Papers Containing Tag(s): 'Survey of Consumer Finances'
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Viewing papers 11 through 12 of 12
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Working PaperAccess to Financial Capital Among U.S. Businesses: The Case of African-American Firms
December 2006
Working Paper Number:
CES-06-33
The differences between African-American business ownership rates and white business ownership rates are striking. Estimates from the 2000 Census indicate that 11.8 percent of white workers are self-employed business owners, compared with only 4.8 percent of black workers. Furthermore, black-white differences in business ownership rates have remained roughly constant over most of the twentieth century (Fairlie and Meyer 2000). In addition to lower rates of business ownership, black-owned businesses are less successful on average than are white or Asian firms. In particular, black-owned businesses have lower sales, hire fewer employees and have smaller payrolls than white- or Asian-owned businesses, on average (U.S. Census Bureau 2001, U.S. Small Business Administration 2001). Black firms also have lower profits and higher closure rates than white firms (U.S. Census Bureau 1997, U.S. Small Business Administration 1999). For most outcomes, the disparities are extremely large. For example, estimates from the 2002 Survey of Business Owners (SBO) indicate that white firms have average sales of $437,870 compared with only $74,018 for black firms.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
