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Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion

Author

Listed:
  • T. Kirk White

    (Center for Economic Studies, U.S. Census Bureau)

  • Jerome P. Reiter

    (Duke University)

  • Amil Petrin

    (University of Minnesota, Twin Cities and NBER)

Abstract

In the U.S. Census Bureau’s 2002 and 2007 Censuses of Manufactures, 79% and 73% of observations, respectively, have imputed data for at least one variable used to compute total factor productivity (TFP). The bureau primarily imputes for missing values using mean-imputation methods, which can reduce the underlying variance of the imputed variables. For five variables entering TFP, we show that dispersion is significantly smaller in the Census mean-imputed versus the nonimputed data. We use classification and regression trees (CART) to produce multiple imputations with observed data for similar plants. For 90% of the 473 industries in 2002 and 84% of the 471 industries in 2007, we find that TFP dispersion increases as we move from Census mean-imputed data to nonimputed data to the CART-imputed data.

Suggested Citation

  • T. Kirk White & Jerome P. Reiter & Amil Petrin, 2018. "Imputation in U.S. Manufacturing Data and Its Implications for Productivity Dispersion," The Review of Economics and Statistics, MIT Press, vol. 100(3), pages 502-509, July.
    • Handle: RePEc:tpr:restat:v:100:y:2018:i:3:p:502-509
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    Cited by:

    1. Ezra Oberfield & Devesh Raval, 2021. "Micro Data and Macro Technology," Econometrica, Econometric Society, vol. 89(2), pages 703-732, March.
    2. Nick Huntington‐Klein & Andreu Arenas & Emily Beam & Marco Bertoni & Jeffrey R. Bloem & Pralhad Burli & Naibin Chen & Paul Grieco & Godwin Ekpe & Todd Pugatch & Martin Saavedra & Yaniv Stopnitzky, 2021. "The influence of hidden researcher decisions in applied microeconomics," Economic Inquiry, Western Economic Association International, vol. 59(3), pages 944-960, July.
    3. Besley, T. & Roland, I. & Van Reenen, J., 2019. "The Aggregate Consequences of Default Risk: Evidence from Firm-level Data," Cambridge Working Papers in Economics 2061, Faculty of Economics, University of Cambridge.
    4. Kuosmanen, Timo & Kuosmanen, Natalia, 2021. "Structural change decomposition of productivity without share weights," Structural Change and Economic Dynamics, Elsevier, vol. 59(C), pages 120-127.
    5. Gene M. Grossman & Elhanan Helpman & Ezra Oberfield & Thomas Sampson, 2017. "The productivity slowdown and the declining labor share: a neoclassical exploration," CEP Discussion Papers dp1504, Centre for Economic Performance, LSE.
    6. Falco J. Bargagli-Dtoffi & Massimo Riccaboni & Armando Rungi, 2020. "Machine Learning for Zombie Hunting. Firms Failures and Financial Constraints," Working Papers 01/2020, IMT School for Advanced Studies Lucca, revised Jun 2020.
    7. Daniel H. Weinberg & John M. Abowd & Robert F. Belli & Noel Cressie & David C. Folch & Scott H. Holan & Margaret C. Levenstein & Kristen M. Olson & Jerome P. Reiter & Matthew D. Shapiro & Jolene Smyth, 2017. "Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System?," Working Papers 17-59r, Center for Economic Studies, U.S. Census Bureau.
    8. Erik Brynjolfsson & Wang Jin & Kristina McElheran, 2021. "The power of prediction: predictive analytics, workplace complements, and business performance," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 56(4), pages 217-239, October.
    9. Mark Bils, 2017. "Misallocation or Mismeasurement?," 2017 Meeting Papers 715, Society for Economic Dynamics.
    10. Sebastian Zalas & Hubert Drążkowski, 2023. "The Evolution of the Labour Share in Poland: New Evidence from Firm-Level Data," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 13-33.
    11. Falco J Bargagli-Stoffi & Fabio Incerti & Massimo Riccaboni & Armando Rungi, 2024. "Machine learning for zombie hunting: predicting distress from firms’ accounts and missing values," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 33(5), pages 1063-1097.
    12. Zalas, Sebastian & Drążkowski, Hubert, . "Kształtowanie się udziału płac w wartości dodanej w Polsce. Nowe szacunki z danych jednostkowych," Gospodarka Narodowa-The Polish Journal of Economics, Szkoła Główna Handlowa w Warszawie / SGH Warsaw School of Economics, vol. 2023(3).
    13. Cindy Cunningham & Lucia Foster & Cheryl Grim & John Haltiwanger & Sabrina Wulff Pabilonia & Jay Stewart & Zoltan Wolf, 2023. "Dispersion in Dispersion: Measuring Establishment‐Level Differences in Productivity," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 69(4), pages 999-1032, December.
    14. Riandy Laksono & Arianto A. Patunru, 2024. "The dynamics of labor share decline in manufacturing: Evidence from Indonesia," Departmental Working Papers 2024-3, The Australian National University, Arndt-Corden Department of Economics.
    15. Lorenz K.F. Ekerdt & Kai-Jie Wu, 2024. "The Rise of Specialized Firms," Working Papers 24-06, Center for Economic Studies, U.S. Census Bureau.
    16. Bils, Mark & Klenow, Peter J. & Ruane, Cian, 2021. "Misallocation or Mismeasurement?," Journal of Monetary Economics, Elsevier, vol. 124(S), pages 39-56.
    17. Francis,David C. & Karalashvili,Nona & Maemir,Hibret Belete & Rodriguez Meza,Jorge Luis, 2020. "Measuring Total Factor Productivity Using the Enterprise Surveys : A Methodological Note," Policy Research Working Paper Series 9491, The World Bank.
    18. Josh Martin & Rebecca Riley, 2025. "Productivity measurement: Reassessing the production function from micro to macro," Journal of Economic Surveys, Wiley Blackwell, vol. 39(1), pages 246-279, February.
    19. Juana Sanchez & Sydney Noelle Kahmann, 2017. "R&D, Attrition and Multiple Imputation in BRDIS," Working Papers 17-13, Center for Economic Studies, U.S. Census Bureau.
    20. Pierce, Justin R. & Schott, Peter K., 2018. "Investment responses to trade liberalization: Evidence from U.S. industries and establishments," Journal of International Economics, Elsevier, vol. 115(C), pages 203-222.
    21. Hang Kim & Martin Rotemberg & T. Kirk White, 2025. "Manufacturing Dispersion: How Data Cleaning Choices Affect Measured Misallocation and Productivity Growth in the Annual Survey of Manufactures," Working Papers 25-67, Center for Economic Studies, U.S. Census Bureau.

    More about this item

    JEL classification:

    • C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • L60 - Industrial Organization - - Industry Studies: Manufacturing - - - General

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