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Some factors influencing the comparability and reliability of poverty estimates across household surveys


  • Derek Yu

    () (Department of Economics, University of Stellenbosch)


In order to evaluate the extent to which a country achieves the objectives of poverty and inequality reduction, up-to-date, reliable and comparable survey data is required. This paper critically reviews the factors which could affect the comparability and reliability of poverty estimates and trends across various household surveys. First, whether income or expenditure variable should be used for the analyses and whether the diary approach is associated with more reliable capture of income and expenditure information compared with the conventional recall method are looked at. If the respondents are asked to declare the income and expenditure in exact amounts, whether they are asked to report these as ‘one-shot’ amounts (single estimation approach) or aggregate amount derived from the sum of the amounts for sub-categories (aggregation approach) could affect the poverty estimates. If the respondents are asked to report income and expenditure in intervals, issues that could affect the reliability of this approach, such as the number and width of the intervals, the appropriate method used to approximate the income (expenditure) amount in each interval, as well as the possible methods to deal with households reporting zero or unspecified income (expenditure) are investigated. In addition, survey data is validated against external sources such as national accounts data to investigate if it would lead to improved reliability of the former data for the subsequent poverty analyses. Furthermore, since the survey data are, strictly speaking, not time-series data, the data are re-weighted by means of the cross entropy approach in order to be consistent with demographic and geographic numbers presented by the Actuarial Society of South Africa (ASSA) model and Census data so as to find out if the comparability and reliability of the poverty estimates and trends are improved.

Suggested Citation

  • 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.
  • Handle: RePEc:sza:wpaper:wpapers179

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    References listed on IDEAS

    1. Martin Browning & Thomas F. Crossley & Guglielmo Weber, 2003. "Asking consumption questions in general purpose surveys," Economic Journal, Royal Economic Society, vol. 113(491), pages 540-567, November.
    2. Dieter von Fintel, 2006. "Earnings bracket obstacles in household surveys – How sharp are the tools in the shed?," Working Papers 08/2006, Stellenbosch University, Department of Economics.
    3. Richard Blundell & Ian Preston, 1998. "Consumption Inequality and Income Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 113(2), pages 603-640.
    4. Miguel Lacerda & Cally Ardington & Murray Leibbrandt, 2007. "Sequential Regression Multiple Imputation for Incomplete Multivariate Data using Markov Chain Monte Carlo," SALDRU Working Papers 13, Southern Africa Labour and Development Research Unit, University of Cape Town.
    5. Servaas van der Berg & Ronelle Burger & Rulof Burger & Megan Louw & Derek Yu, 2005. "Trends in poverty and inequality since the political transition," Working Papers 01/2005, Stellenbosch University, Department of Economics.
    6. Martin Ravallion, 2003. "Measuring Aggregate Welfare in Developing Countries: How Well Do National Accounts and Surveys Agree?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 645-652, August.
    7. Dieter Von fintel, 2007. "Dealing With Earnings Bracket Responses In Household Surveys - How Sharp Are Midpoint Imputations?," South African Journal of Economics, Economic Society of South Africa, vol. 75(2), pages 293-312, June.
    8. Servaas van der Berg & Ronelle Burger & Rulof Burger & Megan Louw & Derek Yu, 2007. "A series of national accounts-consistent estimates of poverty and inequality in South Africa," Working Papers 09/2007, Stellenbosch University, Department of Economics.
    9. Derek Yu, 2008. "The comparability of Income and Expenditure Surveys 1995, 2000 and 2005/2006," Working Papers 11/2008, Stellenbosch University, Department of Economics.
    10. Cally Ardington & David Lam & Murray Leibbrandt & Matthew Welch, 2005. "The Sensitivity of Estimates of Post-Apartheid Changes in South African Poverty and Inequality to key Data Imputations," SALDRU/CSSR Working Papers 106, Southern Africa Labour and Development Research Unit, University of Cape Town.
    11. 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.
    12. Servaas van der Berg & Megan Louw & Leon du Toit, 2009. "Poverty trends since the transition: What we know," Working Papers 19/2009, Stellenbosch University, Department of Economics.
    13. Jonathan Haughton & Shahidur R. Khandker, 2009. "Handbook on Poverty and Inequality," World Bank Publications, The World Bank, number 11985.
    14. Naeem Ahmed & Matthew Brzozowski & Thomas Crossley, 2006. "Measurement errors in recall food consumption data," IFS Working Papers W06/21, Institute for Fiscal Studies.
    15. Charlotte Guénard & Sandrine Mesplé-Somps, 2010. "Measuring Inequalities: Do Household Surveys Paint A Realistic Picture?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 56(3), pages 519-538, September.
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    More about this item


    poverty; income; expenditure; recall method; diary method; imputations; ASSA model; South Africa;

    JEL classification:

    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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