share OVERALLEXPERT, in the “All control variables” matched sample (Column IV, OVERALLEXPERT = -0.008*). The negative association in this regression model is consistent with our baseline model; however, the general insignificance of the expertise variables across the various matched samples implies that the use of a specialist is not consistently associated with tax avoidance. None of the APTS variables are significant across these regressions. In Table 4, Panels B and C, our CETR and BTD tax avoidance proxies are not significantly associated with either measure of expertise. Generally, these tax avoidance proxies are not significantly associated with the APTS variables, with the exception of APTS_HD in Panel C, Column II (APTS = -0.004**).
In Table 4, Panel D, our DTAX proxy of tax avoidance is associated with expertise using the joint audit and tax market share OVERALLEXPERT, in the “All control variables” matched sample (Column IV, OVERALLEXPERT = 0.029*). The positive coefficient on OVERALLEXPERT suggests higher levels of tax avoidance. The APTS variables in this panel are also generally insignificant, with the exception of positive coefficients on APTS in Column III (APTS = 0.039**) and Column IV (APTS = 0.049**) Finally, Table 4, Panel D shows that our proxy for tax aggressiveness UTBSC is not significantly associated with expertise in any of our matched samples. The control variables are also generally insignificant, although we find a statistically negative coefficients on APTS in Column V (APTS = -0.003*). This coefficient suggests lower tax aggressiveness in firms that use their auditor for tax services. As a whole, the results in Table 4 show a lack of consistent evidence that tax avoidance and tax aggressiveness are associated with market-share measures of tax expertise. Specifically, we find no association between TAXEXPERT and tax avoidance in any of our matched samples. In addition, little evidence exists to support an association between tax avoidance or tax aggressiveness and the market-share measure of overall expertise OVERALLEXPERT. There are two important issues concerning these results. First, the weak results using the OVERALLEXPERT measure and the statistically insignificant results using the TAXEXPERT measure are difficult to interpret, given that TAXEXPERT is expected to have a more direct impact on tax avoidance. Second, the difference in results between the book-tax rate ETR proxy and the cash-tax rate CETR proxy using the OVERALLEXPERT measure casts doubt on whether there is a true pattern of tax avoidance in the data.
VII. ADDITIONAL AND SENSITIVITY ANALYSES Full samples including client fixed effects As an alternative to our matched sample tests, we estimate our models using the full sample of observations with client fixed effects included. This approach mitigates bias in our baseline tests that could result from the omission of static client characteristics. As mentioned in Section III, these fixed effects models can be interpreted as the effect of clients alternating between specialist and non-specialist auditors. Consistent with our results from the matched samples, in Table 5 we find no evidence of an association between our tax avoidance proxies and TAXEXPERT and no association between our CETR and BTD tax avoidance proxies and OVERALLEXPERT. Furthermore, our tax aggressiveness proxy UTBSC also lacks an association with our expertise measures. The negative association between ETR and OVERALLEXPERT (Column VI, OVERALLEXPERT = -0.007*) and the positive association between DTAX and OVERALLEXPERT (Column VIII, OVERALLEXPERT = 0.031**) is consistent with our matched results. A potential limitation of fixed-effect models is that they may have low power when the underlying variables vary slowly over time (Li and Prabhala 2007); however, these results are incremental to our match sample analyses and appear to confirm the results in Table 4. Alternative measures of expertise based on the size and importance of the tax function at the office-level Francis and Yu (2009) provide evidence that large auditor offices have higher audit quality than small auditor offices. Their evidence suggests that large offices may have more collective experience in auditing public companies. We propose two alternative measures of tax expertise, related to the size and importance of the tax function at the city level. Francis and Yu