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 and no association between TAXEXPERT our and tax avoidance proxies and Furthermore, our tax CETR BTD OVERALLEXPERT. aggressiveness proxy also lacks an association with our expertise measures. The negative UTBSC association between and (Column VI, = -0.007*) ETR OVERALLEXPERT OVERALLEXPERT and the positive association between and (Column VIII, DTAX OVERALLEXPERT = 0.031**) is consistent with our matched results. A potential limitation of OVERALLEXPERT 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
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
Meza-BMS-Sept-Revised-auditors-industry-specialization-a-reliable-indicator-of-tax-avoidance.docx