Thesis (MBA) -- Stellenbosch University, 2010. / As major corporate entities are investing into Sub-Saharan Africa and other African countries at a fast pace, percentages like the weighted average cost of capital (WACC) and the impairment discount rate, are becoming important measurements of assessing current investments for impairment and/or proposals of future capital investments. One of the important constituents of these percentages is the country/equity risk premium. The country risk premium can be defined as the price for taking risk for investing in that specific country. A widely used method to determine the country risk premium is to multiply the country bond default spread with an equity to bond market risk adjustment. Country bond default spreads are the spreads that investors charge for buying bonds issued by the country. These ratings measure default risk, rather than equity risk, but they are affected by many factors that drive equity risk, like the stability of a country’s currency, the budget and trade balances and the political stability. Analysis that uses spreads as a measure of country risk, usually adds them to both the cost of equity and debt of entities that trade in that country. There are several ways in determining the bond default spreads, but it is most often done in a random and unsystematic manner. Two of the major obstacles in determining these spreads for countries, especially countries of sub-Saharan Africa, are when countries do not issue bonds in another currency such as Euro or US dollar and/or do not have a sovereign credit rating. What could also be a measure of country risk, are the two major country risk polls conducted globally: 1) Euromoney Country Risk Poll; and 2) PRS (Political Risk Group) Composite Risk Ratings. Most of sub-Saharan African countries form part of these risk polls. The usefulness of the PRS scores as a measure of country risk has been previously examined to find that they are correlated with the cost of capital of emerging markets. The aim of the research is to overcome the obstacles in determining default spreads for countries such as sub-Saharan Africa where bond markets are inactive and/or sovereign credit ratings are not assigned, by deriving a predictive model. The predictive model is derived by analysing the relationship between the available estimated default spreads that are assigned to a specific country, depending on their Moody’s sovereign local currency rating and the countries’ respective country risk scores conducted by Euromoney and PRS respectively. The stability of the relationship is also analysed by comparing the prediction of the sub-Saharan’s Africa default spreads based on the 2010 predictive model to the analyses conducted on 2008 data sets. Other similar models have been developed, but this model is focused on the total risk score of a country and not only on the credit risk or related constituents. One of the definitions of country risk is that it relates to the likelihood that changes in the business environment will occur that reduce the profitability of doing business in a country, which can negatively affect operating profits as well as the value of assets. One can conclude that this derived model is a good reflection of prevailing political and economic stability of the countries and a useful measure of country risk that can be used in assessing the profitability of current investments in a specific country and for proposals of future capital investments. Key words: Country bond default spreads, Sovereign credit ratings, Euromoney risk scores, PRS composite ratings, sub-Saharan African countries.

In this study, a household resilience score was developed as a measure of rural household resilience to identify households with low resilience and to measure progress towards improved household resilience. Resilience is the ability of households to cope with risk. The motivation for the study originated from the first objective of the Framework of African Food Security (FAFS) of improved household risk management, and the indicator of progress towards this objective – proposed by the FAFS - a resilience score. A review of the literature indicated that the assets owned by a household could be used as a proxy for resilience. The household component of the Demographic and Health Surveys for six African countries was used to develop and apply the resilience score. The score was estimated using an index of assets owned by the household and information regarding household access to certain services and characteristics of the dwelling. There is disagreement in the literature concerning the best method of constructing an asset index in terms of how to weight the variables included in the index. As a result, four methods of constructing an index of socio-economic status (SES) were selected for comparison in this study: two linear principal component analysis (PCA) techniques; a non-linear or categorical principal component analysis (CATPCA) method; and a simple sum of assets technique. The results from the application of each of the four indices to the country data and the resulting classification of households into quintiles of SES were compared across several assessment criteria. No single method out-performed the others across all the assessment criteria. However, the CATPCA method performed better in terms of the proportion of variance explained by the first principal component and the stability of the solution. The results showed that for all methods, SES was not evenly distributed across the sample populations for the countries analysed. This violates the assumption of uniformity implied when using quintiles as classification cut-off points. As an alternate to the quintile split cluster analysis was applied to the SES scores derived for each country. The classification of households into SES groups was repeated using k-means cluster analysis of the household SES scores estimated by the CATPCA method for each country. The results showed that a greater proportion of households fell into relatively lower levels of SES, which is in contrast to the assumption of uniformity of SES made when using the quintile cut-off approach. Cluster analysis better reflected the clustered nature of the household data analysed in this study, compared to the quintile cut-off method. In a final analysis, the index of SES along with k-means cluster analysis was applied to household data from two different time periods for five African countries to determine whether the resilience measure was able to detect changes in household SES between the two periods and, therefore, whether the tool could be used to monitor changes in household resilience over time. The results showed evidence of adjustments in SES over time: there were differences in the per cent of households allocated to the clusters of SES between the two periods. Using the CATPCA index and k-means cluster analysis, Egypt, Uganda and Mali showed an increase in the per cent of 'poor' households, while for Kenya and Tanzania there was a reduction in the per cent of households allocated to the first cluster between time periods: the decrease for Kenya from 2003 to 2008 was as much as 13 percentage points. The observed changes in SES were then compared to changes in national poverty estimates reported in the literature. The resilience score developed in the study displayed an ability to track changes in household SES over time and could be used as a measure of progress towards improved household resilience. As such, the resilience measure could be valuable to policy-makers for monitoring the impacts of policies aimed at improving household resilience. Future research is recommended before the reliability of the resilience measure developed here can be fully ascertained. / Thesis (M.Sc.Agric.)-University of KwaZulu-Natal, Pietermaritzburg, 2011.

