Measurement and data
This article argues that there is a time-series break in StatsSA’s Quarterly Labour Force Survey (QLFS) in the first quarter of 2015. The principal cause appears to be differences between the sampling frames before and after 2015, respectively based on Census 2001 and Census 2011. The break is signalled by a significant increase in the number of one-person households surveyed in the QLFS. It is critical for economists, policy analysts and policy-makers to be aware of this break in the data.
Calculating the earnings Gini coefficient with survey data from the Quarterly Labour Force Survey (QLFS) may lead to an underestimation of inequality. When one compares earnings in the tax assessments data to those in the QLFS, it appears that the earnings of employees in the QLFS are underreported. Benefits and annual bonuses contribute substantially to the gap. In the case of self-employment incomes, the top earnings in the QLFS are also underreported, but the tax data seems to miss many mid- and low-income earners.
The reliability of Census data on demography and migration comes under attack periodically. This article sheds light on the reliability of survey results with respect to migration into the Western Cape. Census data and two independent studies are compared and the convergence or divergence of the findings assessed. There is greater consistency for more aggregate-level measures than for disaggregated measures (whether by geographical unit or by race). Such comparisons of surveys are important for gauging the reliability of our knowledge of migration.
While everybody seems to favour the pursuit of inclusive growth, this concept is rarely clearly defined in the policy debate. Inclusive growth is often confused or conflated with pro-poor growth or broad-based growth. A recent definition from researchers at the UNDP integrates the latter two concepts to include employment, poverty and inequality. A derivative Inclusiveness Index shows that South Africa has a very low degree of inclusiveness compared to other developing countries and that its growth since 1996 has not been inclusive.
Former homeland areas continue to have significantly higher levels of deprivation and poverty than the rest of South Africa. Of all the former homeland areas, the erstwhile Transkei in the Eastern Cape has the highest levels of deprivation (measured using the Index of Multiple Deprivation for 2011) as well as income poverty. Indeed, the deprivation gap between former homelands and the rest of South Africa has not declined in the period 2001 to 2011.
Amidst a decline in general poverty rates since 2000, women and people living in female-headed households still are significantly worse off. Women are up to 30% poorer than men on average. There is an even larger poverty gap between female- and male-headed households – a difference of as much as 100%, despite improved education, health and basic services. Better health, water and sanitation services, especially in rural areas, should narrow these gaps significantly.
New evidence suggests that non-searching unemployed people are significantly less satisfied with their lives than people who are not economically active. Indeed, the non-searching unemployed have hit rock bottom. Assuming that people do not freely choose an unsatisfactory state of living, a case is made that the non-searching unemployed – or ‘discouraged workers’ – are involuntarily unemployed and should be included in the definition and measurement of the labour force. Consequently, a case is made for the adoption of the broad measure of unemployment.
The frequently reported ‘crisis in graduate unemployment’ in South Africa is a fallacy based on questionable research. Not only is graduate unemployment low at less than 6%, but it also compares well with rates in developed countries. The large expansion of black graduate numbers has not significantly exacerbated unemployment amongst graduates. Contrary to popular perception, such graduates – many from ‘formerly disadvantaged’ universities – have been snapped up by the private sector. Black graduates are, however, still more likely to be unemployed than white graduates.
The official rate of unemployment includes only the unemployed who are actively searching for work. However, findings from new data challenge this practice. The ‘searching unemployed’ are no more likely to find employment than the ‘non-searching unemployed’. This casts doubt on the idea that non-searchers are not committed to finding work. Furthermore, many people find jobs through social networks – but this job-finding strategy is not adequately recognised as ‘searching for work’ in official statistical surveys. StatsSA should reconsider how they count the ‘officially’ unemployed.
Adcorp’s estimated unemployment rate is so low that it disposes of the unemployment crisis. But Adcorp uses a crude currency-demand method to estimate the size of the unrecorded economy, despite researchers’ strong criticism of this method. To estimate informal sector employment, Adcorp mixes up definitions of informal employment and the unrecorded economy and guesses at the labour intensity of the unrecorded economy. They also guess at the number of illegal immigrants. Moreover, Adcorp’s estimates have no statistical precision. Its figures are neither reliable nor credible.