Unemployment Insurance, Duration of Unemployment, and Subsequent Wage Gain

[Excerpt] In order to evaluate what the optimal level of UI benefits is, one must therefore first estimate the magnitude of the relationships between UI benefits levels and unemployed workers\u27 durations of unemployment and post-unemployment wages. There have been several previous studies of the impact of UI benefits on duration of spells of unemployment, however none have been completely satisfactory methodologically. To our knowledge, there have been no previous studies of the system\u27s impact on subsequent wage rates. We attempt to fill these gaps, utilizing data from the National Longitudinal Survey (NLS) to estimate both relationships. The plan of our paper is as follows. First, we sketch the implications of theories of job search for our estimating equations. Next, we briefly discuss the NLS data. The following four sections summarize the empirical results we have obtained for four cohorts of data: older males, ages 45-59; women, ages 30-44; and younger males and females, ages 14-24. Finally, we consider the implications of our results for public policy. Due to space limitations our discussion here is necessarily brief and details of our research are found elsewhere

The Equivalence of Panel Data Estimators under Orthogonal Experimental Design

This paper demonstrates the equivalence between pooled OLS, Fixed Effects, and Random Effects estimates when applied to data generated from an orthogonal experimental design under certain conditions. We show that the point estimates of the treatment effects are identical between these three panel data estimators but that the estimated standard errors differ. Specifically, the estimated variance covariance matrices are identical between FE and RE but differ from that of OLS. Despite the equivalence it is meaningful to test for OLS vs FE/RE because the error distributional assumptions are different.

Statistical Discrimination in Labor Markets: An Experimental Analysis

Statistical discrimination occurs when distinctions between demographic groups are made on the basis of real or imagined statistical distinctions between the groups. While such discrimination is legal in some cases (e.g., insurance markets), it is illegal and/or controversial in others (e.g., racial profiling and gender-based labor market discrimination). First moment statistical discrimination occurs when, for example, female workers are offered lower wages because females are perceived to be less productive, on average, than male workers. Second moment discrimination occurs when risk averse employers offer female workers lower wages based not on lower average productivity but on a higher variance in their productivity. Empirical work on statistical discrimination is hampered by the difficulty of obtaining suitable data from naturally-occurring labor markets. This article reports results from controlled laboratory experiments designed to study second moment statistical discrimination in a simulated labor marker setting. Since decision-makers may not view risk in the same way as economists or statisticians (i.e., risk=variance of distribution), we also examine two possible alternative measures of risk: the support of the distribution, and the probability of earning less than the expected (maximum) profits for the employer. Our results indicate that individuals do respond to these alternative measures of risk, and employers made statistically discriminatory wage offers consistent with loss-aversion in our full sample (though differences between male and female employers can be noted). If one can transfer these results outside of the laboratory, they indicate that labor market discrimination based only on first moment discrimination is biased downward. The public policy implication is that efforts and legislation aimed at reducing discrimination of various sorts face an additional challenge in trying to identify and limit relatively hidden, but significant, forms of statistical discrimination.

Technological Change and Gender Wage Differentials

This paper investigates the impact of non-neutral technological change on the recent narrowing of the gender wage differentials. The relation between technological change and relative wages of female and male workers is modeled through a constant elasticity of substitution production function that incorporates male and female labor inputs by occupation in each industry, a non-labor input and a productivity parameter function that captures non-neutral technological change. Data from 1979 to 2001 on employment and wages by industry and occupation come from the Current Population Survey. Using non-linear two stage least squares with cross-equation restrictions, the estimated results provide evidence that non-neutral technological change partially explains the documented narrowing of the gender wage gap during the 1980s and 1990s, even after controlling for unexplained differences in gender relative wages. Specifically, changes in non-neutral technological change explain between 5 % and 9 % of the overall increase of women’s wages relative to men’s in the sample. The strongest effect is found for the highest pay occupation level, while the smallest effect is found for the lower pay occupations. Finally, this paper brings evidence that ignoring the unexplained component of the gender wage differentials could result in a biased estimation of the effect on non-neutral technological change on the gender wage gap.

New Market Power Models and Sex Differences in Pay

In the context of certain general equilibrium search models, it is possible to infer the elasticity of labor supply to the firm from the elasticity of the quit rate with respect to the wage. We use this framework to estimate the elasticity of labor supply for men and women workers at a chain of grocery stores operating in the southwestern United States, identifying separation elasticities from differences in wages and separation rates across different job titles within the firm. We estimate elasticities of labor supply to the firm of about 2.7 for men and about 1.5 for women, suggesting significant wage-setting power for the firm. Since women have lower elasticities of labor supply to the firm, a Robinson-style monopsony model might explain lower relative pay of women in the grocery industry. The wage gaps we observe among workers in US retail grocery stores are close to what the monopsony model predicts for the elasticities we have estimated.monopsony papers, labor supply, grocery stores, elasticity

New Wine in Old Bottles: A Sequential Estimation Technique for the LPM

The conditions under which ordinary least squares (OLS) is an unbiased and consistent estimator of the linear probability model (LPM) are unlikely to hold in many instances. Yet the LPM still may be the correct model or, perhaps, justified for practical reasons. A sequential least squares (SLS) esti-mation procedure is introduced that may outperform OLS in terms of finite sample bias and yields a consistent estimator. Monte Carlo simulations reveal that SLS outperforms OLS, probit and logit in terms of mean squared error of the predicted probabilities. An empirical example is provided.Linear Probability Model, Sequential Least Squares, Consistency, Monte Carlo