Papers written by Author(s): 'Gary Benedetto'
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Working PaperA Task-based Approach to Constructing Occupational Categories with Implications for Empirical Research in Labor Economics
September 2019
Working Paper Number:
CES-19-27
Most applied research in labor economics that examines returns to worker skills or differences in earnings across subgroups of workers typically accounts for the role of occupations by controlling for occupational categories. Researchers often aggregate detailed occupations into categories based on the Standard Occupation Classification (SOC) coding scheme, which is based largely on narratives or qualitative measures of workers' tasks. Alternatively, we propose two quantitative task-based approaches to constructing occupational categories by using factor analysis with O*NET job descriptors that provide a rich set of continuous measures of job tasks across all occupations. We find that our task-based approach outperforms the SOC-based approach in terms of lower occupation distance measures. We show that our task-based approach provides an intuitive, nuanced interpretation for grouping occupations and permits quantitative assessments of similarities in task compositions across occupations. We also replicate a recent analysis and find that our task-based occupational categories explain more of the gender wage gap than the SOC-based approaches explain. Our study enhances the Federal Statistical System's understanding of the SOC codes, investigates ways to use third-party data to construct useful research variables that can potentially be added to Census Bureau data products to improve their quality and versatility, and sheds light on how the use of alternative occupational categories in economics research may lead to different empirical results and deeper understanding in the analysis of labor market outcomes.View Full Paper PDF
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Working PaperDistribution Preserving Statistical Disclosure Limitation
September 2006
Working Paper Number:
tp-2006-04
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.View Full Paper PDF
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Working PaperUsing Worker Flows in the Analysis of the Firm
August 2003
Working Paper Number:
tp-2003-09
This paper uses a novel approach to measure firm entry and exit, mergers and acquisition. It uses information about the flows of clusters of workers across business units to identify longitudinal linkage relationships in longitudinal business data. These longitudinal relationships may be the result of either administrative or economic changes and we explore both types of newly identified longitudinal relationships. In particular, we develop a set of criteria based on worker flows to identify changes in firm relationships ? such as mergers and acquisitions, administrative identifier changes and outsourcing. We demonstrate how this new data infrastructure and this cluster flow methodology can be used to better differentiate true firm entry/exit and simple changes in administrative identifiers. We explore the role of outsourcing in a variety of ways but in particular the outsourcing of workers to the temporary help industry. While the primary focus is on developing the data infrastructure and the methodology to identify and interpret these clustered flows of workers, we conclude the paper with an analysis of the impact of these changes on the earnings of workers.View Full Paper PDF
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