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Our offices are located at:

Room 3.02, Level 3,
School of Economics Building,
Middle Campus,
University of Cape Town,
Rondebosch 7700


Our postal address is:
SALDRU 
University of Cape Town, 
Private Bag X3, 
Rondebosch 7701, 
South Africa

Tel: +27 (0)21 650 5696
Fax: +27 (0)21 650 5697
Email: Brenda Adams

 


 

 

 

Welcome

 

The Southern Africa Labour and Development Research Unit (SALDRU) carries out research in applied empirical microeconomics with an emphasis on labour markets, human capital, poverty, inequality and social policy. We strive for academic excellence and policy relevance. SALDRU has implemented a range of innovative surveys in South Africa since it was founded in 1975, and is based in the School of Economics in the University of Cape Town. We have three permanent research staff, and twenty two Research Associates who share common research interests, and are drawn from the UCT School of Economics, as well as other South African and international universities.

 

Latest  News

 


Seminar series - “Poverty and ethnicity among black South Africans”


This Wednesday’s seminar will be presented by Carlos Gradín.  

 

 “Poverty and ethnicity among black South Africans

 

Abstract:

The huge white-black differential in poverty in South Africa, its determinant factors and evolution after the Apartheid have been already studied (e.g. Gradín, forthcoming in Journal of African Studies). However, intra-group differentials among blacks of different ethnicities have been less investigated so far. Given that these groups represent about 80% of the country’s population, and given the increasing role of their growing heterogeneity in shaping the country’s income distribution, a better understanding of these differentials is crucial to ascertain the present and future of wellbeing in South Africa.  The aim of this paper is to analyze differences in poverty levels among the main ethnic African groups in South Africa. In this paper, we first document ethnic differences in poverty levels and then study the extent to which they are driven by diverging socioeconomic characteristics across ethnic groups or, otherwise, are the consequence of these characteristics having a different impact on poverty across ethnic groups.  For the analysis, we will use a nationally representative sample of private households in South Africa with rich information on households’ living conditions: the National Income Dynamics Study (NIDS, 2008 and 2010 waves) provided by the Southern Africa Labour and Development Research Unit (SALDRU, University of Cape Town). These datasets will be complemented with data from other sources, such as the Project for Statistics on Living Standards and Development (PSLSD 1993), in order to analyze post-Apartheid trends.   Ethnicity is approached here by the language spoken at home: IsiZulu (29.1%), IsiXhosa (21.6%), Sepedi (12.2%), Sesotho (10.6%), Setswana (8.5%) or other language (the remaining 18%, including IsiNdebele, SiSwati, Tshivenda, Xitsonga, Afrikaans, English, other language and unknown).   Poverty levels will be measured based on income and expenditure, using the usual national poverty thresholds. The main methodology to disentangle the driving factors of ethnic poverty differentials will be Blinder-Oaxaca-type regression-based techniques, adapted for the study of poverty rate differentials (e.g. Yun, Economic Letters, 2004 and related literature).   Preliminary results show that per capita income of the two main groups (Xhosa and Zulu) is the lowest among all ethnic groups, around 76-78% of the average for Africans, compared with 135% of Setswana or 128% of “other groups”. Similarly, their poverty rates are highest among all, 62% for Xhosa and 65% for Zulu, using the lower poverty line, compared with 42% of Setswana or 45% of Sesotho. About 68% of the higher poverty rates of Xosha and Zulu, compared with Setswana, are explained by their poorer characteristics, although with some differences. In both cases the most important factors are the demographic differences (especially having more children and dependent adults) explaining around 25% of the differential. Then comes education, that explains a larger share of the differential for Xhosa (22%) than for Zulu (19%), and the labor market participation of household members, which explains a higher proportion for Zulu (19%) than for Xhosa (13%). The overrepresentation of these groups in rural areas also explains a significant 8% in the case of Xhosa (but is not statistically significant for Zulu).

 

As usual we start at 1pm in Economics' Seminar Room, Level 4, Economics Building, Middle Campus

 

 

Reza DanielsReza Daniels, lecturer in the School of Economics and SALDRU associate, was recently placed second in the Cochran-Hansen competition for the best paper on survey research methods submitted by a young statistician from a developing country or transition country. This prize is awarded every two years by the International Association of Survey Statisticians, a division of the International Statistical Institute.

 

 

 

cardingtonCally Ardington, an Associate Professor within SALDRU, has recently been made a J-PAL Africa affiliate. Within the sphere of randomised control trials, she is currently involved in an evaluation of a holiday program designed to support Grade 4 learners in the transition to English as the language of teaching and learning in South Africa.

 

 

 

Latest Publications

Working Papers

Title: Effects of Household Shocks and Poverty on the Timing of Traditional Male Circumcision and HIV Risk in South Africa


Author(s): Atheendar S. Venkataramani and Brendan Maughan-Brown
Date of Publication: February 2013
Keywords: Economic shocks, poverty, male circumcision, HIV, South Africa

 

Abstract

 

Poverty may influence HIV risk by increasing vulnerability to economic shocks and thereby preventing key health investments. We explored this by examining the relationship between household shocks and the timing of traditional male circumcision, a practice associated with considerable expense and whose HIV-prevention benefits are larger when done earlier, even within young adulthood. Using unique data on a sample of Xhosa men, a group that almost universally practice traditional circumcision, we found that respondents in the poorest households delayed circumcision by two years if a household member experienced loss of income or death and/or illness. The impact of these shocks declined with increasing household income. Our findings suggest that interventions that work to mitigate the impact of shocks among the poor may be useful in HIV prevention efforts. More generally, they illustrate that the relationship between HIV and wealth may be more nuanced than assumed in previous work.

 

Effects of Household Shocks and Poverty on the Timing of Traditional Male Circumcision and HIV Risk in South Africa by Atheendar S. Venkataramani and Brendan Maughan-Brown - Working paper 93

 

Job Creation and Destruction in South Africa by Andrew Kerr, Martin Wittenberg and Jairo Arrow - Working paper 92

 

Policy Briefs

Revisiting the 'crisis' in teen births: What is the impact of teen births on young mothers and their children? This brief has been updated.

 

Other Briefing Papers

DEV Research Briefing 4, Aug 2012 - Chinese Competition and the Restructuring of South African Manufacturing.

 

Newsletter

February 2013 Edition

 
 

Courses


Social Welfare Measurement

18 - 21 June 2013 and 24 - 27 June 2013.

This is a joint SALDRU and DataFirst 8 day course.  The purpose of this short course is to introduce students, researchers, and officials in state agencies to the key concepts in measuring income, expenditure, poverty and inequality.  More information.

 

NIDS Panel Data Course

Analysing Waves 1 & 2 using Stata

1 - 5 July 2013

This five day training course is designed for post-graduate students, university staff and government employees who are interested in using the NIDS panel data. Participants must have Honours-level Econometrics, and prior experience using STATA to analyse cross-sectional household survey data. Experience working with panel data is not necessary. See here for more detailed information.

If you would like to apply, please fill out the online application form by 10 June 2013. Successful applicants will be notified by 15 June 2013.