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Measuring Unemployment in Crisis: Effects of COVID-19 on Potential Biases in the CPS

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  • Ori Heffetz
  • Daniel Reeves

Abstract

From February to April 2020, as COVID-19 hit the U.S. economy, the official unemployment rate (UR) climbed from 3.5 percent—the lowest in more than 50 years—to 14.7—the highest since current measurement began in January 1948. This unprecedented, speedy quadrupling of UR coincided with major disruptions in survey-data-collection procedures and a dramatic, differential drop in response rates. To what extent did measurement issues contribute to this quadrupling? We revisit two recently studied potential biases in the Current Population Survey: rotation group bias (Krueger, Mas and Niu, 2017) and difficulty-of-reaching bias (Heffetz and Reeves, 2019). We extend the original analyses to the years prior to the crisis and focus on the six months of peak UR, from April to September 2020. Our ballpark estimates suggest that the peak official UR figure could be biased by up to ∼1.5 percentage points in either direction.

Suggested Citation

  • Ori Heffetz & Daniel Reeves, 2020. "Measuring Unemployment in Crisis: Effects of COVID-19 on Potential Biases in the CPS," NBER Working Papers 28310, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28310
    Note: AG LS
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    References listed on IDEAS

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    1. Alan B. Krueger & Alexandre Mas & Xiaotong Niu, 2017. "The Evolution of Rotation Group Bias: Will the Real Unemployment Rate Please Stand Up?," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 258-264, May.
    2. Jeehoon Han & Bruce D. Meyer & James X. Sullivan, 2020. "Income and Poverty in the COVID-19 Pandemic," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 51(2 (Summer), pages 85-118.
    3. Solon, Gary, 1986. "Effects of Rotation Group Bias on Estimation of Unemployment," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 105-109, January.
    4. Ori Heffetz & Daniel B. Reeves, 2019. "Difficulty of Reaching Respondents and Nonresponse Bias: Evidence from Large Government Surveys," The Review of Economics and Statistics, MIT Press, vol. 101(1), pages 176-191, March.
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    Cited by:

    1. James C. Davis & Holden A. Diethorn & Gerald R. Marschke & Andrew J. Wang, 2021. "STEM Employment Resiliency During Recessions: Evidence from the COVID-19 Pandemic," NBER Working Papers 29568, National Bureau of Economic Research, Inc.
    2. Osuna-Gomez, Daniel, 2023. "The impact of the COVID-19 pandemic on post-great recession entrants: Evidence from Mexico," Labour Economics, Elsevier, vol. 81(C).
    3. Osuna Gómez Daniel, 2022. "The Impact of the COVID-19 Pandemic on Post-Great Recession Formal Entrants: Evidence from Mexico," Working Papers 2022-19, Banco de México.

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    More about this item

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • J60 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - General

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