Are African workers getting ahead in the new South Africa? Evidence from KwaZulu‐Natal, 1993–1998

Abstract

Excerpt] In this paper, we use the KIDS panel data to answer three questions about the 'progress' of African workers in this one province in post-apartheid South Africa. First, how have African workers progressed as a group? Secondly, which African workers have progressed the most, and by how much have they progressed? Thirdly, to what extent is the progress made by workers driven by transitions between employment and unemployment, or between informal and formal sector employment? We reach the following major findings. First, African workers in KwaZulu-Natal had quite diverse experiences, but experienced positive progress on average. Second, those who progressed the most during the 1993 to 1998 period were individuals who started the least well off. This is a surprising conclusion given the kinds of evidence produced from cross-sectional data. Transitions between employment and unemployment and between formal and informal employment have a strong impact on real earnings, but these real earnings changes are not always as we might expect. Additionally, the sizeable changes that take place for those who do not experience such an employment transition implies that dynamics within sectors also deserve attention. A more technical analysis, complete with a variety of caveats, can be found in a Report to the Ministry of Finance (Cichello, Fields and Leibbrandt, 2001). The purpose of this article is to share these findings at a less technical level and to highlight the necessity of panel data in answering these questions.

Tracking poverty with coarse data: evidence from South Africa

“…Finally, when earnings are missing, they are imputed following a normal 5 See Appendix. 6 The use of five imputations is recommended by [16] on the basis of Monte Carlo simulations on LFS data, and confirmed by [18]. 7 Controlling for hours worked.…”

“…Ten cycles are used to produce each of the five imputed datasets. 6 The resulting datasets are analysed by applying the user-written function Stata function mim, which combines the estimates using Rubin's rules. Missing values are imputed on all covariates in the imputation model, including but not limited to the worker's highest level of education attained, hours of work, firm size and the location of work in the formal or informal sector, using an Ordinary Least Squares, logit, ordered logit or multinomial logit model, where appropriate.…”

“…Leite et al [20] find a decrease in earnings inequality from 2000 to 2004, while [6,7] show that African workers in KwaZulu-Natal experienced large and progressive wage gains between 1993 and 1998. Elsewhere, the number of the employed earning less than $2 a day in real terms is estimated to have more than doubled between 1995 and 2003 [5,27].…”

Earnings and Employment Dynamics for Africans in Post-apartheid South Africa: A Panel Study of KwaZulu-Natal1

“…Table 8 presents a univariate look at the influence these characteristics have on earnings change. These univariate results are discussed in detail in Cichello et al (2001a) and we will only summarise them here. Differences amongst demographic characteristics are fewer than expected.…”

An Evaluation of the Determinants and Implications of Panel Attrition in the National Income Dynamics Survey (2008‐2010)

“…This is a common occurrence. Cichello et al (2001) note that much of the existing literature reaches the same conclusion: that "despite these mean differences in attritors' characteristics, structural relationships are not affected" (cited in Magruder and Nattrass, 2006). This "neutrality of attrition" (Lee, 2003) is entirely model specific, and there may be biases in other structural equations in the NIDS, particularly studies of mobility dynamics, which are a major correlate of attrition.…”