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. When imputing confidential values, a mis-specified model...

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...

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...

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...

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...

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...