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Exploring the quality of income data in two African household surveys for the purpose of tax-benefit microsimulation modelling: Imputing employment income in Tanzania and Zambia

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Listed:
  • David McLennan
  • Michael Noble
  • Gemma Wright
  • Helen Barnes
  • Faith Masekesa

Abstract

The quality of data on employment income is explored using Tanzanian and Zambian household survey datasets. The extent of missing and implausible income data is assessed and four different methods are applied to impute missing or implausible values. The four imputation methods are also applied to artificial missing data for Tanzania and Zambia, and—using one approach—for a South Africa dataset. Post-imputation results are assessed.

Suggested Citation

  • David McLennan & Michael Noble & Gemma Wright & Helen Barnes & Faith Masekesa, 2021. "Exploring the quality of income data in two African household surveys for the purpose of tax-benefit microsimulation modelling: Imputing employment income in Tanzania and Zambia," WIDER Working Paper Series wp-2021-134, World Institute for Development Economic Research (UNU-WIDER).
    • Handle: RePEc:unu:wpaper:wp-2021-134
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    1. Francisco H. G. Ferreira & Shaohua Chen & Andrew Dabalen & Yuri Dikhanov & Nada Hamadeh & Dean Jolliffe & Ambar Narayan & Espen Beer Prydz & Ana Revenga & Prem Sangraula & Umar Serajuddin & Nobuo Yosh, 2016. "A global count of the extreme poor in 2012: data issues, methodology and initial results," The Journal of Economic Inequality, Springer;Society for the Study of Economic Inequality, vol. 14(2), pages 141-172, June.
    2. 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.
    3. 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.
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    Cited by:

    1. Kerr,Andrew Nicholas & Zondi,Mxolisi, 2024. "Measuring the Upper Tail of the Income and Wealth Distributions," Policy Research Working Paper Series 10836, The World Bank.

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