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Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania

  • Bardasi, Elena


    (World Bank)

  • Beegle, Kathleen


    (World Bank)

  • Dillon, Andrew


    (Michigan State University)

  • Serneels, Pieter


    (University of East Anglia)

Labor market statistics are critical for assessing and understanding economic development. In practice, widespread variation exists in how labor statistics are measured in household surveys in low-income countries. Little is known whether these differences have an effect on the labor statistics they produce. This paper analyzes these effects by implementing a survey experiment in Tanzania that varied two key dimensions: the level of detail of the questions and the type of respondent. Significant differences are observed across survey designs with respect to different labor statistics. Labor force participation rates, for example, vary by as much as 10 percentage points across the four survey assignments. Using a short labor module without screening questions on employment generates lower female labor force participation and lower rates of wage employment for both men and women. Response by proxy rather than self-report yields lower male labor force participation, lower female working hours, and lower employment in agriculture for men. The differences between proxy and self reporting seem to come from information imperfections within the household, especially with the distance in age between respondent and subject playing an important role, while gender and educational differences seem less important.

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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 4733.

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Length: 58 pages
Date of creation: Jan 2010
Date of revision:
Publication status: published in: World Bank Economic Review, 2012, 25 (3), 418-447
Handle: RePEc:iza:izadps:dp4733
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  1. Hyslop, Dean R & Imbens, Guido W, 2001. "Bias from Classical and Other Forms of Measurement Error," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 475-81, October.
  2. Ucw, 2011. "Understanding the Brazilian success in reducing child labour: empirical evidence and policy lessons. Drawing policy lessons from the Brazilian experience," UCW Working Paper 55, Understanding Children's Work (UCW Programme).
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  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. Abowd, John M & Zellner, Arnold, 1985. "Estimating Gross Labor-Force Flows," Journal of Business & Economic Statistics, American Statistical Association, vol. 3(3), pages 254-83, June.
  6. Jerry Hausman, 2001. "Mismeasured Variables in Econometric Analysis: Problems from the Right and Problems from the Left," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 57-67, Fall.
  7. de Mel, Suresh & McKenzie, David & Woodruff, Christopher, 2007. "Measuring microenterprise profits : don't ask how the sausage is made," Policy Research Working Paper Series 4229, The World Bank.
  8. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
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