| My dissertation consists of three essays. Chapter 1 tests the hypothesis that bidders in stock-for-stock acquisitions manipulate earnings more aggressively around merger transactions than otherwise similar firms. Using data on 449 merger bids between 1989 and 2000, we find that stock acquirers' accruals are abnormally high before announcing merger bids, while cash acquirers' are not. Incentives for earnings management are stronger for relatively undervalued firms, cash constrained acquirers, and firms that receive less attentive market supervision. Finally, we find that pre-merger earnings manipulation predicts post-merger underperformance.; Chapter 2 measures the impact of crime on corporate investment by exploiting variation in kidnappings in Colombia from 1996 to 2002. The central result is that firms invest less when kidnappings target firms. By contrast, aggregate crime rates---like homicides or guerrilla attacks---have no significant effect on investment. This finding alleviates concerns that our main result may be driven by unobserved variables that explain both overall criminal activity and investment. Furthermore, kidnappings that target firms reduce not only the investment of firms that sell in local markets, but also the investment of firms that sell in foreign markets. The results are consistent with the hypothesis that managers are reluctant to invest when their freedom and life are at risk.; Chapter 3 analyzes the validity of the bootstrap and higher-order expansions in an IV setting with weak instruments. It is well known that size-adjustments based on Edgeworth expansions for the t-statistic perform poorly when instruments are weakly correlated with the endogenous explanatory variable. This paper shows, however, that the lack of Edgeworth expansions and bootstrap validity are not tied to the weak instrument framework, but instead depends on which test statistic is examined. In particular, Edgeworth expansions are valid for the score and conditional likelihood ratio approaches, even when the instruments are uncorrelated with the endogenous explanatory variable. Contrary to the belief that bootstrap methods fail when instruments are weak, we provide a proof of the validity of the bootstrap for the score test and the validity of the conditional bootstrap for many conditional tests. Monte Carlo simulations show that the bootstrap actually decreases size distortions in both cases. |
