The Psychological cost of Indebtedness in South Africa: Evidence from NIDS Wave 2 and 4

Type Thesis or Dissertation - Masters Thesis
Title The Psychological cost of Indebtedness in South Africa: Evidence from NIDS Wave 2 and 4
Publication (Day/Month/Year) 2020
URL viwe.pdf?sequence=1
The mechanisms that perpetuate over-indebtedness at individual level (or household) are still not well researched. Emerging literature on debt and mental health shows that being highly indebted is stressful and it leads to psychological problems. This paper explores the relationship between psychological well-being and debt in South Africa. We rely on Wave 2 and 4 of the National Income Dynamics Study (NIDS), a nationally representative household panel survey in South Africa. We have two indebtedness measures; Negative Asset Value and Financial Stress which are both constructed from the NIDS dataset. Negative asset value is based on the net worth of the respondent, whereas Financial Stress is based on household expenditure over income. In the sample data, we observe that respondents have higher CES-D scores on average in Wave 4 than in Wave 2. As a result, the number of people who report depression is also higher in this wave. The proportion of the sample that is indebted increases between Wave 2 and 4,with more household reporting financial stress in wave 4.

In the empirical analysis, we firstly use cross-sectional data to estimate a probit model between debt and depression, while controlling for socioeconomic variables on the individual and the household they belong to. The results suggest that indebtedness is positively associated with depression. Debt and depression tend to be endogenous since poor mental health can lead to indebtedness. To deal with the endogeneity that exists between debt and depression, we estimate a recursive bivariate probit with the cross-sectional data. We find that the negative asset value is still positively associated with depression, but financial stress is not. Due to the inconsistency of results when using cross-sectional data, we shift to panel data. A fixed effects logit model is estimated to look at the changes in debt and changes in depression. The results show that both debt variables are significant determinants of the onset of depression. Lastly, in the fixed effects logit model, we swap out the debt variables with debt types to look at the changes in debt types and changes in the depression outcome. A personal loan from a bank, a loan from Mashonisa and Hire purchase debt are the significant determinants of changes depression.

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