WIDER Working Paper 2020/32
Measuring labour earnings inequality in post-
apartheid South Africa
Serena Merrino*
March 2020
* UNU-WIDER, Helsinki, Finland, and South African Reserve Bank, Pretoria, South Africa; sm147@soas.ac.uk
This study has been prepared within the UNU-WIDER project Southern Africa – Towards Inclusive Economic Development
(SA-TIED).
Copyright © UNU-WIDER 2020
Information and requests: publications@wider.unu.edu
ISSN 1798-7237 ISBN 978-92-9256-789-7
https://doi.org/10.35188/UNU-WIDER/2020/789-7
Typescript prepared by Luke Finley.
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Abstract: This paper investigates the validity of household survey data published by Statistics
South Africa since 1993 and later integrated into the Post-Apartheid Labour Market Series
(PALMS). A series of statistical adjustments are proposed, compared, and applied to primary
data with the purpose of generating time-comparable, unbiased estimates, and accurate standard
errors of labour earnings inequality coefficients. In particular, corrections deal with
outliers and implausible data records, missing observations, bracket responses, breaks in the
series, under-reporting of high incomes, and quarterly frequency. This work lays the ground for
future research on the redistributive dynamics of economic policy in South Africa, which notably
suffers from the presence of spurious shifts in repeated cross-sections.
Key words: income inequality, distribution, heterogeneity, survey data, imputation
JEL classification: C31, D31, O15, R20
Acknowledgements: I am grateful to Professor Laurence Harris for his mentorship, and to the
South African Reserve Bank, in the persons of Dr Chris Loewald and Dr Konstantin Makrelov,
for hosting my field research.
1
1 The Post-Apartheid Labour Market Series
Despite there being a rich literature examining cross-sectional inequality in South Africa, no
consensus has been reached on the quality of long-run time series. In effect, multiple generations
of household surveys have been produced since the end of the apartheid regime by local statistical
and research agencies—first and foremost the parastatal Statistics South Africa (Stats SA)—which
provide nationally representative micro-level information on the labour market.
1
Although today
these resources constitute an abundant pool of information, they were not originally designed for
dynamic analysis and do not allow for straightforward comparability and immediate use in
longitudinal studies. In other words, the nature of the data collected differs more or less
substantially in each survey wave because of differences in, for example, the sample design
instrument and definitions.
As a response to rising concerns over the validity of using distributional data to undertake time-
comparative exercises, the University of Cape Town’s DataFirst initiated a study of successive
labour market cross-sections and integrated them into a single longitudinal data set. This project
produced the so-called Post-Apartheid Labour Market Series (PALMS): a stacked cross-section
consisting of a harmonized compilation of four household surveys
2
conducted after 1993 and
focused on socioeconomic topics (Kerr et al. 2013). Specifically, PALMS consists of:
• The 1993 Project for Statistics on Living Standards and Development (PSLSD); Southern
Africa Labour and Development Research Unit (SALDRU UCT); annual.
• The 1994–99 October Household Surveys (OHS); Stats SA; annual.
• The 2000–07 Labour Force Surveys (LFS); Stats SA; biannual (March and September).
• The 2008–18 Quarterly Labour Force Surveys (QLFS); Stats SA; quarterly. QFLS earnings
data are released separately in Labour Market Dynamics (LMDSA).
Notably, the major advantage related to the latest release (PALMS version 3.3) is that it exhibits a
labour income variable at individual level that is consistent from 1993 to 2017.
3
This is labelled
‘realearnings’ and reports monthly earnings per capita before taxes and at constant prices as for
December 2015. The full description given in Kerr and Wittenberg (2019b: 16) is as follows:
Monthly REAL earnings variable generated from the earnings amount data (not
bracket information) across all waves where earnings amounts were asked and data
have been released (all waves except OHS 1996 and QFLS waves 2008, 2009 and
2012). This is the earnings variable deflated to 2015 Rands using CPI.
For this reason, PALMS has generated a new strand of academic literature that explores the short-
and long-term dynamics of wage inequality in post-transition South Africa, as well as a vibrant
discussion on the need for higher-quality time-consistent and more frequent microeconomic data.
Although PALMS yields significant improvements in the treatment of labour data in South Africa,
1
According to Devereux (1983), until the 1980s, government censuses ignored the personal incomes of black people,
which had to be calculated as a residual of national accounts. For this and other reasons, this paper refers only to the
post-apartheid period.
2
For a detailed description of primary sources available, see Kerr and Wittenberg (2019a).
3
PALMS version 3.3 includes the 2017 LMDSA data on earnings in quarters 3 and 4.
2
it still preserves a number of incongruities inherited from primary sources. To date, the South
African literature that assesses the sensitivity to economic policy shocks of distributional trends is
almost non-existent precisely because dynamic analyses would suffer from the presence of
methodological shortcomings: spurious shifts among repeated cross-sections are inevitably related
to real changes in the variables of interest. It is nonetheless necessary to use available resources to
identify time trends and changes such that a more granular picture can shed light beyond stylized
facts.
This paper investigates the features inherent in PALMS,
4
thoroughly reviews the literature
addressing issues in South African labour data, and complements earlier studies by constructing a
complete and robust time series of inequality to be used for dynamic economic policy analysis.
The ultimate purpose of the paper is to improve longitudinal analysis on inequality in post-
apartheid South Africa by generating unbiased estimates and accurate standard errors of inequality
coefficients that can be better compared over time with quarterly-frequency data. It lays the ground
for a second paper analysing the impact of monetary policy on labour income inequality in South
Africa.
