How much volatility is there in earnings in South Africa? The South African labour market has been shown to be a key determinant of welfare, both in terms of poverty and inequality. These are a function of both the high levels of unemployment as well as the wage distribution, conditional on being employed. One aspect of welfare that derives from the labour market, which has been relatively understudied to date, is the amount of volatility in earnings that various groups of South Africans experience over time. This has implications directly for welfare, as well as for inequality. We make use of the first three waves of data from the National Income Dynamics Study to describe the amount of earnings volatility experienced by different demographic groups. We then make use of a regression model to estimate the partial correlation between the various characteristics that we use and earnings volatility. Our main findings are that earnings volatility is high over a four year interval. The mean within-person standard deviation in earnings across the three waves lies between 50% and 66% of the mean earnings depending on the time period, and the mean within-person coefficient of variation in earnings is 0.641." -- Abstract.
South Africa’s high rate of unemployment (26.4%) makes it a complete outlier compared with other middle-income countries. Indeed, the unemployment rate rises to 36% if discouraged workers are taken into account. It underpins extreme poverty and inequality and is a major contributor to social dislocation. If it were not for increased social payments, poverty would have continued to increase since the advent of democracy in 1994. Unemployment also represents a huge cost to growth. This book focuses on the growth path of the economy. The starting point is that while more rapid economic expansion is an important objective, at any given level of growth, the economy as a whole needs to become more labour-absorbing. The central question posed is how to bring about changes in the economic structure and pattern of development, which would lead to the attainment of this objective. The authors argue that employment needs to be much more centrally positioned within the economic and social policy arena. They emphasise innovative approaches within a broader focus on the growth path, and employment-intensive growth. And they posit that the negative impact of previous ‘distortions’ requires much more than a levelling of the playing field via market-based reforms. Apart from presenting an alternative growth path which could start to shift the economy in new directions, the book tackles themes which have received only limited attention, such as wage subsidies, youth unemployment and employment growth in rural areas.
This paper investigates the impact of Texas's Top Ten Percent Rule - which grants automatic to any public college in Texas for Texas high school graduates who graduate in the top decile - and subsequent targeted recruitment programs initiated by Texas's flagship universities. Using data on SAT test takers in Texas from 1996-2004, we find that the Top Ten Percent rule affects the set of colleges that students consider, and the targeted recruitment programs are able to attract the attention of students from poor high schools that were not traditional sources of students for the flagships in Texas"--Publisher description.
We document how we diagnosed data fabrication in the National Income Dynamics Study. Since the fabrication was detected while fieldwork was still on-going, the relevant interviews were re-conducted and the fabricated data were replaced with authentic data. To the best of our knowledge, this is the first time that this has been done. We thus have an observed counterfactual that allows us to measure how problematic such fabrication would have been, had it remained undetected. We implement a number of estimators using the data that include the fabricated interviews, and compare these with the corresponding estimates that include the corrected data instead. For the outcomes that we consider, we find that the fabrication would not have substantially affected our univariate estimates. However, the fabricated data do impact substantially on some key covariates when panel estimators are used. -- Abstract.
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