Non-Standard Errors

In statistics, samples are drawn from a population in a data‐generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence‐generating process (EGP). We claim that EGP variation across researchers adds uncertainty—nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer‐review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Nonstandard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Essays on corporate finance

This dissertation contains two chapters that are related with corporate finance and law. Below are the individual abstracts for each chapter. Chapter 1: Do Patent Lawsuits Cause M&A? An Experiment Using Uncertain Lawsuits I investigate whether there exists a causal relation between result of a patent lawsuit and alleged infringer's subsequent M&A activity. I find that if the court gives an infringement decision, then the infringer sharply increases spending on focused M&A and decreases on diversifying M&A. Moreover, the infringer specifically acquires targets that have substitute patents so that it can redesign its products or form a shield against future lawsuits. Patent motivated acquisition channel is new to our literature and different than the traditional knowledge transfer channel. For the experiment, I hand collect detailed data on all patent lawsuits that were appealed to Court of Appeals for the Federal Circuit (CAFC). In this court, decisions are given by majority in randomly assigned 3 judge panels. In a setting that resembles regression discontinuity design, I use only the lawsuits where there was a dissenting judge (i.e, decision was given by 2 to 1). Since CAFC is the only appellate court for patents and has federal jurisdiction, my experiment is not subject to endogeneity problem stemmed from court selection. This is the first paper to use dissenting judge lawsuits for identification strategy. The same approach be can be generalized to other types of litigations. Chapter 2: Do Uncertainties in Bankruptcy Law Affect Optimal Loan Contracts? A Quasi Natural Experiment I investigate whether uncertainties in bankruptcy procedures shape financial contracting in the U.S. syndicated loan market. Utilizing a novel hand-collected data set, I exploit the application of substantive consolidation procedure in the U.S. bankruptcy courts. This procedure has two unique features. First, it removes seniorities granted in the original con- tracts, resulting unexpected huge losses on unsecured bank loans. Second, there is consensus among practitioners that its application is unpredictable since there is no specific provision in the U.S. Code. I find that after exposure, lenders transmit this shock to other clients as requiring collateral more often in their new loans. Moreover, if exposed lenders issue new unsecured loans, then they demand higher interest rate and tighter covenants, even control- ling for bank capitalization, borrower and time fixed effects. To my knowledge, this is the first paper to show that uncertainties in the bankruptcy procedures provide an important friction in the loan market. Furthermore, this work complements the previous literature by providing a new channel for the determinants of optimal financial contracts. Results of this paper are also important for policy makers, who want to ease bank lending standards