Taxes and Fringe Benefits Offered by Employers

Using cross-sectional data for blue and white collar workers for U.S. cities, we examine how the tax treatment of fringe benefits affects whether employers offer benefits. Differences in state-level income taxes cause variation across places in the tax incentives for fringe benefits. We find that employers respond to tax incentives to offer fringe benefits, especially to blue collar workers. The tax incentives affect both the probability of basic benefits, such as medical coverage, and more 'marginal' benefits, such as vision and dental coverage. Higher taxes also reduce the amount of explicit cost sharing for some benefits between employers and employees.

A Unified Approach to Measurement Error and Missing Data: Overview and Applications. Sociological Methods and Research

Abstract Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (mi) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like mi, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extension