Table 5

Effect of bank stress tests on bank effective tax planning

VariableAll relevant banksMSE optimal
LinearQuadraticLinearQuadratic
Effective tax planning score1−0.176**−0.212*−0.284***−0.856***
(0.068)(0.125)(0.064)(0.124)
[226][226][68][68]
Effective tax planning score2−0.198***−0.241*−0.373***−1.437***
(0.072)(0.138)(0.080)(0.120)
[222][222][64][64]
ControlsYYYY
Fixed-effectsYYYY

Note(s): This table reports the regression discontinuity coefficients for bank effective tax planning. The table reports both the local linear and quadratic regression discontinuity coefficients first by specifying a sub-sample of “All Relevant Banks,” which includes banks between $10B and $200B in total asset size, and second, by using the MSE optimal bandwidth measure. The table reports the results for the Effective Tax Planning measures from Schwab et al. (2022) based on the tax planning paradigm described in Scholes et al. (1992). Effective Tax PlanningScoreit was created using (Schwab et al., 2022)’s full sample of firms while Effective Tax PlanningScore2 uses only profitable firms in the sample. Regressions are triangular kernel weighted and are allowed to capture more banks in a larger bandwidth in the first two columns and are restricted to having at least 65 observations within the selected optimal bandwidth in the last two columns. Control variables include the first 12 bank characteristics shown in Table 2. All regressions include bank and year-fixed effects. Standard errors are clustered at the bank level and are reported in parentheses while observation counts are reported in brackets. ***, ** and * indicate significance at the 1%, 5% and 10% levels, respectively

Source(s): Authors’ own work

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