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Evolving Measurement for an Evolving Economy: Thoughts on 21st Century US Economic Statistics

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  • Ron S. Jarmin

Abstract

The system of federal economic statistics developed in the 20th century has served the country well, but the current methods for collecting and disseminating these data products are unsustainable. These statistics are heavily reliant on sample surveys. Recently, however, response rates for both household and business surveys have declined, increasing costs and threatening quality. Existing statistical measures, many developed decades ago, may also miss important aspects of our rapidly evolving economy; moreover, they may not be sufficiently accurate, timely, or granular to meet the increasingly complex needs of data users. Meanwhile, the rapid proliferation of online data and more powerful computation make privacy and confidentiality protections more challenging. There is broad agreement on the need to transform government statistical agencies from the 20th century survey-centric model to a 21st century model that blends structured survey data with administrative and unstructured alternative digital data sources. In this essay, I describe some work underway that hints at what 21st century official economic measurement will look like and offer some preliminary comments on what is needed to get there.

Suggested Citation

  • Ron S. Jarmin, 2019. "Evolving Measurement for an Evolving Economy: Thoughts on 21st Century US Economic Statistics," Journal of Economic Perspectives, American Economic Association, vol. 33(1), pages 165-184, Winter.
    • Handle: RePEc:aea:jecper:v:33:y:2019:i:1:p:165-84
    Note: DOI: 10.1257/jep.33.1.165
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    Citations

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    Cited by:

    1. Orazem, Peter F. & Tran, Thu, 2020. "To Inform or Influence? The Difference between Data Released by Nonprofits and by the Government," Journal of Regional Analysis and Policy, Mid-Continent Regional Science Association, vol. 50(1), March.
    2. Rickard Nyman & Paul Ormerod, 2020. "Text as Data: Real-time Measurement of Economic Welfare," Papers 2001.03401, arXiv.org.
    3. Buda, Gergely & Carvalho, Vasco & Hansen, Stephen & Ortiz, Alvaro & Rodrigo, Tomasa & Rodríguez Mora, José V, 2022. "National Accounts in a World of Naturally Occurring Data: A Proof of Concept for Consumption," CEPR Discussion Papers 17519, C.E.P.R. Discussion Papers.
    4. Gabriel Ehrlich & John C. Haltiwanger & Ron S. Jarmin & David Johnson & Matthew D. Shapiro, 2020. "Reengineering Key National Economic Indicators," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 25-68, National Bureau of Economic Research, Inc.
    5. Katharine G. Abraham, 2022. "Big Data and Official Statistics," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 68(4), pages 835-861, December.
    6. Gabriel Ehrlich & John Haltiwanger & Ron Jarmin & David Johnson & Matthew D. Shapiro, 2019. "Minding Your Ps and Qs: Going from Micro to Macro in Measuring Prices and Quantities," AEA Papers and Proceedings, American Economic Association, vol. 109, pages 438-443, May.
    7. Binswanger, Johannes & Oechslin, Manuel, 2020. "Better statistics, better economic policies?," European Economic Review, Elsevier, vol. 130(C).
    8. Battistin, Erich & De Nadai, Michele & Krishnan, Nandini, 2023. "The insights and illusions of consumption measurements," Journal of Development Economics, Elsevier, vol. 161(C).
    9. Evan S. Totty & Thor Watson, 2024. "Privacy Protection and Accuracy: What Do We Know? Do We Know Things?? Let's Find Out!," NBER Chapters, in: Data Privacy Protection and the Conduct of Applied Research: Methods, Approaches and their Consequences, National Bureau of Economic Research, Inc.
    10. Webel, Karsten, 2022. "A review of some recent developments in the modelling and seasonal adjustment of infra-monthly time series," Discussion Papers 31/2022, Deutsche Bundesbank.
    11. Lachowska, Marta & Mas, Alexandre & Woodbury, Stephen A., 2022. "How reliable are administrative reports of paid work hours?," Labour Economics, Elsevier, vol. 75(C).
    12. Luke Mosley & Idris A. Eckley & Alex Gibberd, 2022. "Sparse temporal disaggregation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2203-2233, October.
    13. Lucas, David S., 2024. "The effect of regime change on entrepreneurship: A real options approach with evidence from US gubernatorial elections," Journal of Business Venturing, Elsevier, vol. 39(4).
    14. Lucia Foster, 2020. "Panel Remarks: Measuring Business Innovation Using a Multidimensional Approach," NBER Chapters, in: The Role of Innovation and Entrepreneurship in Economic Growth, pages 569-575, National Bureau of Economic Research, Inc.
    15. Barbara Guardabascio & Filippo Moauro & Luke Mosley, 2024. "Indirect estimation of the monthly transport turnover indicator in Italy," Empirical Economics, Springer, vol. 67(2), pages 531-566, August.
    16. Luke Mosley & Idris Eckley & Alex Gibberd, 2021. "Sparse Temporal Disaggregation," Papers 2108.05783, arXiv.org, revised Oct 2022.
    17. Katharine G. Abraham & Ron S. Jarmin & Brian C. Moyer & Matthew D. Shapiro, 2020. "Introduction: Big Data for Twenty-First-Century Economic Statistics: The Future Is Now," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 1-22, National Bureau of Economic Research, Inc.
    18. N. M. Rozanova, 2021. "Methodological Issues of Modern Competition Policy," Studies on Russian Economic Development, Springer, vol. 32(5), pages 492-498, September.

    More about this item

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

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