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Socioeconomic related health inequalities in South Africa
Includes bibliographical references
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| Other Authors: | |
| Format: | Thesis |
| Language: | English |
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School of Economics
2016
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| _version_ | 1867613286991659008 |
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| access_status_str | Open Access |
| author | Khaoya, David Wanyama |
| author2 | Leibbrandt, Murray |
| author_browse | Khaoya, David Wanyama Leibbrandt, Murray |
| author_facet | Leibbrandt, Murray Khaoya, David Wanyama |
| author_sort | Khaoya, David Wanyama |
| collection | Thesis |
| description | Includes bibliographical references |
| format | Thesis |
| id | oai:open.uct.ac.za:11427/16557 |
| institution | University of Cape Town (South Africa) |
| language | eng |
| last_indexed | 2026-06-10T12:33:43.673Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UCTD — University of Cape Town Open Access Repository |
| publishDate | 2016 |
| publishDateRange | 2016 |
| publishDateSort | 2016 |
| publisher | School of Economics |
| publisherStr | School of Economics |
| record_format | dspace |
| source_str | UCTD — University of Cape Town Open Access Repository |
| spelling | oai:open.uct.ac.za:11427/16557 Socioeconomic related health inequalities in South Africa Khaoya, David Wanyama Leibbrandt, Murray Woolard, Ingrid Health Economics socioeconomic factors Includes bibliographical references This thesis uses the National Income Dynamics Study (NIDS) data to estimate the extent of, and the factors correlated with, socio economic related health inequalities in South Africa. We extend our analysis by investigating whether income has a causal effect on health outcomes. The thesis is divided into four separate, but related chapters. In chapter two, we describe the data and the variables used in the study. We then check the quality of health related data in the NIDS by analyzing attrition trends and establishing whether attrition affects the representativeness of the data in subsequent waves. We use three health outcomes, self-assessed health, body mass index and depression, to test for the potential effects of attrition bias on parameter estimates. We test using the attrition probit and Becketti, Gould, Lillard and Welch (BGLW) tests, which are two well-known tests for attrition bias in panel data. We find that although the attrition rates of individuals from the sample are high in wave 2 and 3 (21% and 20% respectively), their attrition is random with respect to the health outcomes we use. In chapter three, we establish the socioeconomic factors correlated with health outcomes in South Africa. We use bivariate and panel data approaches. We find significant correlations between health outcomes and socioeconomic factors (income, educational attainment, and demographic factors). Income is positively correlated with self-assessed health and body mass index, and it is negatively correlated with depressive symptoms. In chapter four, we build on the findings discussed in chapter three to estimate the extent of Income Related Health Inequality (IRHI). We estimate the index of inequality using a health concentration index. We then decompose the concentration index to establish the extent to which the correlates of health outcome drive the IRHI. The panel nature of the data allows us to investigate whether IRHI is narrowing or widening. We find a positive health concentration index. This implies that better health is concentrated among the rich. The decomposition of the index reveals that these differences are explained by disparities in income and educational attainment. We also find that the IRHI has narrowed from 2008 to 2012. Most of the narrowing is unexplained but about 21% and 20% of the decrease is correlated with the changes in the distribution and response to covariates respectively. One of the socioeconomic determinants identified from the previous chapters to be correlated with health is income. In the last part of this thesis, we extend the analysis to investigate whether this relationship is causal. To do so, we use the Old Age Pension (OAP) programme as a natural experiment. The OAP is based on age eligibility. Therefore, we use this age eligibility as an exogenous income shock to isolate the effect of income on health. We apply a Regression Discontinuity Design on the NIDS data to identify this effect. We do not find any contemporaneous effect of income on three health outcomes considered, namely; self assessed health (SAH), body mass index (BMI), and depression. 2016-01-26T11:01:47Z 2016-01-26T11:01:47Z 2015 Doctoral Thesis Doctoral PhD http://hdl.handle.net/11427/16557 eng application/pdf School of Economics Faculty of Commerce University of Cape Town |
| spellingShingle | Health Economics socioeconomic factors Khaoya, David Wanyama Socioeconomic related health inequalities in South Africa |
| thesis_degree_str | Doctoral |
| title | Socioeconomic related health inequalities in South Africa |
| title_full | Socioeconomic related health inequalities in South Africa |
| title_fullStr | Socioeconomic related health inequalities in South Africa |
| title_full_unstemmed | Socioeconomic related health inequalities in South Africa |
| title_short | Socioeconomic related health inequalities in South Africa |
| title_sort | socioeconomic related health inequalities in south africa |
| topic | Health Economics socioeconomic factors |
| url | http://hdl.handle.net/11427/16557 |
| work_keys_str_mv | AT khaoyadavidwanyama socioeconomicrelatedhealthinequalitiesinsouthafrica |