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Small-area estimates from consumer trace data

Author

Listed:
  • Arthur Acolin

    (University of Washington)

  • Ari Decter-Frain

    (Cornell University)

  • Matt Hall

    (Cornell University)

Abstract

Background: Timely, accurate, and precise demographic estimates at various levels of geography are crucial for planning, policymaking, and analysis. In the United States, data from the decennial census and annual American Community Survey (ACS) serve as the main sources for subnational demographic estimates. While estimates derived from these sources are widely regarded as accurate, their timeliness is limited and variability sizable for small geographic units like towns and neighborhoods. Objective: This paper investigates the potential for using nonrepresentative consumer trace data assembled by commercial vendors to produce valid and timely estimates. We focus on data purchased from Data Axle, which contains the names and addresses of over 150 million Americans annually. Methods: We identify the predictors of over- and undercounts of households as measured with consumer trace data and compare a range of calibration approaches to assess the extent to which systematic errors in the data can be adjusted for over time. We also demonstrate the utility of the data for predicting contemporaneous (nowcasting) tract-level household counts in the 2020 Decennial Census. Results: We find that adjusted counts at the county, ZIP Code Tabulation Areas (ZCTA), and tract levels deviate from ACS survey-based estimates by an amount roughly equivalent to the ACS margins of error. Machine-learning methods perform best for calibration of county- and tract-level data. The estimates are stable over time and across regions of the country. We also find that when doing nowcasts, incorporating Data Axle estimates improved prediction bias relative to using the most recent ACS five-year estimates alone. Contribution: Despite its affordability and timeliness compared to survey-based measures, consumer trace data remains underexplored by demographers. This paper examines one consumer trace data source and demonstrates that challenges with representativeness can be overcome to produce household estimates that align with survey-based estimates and improve demographic forecasts. At the same time, the analysis also underscores the need for researchers to examine the limits of the data carefully before using them for specific applications.

Suggested Citation

  • Arthur Acolin & Ari Decter-Frain & Matt Hall, 2022. "Small-area estimates from consumer trace data," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(27), pages 843-882.
    • Handle: RePEc:dem:demres:v:47:y:2022:i:27
    DOI: 10.4054/DemRes.2022.47.27
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    as
    1. Alexandre Belloni & Victor Chernozhukov & Christian Hansen, 2014. "High-Dimensional Methods and Inference on Structural and Treatment Effects," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 29-50, Spring.
    2. Raj Chetty & John N Friedman & Michael Stepner & Opportunity Insights Team & Camille Baker & Harvey Barnhard & Matt Bell & Gregory Bruich & Tina Chelidze & Lucas Chu & Westley Cineus & Sebi Devlin-Fol, 2024. "The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 139(2), pages 829-889.
    3. Lewandowski, Daniel & Kurowicka, Dorota & Joe, Harry, 2009. "Generating random correlation matrices based on vines and extended onion method," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 1989-2001, October.
    4. Dragan, Kacie & Ellen, Ingrid Gould & Glied, Sherry, 2020. "Does gentrification displace poor children and their families? New evidence from medicaid data in New York City," Regional Science and Urban Economics, Elsevier, vol. 83(C).
    5. Rebecca Diamond & Tim McQuade & Franklin Qian, 2019. "The Effects of Rent Control Expansion on Tenants, Landlords, and Inequality: Evidence from San Francisco," American Economic Review, American Economic Association, vol. 109(9), pages 3365-3394, September.
    6. Steven Ruggles & Catherine Fitch & Diana Magnuson & Jonathan Schroeder, 2019. "Differential Privacy and Census Data: Implications for Social and Economic Research," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 403-408, May.
    7. Jack DeWaard & Janna Johnson & Stephan Whitaker, 2019. "Internal migration in the United States: A comprehensive comparative assessment of the Consumer Credit Panel," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(33), pages 953-1006.
    8. Andrew J. Greenlee, 2019. "Assessing the Intersection of Neighborhood Change and Residential Mobility Pathways for the Chicago Metropolitan Area (2006–2015)," Housing Policy Debate, Taylor & Francis Journals, vol. 29(1), pages 186-212, January.
    9. W. R. Bell & G. S. Datta & M. Ghosh, 2013. "Benchmarking small area estimators," Biometrika, Biometrika Trust, vol. 100(1), pages 189-202.
    10. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    11. David C. Phillips, 2020. "Measuring Housing Stability With Consumer Reference Data," Demography, Springer;Population Association of America (PAA), vol. 57(4), pages 1323-1344, August.
    12. Donghoon Lee & Wilbert Van der Klaauw, 2010. "An introduction to the FRBNY Consumer Credit Panel," Staff Reports 479, Federal Reserve Bank of New York.
    13. Kacie Dragan & Ingrid Ellen & Sherry A. Glied, 2019. "Does Gentrification Displace Poor Children? New Evidence from New York City Medicaid Data," NBER Working Papers 25809, National Bureau of Economic Research, Inc.
    14. Malay Ghosh & Rebecca Steorts, 2013. "Two-stage benchmarking as applied to small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(4), pages 670-687, November.
    15. Boarnet, Marlon G. & Bostic, Raphael W. & Burinskiy, Evgeniy & Rodnyansky, Seva & Prohofsky, Allen, 2018. "Gentrification Near Rail Transit Areas: A Micro-Data Analysis of Moves into Los Angeles Metro Rail Station Areas," Institute of Transportation Studies, Working Paper Series qt4p4584w8, Institute of Transportation Studies, UC Davis.
    16. David C. Folch & Daniel Arribas-Bel & Julia Koschinsky & Seth E. Spielman, 2016. "Spatial Variation in the Quality of American Community Survey Estimates," Demography, Springer;Population Association of America (PAA), vol. 53(5), pages 1535-1554, October.
    17. Mathew E. Hauer, 2017. "Migration induced by sea-level rise could reshape the US population landscape," Nature Climate Change, Nature, vol. 7(5), pages 321-325, May.
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    More about this item

    Keywords

    small area estimation; nontraditional data; consumer data; calibration techniques;
    All these keywords.

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics
    • Z0 - Other Special Topics - - General

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