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Research Spending & Results

Award Detail

Awardee:REGENTS OF THE UNIVERSITY OF MICHIGAN
Doing Business As Name:University of Michigan Ann Arbor
PD/PI:
  • Matthew D Shapiro
  • (734) 764-5419
  • shapiro@umich.edu
Co-PD(s)/co-PI(s):
  • Margaret Levenstein
Award Date:09/19/2011
Estimated Total Award Amount: $ 2,995,165
Funds Obligated to Date: $ 4,134,059
  • FY 2012=$262,551
  • FY 2011=$2,995,165
  • FY 2013=$338,263
  • FY 2016=$538,080
Start Date:10/01/2011
End Date:09/30/2018
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.075
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:NCRN-MN: Linking Surveys to the World: Administrative Data, the Web, and Beyond
Federal Award ID Number:1131500
DUNS ID:073133571
Parent DUNS ID:073133571
Program:NSF-Census Research Network
Program Officer:
  • Cheryl Eavey
  • (703) 292-7269
  • ceavey@nsf.gov

Awardee Location

Street:3003 South State St. Room 1062
City:Ann Arbor
State:MI
ZIP:48109-1274
County:Ann Arbor
Country:US
Awardee Cong. District:12

Primary Place of Performance

Organization Name:University of Michigan Ann Arbor
Street:3003 S. State St
City:Ann Arbor
State:MI
ZIP:48104-1284
County:Ann Arbor
Country:US
Cong. District:12

Abstract at Time of Award

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.

Publications Produced as a Result of this Research

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Antenucci, D., Cafarella, M. J., Levenstein, M. C., Ré, C., & Shapiro, M. "Ringtail: Feature Selection for Easier Nowcasting" WebDB, v., 2013, p.49.

Shaefer, H. L., and Ybarra, M. "The welfare reforms of the 1990s and the stratification of material well-being among low-income households with children" Children and Youth Services Review, v.34, 2012, p.1810.

Shaefer, H.L., Edin, K. & Talbert, E. "Understanding the Dynamics of $2-a-Day Poverty in the United States" RSF: A Journal of the Social Sciences, special issue on ?Severe Deprivation.?, v.1, 2015, p.120.

Shaefer, H. L., Song, X., and Williams Shanks, T. R. "Do single mothers in the United States use the Earned Income Tax Credit to reduce unsecured debt?" Review of Economics of the Household, v., 2013, p.659.

Wiederspan, J., Rhodes, E., & Shaefer, H. L. "Expanding the Discourse on Antipoverty Policy: Reconsidering a Negative Income Tax" Journal of Poverty, v.19, 2015, p.218.

Wilson, C. R., & Brown, D. G. "Change in Visible Impervious Surface Area in Southeastern Michigan Before and After the ?Great Recession:? Spatial Differentiation in Remotely Sensed Land-Cover Dynamics" Population and Environment, v.36, 2015, p..

Fusaro, V.* & Shaefer, H.L. "How Should We Define Low-Wage Work? An Analysis Using the Current Population Survey" Monthly Labor Review, v., 2016, p..

Friedline, T., Despard, M., & Chowa, G. "Preventive policy strategy for banking the unbanked: Savings accounts for teenagers?" Journal of Poverty, v., 2015, p..

Shaefer, H. L., & Ybarra, M. "The welfare reforms of the 1990s and the stratification of material well-being among low-income households with children" Children and Youth Services Review, v.34, 2012, p.1810.

Antenucci, D., Li, E., Liu, S., Zhang, B., Cafarella, M. J., & Ré, C. "Ringtail: a generalized nowcasting system" WebDB, v.6, 2013, p.1358.

Antenucci, D., Cafarella, M. J., Levenstein, M. C., Ré, C., & Shapiro, M.D. "Ringtail: Feature Selection for Easier Nowcasting" WebDB, v., 2013, p.49.

Gelman, M., Kariv, S., Shapiro, M. D., Silverman, D., & Tadelis, S. "Harnessing Naturally Occurring Data to Measure the Response of Spending to Income" Science, v.345, 2014, p.212.

Wasi, N. and A. Flaaen "Record linkage using Stata: Preprocessing, linking, and reviewing utilities" Stata Journal, v.15, 2015, p.672.

