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 - David McLennan; Michael Noble; Gemma Clare Wright; Helen Barnes; Faith Masekesa (The United Nations University World Institute for Development Economics Research (UNU-WIDER), 2021)
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. It is argued that the treatment of missing data cannot be generalized, and that tax compliance should also be taken into account when assessing the validity of a microsimulation model's simulation of direct taxes.
Additional information: Update (September 2021): The technical note mentioned in the text is available as WIDER Technical Note 2021/15 here: https://doi.org/10.35188/UNU-WIDER/WTN/2021-15
Keywords: C63; C81; H24; D31; 330; income imputation; microsimulation; missing data; Tanzania; Zambia