This project will undertake research that responds to the specific analytic and operational requirements of the Census Bureau and other federal statistical agencies to improve their estimates while reducing costs and respondent burden. The project will use administrative data, and more generally, data generated by households and businesses in the course of their normal activities to produce economic and demographic measurements that currently rely on surveys. The project will develop and evaluate methodologies that use the vast constellation of data generated by ordinary activity in a modern society and that protect the privacy of individuals and businesses. The project will examine administrative records created by businesses, individuals, and governments, streams of data from social media sites on the World Wide Web, and detailed geospatial data. The project will analyze these multiple source of data and relate them to data collected on surveys. It aims to improve survey measurements of economic and demographic data and potentially supplement or replace surveys with statistics based on administrative, Web-based, and geospatial data. Applications of these approaches include the following: using linked survey-administrative data to assess attrition, selective non-response, and measurement error in surveys; using Web-based social media to measure job loss, job creation, small business creation, and informal economic activity; using administrative geo-spatial data to enhance small-area estimates; and training in the use and creation of linked survey-administrative datasets.

The Federal statistical agencies have pressing needs to innovate in light of the rapidly changing structure of the economy and the interaction of these changes with the fundamental ways in which households and businesses produce and use information. This project will combine expertise in social science, survey research, and information science to address the scientific and practical problems that the statistical system must confront. The project will advance the science of measurement and serve to renew the statistical system both by bringing frontier methodology to measurement problems faced by the statistical agencies and by nurturing a new generation of scholars, both within the statistical agencies and academia, who will collaboratively address these issues. This activity is supported by the NSF-Census Research Network funding opportunity.

Agency
National Science Foundation (NSF)
Institute
Division of Social and Economic Sciences (SES)
Type
Standard Grant (Standard)
Application #
1131500
Program Officer
Cheryl L. Eavey
Project Start
Project End
Budget Start
2011-10-01
Budget End
2016-09-30
Support Year
Fiscal Year
2011
Total Cost
$3,595,979
Indirect Cost
Name
University of Michigan Ann Arbor
Department
Type
DUNS #
City
Ann Arbor
State
MI
Country
United States
Zip Code
48109