INDICADORES BASADOS EN CARACTERÍSTICAS DE LOS PACIENTES

This section discusses robustness tests of the empirical results. First, I consider the influence of outliers in the data. I note that I use the natural logarithm transformations LN Deal Value to Sales and LN Private to Public Value in empirical tests to mitigate the influence of outliers. To further examine the influence of outliers on my empirical results, I winsorize the data at the 10th and 90th percentiles. I find that the average of Deal Value to Sales falls from 15.49 when winsorization is at the 1st and 99th percentiles to 3.20 when winsorization is at the 10th and 90th percentiles. The average of LN Deal Value to Sales falls from 0.76 to 0.65 while the average of LN Private to Public Value falls from 0.07 to 0.001.

I replicate Tables 5 through 9 on the sample of sellouts where winsorization is done at the 10th and 90th percentiles and present the coefficients on the main variable of interest in Table 11. Panels A, B, C, and D show the coefficient on Dereg State x After from replications of Tables 5, 6, 7, and 8, respectively. Panel E shows the coefficient on Dereg State x After x Small from replications of Table 9, Panel A, columns (1) through (4). Panel F shows the coefficient on

Dereg State x After x Small from replications of Table 9, Panel A, columns (5) through (8). I do not present replication results of Table 9, Panel B because the results in the original panel (and in replications of that panel) are not statistically significant. The results in Table 11, Panels A through F are very similar to the original results in Tables 5 through 9. Thus, my empirical results do not appear to be driven by large outliers in the data.

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[Insert Table 11: Robustness Test]

Second, I examine whether or not my results are robust to excluding private targets headquartered in California. This robustness test addresses two concerns. First, private targets headquartered in California represent 17.06% of the sample (95 deals), much more than any other state. Therefore, private targets in this state could have a large influence on my tests. Second, California is home to many “internet firms” during the latter part of the sample period. Such firms can receive systematically high valuations and drive my results. I exclude 95 private targets headquartered in California and replicate the empirical results in Tables 5 through 9. Table 12 summarizes the findings and is structured the same as Table 11. As show in Table 12, the empirical results in Panels A, B, C, D, and F are similar to the original results. However, Panel E shows that the statistical significance of the coefficient on Dereg State x After x Small is noticeably reduced in the first two regressions. In columns (1) and (2) the coefficient is not statistically significant while in columns (3) and (4), it is significant at the 10% and 1% levels, respectively. Overall, the main results in the paper are robust to excluding private targets headquartered in California.

[Insert Table 12: Robustness Test]

Third, factors that influence a state to deregulate or not may also be correlated with private firm valuations. For example, firms with large growth opportunities may lobby the state to deregulate interstate bank branching. In that case, IBBEA may not be a truly exogenous event. I conduct a robustness test which eliminates deals in states that are very likely to deregulate or not (i.e., the test retains deals in states where the choice to deregulate is less predictable). Motivated by Kroszner and Strahan’s (1999) analysis of the factors that influence deregulation, I estimate the likelihood that each state deregulates based on the following

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independent variables: small bank asset share of bank assets in the state, small firm share of firms in the state, whether or not banks can sell insurance in the state, relative size of insurance and banking sectors in states where banks can (cannot) sell insurance, state income growth, and a dummy if the governor of the state is a Democrat.16 Then, I eliminate 112 deals (20.1% of the sample) where private targets are headquartered in states that had a high likelihood of making the choice that they eventually made. These deals are headquartered in the two deregulating states with the highest likelihood of deregulating (Arizona and California) and the two non- deregulating states with the lowest likelihood of deregulating (Arkansas and Iowa). Table 13 summarizes the findings and is structured the same as Table 11. Table 13, Panels A through F, show that the results are qualitatively similar to the original results in Tables 5 through 9. In Appendix B: Supplemental Results, I exclude 125 deals in eight states that are likely to deregulate or not and 151 deals in twelve states that are likely to deregulate or not and continue to find similar results. Thus, factors that influence the state’s choice to deregulate do not appear to drive my results.

[Insert Table 13: Robustness Test]

Fourth, I calculate LN Private to Public Value using different matching criteria. Similar to the results presented above, I match private targets to public targets where the takeover involved a public acquirer who purchases 100% of the public target’s equity and has no toehold in the target. Now, I alter the matching criteria in four ways: 1) allow failed takeovers as long as the public acquirer sought 100% of the equity, 2) allow failed takeovers and include deal values within +/-50% of the private target’s deal value, 3) choose public targets where the deal is announced within 3 years of the target’s home state’s response to IBBEA, and 4) match on Fama French 48 industry codes rather than two-digit SIC industry codes. In Appendix B: Supplemental

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Results, I show that my empirical results are similar using each of these four different matching criteria.

Fifth, I construct Acquirer CAR and Acquirer Wealth to Deal Value based on announcement returns measured over the (-2, +2) day window around the deal announcement. The results are shown in Appendix B: Supplemental Results and are largely similar to those reported in Tables 7 and 8. For example, for sellouts announced two years before to two years after the state’s response to IBBEA, with Acquirer CAR measured over the (-2, +2) day window, the coefficient on Dereg State x After is -3.792 (significant at the 5% level) in the specification without state fixed effects and is -5.134 (significant at the 1% level) in the specification with state fixed effects.

Finally, I examine an alternative definition of deregulation that groups deregulating states into finer categories based on the number of restrictions that were relaxed. The variable Dereg

State is decomposed into four dummy variables according to whether the state relaxed one, two,

three, or four restrictions on interstate bank branching. The results are presented in Appendix B: Supplemental Results. Under this definition of deregulation, I obtain similar conclusions as under the original definition of deregulation. Deregulation results in higher valuations for private targets, higher valuations for private targets benchmarked to public targets, and acquirer returns are lower.