IDEAS home Printed from https://ideas.repec.org/p/ldr/wpaper/90.html
   My bibliography  Save this paper

A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys

Author

Listed:
  • Reza C. Daniels

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

Abstract

In this paper the Total Survey Error (TSE) paradigm is combined with detailed data quality indicators to develop a framework for investigating micro data quality. The TSE framework is widely used in the survey methodology literature to identify different components of error that arise in the survey process. Consequently, it provides a very useful typology for researchers to understand which data quality issues are relevant in applied work based on these surveys. In order to demonstrate how the framework sheds light on micro data quality, two labour market household surveys conducted by Statistics South Africa are reviewed, spanning a time-frame from 1995-2007. It is argued that efforts to improve data quality should involve a virtuous interaction between producers and consumers of micro data and should be considered an evolving process. For producers of data, the preparation and publication of detailed data quality frameworks is recommended, and two examples of these frameworks are reviewed. For consumers of data, judicious analyses of the univariate, bivariate and multivariate relationships in public-use versions of the datasets can help shed light on different components of survey error, and should be communicated back to survey organisations. Ultimately, improving data quality is about being more explicit about the limitations of data production at each stage of the process, which does not stop at initial public release.

Suggested Citation

  • Reza C. Daniels, 2012. "A Framework for Investigating Micro Data Quality, with Application to South African Labour Market Household Surveys," SALDRU Working Papers 90, Southern Africa Labour and Development Research Unit, University of Cape Town.
    • Handle: RePEc:ldr:wpaper:90
    as

