The South African labour market: 1995 – 2006

Given the importance of the labour market to economic activity in any country, it is important to correctly infer trends from the available labour data. In South Africa, several researchers have compared selected household surveys with each other and then drew conclusions about the ‘trends’ in the labour market for the entire period between surveys. It is argued that such a methodology is imperfect and could give misleading results. A better methodology would entail looking at all the available surveys to ascertain the real trends over time. Therefore, this paper seeks to examine the trends of the labour force (LF), labour force participation rate (LFPR) and employment, as well as the working conditions of the employed, and the personal and household characteristics of the unemployed from 1995 to 2006, using the October Household Survey (OHS) data from 1995 to 1999, and the Labour Force Survey (LFS) data from 2000 to 2006. The paper finds that, with the exception of an unusual slight decrease between 1995 and 1996, the LF and LFPR in both narrow and broad terms experienced a rapid increase during the OHSs, followed by an abrupt increase during the changeover from OHS to LFS. The narrow LF and LFPR have since increased slightly, while the broad LF and LFPR have stabilized. The trends over the LFS period do not suggest any further “feminization of the LF” (Casale 2004; Casale, Muller & Posel 2005), and the abrupt break in this trend between the LFS and OHS periods may suggest that the observed trend over the former period could perhaps have been the result of improved capturing of participation rather than a real shift in LFPR. In addition, the number of employed clearly shows enormous fluctuations, and it is only since LFS2004b that employment growth enjoyed a stable and continuous increase. Therefore, it is possible to obtain contrasting conclusions on whether job creation or jobless growth has taken place in the South African economy, if different reference points are used for comparison. Finally, both the narrow and broad unemployment rates increased continuously from OHS1995 to LFS2003a, before this was replaced by a continuous downward trend since LFS2003b. Such a decline needs to be more rapid before the ASGISA goal of reducing the narrow unemployment rate to below 15% in 2014 could be achieved.South Africa, Household survey, Labour market trends

Poverty and inequality trends in South Africa using different survey data

There is an abundance of literature adopting the monetary approach (i.e., using per capita income or expenditure variables) to derive poverty and inequality trends for South Africa since the transition. The most commonly used data sets used for these analyses are the censuses and the Income Expenditure Surveys (IESs) conducted by Statistics South Africa (Stats SA). However, in some recent studies, alternative data sources were used, namely the All Media Products Survey (AMPS) by the South African Advertising Research Foundation (SAARF), as well as the National Dynamic Income Study (NIDS), which is conducted by Southern African Labour and Development Research Unit (SALDRU). Some of the data sets are problematic in a particular year or in more than one year, which in turn makes the comparison of poverty and inequality results across the years difficult. Examples of these problems are as follows: the serious decline of income and expenditure between the 1995 and 2000 IES; the high proportion of households with zero or unspecified income in the censuses; too few household expenditure bands in the General Household Surveys (GHSs). In addition, in the various studies mentioned above, different poverty lines were used in the poverty analysis, with the most commonly used poverty line values being R250 per month in 1996 Rand, US$1 a day, US$2 a day, as well as R211 per month and R322 per month in 2000 Rand (i.e., the two official poverty lines proposed by Woolard and Leibbrandt (2006). This paper aims to consistently apply the same poverty lines (i.e., the proposed official poverty lines mentioned above) across all the available survey data, in order to explore the poverty and inequality trends over the years, and to find out if these trends are consistent across different surveys during the period under investigation. The data quality problems mentioned above are addressed (if possible), before the poverty and inequality trends are derived.South Africa, Household survey, Poverty, Inequality, Missing data, Imputation

The comparability of Income and Expenditure Surveys 1995, 2000 and 2005/2006

The Income and Expenditure Survey (IES) conducted by Statistics South Africa (Stats SA) between September 2005 and August 2006 was the third of its kind, after similar surveys in October 1995 and October 2000. The main purpose of the IES is to collect and provide information on income and expenditure patterns of a representative sample of households, so as to update the basket of goods and services required for the compilation of the Consumer Price Index (CPI). Nonetheless, these surveys have also become an important source of information for poverty and inequality analysis, mainly because of the absence of other detailed datasets containing income and expenditure data. There are, however, important reasons why these datasets cannot be unquestioningly compared. This paper attempts to show why. The IESs conducted in 1995 and 2000 used the recall method. In the recall method, a single questionnaire was administered to a household at a selected dwelling unit in the sample, and the responding household was required to recall income and expenditure either during the month prior to the survey or for the twelve months prior to the survey. However, in the IES conducted in 2005-2006, the diary method was used extensively for the first time in order to record the household’s daily acquisitions on a daily basis. In addition to the adoption of the diary method, the 2005-2006 IES is also different from the previous IESs in many aspects, such as sampling design, questionnaire structure, number of visits to the households, additions of some new expenditure items in the questionnaire, categorization of income and expenditure items, etc., and the focus of this paper is to look at how different the three IESs are, so as to assist researchers and policy makers when they try to analyze the IES data.South Africa, Household survey