Friedline, T., & Nam, I. "Savings from ages 16 to 35: A test to inform Child Development Account policy" Poverty & Public Policy, v.6, 2014, p.46.

Shaefer, H. L., & Edin, K. "Understanding the Dynamics of $2-a-Day Poverty in the United States" RSF: The Russell Sage Foundation Journal of the Social Sciences, v.1, 2015, p.120.

Shaefer, H. Luke, & Edin, K. "Rising extreme poverty in the United States and the response of means-tested transfers" Social Service Review, v.87, 2013, p.250.

Shaefer, H. L., & Edin, K. "Extreme Poverty in the United States, 1996 to 2011" Policy Brief, N. P. Center (Ed.), University of Michigan, v., 2012, p.5.

Gelman, M., Kariv, S., Shapiro, M. D., Silverman, D., & Tadelis, S. "Harnessing Naturally Occurring Data to Measure the Response of Spending to Income" Science, v.345, 2014, p.212.

Friedline, T., & Nam, I. "Savings from ages 16 to 35: A test to inform Child Development Account policy" Poverty & Public Policy, v.6, 2014, p.46.

Antenucci, D., Li, E., Liu, S., Zhang, B., Cafarella, M. J., & Ré, C. "Ringtail: a generalized nowcasting system" WebDB, v.6, 2013, p.1358.

Shaefer, H. L., & Ybarra, M. "The welfare reforms of the 1990s and the stratification of material well-being among low-income households with children" Children and Youth Services Review, v.34, 2012, p.1810.

Shaefer, H. L., Song, X., & Williams Shanks, T. R. "Do single mothers in the United States use the Earned Income Tax Credit to reduce unsecured debt?" Review of Economics of the Household, v.11, 2013, p.659.


Project Outcomes Report

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

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 produced innovations in using such data—often called “big data”—that are foundational for modernizing how Federal statistical agencies measure the economy in the twenty-first century. 

This project provided novel estimates and analyses including the following:

Using financial transactions data, the project produced a new way to estimate households’ spending, income, and debt at high frequency and with great accuracy.  Analyses using these data provided evidence on how households manage their spending and debt payments over their pay period.  It also showed how households adjust to adverse shocks to income by drawing down cash and delaying payments. These results show the extent to which households face financial risks if they have very low cash on hand to meet unexpected events.

Using social media data, this projected produced new indices of job loss and job finding that complement official statistics.   This approach demonstrates the usefulness of using new sources of data—specifically Tweets about jobs—in identifying labor market trends.  In particular, social media provides novel insight into how workers feel about the strength or weakness of employment prospects.  Techniques developed using social media data will be useful for economists and other social scientists aiming to construct real time indices of social and economic behavior and outcomes.  Collaboration between social scientists and computer scientists produced tools that can be applied to extracting time series of social and economic indicators from social media.

Geospatial images also provide a novel source of data for assessing economic trends with greater resolution than conventional data sources.  This project used satellite measurements of land cover to measure the impact of the 2008 financial crisis on economic activity in southeast Michigan.

A modern statistical infrastructure combines survey and administrative data. Some information, including inherently subjective variables, can best obtained from surveys of individuals.  Other data are much better measured by administrative data.  This project advanced the joint use of measure survey and administrative data by developing new techniques for matching survey responses about employers to administrative data.  These techniques have better performance—better match probabilities and fewer false positive matches—than conventional procedures. These innovations will be available to statistical agencies as they improve official measures by combining survey and administrative data.  In addition to improving data quality, the use of such record linkage can reduce the cost of administering surveys and the time burden of households when they respond to surveys.

The project compared survey and administrative measurement of permanent job loss. It examined the implications of these different measures for estimates of how job loss affects workers’ prospects for future earnings and employment.  This work allows novel assessment of how business conditions are perceived by workers and the differential effects on workers.

The Federal statistical system faces important challenges in renewing its workforce.  This project sponsored training in using techniques related to its findings, engaged in collaborative methodological research with researchers at the Census Bureau and other participants in the NSF-Census Research Network, and disseminated the project’s results via participation in conferences and scholarly publication.

 


Last Modified: 01/31/2019
Modified by: Matthew D Shapiro

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