Information on firm dynamics is critical to understanding economic activity, yet is fundamentally difficult to measure. In this article we introduce a new way of capturing dynamics: following clusters of workers as they move across administrative entities. We show that a worker flow approach improves linkages across firms in longitudinal business databases. The approach also provides conceptual insights into the changing structure of businesses and employer-employee relationships. Many worker-cluster flows involve changes in industry particularly movements into and out of personnel supply firms. Another finding, that a nontrivial fraction of firm entry is associated with such flows, suggests that a path for firm entry is a group of workers at an existing firm starting a new firm.
, members of the LEHD Program sta¤, and participants at the 2005 Meeting of the Canadian Econometric Study Group for helpful comments and suggestions. We also thank Bryan Richetti for providing SAS code used in the re-identi…cation analysis. We gratefully acknowledge the …nancial support of National Science Foundation grants SES-0427889 and SES-0339191 awarded to Cornell University, and the Simon Fraser University President's Research Grant. AbstractOne 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 con…dential data replaced by multiply-imputed synthetic values. A mis-speci…ed 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 con…dential 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.
, members of the LEHD Program sta¤, and participants at the 2005 Meeting of the Canadian Econometric Study Group for helpful comments and suggestions. We also thank Bryan Richetti for providing SAS code used in the re-identi…cation analysis. We gratefully acknowledge the …nancial support of National Science Foundation grants SES-0427889 and SES-0339191 awarded to Cornell University, and the Simon Fraser University President's Research Grant. AbstractOne 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 con…dential data replaced by multiply-imputed synthetic values. A mis-speci…ed 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 con…dential 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.
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