Capitol Gains: the Returns to Elected Office from Corporate Board Directorships

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Capitol Gains: the Returns to Elected Office from Corporate Board Directorships Capitol Gains: The Returns to Elected Office from Corporate Board Directorships Online Appendix A Robustness Checks This section of the appendix presents a series of robustness checks that illustrate how the effects we report hold up under a variety of different specifications and bandwidths. We consider a host of alternative approaches meant to deal with potential bias in construction of the sample as well as the outcome measures. We also report results from placebo tests. 1 A.1 Alternate Functional Forms Table A1: Fuzzy Regression Discontinuity (First Election): Effect of Holding Office on Pr(Board) (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Senator, Bandwidth = ±:5, N = 429 Estimate 0.255∗∗∗ 0.295∗∗∗ 0.219∗∗∗ 0.200∗∗ 0.288∗∗∗ 0.156 0.179 0.338∗∗ (0.0705) (0.0784) (0.0835) (0.0931) (0.0777) (0.100) (0.126) (0.157) Panel B: Governor, Bandwidth = ±:5, N = 375 Estimate 0.261∗∗∗ 0.288∗∗∗ 0.276∗∗∗ 0.277∗∗∗ 0.284∗∗∗ 0.278∗∗∗ 0.300∗∗ 0.265∗ (0.0682) (0.0751) (0.0872) (0.0890) (0.0733) (0.0990) (0.127) (0.155) Panel C: Senator, Bandwidth = ±:2, N = 370 Estimate 0.197∗∗ 0.203∗∗ 0.227∗∗ 0.174 0.193∗∗ 0.224∗ 0.468∗∗∗ 0.263 (0.0836) (0.0953) (0.108) (0.121) (0.0932) (0.134) (0.180) (0.218) Panel D: Governor, Bandwidth = ±:2, N = 341 Estimate 0.271∗∗∗ 0.277∗∗∗ 0.282∗∗∗ 0.298∗∗∗ 0.278∗∗∗ 0.296∗∗ 0.260∗ 0.467∗∗ (0.0794) (0.0843) (0.0993) (0.104) (0.0829) (0.116) (0.149) (0.192) Linear Yes Yes Yes Yes Yes Yes Yes Yes Quadratic No Yes Yes Yes No Yes Yes Yes Cubic No No Yes Yes No No Yes Yes Quartic No No No Yes No No No Yes Separate Fit No No No No Yes Yes Yes Yes Standard errors in parentheses Standard errors are clustered at the state-year level. ∗ p < 0:10, ∗∗ p < 0:05, ∗∗∗ p < 0:01 Table A1 reports results for regressions where the outcome is a binary variable indicating whether a candidate served on a board. It illustrates the robustness of the result for senators and governors across specifications up to a fourth degree polynomial as well as separate fits on each side of the threshold. 2 Table A2: Fuzzy Regression Discontinuity (First Election): Effect of Holding Office on Boards Per Year (1) (2) (3) (4) (5) (6) (7) (8) Panel A: Senator, Bandwidth = ±:5, N = 429 Estimate 0.466∗∗∗ 0.511∗∗∗ 0.381∗∗ 0.331∗ 0.496∗∗∗ 0.229 0.163 0.388 (0.145) (0.162) (0.172) (0.195) (0.160) (0.214) (0.272) (0.341) Panel B: Governor, Bandwidth = ±:5, N = 375 Estimate 0.444∗∗∗ 0.482∗∗∗ 0.466∗∗∗ 0.469∗∗∗ 0.484∗∗∗ 0.502∗∗∗ 0.640∗∗∗ 0.609∗∗ (0.116) (0.131) (0.158) (0.164) (0.129) (0.180) (0.241) (0.299) Panel C: Senator, Bandwidth = ±:2, N = 370 Estimate 0.338∗∗ 0.294 0.301 0.194 0.283 0.250 0.644 0.371 (0.171) (0.205) (0.224) (0.259) (0.199) (0.288) (0.396) (0.522) Panel D: Governor, Bandwidth = ±:2, N = 341 Estimate 0.429∗∗∗ 0.434∗∗∗ 0.560∗∗∗ 0.634∗∗∗ 0.445∗∗∗ 0.671∗∗∗ 0.499∗ 0.698∗ (0.141) (0.162) (0.178) (0.190) (0.156) (0.220) (0.291) (0.363) Linear Yes Yes Yes Yes Yes Yes Yes Yes Quadratic No Yes Yes Yes No Yes Yes Yes Cubic No No Yes Yes No No Yes Yes Quartic No No No Yes No No No Yes Separate Fit No No No No Yes Yes Yes Yes Standard errors in parentheses Standard errors are clustered at the state-year level. ∗ p < 0:10, ∗∗ p < 0:05, ∗∗∗ p < 0:01 Table A2 reports results for regressions where the outcome is boards per year. It illustrates the robustness of the result for senators and governors across specifications up to a fourth degree polynomial as well as separate fits on each side of the threshold. 3 A.2 Sample Selection and Bias in Outcome Measures The empirical strategy used for our main results seeks to minimize bias while still including enough observations to estimate an effect with some degree of precision. However, the approach we take (constructing a sample of all officials who held or ran for office in 1992 and after and measuring board service with (1) a binary outcome and (2) boards per year) is still a second-best approach. Ideally, we would construct a sample of first-time candidates who ran against each other in close elections, and use a binary outcome to estimate effects along the extensive margin and a measure of earnings to estimate effects along the intensive margin. However, simply not enough of these cases exist for application to Senate candidates, and the approach restricts sample size considerably for governors as well. In addition, there is substantial missingness in the yearly compensation data for board directors, which limits the utility of an estimate in dollars. Departing from the ideal estimation strategy introduces potential bias along two dimen- sions: (1) who is included in the sample, and (2) how