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Vulnerability and the Middle Class in South Africa
No full textApartheid imposed a rigid racialised system of unequal resource distribution on South African society, resulting in one of the highest rates of inequality in the world. Since apartheid ended in 1994, this aggregate income inequality has not improved. The persistence of extraordinarily high levels of poverty and inequality makes the definition and measurement of the ‘middle class’ particularly challenging. A review of previous work on the middle class, both in South Africa and in other developing countries, illustrates the difficulty of addressing this challenge. Recent research showing growth in the South African middle class often classifies as ‘middle class’ households which either fall below the basic‐needs poverty line or are vulnerable to poverty. This notion of economic insecurity conflicts with the sociological understanding of the middle class as an ‘empowered’ class.
In this paper, we attempt to develop a conceptually and empirically rigorous approach to defining and measuring the middle class in South Africa. Arguing that the notion of ‘empowerment’ is central to the social and political meanings of ‘middle class’, we propose an empirical strategy that uses (in)vulnerability to poverty as the key criterion defining middle class status. Using the panel dimension of the nationally representative National Income Dynamics Study (NIDS), we present a probability model that predicts the risk of staying in or falling into poverty over a six‐year time frame, depending on a broad array of initial household conditions and resources. We select the expenditure level associated with a maximum risk to poverty of 10 percent as the lower bound of the middle class and the expenditure level associated with effective invulnerability to poverty as the upper bound. This gives us a monthly per capita expenditure range of R3,104 to R10,387 (January 2015 prices). Using these thresholds, we find that the middle class in South Africa is smaller than previous research has suggested (with a population share of about 13.5 percent in 2014), and has grown sluggishly since
1993. Despite this, there has been considerable demographic transformation within the middle class, with Africans now outnumbering whites by a significant margin.Rocco Zizzamia is a graduate student at the Oxford Department of International Development, University of Oxford, and a Researcher at the Southern Africa Labour and Development Research Unit at the University of Cape Town.
Simone Schotte is a doctoral student and research fellow at the German Institute of Global and Area
Studies and the Georg-August-University Göttingen.
Murray Leibbrandt is a Professor in the School of Economics at the University of Cape Town and the
Director of the Southern Africa Labour and Development Research Unit. He holds the DSD/NRF National Research Chair of Poverty and Inequality Research and is a Principal Investigator on the National Income Dynamics Study.
Vimal Ranchhod is the Chief Research Officer at the Southern Africa Labour and Development Research Unit at the University of Cape Town.
Acknowledgements:
This publication has been produced with the financial assistance of the Programme to Support Pro-Poor Policy Development (PSPPD), located in the Department of Planning, Monitoring and Evaluation
(DPME), and is a product of the strategic partnership between South African government and the
European Union. The content of this publication can in no way be taken to reflect the views of the DPME or the European Union.
