Reweighting South African National Household Survey Data to Create a Consistent Series Over Time: A Cross‐Entropy Estimation Approach

Abstract

In the absence of established longitudinal panel surveys in South Africa, national cross-sectional household survey data are frequently used to analyse change. When these data are stacked side by side, however, inconsistencies both in time trends and between household-and person-level data are found. This study uses a new set of weights calibrated to the Actuarial Society of South Africa 2003 model projected totals using a cross-entropy estimation approach. These weights are favoured because they produce consistent demographic and geographic trends. The calculated weights are similar to the initial sample weights (and hence retain the survey design benefits) but match to a series of age-sex-race and province marginal totals that are consistent over time. The weights are publicly available for the 14-year period between 1994 and 2007. JEL Classification: J11, C81, C83

Evidence on the impact of minimum wage laws in an informal sector: Domestic workers in South Africa

“…Indeed, the original sample weights that he refers to have been shown to produce inconsistent aggregate statistics over time. More formally, Branson (2009) explains that "The StatsSA weights presented in the data are problematic for analyses over time … the auxiliary data used as a benchmark in the poststratification adjustment are unreliable and inconsistent over time and hence result in temporal inconsistencies even at the aggregate level. "…”

“…First, we focus on urban workers only, because we are concerned that identification of the impact of the new minimum wage law for rural domestic workers is likely confounded by the concomitant introduction of a minimum wage for agricultural workers, a plausible alternative sector of work for low-skilled workers in rural areas. 13 Second, we use an updated and consistent set of survey weights for the LFS data (Branson, 2009) which were not available for these studies. 14 Third, we use a higher level of aggregation (the province) to define areas in which the new law was more or less binding, based on prelaw characteristics.…”

Estimating the Impact of Minimum Wages on Employment, Wages and Non-Wage Benefits: The Case of Agriculture in South Africa

“…(1) 6 We do not use the standard LFS individual-level weights but rather those provided by Branson (2009). We continue to use the post-stratification unit (PSU) and district level weights from the LFS which adjust for the survey design.…”

Revisiting unemployment levels and trends in South Africa since the transition

“…the mid-year population estimates at the time of the survey derived using the Census 1991Census , 1996Census and 2001, with the precensus and post-census year's population being calculated using exponential interpolation and extrapolation. Nonetheless, some concerns are raised regarding the reliability of the post-stratification design weights (Branson, 2009), as the mid-year population estimates could be unreliable, inconsistent over time and of poor quality, thereby resulting in temporal inconsistencies even at the aggregate level. Furthermore, since the survey data are cross-sectional, the purpose of the post-stratification adjustment is to produce the best estimates of the population, given the information available at the time of the survey.…”

“…Finally, Branson (2009) takes the inconsistent weighting techniques across the surveys into consideration by adopting the entropy approach (to be discussed in detail in Section 3) to re-weight the OHS 1995 -99 and the March LFSs in 2000 -04 data. She finds that even after using the new weights derived by this approach, the female labour force and employment trends do not show any significant changes.…”