The comparability of the Statistics South Africa October Household Surveys and Labour Force Surveys

Statistics South Africa (Stats SA) has been collecting labour market data with household surveys and in a fairly comparable format since 1993. These datasets have been studied and compared extensively in order to better understand the workings of the South African labour market. Many of these studies compare household surveys of different periods in order to identify trends, but the validity of such trends is conditional on the comparability of the different datasets. Besides, the naïve comparisons of the different datasets have been questioned. Other problems include inconsistencies in questionnaire design, coding errors, changes in the sampling frame, the oversampling of agricultural workers in OHS1995, the oversampling of subsistence agricultural workers in LFS2000a and LFS2000b, as well as the oversampling of informal workers in LFS2001a. Most of these issues have received attention in papers by Burger and Yu (2006), Casale, Muller and Posel (2005), and Wittenberg (2004). By drawing attention to a few of the lesser known problems, this paper aims to build on the existing literature by further stimulating debate around the strengths and weaknesses of the existing survey data, as well as considering the best ways in which to analyse the existing data. The inconsistencies that occur in the data independently of the way in which questions are asked by the interview, as well as the inconsistencies that result from the way in which the survey questions are formulated or placed in a given sequence are discussed. Where possible, adjustments that may contribute towards increased consistency in the responses are suggested. Ultimately, it is hoped that the lessons learnt from such discussions will serve to inform questionnaire design in future.South Africa, Household Survey, Labour Market Trends, Earnings

The comparability of Labour Force Survey (LFS) and Quarterly Labour Force Survey (QLFS)

Statistics South Africa (Stats SA) has been collecting labour market data since 1993 with the October Household Survey (OHS), which was conducted annually between 1993 and 1999, as well as the Labour Force Survey (LFS), which was a biannual survey introduced in 2000 to replace the OHS. In March 2005, consultants from the International Monetary Fund (IMF) were appointed to revise all aspects of the LFS. All documents, processes and procedures relating to the LFS were reviewed, before a report on the findings was presented to Stats SA in June 2005. At the end, it was decided to re-engineer the LFS, and this took place in October 2005. Moreover, consultants were appointed in 2006 to help improve the survey questionnaire, sampling and weighting, data capture and processing systems. Eventually, Stats SA came up with a decision that the LFS would take place on a quarterly basis from 2008, i.e., the Quarterly Labour Force Survey (QLFS) was introduced to replace the LFS. The comparability issues between the OHSs and LFSs have been discussed thoroughly by Burger and Yu (2006), Casale, Muller and Posel (2005), Wittenberg (2004) and Yu (2007), focusing on changes in the sampling frame, inconsistencies in the questionnaire design, changes in the methodology to derive labour market status, trends in numerous variables (e.g., demographics, educational attainment, labour force participation rates, unemployment rates, earnings, etc.), oversampling of informal sector workers in 2000, overestimation of the earnings of self-employed in the OHSs, and the continuous improvement of the questionnaire by Stats SA. Therefore, this paper rather focuses on the comparability between LFS and QLFS, so as to assist researchers and policy makers when they try to analyze or compare both the LFS and QLFS data. As only four QLFSs have taken place at the time of writing, trends in variables will not be the focus of this paper. Instead, this paper will mainly look at the changes in questionnaire design, sampling method, derivation of new variables (i.e., underemployment status and unemployment status), a new methodology to capture the formal/informal status of the employed, as well as the drastic changes in methodology to capture labour market status. With regard to the latter, it is found that there is no longer a clear distinction between strict and broad labour market status in the QLFS, and this makes it difficult to derive long-term trends in the labour force participation rates (LFPRs) and unemployment rates under both strict and broad definitions.South Africa, Household survey

Defining and measuring informal employment in South Africa: A review of recent approaches

This paper reviews the Stats SA methodologies to measure informal employment before and after the introduction of the Quarterly Labour Force Survey (QLFS), as well as other recently proposed approaches (e.g., Devey, Skinner and Valodia, Heintz and Posel, etc.), so as to investigate the congruence, if any, between the various measures of the rate of informality. Furthermore, econometric techniques are used to investigate commonalities and differences in the way in which the different measures of informality are associated with demographic and employment characteristics. The results suggest that informal employment is much bigger if the post-2007 Stats SA methodology, which considers employment as informal regardless of whether the activities take place in the informal sector or not, is adopted.South Africa, Household survey, Labour market trends, Informal employment

