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Impact of the collection mode on labor income data. A study in the times of COVID19

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Listed:
  • García-Suaza, A
  • Lobo, J
  • Montoya, S
  • Ordóñez, J
  • Oviedo, J. D

Abstract

The strict confinement implemented by the National Government of Colombia to contain the expansion of the pandemic caused by COVID-19 generated challenges in data collection operations through household surveys. As a result, the surveys with face-to-face collection methods migrated to a remote mode, through telephone surveys, which could have changed the possible reporting biases of variables, such as income. This paper studies the effect of the change in the information collection model in the Great Integrated Household Survey (Gran Encuesta Integrada de Hogares) of Colombia on the report of labor income. To do this, we exploit the geographical variation in implementing collection methods and an integration of the survey with a social security administrative record to quantify the variation on the report.

Suggested Citation

  • García-Suaza, A & Lobo, J & Montoya, S & Ordóñez, J & Oviedo, J. D, 2022. "Impact of the collection mode on labor income data. A study in the times of COVID19," Documentos de Trabajo 20396, Universidad del Rosario.
    • Handle: RePEc:col:000092:020396
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    References listed on IDEAS

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    1. Bollinger, Christopher R, 1998. "Measurement Error in the Current Population Survey: A Nonparametric Look," Journal of Labor Economics, University of Chicago Press, vol. 16(3), pages 576-594, July.
    2. Andrew Chesher & Christian Schluter, 2002. "Welfare Measurement and Measurement Error," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(2), pages 357-378.
    3. John M. Abowd & Martha H. Stinson, 2013. "Estimating Measurement Error in Annual Job Earnings: A Comparison of Survey and Administrative Data," The Review of Economics and Statistics, MIT Press, vol. 95(5), pages 1451-1467, December.
    4. Bound, John & Brown, Charles & Mathiowetz, Nancy, 2001. "Measurement error in survey data," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 59, pages 3705-3843, Elsevier.
    5. Bound, John & Krueger, Alan B, 1991. "The Extent of Measurement Error in Longitudinal Earnings Data: Do Two Wrongs Make a Right?," Journal of Labor Economics, University of Chicago Press, vol. 9(1), pages 1-24, January.
    6. Stefan Angel & Richard Heuberger & Nadja Lamei, 2018. "Differences Between Household Income from Surveys and Registers and How These Affect the Poverty Headcount: Evidence from the Austrian SILC," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 138(2), pages 575-603, July.
    7. Stefan Angel & Franziska Disslbacher & Stefan Humer & Matthias Schnetzer, 2019. "What did you really earn last year?: explaining measurement error in survey income data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1411-1437, October.
    8. Pirmin Fessler & Maximilian Kasy & Peter Lindner, 2018. "Survey mode effects on measured income inequality," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 16(4), pages 487-505, December.
    9. Bound, John & Brown, Charles & Duncan, Greg J & Rodgers, Willard L, 1994. "Evidence on the Validity of Cross-Sectional and Longitudinal Labor Market Data," Journal of Labor Economics, University of Chicago Press, vol. 12(3), pages 345-368, July.
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    More about this item

    Keywords

    Household surveys; measurement bias; labor income; administrative data; COVID-19; Colombia.;
    All these keywords.

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials

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