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Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?
January 2017
Authors:
Lars Vilhuber,
John M. Abowd,
Daniel Weinberg,
Jerome P. Reiter,
Matthew D. Shapiro,
Robert F. Belli,
Noel Cressie,
David C. Folch,
Scott H. Holan,
Margaret C. Levenstein,
Kristen M. Olson,
Jolene Smyth,
Leen-Kiat Soh,
Bruce D. Spencer,
Seth E. Spielman,
Christopher K. Wikle
Working Paper Number:
CES-17-59R
The National Science Foundation-Census Bureau Research Network (NCRN) was established in 2011 to create interdisciplinary research nodes on methodological questions of interest and significance to the broader research community and to the Federal Statistical System (FSS), particularly the Census Bureau. The activities to date have covered both fundamental and applied statistical research and have focused at least in part on the training of current and future generations of researchers in skills of relevance to surveys and alternative measurement of economic units, households, and persons. This paper discusses some of the key research findings of the eight nodes, organized into six topics: (1) Improving census and survey data collection methods; (2) Using alternative sources of data; (3) Protecting privacy and confidentiality by improving disclosure avoidance; (4) Using spatial and spatio-temporal statistical modeling to improve estimates; (5) Assessing data cost and quality tradeoffs; and (6) Combining information from multiple sources. It also reports on collaborations across nodes and with federal agencies, new software developed, and educational activities and outcomes. The paper concludes with an evaluation of the ability of the FSS to apply the NCRN's research outcomes and suggests some next steps, as well as the implications of this research-network model for future federal government renewal initiatives.
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Who Files for Personal Bankruptcy in the United States?
January 2017
Working Paper Number:
CES-17-54
Who files for bankruptcy in the United States is not well understood. Previous research relied on small samples from national surveys or a small number of states from administrative records. I use over 10 million administrative bankruptcy records linked to the 2000 Decennial Census and the 2001-2009 American Community Surveys to understand who files for personal bankruptcy. Bankruptcy filers are middle income, more likely to be divorced, more likely to be black, more likely to have terminal high school degree or some college, and more likely to be middle-aged. Bankruptcy filers are more likely to be employed than the U.S. as a whole, and they are more likely to be employed 50-52 weeks. The bankruptcy population is aging faster than the U.S. population as a whole. Lastly, using the pseudo-panels I study what happens in the years around bankruptcy. Individuals are likely to get divorced in the years before bankruptcy and then remarry. Income falls before bankruptcy and then rises after bankruptcy.
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Decennial Census Return Rates: The Role of Social Capital
January 2017
Working Paper Number:
CES-17-39
This paper explores how useful information about social and civic engagement (social capital)
might be to the U.S. Census Bureau in their efforts to improve predictions of mail return rates for the Decennial Census (DC) at the census tract level. Through construction of Hard-to-count (HRC) scores and multivariate analysis, we find that if information about social capital were available, predictions of response rates would be marginally improved.
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Medicare Coverage and Reporting
December 2016
Working Paper Number:
carra-2016-12
Medicare coverage of the older population in the United States is widely recognized as being nearly universal. Recent statistics from the Current Population Survey Annual Social and Economic Supplement (CPS ASEC) indicate that 93 percent of individuals aged 65 and older were covered by Medicare in 2013. Those without Medicare include those who are not eligible for the public health program, though the CPS ASEC estimate may also be impacted by misreporting. Using linked data from the CPS ASEC and Medicare Enrollment Database (i.e., the Medicare administrative data), we estimate the extent to which individuals misreport their Medicare coverage. We focus on those who report having Medicare but are not enrolled (false positives) and those who do not report having Medicare but are enrolled (false negatives). We use regression analyses to evaluate factors associated with both types of misreporting including socioeconomic, demographic, and household characteristics. We then provide estimates of the implied Medicare-covered, insured, and uninsured older population, taking into account misreporting in the CPS ASEC. We find an undercount in the CPS ASEC estimates of the Medicare covered population of 4.5 percent. This misreporting is not random - characteristics associated with misreporting include citizenship status, year of entry, labor force participation, Medicare coverage of others in the household, disability status, and imputation of Medicare responses. When we adjust the CPS ASEC estimates to account for misreporting, Medicare coverage of the population aged 65 and older increases from 93.4 percent to 95.6 percent while the uninsured rate decreases from 1.4 percent to 1.3 percent.