Wage trends in post-apartheid South Africa: Constructing an earnings series from household survey data

This paper examines South African wage earnings trends using all the available post-1994 household survey datasets. This allows us to identify and address the sources of data inconsistencies across surveys in order to construct a more comparable earnings time series. Taking account of the inconsistencies in questionnaire design and the presence of outliers, we find that it is possible to construct a fairly stable earnings series for formal sector employees. We find that claims that workers have on average experienced a substantial decrease in their real wage earnings in the post-apartheid era is based on choosing datasets on either side of Statistics South Africa’s changeover from October Household Surveys (OHS) to the more consistent Labour Force Surveys (LFS), which caused a discontinuous and inexplicably large drop in average earnings. The data actually show an increase in real wage earnings in the post-transition period for formal sector employees, and does not appear to provide strong evidence of decreasing wages in the informal economy. The paper also investigates the change in the distribution of earnings, as well as mean earnings trends by population group, gender and skill category.South Africa, Earnings, Wages, Labour market trends

Poverty and Migration: Evidence from the Khayelitsha/Mitchell's Plain Area

The consequences of misguided economic and social policies in the previous dispensation remain evident in the structure of poverty in South Africa. Enforced migration control, job reservation for whites and inadequate education and public services have all left their mark on the social and economic structure of the population. Migration and the pattern thereof play a significant role in explaining poverty in South Africa. This paper asks who the poor are, but with a focus that enables us both to utilise a new dataset (the Khayelitsha-Mitchells Plain survey) and to investigate the migration process which links the socio-economic situation of Khayelitsha (the destination of many migrants) to the source area, the Eastern Cape. A poverty profile is constructed of South Africa and also of the Eastern and Western Cape. Further analysis of household data for the Khayelitsha/Mitchells Plain area allows us to further explain the impact of migration on the structure of poverty. Within this context we investigate persistent biases in patterns of poverty and differences in the socio-economic characteristics (such as race, location, employment status and access to services) of three poverty categories, the ultra-poor, moderately poor and non-poor.

Alternative definitions of informal sector employment in South Africa

Before the introduction of the Quarterly Labour Force Survey (QLFS) in 2008, Statistics South Africa (Stats SA) has been using the same methodology to derive the informal sector employment throughout the years, focusing on the enterprise registration status to classify workers (which include both self-employed and employees) as either formal or informal sector workers. Although there are difficulties with attempting to provide any consistent trend data (Yu, 2007 & Essop & Yu, 2008), it is generally accepted that informal sector employment grew relatively more rapidly in the late 1990s, and then stabilized at about 2 million in the early 2000s before it increased (albeit more slowly) again since 2005. Nonetheless, recent papers by Devey, Skinner & Valodia (2006) as well as Heintz & Posel (2008) argue that the current classifications used by Stats SA hide a significant degree of informality in the formal economy, as some formal jobs are characterized by conditions that are typical of informal work. Therefore, they propose alternative definitions of informal sector employment, focusing on worker characteristics instead of enterprise characteristics. This paper aims to address the reliability or otherwise of these recent approaches, as well as to suggest better ways to define informal sector employment.South Africa, Household survey, Labour market trends, Informal sector

What explains the academic success of second-year economics students? An exploratory analysis

The factors influencing academic success of first-year Economics students have received much attention from researchers. Very little attention, however, has been given to the determinants of success of senior Economics students. In the USA, Graunke and Woosley (2005: 367) indicate that college sophomores (second years) face academic difficulties, but this receives little attention in the literature. Economics is an elective subject for second-year students at Stellenbosch University. The academic performance of the second-year students has shown a decline, as compared to the first-year Economics performance and the faculty’s average performance. An observed phenomenon at Stellenbosch University is the poor attendance of lecture and tutorials by second year students, some of the factors than can perhaps explain why students perform poorly. This phenomenon may be explained in part by second year students losing interest in academic activities, focusing on other social commitments. This study investigates the academic success of second-year Economics students. It adds to the existing literature on the factors affecting the academic success of Economics students by focusing on the second-year students (a much neglected group in empirical studies, particularly in South Africa). The empirical analyses confirm some of the existing findings in the literature, namely that lecture and tutorial attendance are important contributors to academic success. We also find that as students progress to Economics at the second-year level, their performance in individual matriculation subjects is less relevant, except for those students who had taken Additional Mathematics. However, the matriculation aggregate mark is significant in explaining the academic performance, in a non-linear way. An important finding is that non-White students tend to perform more poorly in essay writing (one of the components of the course mark in the second year) than White students.Education, Undergraduate, Second-year economics, Academic performance