This thesis is an investigation into the microeconomic mechanisms that govern some of the occupational choices faced by workers in Sub-Saharan Africa, and into the monetary and non-monetary returns to their decisions. Chapter 1 begins by exploring the decision process that leads workers to allocate themselves to different occupations within the economy. In particular, I investigate the role of risk-aversion in the allocation of workers between formal and informal jobs in Ghana, hence attempting to explain a fundamental dimension of duality through an investigation into workers' preferences. In my model of sectoral allocation risk-averse workers can opt between entering the free-entry informal sector and queuing for formal occupations. Conditional on identifying the riskier option, the model yields testable implications on the relationship between risk-aversion and workers' allocation. My testing strategy proceeds in two steps. First, using the first three waves of the Ghana Household Urban Panel Survey (GHUPS) dataset, I estimate expected income uncertainty and find it considerably higher in the informal sector than in formal employment. Second, using experimental data to elicit risk-attitudes I estimate the effect of risk-aversion on occupational choices and I find that, in line with the first result, more risk-averse workers are more likely to queue for formal jobs and less likely to be in the informal sector. The conclusion of the first chapter is that attitudes to risk should feature more prominently in models of sector allocation and in the design of labour market policies, in particular when those policies aim to impact workers' vulnerability to risk and uncertainty. Chapter 2 focuses on the largest occupational category in the Developing world, self-employed workers with small productive activities, and it tries to estimate the returns to different productive assets, namely physical capital, labour and human capital. These are the workers that form most of the informal sector analysed in chapter 1, which allows me to draw a direct link with the analysis so far. The chapter begins by specifying a model for the income-generating process grounded in the literature on firms' production and hence abridging the gap between the analysis of individual earnings and the study of firms' value added. Identification in the empirics is achieved by means of panel estimators that are suitable to address the endogeneity of input choices, which derives from both time-varying and time-invariant unobservable heterogeneity. The use of these estimators is made feasible by the length of the Ghanaian Household Urban Panel Survey dataset at CSAE. I also explore issues of endogeneity in the selection of different technologies, defined by their relative capital and labour intensity. Finally, I analyse the shape of returns to capital, with the aim to detect potential non-convexities in technology. The results show that capital and work-experience play the strongest role in income-generation, while the shares of value added attributed to labour and to formal schooling are low. Marginal returns to investment are high at low capital levels and they decrease very rapidly, pointing against the existence of non-convexities due to minimum scale requirements, but implying that real income gains resulting form micro-investment are modest. Chapter 3 returns to the issue of earnings uncertainty and risk-aversion explored in Chapter 1, but it now takes the allocation choice as given and explores the direct welfare implications of income uncertainty for worker's well-being. Namely, the chapter explores the relationship between income and welfare, with a particular attention on the link between income vulnerability and happiness. Using unique longitudinal data on life-satisfaction and labour market outcomes, I estimate an individual measure of vulnerability (defined as the probability of falling below a low-income threshold) and investigate its effect on well-being. After controlling for unobservable individual fixed effects, work-satisfaction, relative income and other relevant worker characteristics, I find a sizable impact of vulnerability, over and above the income effect. When I explore the mechanisms behind my results, I find that aspiration adaptation to current income may result in a transitory income effect. Moreover, using my direct measure of attitudes to risk from field-experiments (already used in chapter 1), I can test directly the hypothesis that more risk-averse agents suffer more heavily from a given increase in income vulnerability. Overall, my findings support policy interventions that aim to reduce vulnerability, as I expect such policies to have a 'direct' impact on agents' happiness given the prevailing attitudes to risk and uncertainty in the population. Finally, from the point of view of overall social welfare, my results suggest that non-Rawlsian growth models, whereby 'someone may be left behind', may fail to enhance general welfare, for high enough levels of risk-aversion in the population, if the risk of falling behind is sufficiently widespread.