We would also like to thank Joshua Budlender, Arden Finn and participants at the GIGA Workshop on
Inequality and Middle Class Development in Africa held in Cape Town in May 2016 for helpful discussions and comments
Drivers of Inequality in South Africa
No full textThe first democratic elections in 1994 brought about the promise for equal opportunity and an overall improvement of living standards for the majority of the South African population. The newly elected government promised to combat high levels of poverty as well as inequality inherited from the apartheid regime. However, 20 years after the democratization of South Africa, levels of inequality remain stubbornly high. Therefore, this paper analyzes the role of income from different sources in order to investigate which one(s) continue to drive those high levels of inequality. We use data from the 1993 Project for Statistics on Living Standards and Development (PSLSD) to present a detailed snapshot of the level and texture of inequality that was prevalent at the end of the apartheid regime. Furthermore, we use recent data from the National Income Dynamics Study (NIDS) from 2008 and 2014 to assess the role of different income sources in overall inequality and compare these contemporary snapshots to the results from 1993. We do so by applying two different decomposition methods to inequality measured by the Gini coefficient. The first is static, explaining the role of income sources in driving income inequality at each of the three points in time. The second is dynamic, explaining the role of changing income sources in changes in income inequality over time. We find that over the past 20 years, labour income has been the major contributor to overall inequality. The results indicate that a drop in inequality from labour market sources led to a decrease in overall income inequality. A more nuanced decomposition technique within the dynamic decomposition allows us to extract the effect of changes in household demographics on inequality from these results. This shows that when factors of household composition are accounted for, changes in all of the different income sources have led to a decrease in inequality between 2008 and 2014 in particular and over the entire post‐apartheid period in general.Acknowledgments:
This publication has been produced with the nancial assistance of the Programme to
Support Pro-Poor Policy Development (PSPPD), located in the Department of Planning, Monitoring and Evaluation (DPME), and is a product of the strategic partnership between South African government and the European Union. The content of this publication can in no way be taken to reflect the views of the DPME or the European Union
Causes and Consequences of Teen Childbearing: Evidence from a Reproductive Health Intervention in South Africa
No full textThe rollout of the National Adolescent Friendly Clinic Initiative (NAFCI) serves as a natural experiment to study the causes and consequences of early teen child bearing. Geolinking residence histories to the rollout, we estimate that living near a NAFCI clinic during adolescence delayed early childbearing by 1.2 years on average. Adolescents who had access to NAFCI completed more years of schooling and, consistent with increased human capital investments, earn substantially higher wages as young adults. Children born to women who had access to youth-friendly services as teens show substantial health advantages, indicating a strong intergenerational benefit of delayed childbearing.We are grateful to Cally Ardington, Martha Bailey, Diane Cooper, Robert Garlick, David Lam, Murray Leibbrandt, and Jeff Smith for helpful comments and guidance.This project was generously supported by a William and Flora Hewlett Foundation/Institute of International Education Dissertation Fellowship in Population, Reproductive Health and Economic Development and the Hewlett/PRB Global Teams of Research Excellence in Population, Reproductive Health, and Economic Development. Tanya Byker also received support from an NICHD training grant to the Population Studies Center at the University of Michigan (T32 HD007339)
Estimating the size and impact of Affirmative Action at the University of Cape Town
No full textIn this paper we estimate the extent and targeting of affirmative action at the University of Cape Town, a large public university in South Africa. To do this we use admissions data from the University of Cape Town (UCT), as well as South African population census data and administrative enrolment and graduation data from the South African Department of Higher Education. We find that affirmative action does have a significant effect on the racial distribution of who is made an offer by the university. We also find that affirmative action is well targeted, with those who we estimate to be beneficiaries being of much lower socioeconomic status than those who we estimate are displaced by affirmative action. Beneficiaries of affirmative action have low graduation rates on average, with those beneficiaries who attend UCT being less likely to graduate than those beneficiaries who enrol at other public universities.We thank Tim Brophy for his help with merging the applicant address data with the 2011 Census
small area data. We thank Jean Skeane and Norman Nkwana from the South African Department of Higher Education and Training for assistance in matching the HEMIS data to the applicant data. Carl Herman in the UCT admissions office, Hugh Amoore the UCT registrar, Judy Favish and Jane Hendry in the UCT Institutional Planning Department all provided very helpful background information and helped to source the applicant data. Participants in two SALDRU seminars at UCT, the MASA 2014 conference in Durban, as well as a UNU-WIDER 2014 conference in Helsinki provided helpful comments
The dynamics of poverty in the first four waves of NIDS
No full textWe analyse the determinants of South Africans moving into and out of poverty over the first four waves of the National Income Dynamics Study (NIDS) for the years 2008 to 2014/2015. We focus on the balanced panel of NIDS respondents and find that a relatively high poverty exit rate was accompanied by a substantial proportion of the population being trapped in severe poverty. The roles of demographic versus income changes over time reveal that changing household composition is the largest trigger of poverty entry and exit, and that increasing income from government grants is the main trigger precipitating poverty exit for about one quarter of our sample. Regression analysis shows that access to the labour market within the household is the single most important determinant of poverty entry and exit after race. We calculate multidimensional poverty rates and find that although MPI poverty is far lower than money‐metric poverty, being chronically MPI poor over the four waves is closely matched by being chronically income poor.Arden Finn: [email protected] Doctoral student and researcher at the Southern Africa Labour and Development Research Unit, University of Cape Town.
