Are winners promoted too often? Evidence from the NFL draft 1999-2012.

I. INTRODUCTION Recruiting new employees is a costly activity for firms. Firms must advertise the position, review resumes, interview job candidates, and ultimately make hiring decisions. Given the costs associated with the search, firms may seek ways to reduce the burden of recruitment. For example, firms may send representatives to university job fairs at their employees' alma maters or offer bonuses to current employees that refer qualified applicants to the firm. As a result of these recruitment methods, qualified applicants from certain institutions, or without the right connections, may find it difficult to secure employment at a particular firm. If firms associate characteristics of the institution of the referring employee with the job candidate, then cost reducing hiring procedures may result in statistical discrimination. As a consequence, high-ability applicants without connections might have lower initial placements than their more connected equals. These hiring practices likely have little impact on the firm's productivity due to their ability to replace underperforming employees, yet the overlooked job candidates may suffer from reduced earnings over their lifetime due to their initial position in the labor market. (1) Evaluating claims of statistical discrimination based on institutional affiliation in most labor markets is a difficult task. Often times, firms do not subject applicants to prescreening exams to measure ability, and if they do, the exam is likely a small component of the selection process. Thus, it is usually not possible to determine if a particular employee was hired due to his or her ability or institutional affiliation. If firms do subject workers to testing, they are likely hesitant to hand over their human resources data. Owing to these data limitations, several authors have turned to the world of high stakes athletic competitions where highly detailed individual-level data are readily available. Data from professional sports leagues have provided several authors the opportunity to examine other forms of discrimination, such as the existence of racial wage gaps (Kahn 1991, 1992, 2000; Kahn and Sherer 1988; McCormick and Tollison 2001). To study the effects of statistical discrimination on job placement and long-run career outcomes, I follow a similar approach by studying a novel setting in professional sports: the National Football League (NFL) draft process. Every spring, college football players declare for the NFL draft. As a part of the draft, the players are subjected to a battery of physical and mental tests at the NFL combine. At the combine, players are tested on speed, agility, strength, and other measures of their overall athletic ability. In addition to these formal tests, professional scouting services rank the players by position according to their ability witnessed during their collegiate career, individual pro days hosted at their home universities, and tryouts at NFL team facilities. These scouting services make these data available to teams and fans for a fee but are not affiliated with the NFL or any member organization as teams employ their own scouts. (2) Prior to joining the NFL, players compete in college on teams that have different levels of success. Team-level success is measured every week from beginning to the end of the college season resulting in a final ordinal ranking of all teams in the National Collegiate Athletic Association (NCAA) for a given division. Examining players entering the NFL makes it possible to determine if players from recently successful institutions are given better job opportunities than equally skilled players from less successful institutions. To measure the impact of being affiliated with a winning program on draft position, I correlate the ranking of an individual's college team in the year prior to their entry in the draft with their NFL draft pick position. One major concern is that there is unobserved player ability that is correlated with their team ranking. …