board service is measured. The potential problems with sample selection arise in part from including members of Congress or governors who were currently serving in 1992. Including these candidates leads to some degree of imbalance in the year of first election. It might appear that including only can- didates who ran for the first time in 1992 or later would remedy this problem. However, this approach too would lead to imbalance in the sample. The reason is that, due to the advantages of incumbency, winners in 1992 are likely to run and win again (possibly even multiple times) against first time candidates. Imbalance between winners and losers in the year of first election could lead to biased estimates if, for example, there were differences in the competitiveness of elections or the availability of board seats over time, or if unobserv- able candidate characteristics that were correlated with board service varied across cohorts. A second sample selection problem occurs when long-serving officeholders do not leave office and are therefore never eligible to serve on a board. Under this scenario, their board service 4 is a censored outcome. While this form of selection almost surely leads us to understate the effect of holding office on board service, it nonetheless leads to bias in our estimates. Our measurements of outcomes (i.e., board service and boards per year) might lead to biased estimates because they are affected by how much time a candidate has spent out of office after running for senator or governor. Spending more time in office translates to less time available once retired to serve on a board.36 For our binary outcome measuring whether a candidate ever served on a board, the bias likely cuts against finding an effect of holding office on board service. Fewer years out of office would mean that office-holders have fewer opportunities to serve. However, the boards-per-year measure may well be biased in favor of finding an effect. Because boards per year includes years eligible in the denominator and winners who spend time in office have fewer years eligible than losers, this outcome may be artificially inflated for winners.37 While no single solution addresses all the issues discussed above, we present a range of different checks to explore sensitivity of our main results to different sample constructions and based on different outcome measures. Comparing these results with our main specification reveals the extent to which bias drives the main results. We consider five possible corrections (1) We include election year fixed effects to control for potential differences across cohort. We also estimate the effects of holding office on board service (2) among first time candidate pairs, (3) in states with term-limits for governors, (4) for candidates who were only elected after 1992, and (5) for two-year windows after leaving office. Including election year fixed effects means that we condition on cohort and then find the difference in board service between winners and losers at the discontinuity, essentially con- trolling for differences across cohorts. Table A3 presents the results. Across all bandwidths, 36Thanks to anonymous reviewers for advice on this point. 37Of course, winners also have fewer opportunities to serve on a board if they are eligible for fewer years, so the direction of the bias is not at all clear. 5 we find a positive effect of holding office on board service. At the narrowest bandwidths of ±:05 and ±:1, three of the four estimates have a 95% confidence interval that does not overlap with zero. These findings suggest that imbalances across cohorts are not responsible for our results.38 Next, we restrict our sample only to first time candidate pairs. This approach eliminates the selection bias in the construction of our sample entirely. Any concern over bias induced by time trends should disappear because the sample is now balanced in terms of first election year by construction.39 While restricting the sample to only open seat elections reduces the number of observations drastically, the estimates nonetheless provide additional evidence that serving as governor results in subsequent board service. In Table A5 we report a positive effect ranging between 0.348 and 0.471 additional boards per year that results from gubernatorial service. For the Pr(Board) outcome, all coefficients are positive, though the confidence intervals tend to overlap with zero. In support of our argument that the matched pairs analysis provides an ideal research design for assessing the effects of holding office on board service, we also present Table A9, which reports results from performing a placebo test on pre-treatment covariates for the matched-pair specification.
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