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Do labor statistics depend on how and to whom the questions are asked ? results from a survey experiment in Tanzania

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  • Bardasi, Elena
  • Beegle, Kathleen
  • Dillon, Andrew
  • Serneels, Pieter

Abstract

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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Bibliographic Info

Paper provided by The World Bank in its series Policy Research Working Paper Series with number 5192.

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Date of creation: 01 Jan 2010
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Handle: RePEc:wbk:wbrwps:5192

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Keywords: Labor Markets; Labor Policies; Work&Working Conditions; Social Analysis; Housing&Human Habitats;

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References

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  1. Dean R. Hyslop & Guido W. Imbens, 2000. "Bias from Classical and Other Forms of Measurement Error," NBER Technical Working Papers 0257, National Bureau of Economic Research, Inc.
  2. 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.
  3. George Judge & Laura Schechter, 2009. "Detecting Problems in Survey Data Using Benford’s Law," Journal of Human Resources, University of Wisconsin Press, vol. 44(1).
  4. 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.
  5. 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).
  6. 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.
  7. 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.
  8. 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.
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Cited by:
  1. Dillon, Andrew & Bardasi, Elena & Beegle, Kathleen & Serneels, Pieter, 2010. "Explaining variation in child labor statistics," Policy Research Working Paper Series 5414, The World Bank.
  2. Duncan Chaplin & Arif Mamun & Thomas Fraker & Kathy Buek & Minki Chatterji & Denzel Hankinson, 2011. "Evaluation of Tanzania Energy Sector Project: Updated Design Report," Mathematica Policy Research Reports 7138, Mathematica Policy Research.
  3. Fox, Louise & Pimhidzai, Obert, 2013. "Different dreams, same bed : collecting, using, and interpreting employment statistics in Sub-Saharan Africa -- the case of Uganda," Policy Research Working Paper Series 6436, The World Bank.
  4. World Bank, 2012. "Uganda - Promoting Inclusive Growth : Transforming Farms, Human Capital, and Economic Geography, Synthesis Report," World Bank Other Operational Studies 12655, The World Bank.
  5. Joachim R. Frick & Kristina Krell, 2009. "Einkommensmessungen in Haushaltspanelstudien für Deutschland: ein Vergleich von EU-SILC und SOEP," SOEPpapers on Multidisciplinary Panel Data Research 237, DIW Berlin, The German Socio-Economic Panel (SOEP).
  6. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2010. "Methods of household consumption measurement through surveys : experimental results from Tanzania," Policy Research Working Paper Series 5501, The World Bank.
  7. Dammert, Ana C. & Galdo, Jose, 2013. "Child Labor Variation by Type of Respondent: Evidence from a Large-Scale Study," World Development, Elsevier, vol. 51(C), pages 207-220.
  8. Oya, Carlos, 2010. "Rural inequality, wage employment and labour market formation in Africa : historical and micro-level evidence," ILO Working Papers 458221, International Labour Organization.

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