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Author: Pinkston, Joshua C.

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1.	Pinkston, Joshua C.
1.	A Test of Screening Discrimination with Employer Learning Industrial and Labor Relations Review 59,2 (January 2006): 267-284. Also: http://www.jstor.org/stable/25067520 Cohort(s): NLSY79 Publisher: New York State School of Industrial and Labor Relations, Cornell University Keyword(s): Black Studies; Discrimination; Discrimination, Employer; Discrimination, Job; Discrimination, Racial/Ethnic; Minorities; Minority Groups 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.
	Bibliography Citation Pinkston, Joshua C. "A Test of Screening Discrimination with Employer Learning." Industrial and Labor Relations Review 59,2 (January 2006): 267-284.

2.	Pinkston, Joshua C.
2.	Model of Asymmetric Employer Learning with Testable Implications BLS Working Paper 365, Bureau of Labor Statistics, Department of Labor, January, 2003 Cohort(s): NLSY79 Publisher: U.S. Department of Labor Keyword(s): Labor Force Participation; Mobility, Labor Market; Wage Growth; Wage Models; Wages, Men This paper develops and tests a unique model asymmetric employer learning. The previous literature on asymmetric learning assumes that a worker's employer is perfectly informed while outside firms possess only public information. This paper relaxes that assumption, allowing firms to profitably bid for employed workers under conditions that were not profitable in previous models. The model in this paper is the first in the literature to predict either wage growth without promotions or mobility between firms without firm- or match-specific productivity. The bidding through which firms compete for a worker produces a sequence of wages that converges to the current employer's conditional expectation of the worker's productivity. This convergence of wages allows the model to be tested using an extension of existing work on employer learning. Wage regressions estimated on a sample of men from the NLSY produce strong evidence of asymmetric learning.
	Bibliography Citation Pinkston, Joshua C. "Model of Asymmetric Employer Learning with Testable Implications." BLS Working Paper 365, Bureau of Labor Statistics, Department of Labor, January, 2003.

3.	Pinkston, Joshua C.
3.	The Dynamic Effects of Obesity on the Wages of Young Workers Economics and Human Biology 27,A (November 2017): 154-166. Also: http://www.sciencedirect.com/science/article/pii/S1570677X16301654 Cohort(s): NLSY97 Publisher: Elsevier Keyword(s): Body Mass Index (BMI); Obesity; Wage Penalty/Career Penalty; Wages 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.
	Bibliography Citation Pinkston, Joshua C. "The Dynamic Effects of Obesity on the Wages of Young Workers." Economics and Human Biology 27,A (November 2017): 154-166.

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