Murray Leibbrandt: [email protected] Professor of economics and director of SALDRU at the University of Cape Town.
Acknowledgements:
Funding for this research from the Department of Planning, Monitoring and Evaluation is gratefully acknowledged. Arden Finn acknowledges the National Research Foundation for financial support for his doctoral work through the Chair in Poverty and Inequality Research. Murray Leibbrandt acknowledges the Research Chairs Initiative of the Department of Science and Technology and National Research Foundation for funding his work as the Chair in Poverty and Inequality Research
Tracking and Tracing Tobacco Products in Kenya
No full textAlthough estimates of the size of the illicit cigarette market in Kenya vary, the government sees it as a problem and has been trying to address the issue since the early 2000's. Between 2003 and 2013 the Kenyan government experimented with numerous measures designed to reduce tobacco tax evasion with varying degrees of success. In the end, it decided to implement a tracking and tracing system for cigarettes (and alcohol products) and joined a small but growing number of countries addressing illicit tobacco trade via a technological solution. The introduction of the new system required a systematic approach, participation of all stakeholders, and an initial investment into infrastructure and enforcement. Preliminary results indicate that the new system, accompanied by an electronic cargo monitoring system, has reduced the size of the illicit cigarette market and substantially increased tax revenue for the Kenya Revenue Authority (KRA). The experience of Kenya highlights the importance of political will, consistency, and comprehensiveness of the system addressing tax evasion, because piecemeal measures have only short-term effects
Changes in self-employment in the agricultural sector, South Africa: 1994-2012
No full textWhile South Africa enjoys a wealth of household and firm data that speaks to the evolution of the labour market since the end of apartheid in 1994, the interpretation of these data is complicated by a variety of measurement and fieldwork changes that have occurred over this time period. These changes have been well documented by Wittenberg (2004, 2014), Casale, Muller, and Posel (2004), and Yu (2007). One of the most dramatic changes that must be considered when examining employment trends over this period is the apparent increase in self-employment that took place with the switch from the October Household Surveys (OHS) to the Labour Force Surveys (LFS). With this change in survey instrument, there was a seeming increase in the number of self-employed agricultural workers from roughly 150 000 in the last wave of the OHS (October 1999) to more than 1.4 million in the first wave of the LFS (February 2000). The number of self-employed agricultural workers (SEAWs) drops somewhat after September 2000 but remains elevated throughout all waves of the LFS compared to previous OHS waves and later Quarterly Labour Force Survey (QLFS) waves. This series, calculated using the Post-Apartheid Labour Market Series (PALMS) which combines all three survey instruments—OHS, LFS, and QLFSAbout the Author(s)
Liz Neyens, Analyst, Analysis Group Inc., Boston
Martin Wittenberg, Director, DataFirst, University of Cape Town
Acknowledgements
This work was begun while Liz Neyens was an intern at DataFirst. We are grateful for the fi nancial support
provided by the Jackson Institute for Global Aff airs at Yale University which made that visit possible. We
would like to thank Andrew Kerr for reading this document and making helpful comments.
Disclaimer
The views presented are those of the authors and do not refl ect those of Analysis Group or its clients.
Research for this paper was partially undertaken while the fi rst author was a graduate student at Yale
University.