The paper is structured as follows. Section 2 offers a selective review of the literature that makes
use of South African income and earnings disaggregated data. Then, in Section 3, the data
underpinning this work is carefully analysed and different methods of adjustment proposed,
compared, and implemented in defiance of data quality issues. In Section 4, I discuss trends of
inequality through distinct measures based on the moments of the earnings distribution. While it
is not feasible to fully address all problems pertaining to primary data collection, the final remarks
discuss what assumptions are needed in order to make defensible comparisons over time. The final
set of complete data on household-level pre-tax wage income at constant prices, along with the
Stata code that was applied to the raw data, is available from the author on request.
2 Labour income in post-apartheid South Africa: a literature review
A number of attempts to quantify inequality dynamics since the advent of democracy in South
Africa explore the quality of surveys and censuses available in the country and eventually comment
on the comparability of relevant variables over time. Cichello et al. (2005) compare 1993 and 1998
earnings in the KwaZulu Natal Income Dynamics Study and reach different results when using
the data as a panel and as a cross-section. Using the cross-sectional data by overlooking specific
workers’ dynamics shows that formal sector workers were better off in 1998. By contrast, the panel
data indicate that workers who were already employed in the formal sector in 1993 experienced a
fall in earnings, while informal workers started at a much lower average earnings point but
experienced a rise due to mobility towards formal employment. Casale et al. (2004) use only the
OHS 1995 and the LFS 2001:2 to analyse the position of women and ethnic groups in the labour
market. In that paper, the authors make no data transformation and assert that ‘these are the years
in which the earnings data are most comparable’ (Casale et al. 2004: 6). Despite data concerns, they
observe that both mean and median earnings declined over the period. Burger and Yu (2007)
compare the OHS and the LFS from 1995 to 2005 by excluding the outliers, the self-employed,
and informal workers. They find that average earnings started to increase and their distribution to
improve after 1998. Following Casale et al. (2004), their figures confirm no improvements in the
relative earnings position of women, non-white population groups, or unskilled and semi-skilled
4
The relatively long span of data necessary to implement this analysis precludes the use of administrative data recently
released by the South African Revenue Service (SARS), which starts in 2011.
3
workers, but they show ‘signs that there has been an decrease in between-group inequality in more
recent years’. Bhorat et al. (2009) utilize the 1995 Income and Expenditure Survey (IES) and the
2005/06 Income and Expenditure Survey, looking at total income, and report increasing inequality
over the period, from an income Gini coefficient of 0.64 in 1995 to 0.72 in 2005. Leibbrandt et al.
(2010) include all forms of labour earnings from three comparable national household survey data
sets: the PSLSD for 1993, the LFS and IES for 2000, and the National Income Dynamics Study
(NIDS) for 2008. With no adjustment, they calculate the income Gini coefficient in South Africa
and report that it rose from 0.66 in 1993 to 0.68 in 2000 and further to 0.70 in 2008. Finn et al.
(2016) use the first four waves of NIDS from 2008 to 2014 and the 1993 PSLSD to investigate
the shape of the association between parental and child earnings across the distribution.
While all previously mentioned authors rely on a few points in time, the most comprehensive study
on long-run trends in labour income inequality in South Africa can be found in the work of
University of Cape Town’s Martin Wittenberg, which indeed serves as the basis for this discussion.
Wittenberg and Pirouz (2013) use PALMSv2 to show the impact of different types of data quality
adjustments (specifically they treat outliers, zero earnings, bracket responses, and missing
observations) on the estimation of the average wage over the period 1994–2011. As already
observed by Casale et al. (2004) and Leibbrandt et al. (2010), Wittenberg and Pirouz (2013) also
evidence how the change in coverage between the OHSs and the successive LFSs generated a gap
in the earnings series at the year 2000. Wittenberg and Pirouz conclude by arguing that it is possible
to identify some real wage growth since 2000 despite the noise generated by these measurement
changes. Wittenberg (2014b) builds on the previous paper to compare PALMS to firm-level data—
namely the Survey of Employment and Earnings (SEE) and the Quarterly Employment Statistics
(QES) surveys. He adds that the top tail of the earnings distribution has received larger gains than
the 75th percentile; that both of them show significant real earnings growth; that the 10th
percentile made real gains relative to the median, therefore experiencing a compression; and that
among the self-employed there is no evidence for systematic shifts in the distribution over the
post-apartheid period. Wittenberg (2017c) effects further adjustments to yield PALMSv2.1 and
calculates wage inequality through the Gini coefficient. He argues that despite some noise in the
estimates, the measurements made after the LFS 2007:1 are noticeably higher than those made
from 2000 to 2006. Finn (2015) calculates the Gini wage inequality in PALMS using the same data-
cleaning procedure suggested by Wittenberg (2014b): in contrast to Leibbrandt et al. (2010), who
calculated overall income inequality, the Gini coefficient of real wages in 2003:1 (0.553) was almost
identical in 2012:1 (0.554). By contrast, using the LFSs, Vermaak (2012) finds no trend that is
robust to alternative coarse data adjustments—particularly the treatment of zero values and the
choice of imputation methods.
3 Working with PALMS
In PALMS, the variable reporting real earnings with no adjustment returns a mean of ZAR8,784
per month and a median of ZAR3,225. The number of observations,
, in the original file
is 963,492; this is higher than in any of the other approaches because every possible earner is
included. However, in the original file more than 5 million real earnings observations are missing,
including all individuals in years 1996, 2008, 2009, and 2018 and the first two quarters of 2019.
Table A1 in the Appendix summarizes the main features of real earnings in PALMSv3.3 before
any adjustment. It can be observed that for each wave the coefficient of variation of the random
variable (standard deviation/mean) is significantly higher than 1: the high variance is due to the
log-normal distribution of real earnings that is not centred on the mean and is positively skewed
with long right tails.
