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Dynarski says NY's Excelsior Scholarship Program could crowd out low-income and minority students

U-M Poverty Solutions funds nine projects

COSSA makes 10 suggestions to next Administration for supporting and using social science research

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2017 PAA Annual Meeting, April 27-29, Chicago

NIH funding opportunity: Etiology of Health Disparities and Health Advantages among Immigrant Populations (R01 and R21), open Jan 2017

Russell Sage 2017 Summer Institute in Computational Social Science, June 18-July 1. Application deadline Feb 17.

Russell Sage 2-week workshop on social science genomics, June 11-23, 2017, Santa Barbara

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Next Brown Bag

Mon, Jan 23, 2017 at noon:
Decline of cash assistance and child well-being, Luke Shaefer

Imputing for Late Reporting in the U.S. Current Employment Statistics Survey

Archived Abstract of Former PSC Researcher

Copeland, Kennon, and Richard L. Valliant. 2007. "Imputing for Late Reporting in the U.S. Current Employment Statistics Survey." Journal of Official Statistics, 23(1): 69--90.

Surveys of economic conditions are often published monthly to provide up-to-date measures of the state of a country’s economy. In establishment surveys, some sample units may not report in time to be included in the current month’s estimates, but eventually do report data. This late reporting can lead to revisions of estimates as more sample data become available. To maintain credibility, it is important that the size of revisions be kept as small as possible. We study this issue using the U.S. Current Employment Statistics (CES) survey. A model-based view of the CES weighted link relative estimator is used to identify potential bias due to model misspecification. An alternative approach, involving imputation for missing data, is used in an attempt to reduce the magnitude of revisions between preliminary and final estimates of employment for a month. The alternative, while not yielding statistically significant improvement in monthly revisions at the industry level, offers the potential for improved estimates for lower level aggregation.

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