The patterns of comorbidity among prevalent mental disorders in adults lead them to load on ‘externalizing,’ ‘distress,’ and ‘fears’ factors. These factors are themselves robustly correlated, but little attention has been paid to this fact. As a first step in studying the implications of these inter-factor correlations, confirmatory factor analyses were conducted on diagnoses of 11 prevalent DSM-IV mental disorders in a nationally representative sample. A model specifying correlated externalizing, distress, and fears factors fit well, but an alternative model was tested in which a ‘general’ bifactor was added to capture what these disorders share in common. There was a modest but significant improvement in fit for the bifactor model relative to the 3-factor oblique model, with all disorders loading strongly on the bifactor. Tests of external validity revealed that the fears, distress, and externalizing factors were differentially associated with measures of functioning and potential risk factors. Nonetheless, the general bifactor accounted for significant independent variance in future psychopathology, functioning, and other criteria over and above the fears, distress, and externalizing factors. These findings support the hypothesis that these prevalent forms of psychopathology have both important common and unique features. Future studies should determine if this is because they share elements of their etiology and neurobiological mechanisms. If so, the existence of common features across diverse forms of prevalent psychopathology could have important implications for understanding the nature, etiology, and outcomes of psychopathology.

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In his 2003 book Moneyball, financial reporter Michael Lewis made a striking claim: the valuation of skills in the market for baseball players was grossly inefficient. The discrepancy was so large that when the Oakland Athletics hired an unlikely management group consisting of Billy Beane, a former player with mediocre talent, and two quantitative analysts, the team was able to exploit this inefficiency and outproduce most of the competition, while operating on a shoestring budget.The publication of Moneyball triggered a firestorm of criticism from baseball insiders (Lewis, 2004), and it raised the eyebrows of many economists as well. Basic price theory implies a tight correspondence between pay and productivity when markets are competitive and rich in information, as would seem to be the case in baseball. The market for baseball players receives daily attention from the print and broadcast media, along with periodic in-depth analysis from lifelong baseball experts and academic economists. Indeed, a case can be made that more is known about pay and quantified performance in this market than in any other labor market in the American economy.In this paper, we test the central portion of Lewis's (2003) argument with elementary econometric tools and confirm his claims. In particular, we find that hitters' salaries during this period did not accurately reflect the contribution of various batting skills to winning games. This inefficiency was sufficiently large that knowledge of its existence, and the ability to exploit it, enabled the Oakland Athletics to gain a substantial advantage over their competition. Further, we find

General patterns of bias in risk beliefs are well established in the literature, but much less is known about how these biases vary across the population. Using a sample of almost 500 people, the regression analysis in this article yields results consistent with the well-established pattern that small risks are overassessed and large risks are underassessed. The accuracy of these risk beliefs varies across demographic factors, as does the switch point at which people go from underassessment to overassessment, which we found to be 1500 deaths annually for the full sample. Better educated people have more accurate risk beliefs, and there are important differences in the risk perception by race and gender that also may be of policy interest.

Using panel data of MLB team attendance from 1950 to 2002, we determined that the attendance “honeymoon” effect of a new stadium—after separating quality-of-play effects—increases attendance by 32% to 37% the opening year of a new stadium. Attendance remains above baseline levels for only two seasons for multipurpose stadiums built during 1960 to 1974 but for 6 to 10 seasons at newer ballparks. Contrary to expectations, there is no systematic interaction between new venues and team performance on attendance or stadium revenues. This noncomplementarity implies that a profit-maximizing team owner would not use a new stadium’s revenue stream to increase team quality of play.