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Genuine Fakes: The prevalence and implications of fieldworker fraud in a large South African survey

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  • Finn, Arden

    (SALDRU, School of Economics, University of Cape Town)

  • Ranchhod, Vimal

    (SALDRU, School of Economics, University of Cape Town)

Abstract

We document how we diagnosed data fabrication in the National Income Dynamics Study. Since the fabrication was detected while fieldwork was still on-going, the relevant interviews were re-conducted and the fabricated data were replaced with authentic data. To the best of our knowledge, this is the first time that this has been done. We thus have an observed counterfactual that allows us to measure how problematic such fabrication would have been, had it remained undetected. We implement a number of estimators using the data that include the fabricated interviews, and compare these with the corresponding estimates that include the corrected data instead. For the outcomes that we consider, we find that the fabrication would not have substantially affected our univariate estimates. However, the fabricated data do impact substantially on some key covariates when panel estimators are used.

Suggested Citation

  • Finn, Arden & Ranchhod, Vimal, 2013. "Genuine Fakes: The prevalence and implications of fieldworker fraud in a large South African survey," SALDRU Working Papers 115, Southern Africa Labour and Development Research Unit, University of Cape Town.
    • Handle: RePEc:ldr:wpaper:115
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    References listed on IDEAS

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    5. Christin Schäfer & Jörg-Peter Schräpler & Klaus-Robert Müller & Gert G. Wagner, 2004. "Automatic Identification of Faked and Fraudulent Interviews in Surveys by Two Different Methods," Discussion Papers of DIW Berlin 441, DIW Berlin, German Institute for Economic Research.
    6. Krueger, Alan B & Summers, Lawrence H, 1988. "Efficiency Wages and the Inter-industry Wage Structure," Econometrica, Econometric Society, vol. 56(2), pages 259-293, March.
    7. Murray Leibbrandt & Ingrid Woolard & Arden Finn & Jonathan Argent, 2010. "Trends in South African Income Distribution and Poverty since the Fall of Apartheid," OECD Social, Employment and Migration Working Papers 101, OECD Publishing.
    8. Cally Ardington & Boingotlo Gasealahwe, 2012. "Health: Analysis of the NIDS Wave 1 and 2 Datasets," SALDRU Working Papers 80, Southern Africa Labour and Development Research Unit, University of Cape Town.
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    Cited by:

    1. De Haas Samuel & Winker Peter, 2016. "Detecting Fraudulent Interviewers by Improved Clustering Methods – The Case of Falsifications of Answers to Parts of a Questionnaire," Journal of Official Statistics, Sciendo, vol. 32(3), pages 643-660, September.
    2. Fiedler, John L. & Mwangi, Dena M., 2016. "Improving household consumption and expenditure surveys’ food consumption metrics: Developing a strategic approach to the unfinished agenda:," IFPRI discussion papers 1570, International Food Policy Research Institute (IFPRI).
    3. Essers, Dennis, 2013. "South African labour market transitions during the global financial and economic crisis: Micro-level evidence from the NIDS panel and matched QLFS cross-sections," IOB Working Papers 2013.12, Universiteit Antwerpen, Institute of Development Policy (IOB).
    4. Kingori, Patricia & Gerrets, René, 2016. "Morals, morale and motivations in data fabrication: Medical research fieldworkers views and practices in two Sub-Saharan African contexts," Social Science & Medicine, Elsevier, vol. 166(C), pages 150-159.
    5. Andrew Kerr, 2015. "Tax(i)ing the poor? Commuting costs in South Africa," SALDRU Working Papers 156, Southern Africa Labour and Development Research Unit, University of Cape Town.
    6. González Fernando Antonio Ignacio, 2019. "Detecting Anomalous Data in Household Surveys: Evidence for Argentina," Journal of Social and Economic Statistics, Sciendo, vol. 8(2), pages 1-10, December.

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    Keywords

    fieldworkers; data fabrication; South Africa; National Income Dynamics Study; NIDS;
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