load in our models as we expect them to or, where no prediction is stated, load SEC_TIER, consistently across the different proxies. Our proxy of tax avoidance increases with BTD NOL and but our and proxies of tax avoidance decrease with respect to these BTM ETR CETR variables. Finally, and generally show consistent associations with our tax EQINC PPE avoidance and aggressiveness proxies, except for and DTAX 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, and TAXEXPERT OVERALLEXPERT. In Table 4, Panel A, only our proxy of tax avoidance is associated with expertise ETR using the joint audit and tax market share in the control variables” OVERALLEXPERT, “All matched sample (Column IV, = -0.008*). The negative association in this OVERALLEXPERT 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 and tax avoidance proxies are not CETR BTD significantly associated with either measure of expertise. Generally, these tax avoidance proxies are not significantly associated with the APTS variables, with the exception of in APTS_HD Panel C, Column II (APTS = -0.004**).
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**).
Meza-BMS-Sept-Revised-auditors-industry-specialization-a-reliable-indicator-of-tax-avoidance.docx