Match Effects

We present an empirical model of earnings that controls for observable and unobservable characteristics of workers (person effects), unmeasured characteristics of their employers (firm effects), and unmeasured characteristics of worker-firm matches (match effects). We interpret these as the returns to general human capital, firm-specific human capital, and match-specific human capital, respectively. We stress the importance of match effects because the returns to match-specific human capital will be incorrectly attributed to general and/or firm-specific human capital when match effects are omitted, and because general and specific human capital have very different implications for the economic cost of job destruction. We find that slightly more than half of observed variation in log earnings is attributable to general human capital, 22 percent is attributable to firm-specific human capital, and 16 percent to match-specific human capital. Specifications that omit match effects over-estimate the returns to experience by as much as 50 percent, over-estimate the returns to a college education by as much as 8 percent, attribute too much variation to person effects, and too little to firm effects. Our results suggest that considerable earnings variation previously attributed to general human capital -- both observed and unobserved -- is in fact attributable to workers sorting into higher-paying firms and better worker-firm matches.human capital; fixed effects; mixed effects; person and firm effects; linked employer-employee data

Wage Differentials in the Presence of Unobserved Worker, Firm, and Match Heterogeneity

We consider the problem of estimating and decomposing wage differentials in the presence of unobserved worker, firm, and match heterogeneity. Controlling for these unobservables corrects omitted variable bias in previous studies. It also allows us to measure the contribution of unmeasured characteristics of workers, firms, and worker-firm matches to observed wage differentials. An application to linked employer-employee data shows that decompositions of inter-industry earnings differentials and the male-female differential are misleading when unobserved heterogeneity is ignored.wage differentials; unobserved heterogeneity; employer-employee data

Wage Differentials in the Presence of Unobserved Worker, Firm, and Match Heterogeneity

We consider the problem of estimating and decomposing wage differentials in the presence of unobserved worker, firm, and match heterogeneity. Controlling for these unobservables corrects omitted variable bias in previous studies. It also allows us to measure the contribution of unmeasured characteristics of workers, firms, and worker-firm matches to observed wage differentials. An application to linked employer-employee data shows that decompositions of inter-industry earnings differentials and the male-female differential are misleading when unobserved heterogeneity is ignored.wage differentials, unobserved heterogeneity, employer-employee data

Glass Ceilings or Glass Doors? Wage Disparity Within and Between Firms

We investigate whether immigrant and minority workers' poor access to high-wage jobs---that is, glass ceilings---is attributable to poor access to jobs in high-wage firms, a phenomenon we call glass doors. Our analysis uses linked employer-employee data to measure mean- and quantile-wage differentials of immigrants and ethnic minorities, both within and across firms. We find that glass ceilings exist for some immigrant groups, and that they are driven in large measure by glass doors. For some immigrant groups, the sorting of these workers across firms accounts for as much as half of the economy-wide wage disparity they face.glass ceilings, wage differentials, immigration, visible minorities, quantile regression, linked employer-employee data

Distribution-Preserving Statistical Disclosure Limitation

One approach to limiting disclosure risk in public-use microdata is to release multiply-imputed, partially synthetic data sets. These are data on actual respondents, but with confidential data replaced by multiply-imputed synthetic values. A mis-specified imputation model can invalidate inferences because the distribution of synthetic data is completely determined by the model used to generate them. We present two practical methods of generating synthetic values when the imputer has only limited information about the true data generating process. One is applicable when the true likelihood is known up to a monotone transformation. The second requires only limited knowledge of the true likelihood, but nevertheless preserves the conditional distribution of the confidential data, up to sampling error, on arbitrary subdomains. Our method maximizes data utility and minimizes incremental disclosure risk up to posterior uncertainty in the imputation model and sampling error in the estimated transformation. We validate the approach with a simulation and application to a large linked employer-employee database.statistical disclosure limitation; confidentiality; privacy; multiple imputation; partially synthetic data

Glass Ceilings or Glass Doors? Wage Disparity Within and Between Firms

We investigate whether immigrant and minority workers’ poor access to high-wage jobs— that is, glass ceilings— is attributable to poor access to jobs in high-wage …rms, a phenomenon we call glass doors. Our analysis uses linked employer-employee data to measure mean- and quantile-wage di¤erentials of immigrants and ethnic minorities, both within and across …rms. We …nd that glass ceilings exist for some immigrant groups, and that they are driven in large measure by glass doors. For some immigrant groups, the sorting of these workers across …rms accounts for as much as half of the economy-wide wage disparity they face.glass ceilings, wage di¤erentials, immigration, visible minorities, quantile regression, linked employer-employee data

Glass Ceilings or Glass Doors? Wage Disparity Within and Between Firms

We investigate whether immigrant and minority workers’ poor access to high-wage jobs – that is, glass ceilings – is attributable to poor access to jobs in high-wage firms, a phenomenon we call glass doors. Our analysis uses linked employer-employee data to measure mean- and quantile-wage differentials of immigrants and ethnic minorities, both within and across firms. We find that glass ceilings exist for some immigrant groups, and that they are driven in large measure by glass doors. For some immigrant groups, the sorting of these workers across firms accounts for as much as half of the economy-wide wage disparity they face.glass ceilings, wage differentials, immigration, visible minorities, quantile regression, linked employer-employee data

Unifying heterogeneous state-spaces with lenses

Most verification approaches embed a model of program state into their semantic treatment. Though a variety of heterogeneous state-space models exists,they all possess common theoretical properties one would like to capture abstractly,such as the common algebraic laws of programming. In this paper,we propose lenses as a universal state-space modelling solution. Lenses provide an abstract interface for manipulating data types through spatially-separated views. We define a lens algebra that enables their composition and comparison,and apply it to formally model variables and alphabets in Hoare and He’s Unifying Theories of Programming (UTP). The combination of lenses and relational algebra gives rise to a model for UTP in which its fundamental laws can be verified. Moreover,we illustrate how lenses can be used to model more complex state notions such as memory stores and parallel states. We provide a mechanisation in Isabelle/HOL that validates our theory,and facilitates its use in program verification