This is a joint SALDRU Working Paper and DataFirst Technical Paper
Pathways to food security in South Africa: Food quality and quantity in NIDS Wave 1
No full textSouth Africa is food secure at the national level; however widespread food insecurity persists at the household level. To understand the dynamics of micro-level food insecurity this paper investigates how two different aspects of ‘food access’ – diet quality and diet quantity – affect two outcomes of ‘food utilisation’ – hunger and nutrition. Diet quantity is captured by food expenditure in Wave 1 of the National Income Dynamics Study (NIDS). To capture diet quality I use dietary diversity, which is not directly available in NIDS. I build and test a food group dietary diversity score and a food variety dietary diversity score using NIDS Wave 1. Both dietary diversity indicators are found to usefully summarise information about food security in South Africa by using methods found in the dietary diversity literature. The paper then turns to testing whether the theoretical differences between diet quality and quantity play out empirically in the case of nutrition (adult BMI) and hunger (self-reported household hunger). The results reveal that food variety and food quantity are complementary in explaining the chance of household hunger, with food quantity having a slightly more important effect. The pathways to BMI differ by gender. Dietary diversity and food expenditure are substitutes in the case of male BMI; however, food variety and food expenditure are complementary to explaining female BMI when food expenditure enters into the model as a quadratic. Overall, food variety proved to be a stronger and more significant correlate of both outcomes than the food group dietary diversity score.I would like to thank Murray Leibbrandt and Cally Ardington for their supervision of this paper. I would also like to express my gratitude to the DST/NRF Centre of Excellence in Food Security, the University of Cape Town, and Murray Leibbrandt in his capacity as NRF/DSD Research Chair in Poverty and Inequality Research for their funding of this research
Towards measuring social cohesion in South Africa
No full textThis paper uses data collected across the four waves of the National Income Dynamics Study (NIDS) to construct a measure of social cohesion for South Africa. We compare our index to one derived using the Afrobarometer data and find a large degree of consistency in trends in the index and its constituent components over time across the two datasets. However, there is less consistency in the measures once one moves to lower levels of geographic disaggregation. We also find far less variability in the constructed index relying on NIDS panel data as opposed to the repeated cross‐sections from Afrobarometer. Having derived the index, we then correlate it with a variety of indicators of social and economic well‐being. We show that higher levels of education, per capita income and employment are positively associated with higher social cohesion while social cohesion is negatively associated with poverty, service delivery protest and perceptions of crime. In addition, municipal policy and competence are closely associated with higher social cohesion. While this work is exploratory, it is encouraging, and suggests new opportunities for future research to begin to take seriously the link between social cohesion and economic and social development.Lindokuhle Njozela: School of Economics, University of Cape Town.
Ingrid Shaw: [email protected]
Justine Burns: School of Economics, University of Cape Town, [email protected].
Acknowledgements:
Funding for this research from the Department of Planning, Monitoring and Evaluation is gratefully acknowledged
Determinants of remittances in South Africa
No full textThis paper analyses household‐level determinants of the probability and level of domestic remittances
in South Africa over the period 2008 to 2014‐2015. We exploit all four waves of the National Income
Dynamics Survey (NIDS) data to analyse the determinants of remittances in a panel setting using
random‐effects Tobit, Heckman selection, and two‐part model approaches. The panel nature of
this data allows us to incorporate individuals’ unobserved time‐constant characteristics (or
unobserved heterogeneity) in the models, a step that enriches the analysis and yields more accurate
results than if we were to use only cross‐sectional analysis. It also allows us to incorporate information about the dynamics of remittance behaviour for the same households. However, data availability restricts the analysis to determinants associated with the recipient households. We find the
determinants of the probability of remitting to be non‐identical to the determinants of the level of
remittances. Determinants of both include the age, race, education level, and employment status of
the household head, and the income and the type of area of the household. The gender of the
household head and the size of the household are also important determinants, but appear to have a
positive effect on the probability of remitting, yet a negative effect on the amount remitted. These
results shed light on the factors that affect whether or not families receive remittances and, if they
do, how much.Mduduzi Biyase: Corresponding Author. Lecturer, Department of Economics and Econometrics, University of Johannesburg
Fiona Tregenna: South African Research Chair in Industrial Development, and Professor, Department of Economics and Econometrics, University of Johannesburg
Acknowledgements:
Funding for this research from the Department of Planning, Monitoring and Evaluation is gratefully
acknowledged