Joshua C. Pinkston

A Test of Screening Discrimination with Employer Learning

This paper tests for the presence of screening discrimination, a type of statistical discrimination that occurs when employers are less able to evaluate the ability of workers from one group than from another. Using data from the 2000 release of the NLSY79, the author examines wage equations in a framework of employer learning to test the hypothesis that the market receives less reliable productivity signals at labor market entry from black men than from white men. The estimation results support this hypothesis. Variables that are difficult for employers to observe, such as the AFQT score, had less influence on the wages of black men (and easily observed variables had more influence) than on the wages of white men. The influence of hard-to-observe variables on wages, however, increased faster with experience for black men.

Adjusting body mass for measurement error with invalid validation data.

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not measure respondents. The standard correction in prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of measured weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples. This makes our correction more robust to differences in measurement error across surveys as long as both surveys represent the same population. We examine three nationally representative datasets and find that misreporting appears to be sensitive to differences in survey context. When we compare predicted BMI distributions using the two validation approaches, we find that the standard correction is affected by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.

How Much Do Employers Learn from Referrals

This paper tests the hypothesis that referrals from various sources provide employers with more information about job applicants than they would have without a referral. I use data from the 1982 EOPP Survey of employers that contain information on two workers in the same job, allowing me to cancel out differences in job and firm characteristics and control for the possibility that workers with referrals from different sources (or no referral at all) might sort into jobs that put different weights on individual performance. My estimation results provide evidence consistent with referrals from friends and family members providing employers with more information than they would have otherwise. Despite the information they provide, however, it appears as though referrals from family members are associated with jobs that put less weight on performance overall. On the other hand, referrals from other employers or labor unions appear to provide little, if any, information but are associated with jobs that put more weight on performance than the average job does. I find no evidence that referrals from schools, community organizations or other sources provide useful information.

Screening discrimination and the determinants of wages

Abstract This paper tests for a type of statistical discrimination, referred to as “screening discrimination,” that assumes signals of worker productivity that employers receive at the time of hiring are noisier for one group than for another. Screening discrimination implies that starting wages depend less on initial productivity signals for women than for men, and that the influence of worker performance on wages increases faster with tenure for women. I test these predictions and find strong evidence suggesting that employers receive less-accurate initial signals from women than from men, even when comparing men and women in the same job.

Can Changing Economic Factors Explain the Rise in Obesity

A growing literature examines the effects of economic variables on obesity, typically focusing on only one or a few factors at a time. We build a more comprehensive economic model of body weight, combining the 1990–2010 Behavioral Risk Factor Surveillance System with 27 state-level variables related to general economic conditions, labor supply, and the monetary or time costs of calorie intake, physical activity, and cigarette smoking. Controlling for demographic characteristics and state and year fixed effects, changes in these economic variables collectively explain 37% of the rise in body mass index (BMI), 43% of the rise in obesity, and 59% of the rise in Class II/III obesity. Quantile regressions also point to large effects among the heaviest individuals, with half the rise in the 90th percentile of BMI explained by economic factors. Variables related to calorie intake—particularly restaurant and supercenter/warehouse club densities—are the primary drivers of the results.

The Dynamic Effects of Obesity on the Wages of Young Workers

This paper considers effects of body mass on wages in the years following labor market entry. The preferred models allow current wages to be affected by both past and current body mass, as well as past wages, while also addressing the endogeneity of body mass. I find that a history of severe obesity has a large negative effect on the wages of white men. White women face a penalty for a history of being overweight, with some evidence of additional penalties that begin above the threshold for severe obesity. Furthermore, the effects of past wages on current wages imply that past body mass has additional, indirect effects on wages, especially for white women.

Adjusting Body Mass for Measurement Error with Invalid Validation Data

We propose a new method for using validation data to correct self-reported weight and height in surveys that do not weigh and measure respondents. The standard correction from prior research regresses actual measures on reported values using an external validation dataset, and then uses the estimated coefficients to predict actual measures in the primary dataset. This approach requires the strong assumption that the expectations of actual weight and height conditional on the reported values are the same in both datasets. In contrast, we use percentile ranks rather than levels of reported weight and height. Our approach requires the much weaker assumption that the conditional expectations of actual measures are increasing in reported values in both samples, making our correction more robust to differences in measurement error across surveys. We then examine three nationally representative datasets and confirm that misreporting is sensitive to differences in survey context such as data collection mode. When we compare predicted BMI distributions using the two approaches, we find that the standard correction is biased by differences in misreporting while our correction is not. Finally, we present several examples that demonstrate the potential importance of our correction for future econometric analyses and estimates of obesity rates.

Breaking Bad: Are Meth Labs Justified in Dry Counties?

This paper examines the influence of local alcohol prohibition on the prevalence of methamphetamine labs. Using multiple sources of data for counties in Kentucky, we compare various measures of meth manufacturing in wet, moist, and dry counties. Our preferred estimates address the endogeneity of local alcohol policies by using as instrumental variables data on religious affiliations in the 1930s, when most local-option votes took place. Alcohol prohibition status is influenced by the percentage of the population that is Baptist, consistent with the “bootleggers and Baptists” model. Our results suggest that the number of meth lab seizures in Kentucky would decrease by 24.4 percent if all counties became wet.This paper examines the influence of local alcohol prohibition on the prevalence of methamphetamine labs. Using multiple sources of data for counties in Kentucky, we compare various measures of meth manufacturing in wet, moist, and dry counties. Our preferred estimates address the endogeneity of local alcohol policies by using as instrumental variables data on religious affiliations in the 1930s, when most local-option votes took place. Alcohol prohibition status is influenced by the percentage of the population that is Baptist, consistent with the “bootleggers and Baptists” model. Our results suggest that the number of meth lab seizures in Kentucky would decrease by 24.4 percent if all counties became wet.

The dynamic effects of obesity on the wages of young workers

HighlightsI consider effects of body mass on the wages of young men and women.I address multiple sources of bias, and carefully test identifying assumptions.Men are penalized for a history of morbid obesity.Women are penalized for a history of being overweight.Penalties persist and accumulate over time, especially for women. ABSTRACT This paper considers effects of body mass on wages in the years following labor market entry. The preferred models allow current wages to be affected by both past and current body mass, as well as past wages, while also addressing the endogeneity of body mass. I find that a history of severe obesity has a large negative effect on the wages of white men. White women face a penalty for a history of being overweight, with some evidence of additional penalties that begin above the threshold for severe obesity. Furthermore, the effects of past wages on current wages imply that past body mass has additional, indirect effects on wages, especially for white women.