    Download full text from publisher

    File URL: https://opensaldru.uct.ac.za/bitstream/handle/11090/606/2012_90.pdf?sequence=1
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. H. Bhorat, 1999. "The October Household Survey, Unemployment and the Informal Sector: A Note," South African Journal of Economics, Economic Society of South Africa, vol. 67(2), pages 143-146, June.
    2. Martin Wittenberg, 2004. "The Mystery Of South Africa'S Ghost Workers In 1996: Measurement And Mismeasurement In The Manufacturing Census, Population Census And October Household Surveys," South African Journal of Economics, Economic Society of South Africa, vol. 72(5), pages 1003-1022, December.
    3. Nicola Branson & Martin Wittenberg, 2014. "Reweighting South African National Household Survey Data to Create a Consistent Series Over Time: A Cross-Entropy Estimation Approach," South African Journal of Economics, Economic Society of South Africa, vol. 82(1), pages 19-38, March.
    4. Daan Steenkamp & Ruan Erasmus, 2022. "South Africa’s yield curve conundrum," Working Papers 07, Economic Research Southern Africa.
    5. R C Daniels & S Rospabe, 2005. "Estimating an Earnings Function From Coarsened Data by an Interval Censored Regression Procedure," Studies in Economics and Econometrics, Taylor & Francis Journals, vol. 29(1), pages 29-46, April.
    6. Nicola Branson & Martin Wittenberg, 2007. "The Measurement Of Employment Status In South Africa Using Cohort Analysis, 1994‐20041," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 313-326, June.
    7. Derek Yu, 2007. "The comparability of the Statistics South Africa October Household Surveys and Labour Force Surveys," Working Papers 17/2007, Stellenbosch University, Department of Economics.
    8. Robert M. Groves & Steven G. Heeringa, 2006. "Responsive design for household surveys: tools for actively controlling survey errors and costs," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(3), pages 439-457, July.
    9. Christopher J. Flinn & Richard Kulka & Robert Moffitt & Kenneth I. Wolpin, 2001. "Introduction to the Journal of Human Resources Special Issue on Data Quality," Journal of Human Resources, University of Wisconsin Press, vol. 36(3), pages 413-415.
    10. Nicola Branson, 2009. "Re-weighting the OHS and LFS National household Survey Data to create a consistent series over time: A Cross Entropy Estimation Approach," SALDRU Working Papers 38, Southern Africa Labour and Development Research Unit, University of Cape Town.
    11. Martin Wittenberg, 2006. "Research Note: Errors In The October Household Survey 1994 Available From The South African Data Archive1," South African Journal of Economics, Economic Society of South Africa, vol. 74(4), pages 766-768, December.
    12. Servaas Van Der Berg & Megan Louw, 2004. "Changing Patterns Of South African Income Distribution: Towards Time Series Estimates Of Distribution And Poverty1," South African Journal of Economics, Economic Society of South Africa, vol. 72(3), pages 546-572, September.
    13. Rosalia Vazquez-Alvarez, 2003. "Anchoring Bias and Covariate Nonresponse," University of St. Gallen Department of Economics working paper series 2003 2003-19, Department of Economics, University of St. Gallen.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Arden Finn & Murray Leibbrandt & Morné Oosthuizen, 2014. "Poverty, Inequality, and Prices in Post-Apartheid South Africa," WIDER Working Paper Series wp-2014-127, World Institute for Development Economic Research (UNU-WIDER).
    2. Finn, Arden & Leibbrandt, Murray & Oosthuizen, Morne, 2014. "Poverty, inequality, and prices in post-apartheid South Africa," WIDER Working Paper Series 127, World Institute for Development Economic Research (UNU-WIDER).
    3. repec:ilo:ilowps:484770 is not listed on IDEAS
    4. Wittenberg, Martin., 2014. "Analysis of employment, real wage, and productivity trends in South Africa since 1994," ILO Working Papers 994847703402676, International Labour Organization.
    5. Andrew Kerr & Martin Wittenberg, 2019. "Earnings and employment microdata in South Africa," WIDER Working Paper Series wp-2019-47, World Institute for Development Economic Research (UNU-WIDER).
    6. Rulof Burger & Servaas Berg & Dieter Fintel, 2015. "The Unintended Consequences of Education Policies on South African Participation and Unemployment," South African Journal of Economics, Economic Society of South Africa, vol. 83(1), pages 74-100, March.
    7. Rulof Burger & Rachel Jafta & Dieter von Fintel, 2016. "Affirmative action policies and the evolution of post-apartheid South Africa's racial wage gap," WIDER Working Paper Series 066, World Institute for Development Economic Research (UNU-WIDER).
    8. Reza C. Daniels, 2012. "Questionnaire Design and Response Propensities for Employee Income Micro Data," SALDRU Working Papers 89, Southern Africa Labour and Development Research Unit, University of Cape Town.
    9. Neil Rankin & Rulof Burger & Friedrich Kreuser, 2015. "The elasticity of substitution and labour-displacing technical change in post-apartheid South Africa," WIDER Working Paper Series 101, World Institute for Development Economic Research (UNU-WIDER).
    10. Jacqueline Mosomi, 2019. "Distributional changes in the gender wage gap in the post-apartheid South African labour market," WIDER Working Paper Series wp-2019-17, World Institute for Development Economic Research (UNU-WIDER).
    11. Magejo, Prudence & Benhura, Miracle, 2015. "A Detailed Decomposition Analysis of the Public-Private Sector Wage Gap in South Africa," IZA Discussion Papers 9271, Institute of Labor Economics (IZA).
    12. Derek Yu, 2013. "Some factors influencing the comparability and reliability of poverty estimates across household surveys," Working Papers 03/2013, Stellenbosch University, Department of Economics.
    13. Early Kirstin & Mankoff Jennifer & Fienberg Stephen E., 2017. "Dynamic Question Ordering in Online Surveys," Journal of Official Statistics, Sciendo, vol. 33(3), pages 625-657, September.
    14. Chun Asaph Young & Schouten Barry & Wagner James, 2017. "JOS Special Issue on Responsive and Adaptive Survey Design: Looking Back to See Forward – Editorial: In Memory of Professor Stephen E. Fienberg, 1942–2016," Journal of Official Statistics, Sciendo, vol. 33(3), pages 571-577, September.
    15. Dinkelman, Taryn & Ranchhod, Vimal, 2012. "Evidence on the impact of minimum wage laws in an informal sector: Domestic workers in South Africa," Journal of Development Economics, Elsevier, vol. 99(1), pages 27-45.
    16. Geeta G. Kingdon & John B. Knight, 2000. "Are searching and non-searching unemployment distinct states when unemployment is high? The case of South Africa," CSAE Working Paper Series 2000-02, Centre for the Study of African Economies, University of Oxford.
    17. Reist, Benjamin M. & Rodhouse, Joseph B. & Ball, Shane T. & Young, Linda J., 2019. "Subsampling of Nonrespondents in the 2017 Census of Agriculture," NASS Research Reports 322826, United States Department of Agriculture, National Agricultural Statistics Service.
    18. Andrew Kerr & Bruce McDougall, 2020. "What is a firm census in a developing country? An answer from Ghana," SALDRU Working Papers 262, Southern Africa Labour and Development Research Unit, University of Cape Town.
    19. Lewis Taylor, 2017. "Univariate Tests for Phase Capacity: Tools for Identifying When to Modify a Survey’s Data Collection Protocol," Journal of Official Statistics, Sciendo, vol. 33(3), pages 601-624, September.
    20. Jiayun Jin & Caroline Vandenplas & Geert Loosveldt, 2019. "The Evaluation of Statistical Process Control Methods to Monitor Interview Duration During Survey Data Collection," SAGE Open, , vol. 9(2), pages 21582440198, June.
    21. Andy Peytchev, 2013. "Consequences of Survey Nonresponse," The ANNALS of the American Academy of Political and Social Science, , vol. 645(1), pages 88-111, January.

    More about this item

    Keywords

    Data Quality Evaluation and Assessment; Total Survey Error;

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ldr:wpaper:90. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Alison Siljeur (email available below). General contact details of provider: https://edirc.repec.org/data/sauctza.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.