November 11, 2016
Dylan Glover, Sciences Po; Amanda Pallais, Harvard University and NBER; and William Pariente, Université Catholique de Louvain
Examining the performance of cashiers in a French grocery store chain, Glover, Pallais, and Pariente find that manager bias negatively affects minority job performance. In the stores studied, cashiers work with different managers on different days and their schedules are determined quasi-randomly. When minority cashiers, but not majority cashiers, are scheduled to work with managers who are biased (as determined by an Implicit Association Test), they are absent more often, spend less time at work, scan items more slowly, and take more time between customers. Manager bias has consequences for the average performance of minority workers: while on average minority and majority workers perform equivalently, on days where managers are unbiased, minorities perform significantly better than do majority workers. This appears to be because biased managers interact less with minorities, leading minorities to exert less effort.
Isaac McFarlin Jr., University of Florida, and Peter Bergman, Columbia University
School choice can improve both access and quality, and a growing form of choice is manifested by charter schools. While certain charter schools produce large gains in student achievement, there are concerns they divert high-performing students from traditional public schools and impede enrollment for students perceived as costly to educate. Bergman and McFarlin design and implement a nationwide field experiment to test whether charter schools impede access to application information for children of a particular race, gender, special need, behavioral problem, and low academic achievement. The researchers find that charter schools are significantly more likely to ignore inquiries or respond with information that could discourage families from applying when it appears the child has a behavior issue, potential high-cost special need or a Hispanic-sounding name. The patterns of discrimination uncovered are concentrated among charter schools with high test scores and schools that are their own local district.
Suzanne Barth and Kyung Park, Wellesley College, and Nikolas Mittag, CERGE-EI
The study of voter discrimination is complicated by the possibility that voters spurn minority candidates due to unobserved candidate characteristics besides race. This paper exploits low-level statewide elections in which voters are plausibly ill-informed about candidates but can still infer race via the informational content in their names. Using nearly two decades of election results from the state of Texas, Barth, Mittag, and Park find considerable evidence of minority disadvantage in democratic elections. Voter bias affects both vote share and the selection of minority candidates.
Stefano DellaVigna, University of California at Berkeley and NBER, and David Card
Zhuan Pei, Cornell University; Jörn-Steffen Pischke, London School of Economics and NBER; and Hannes Schwandt, University of Zurich
Researchers frequently test identifying assumptions in regression based research designs (which include instrumental variables or difference-in-differences models) by adding additional control variables on the right hand side of the regression. If such additions do not affect the coefficient of interest (much) a study is presumed to be reliable. Pei, Pischke, and Schwandt caution that such invariance may result from the fact that the observed variables used in such robustness checks are often poor measures of the potential underlying confounders. In this case, a more powerful test of the identifying assumption is to put the variable on the left hand side of the candidate regression. Authors provide derivations for the estimators and test statistics involved, as well as power calculations, which can help applied researchers interpret their findings. They illustrate these results in the context of various strategies which have been suggested to identify the returns to schooling.
Melvin Stephens, Jr., University of Michigan and NBER, and Desmond J. Toohey, University of Michigan
While economists have long posited that investments in health lead to higher earnings, isolating the causal effect of health on earnings has been challenging both due to reverse causality and unobserved heterogeneity. In this paper, Stephens and Toohey examine the labor market effects of the Multiple Risk Factor Intervention Trial (MRFIT), a randomized controlled trial in which a bundle of treatments was provided to determine their joint impact on coronary heart disease mortality. Nearly 13,000 U.S. men were enrolled in the trial and monitored for more than six years. The researchers find that the MRFIT intervention led to higher earnings and family income. They also find, consistent with the Grossman model prediction that health investments increase earnings through reductions in days missed due to illness, a decrease in major physical injuries and work-limiting disabilities.
Robert E. Hall, Stanford University and NBER, and Andreas I. Mueller, Columbia University and NBER
Hall and Mueller use a rich new body of data on the experiences of unemployed jobseekers to determine the sources of wage dispersion and to create a search model consistent with the acceptance decisions the jobseekers made. Heterogeneity in non-wage job values or amenities among jobseekers and jobs is a central feature of the researchers' model. From the data and the model, they identify the distributions of four key variables: offered wages, offered non-wage job values, the value of the jobseeker's non-work alternative, and the jobseeker's personal productivity. The researchers find that, conditional on personal productivity, the standard deviation of offered log-wages is moderate, at 0.24, whereas the dispersion of the non-wage component of offered job values is substantially larger, at 0.34. The resulting dispersion of offered job values is 0.38. They also find high dispersion of personal productivity, at 0.43.