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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.

Suggested Citation

  • Bardasi, Elena & Beegle, Kathleen & Dillon, Andrew & Serneels, Pieter, 2010. "Do Labor Statistics Depend on How and to Whom the Questions Are Asked? Results from a Survey Experiment in Tanzania," IZA Discussion Papers 4733, Institute for the Study of Labor (IZA).
  • Handle: RePEc:iza:izadps:dp4733

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    References listed on IDEAS

    1. 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).
    2. 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.
    3. 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.
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    7. 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 475-481, October.
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    Cited by:

    1. Joachim Frick & Kristina Krell, 2011. "Einkommensmessungen in Haushaltspanelstudien für Deutschland: Ein Vergleich von EU-SILC und SOEP," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 5(3), pages 221-248, December.
    2. Dillon, Andrew & Bardasi, Elena & Beegle, Kathleen & Serneels, Pieter, 2012. "Explaining variation in child labor statistics," Journal of Development Economics, Elsevier, vol. 98(1), pages 136-147.
    3. Ugo Pica-Ciamarra & Derek Baker & Nancy Morgan & Alberto Zezza & Carlo Azzarri & Cheikh Ly & Longin Nsiima & Simplice Nouala & Patrick Okello & Joseph Sserugga, 2014. "Investing in the Livestock Sector : Why Good Numbers Matter, A Sourcebook for Decision Makers on How to Improve Livestock Data," World Bank Other Operational Studies 17830, The World Bank.
    4. 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.
    5. Palacios-Lopez, Amparo & Christiaensen, Luc & Kilic, Talip, 2017. "How much of the labor in African agriculture is provided by women?," Food Policy, Elsevier, vol. 67(C), pages 52-63.
    6. Beegle, Kathleen & De Weerdt, Joachim & Friedman, Jed & Gibson, John, 2012. "Methods of household consumption measurement through surveys: Experimental results from Tanzania," Journal of Development Economics, Elsevier, vol. 98(1), pages 3-18.
    7. 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.
    8. repec:ilo:ilowps:458221 is not listed on IDEAS
    9. Alwang, Jeffrey & Larochelle, Catherine & Barrera, Victor, 2017. "Farm Decision Making and Gender: Results from a Randomized Experiment in Ecuador," World Development, Elsevier, vol. 92(C), pages 117-129.
    10. 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.
    11. Virginie Comblon & Anne-Sophie Robilliard, 2015. "Are female employment statistics more sensitive than male ones to questionnaire design? Evidence from Cameroon, Mali and Senegal," Working Papers DT/2015/22, DIAL (Développement, Institutions et Mondialisation).
    12. repec:mpr:mprres:7138 is not listed on IDEAS
    13. 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 d946b658c7774742aeeec5d65, Mathematica Policy Research.

    More about this item


    Tanzania; survey design; labor statistics; field experiment;

    JEL classification:

    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments

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