Socioeconomic related health inequalities in South Africa

 

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dc.contributor.advisor Leibbrandt, Murray en_ZA
dc.contributor.advisor Woolard, Ingrid en_ZA
dc.contributor.author Khaoya, David Wanyama en_ZA
dc.date.accessioned 2016-01-26T11:01:47Z
dc.date.available 2016-01-26T11:01:47Z
dc.date.issued 2015 en_ZA
dc.identifier.citation Khaoya, D. 2015. Socioeconomic related health inequalities in South Africa. University of Cape Town. en_ZA
dc.identifier.uri http://hdl.handle.net/11427/16557
dc.description Includes bibliographical references en_ZA
dc.description.abstract 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. en_ZA
dc.language.iso eng en_ZA
dc.subject.other Health Economics en_ZA
dc.subject.other socioeconomic factors en_ZA
dc.title Socioeconomic related health inequalities in South Africa en_ZA
dc.type Doctoral Thesis
uct.type.publication Research en_ZA
uct.type.resource Thesis en_ZA
dc.publisher.institution University of Cape Town
dc.publisher.faculty Faculty of Commerce en_ZA
dc.publisher.department School of Economics en_ZA
dc.type.qualificationlevel Doctoral
dc.type.qualificationname PhD en_ZA
uct.type.filetype Text
uct.type.filetype Image
dc.identifier.apacitation Khaoya, D. W. (2015). <i>Socioeconomic related health inequalities in South Africa</i>. (Thesis). University of Cape Town ,Faculty of Commerce ,School of Economics. Retrieved from http://hdl.handle.net/11427/16557 en_ZA
dc.identifier.chicagocitation Khaoya, David Wanyama. <i>"Socioeconomic related health inequalities in South Africa."</i> Thesis., University of Cape Town ,Faculty of Commerce ,School of Economics, 2015. http://hdl.handle.net/11427/16557 en_ZA
dc.identifier.vancouvercitation Khaoya DW. Socioeconomic related health inequalities in South Africa. [Thesis]. University of Cape Town ,Faculty of Commerce ,School of Economics, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/16557 en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Khaoya, David Wanyama AB - 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. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Socioeconomic related health inequalities in South Africa TI - Socioeconomic related health inequalities in South Africa UR - http://hdl.handle.net/11427/16557 ER - en_ZA


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