"Beauty Is the Promise of Happiness"?

We measure the impact of individuals' looks on their life satisfaction or happiness. Using five data sets from the U.S., Canada, the U.K., and Germany, we construct beauty measures in different ways that allow putting a lower bound on the true effects of beauty on happiness. Personal beauty raises happiness, with a one standard-deviation change in beauty generating about 0.10 standard deviations of additional satisfaction/happiness among men, 0.12 among women. Accounting for a wide variety of covariates, including those that might be affected by differences in beauty, and particularly effects in the labor and marriage markets, the impact among men is more than halved, among women slightly less than halved. The majority of the effect of beauty on happiness may work through its effects on economic outcomes.life satisfaction, measurement error, looks

Charity and Favoritism in the Field: Are Female Economists Nicer (to Each Other)?

Using a very large sample of matched author-referee pairs, we examine how the gender of referees and authors affects the former’s recommendations. Relying on changing matches of authors and referees, we find no evidence of gender differences among referees in charitableness toward authors; nor do we find any effect of the interaction between the referees’ and authors’ gender. With substantial research showing gender differences in fairness, the results suggest that an ethos of objectivity can overcome tendencies toward same-group favoritism/opposite-group discrimination.

Testing for causal e ffects in a generalized regression model with endogenous regressors

A unifying framework to test for causal effects in nonlinear models is proposed. We consider a generalized linear-index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Kendall (1938). The semiparametric model encompasses previous cases considered in the literature (continuous endogenous regressors (Blundell and Powell (2003)) and a single binary endogenous regressor (Vytlacil and Yildiz (2007))), but the testing approach is the first to allow for (i) multiple discrete endogenous regressors, (ii) endogenous regressors that are neither discrete nor continuous (e.g., a censored variable), and (iii) an arbitrary “mix” of endogenous regressors (e.g., one binary regressor and one continuous regressor)

Estimating Fixed Effects: Perfect Prediction and Bias in Binary Response Panel Models, with an Application to the Hospital Readmissions Reduction Program

The maximum likelihood estimator for the regression coefficients, β, in a panel binary response model with fixed effects can be severely biased if N is large and T is small, a consequence of the incidental parameters problem. This has led to the development of conditional maximum likelihood estimators and, more recently, to estimators that remove the O(T–1) bias in β^. We add to this literature in two important ways. First, we focus on estimation of the fixed effects proper, as these have become increasingly important in applied work. Second, we build on a bias-reduction approach originally developed by Kosmidis and Firth (2009) for cross-section data, and show that in contrast to other proposals, the new estimator ensures finiteness of the fixed effects even in the absence of within-unit variation in the outcome. Results from a simulation study document favourable small sample properties. In an application to hospital data on patient readmission rates under the 2010 Affo

Do Gender Quotas Pass the Test? Evidence from Academic Evaluations in Italy

This papers studies how the presence of women in academic committees affects the chances of success of male and female candidates. We use evidence from Italy, where candidates to Full and Associate Professor positions are required to qualify in a nation-wide evaluation known as Abilitazione Scientifica Nazionale. This evaluation was conducted between 2012 and 2014 in 184 academic disciplines and it attracted around 70,000 applications. In each field, committee members were selected from the pool of professors that had volunteered for the task using a random lottery. We estimate the causal effect of committees' gender composition on candidates' chances of success exploiting the existence of this system of random assignment. In a five-member committee, each additional female evaluator decreases by 2 percentage points the success rate of female candidates relative to male candidates. Information from 274,000 individual evaluation reports shows that, in mixed-gender committees, male and female evaluators are equally biased against female candidates, suggesting that the presence of women in the committee affects the voting behavior of male evaluators

A Decomposition of the Black-White Differential in Birth Outcomes

Substantial racial disparities continue to persist in the prevalence of preterm births and lowbirth-weight births. Health policy aimed at reducing these disparities could be better targeted if the differences in birth outcomes are better understood. This study decomposes these racial disparities in birth outcomes to determine the extent to which the disparities are driven by differences in measurable characteristics of black mothers and white mothers as well as the extent to which the gap results from differences in the impact of these characteristics. The analysis is focused on three adverse birth outcomes: preterm, early preterm birth (less than 32 weeks gestation), and low birth weight. The results suggest that differences in covariates accounted for approximately 25 percent of the gap in the incidence of preterm births. The specific characteristics that matter the most are marriage rates, father's characteristics, and prenatal care. For gestation-adjusted birth weight, approximately 16 percent of the racial gap for first births is explained by covariates; for subsequent births this covariate explanation rises to 22 percent of the gap. Furthermore, differences in coefficients explain about another quarter of the gap in preterm birth outcomes but very little of the gap in birth weight

Semiparametric estimation methods for nonlinear panel data models and mismeasured dependent variables

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Economics, 1996.Includes bibliographical references (p. 90-91).by Jason Abrevaya.Ph.D