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Response Error & the Medicaid undercount in the CPS
December 2016
Working Paper Number:
carra-2016-11
The Current Population Survey Annual Social and Economic Supplement (CPS ASEC) is an important source for estimates of the uninsured population. Previous research has shown that survey estimates produce an undercount of beneficiaries compared to Medicaid enrollment records. We extend past work by examining the Medicaid undercount in the 2007-2011 CPS ASEC compared to enrollment data from the Medicaid Statistical Information System for calendar years 2006-2010. By linking individuals across datasets, we analyze two types of response error regarding Medicaid enrollment - false negative error and false positive error. We use regression analysis to identify factors associated with these two types of response error in the 2011 CPS ASEC. We find that the Medicaid undercount was between 22 and 31 percent from 2007 to 2011. In 2011, the false negative rate was 40 percent, and 27 percent of Medicaid reports in CPS ASEC were false positives. False negative error is associated with the duration of enrollment in Medicaid, enrollment in Medicare and private insurance, and Medicaid enrollment in the survey year. False positive error is associated with enrollment in Medicare and shared Medicaid coverage in the household. We discuss implications for survey reports of health insurance coverage and for estimating the uninsured population.
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Using Partially Synthetic Microdata to Protect Sensitive Cells in Business Statistics
February 2016
Working Paper Number:
CES-16-10
We describe and analyze a method that blends records from both observed and synthetic microdata into public-use tabulations on establishment statistics. The resulting tables use synthetic data only in potentially sensitive cells. We describe different algorithms, and present preliminary results when applied to the Census Bureau's Business Dynamics Statistics and Synthetic Longitudinal Business Database, highlighting accuracy and protection afforded by the method when compared to existing public-use tabulations (with suppressions).
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Food and Agricultural Industries: Opportunities
for Improving Measurement and Reporting
January 2016
Working Paper Number:
CES-16-58
We measure one component of off-farm food and agricultural industries using establishment
level microdata in the federal statistical system. We focus on services for crop production, and compare measures of firm and employment dynamics in this sector during the period 1992-2012 with county-level publicly available data for the same measures. Based on differences across data sources, we establish new facts regarding the evolution of food and agricultural industries, and demonstrate the value of working with confidential microdata. In addition to the data and results we present, we highlight possibilities for collaboration across universities and federal agencies to improve reporting in other segments of food and agricultural industries.
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The Management and Organizational Practices Survey (MOPS): Cognitive Testing*
January 2016
Working Paper Number:
CES-16-53
All Census Bureau surveys must meet quality standards before they can be sent to the public for data collection. This paper outlines the pretesting process that was used to ensure that the Management and Organizational Practices Survey (MOPS) met those standards. The MOPS is the first large survey of management practices at U.S. manufacturing establishments. The first wave of the MOPS, issued for reference year 2010, was subject to internal expert review and two rounds of cognitive interviews. The results of this pretesting were used to make significant changes to the MOPS instrument and ensure that quality data was collected. The second wave of the MOPS, featuring new questions on data in decision making (DDD) and uncertainty and issued for reference year 2015, was subject to two rounds of cognitive interviews and a round of usability testing. This paper illustrates the effort undertaken by the Census Bureau to ensure that all surveys released into the field are of high quality and provides insight into how respondents interpret the MOPS questionnaire for those looking to utilize the MOPS data.
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Measuring Cross-Country Differences in Misallocation
January 2016
Working Paper Number:
CES-16-50R
We describe differences between the commonly used version of the U.S. Census of Manufactures available at the RDCs and what establishments themselves report. The originally reported data has substantially more dispersion in measured establishment productivity. Measured allocative efficiency is substantially higher in the cleaned data than the raw data: 4x higher in 2002, 20x in 2007, and 80x in 2012. Many of the important editing strategies at the Census, including industry analysts' manual edits and edits using tax records, are infeasible in non-U.S. datasets. We describe a new Bayesian approach for editing and imputation that can be used across contexts.
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Statistics on the International Trade Administration's Global Markets Program
September 2015
Working Paper Number:
CES-15-17
Recent mandates for evidence-based policy choices from both the Executive and Legislative branches of the federal government underscore the importance of understanding the relationship between program participation and business outcomes. In this paper, we examine the correlations between participation in an export-promotion program and business outcomes. We use this experience to provide more general lessons learned about combining program data on treatments with Census Bureau micro data that can be used as a control. Note this paper does not evaluate a program, but instead provides critical information about a program.
The mission of the Commercial Service/Global Markets program is to help companies either start or increase their exports of goods and services. It pursues this mission through advocacy, events, and counseling. This study looks at a very small part of the overall program. While we cannot rule-out several sources of bias in our results, we do observe several consistent patterns across our models. In particular, program participation is positively correlated with export growth and change and, for small businesses, also with positive employment growth. However, overall, and for large firms in particular, there is a negative correlation with employment growth and counseling. The paper concludes with a 'Lessons Learned' section that highlights areas where measurement can be improved.
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