The main part of this thesis is composed of three separate chapters, each using an innovative approach to analysing externalities from economic activity. The general introduction and overall conclusion sections complete the structure of the thesis. Chapter one examines the value of statistical life, an essential parameter used in ascribing monetary values to the mortality costs of air pollution in health risk analyses. This willingness to pay estimate is virtually non-existent for most developing countries. In the absence of local estimates, two major benefit transfer approaches lend themselves to the estimation of the value of statistical life: the value transfer method and the meta-regression analysis. Using Nigeria as a sample country, we find that the latter method is better tailored than the former for incorporating many characteristics that vary between study sites and policy sites into its benefit transfer application. It is therefore likely to provide more accurate value of statistical life predictions for very low-income countries. Employing the meta-regression method, we find Nigeria’s value of statistical life estimate to be $489,000. Combining this estimate with dose response functions from the epidemiological literature, it follows that if Nigeria had mitigated its 2006 particulate air pollution to the World Health Organisation standards, it could have avoided at least 58,000 premature deaths and recorded an avoided mortality related welfare loss of about $28 billion or 19 percent of the nation’s GDP for that year. The second chapter applies the quantile fixed effects technique in exploring the CO2 environmental Kuznets curve within two groups of economic development (OECD and Non-OECD countries) and six geographical regions – West, East Europe, Latin America, East Asia, West Asia and Africa. A comparison of the findings with those of the conventional fixed effects method reveals that the latter may depict a flawed summary of the prevailing income-emissions nexus depending on the conditional quantile examined. We also extend the Machado and Mata decomposition method to the Kuznets curve framework to explore the most important explanations for the carbon emissions gap between OECD and Non-OECD countries. We find a statistically significant OECD-Non-OECD emissions gap and this contracts as we ascend the emissions distribution. Also, had the Non-OECD group the incomes of the OECD group, the former would pollute 26 to 40 percent more than the latter ceteris paribus. The decomposition further reveals that there are non-income related factors working against the Non-OECD group’s greening. We tentatively conclude that deliberate and systematic mitigation of current CO2 emissions in the Non-OECD group is required. The final chapter employs the Arellano-Bond difference GMM method in investigating the oil curse in OECD and Non-OECD oil exporting countries. Empirical studies investigating the natural resource curse theory mostly employ cross-country and panel regression techniques subject to endogeneity bias. Also, most of these studies employ GDP in its aggregate or per-capita terms as the outcome variable in their analyses. However, the use of GDP measures of income for resource curse investigations does not portray the true incomes of resource intensive economies. Standard national accounts treat natural resource rents as a positive contribution to income without making a corresponding adjustment for the value of depleted natural resource stock. This treatment, inconsistent with green national accounting, leads to a positive bias in the national income computations of resource rich economies. Our paper deviates from most empirical studies in the literature by using the Arellano-Bond difference GMM method. We test the robustness of the curse in the predominantly used measures of national income, GDP, by investigating the theme in genuine income measures of economic output as well. We employ two alternative measures of resource intensity in our explorations: the share of oil rents in GDP and per-capita oil reserves. Our results provide evidence of the curse in Non-OECD countries employing aggregate and per-capita measures of genuine income. On the other hand, we find oil abundance to be a blessing rather than a curse to the OECD countries in our sample.

The present thesis is concerned with the existence of political cycles in European Union member states between 1990 and 2013. These cycles are of two types: political business cycles and political budget cycles. The analysis was performed by means of the fixed effects method (using first differences) along with a visual analysis of data. The results obtained from three types of data sets show that two years before elections, inflation grows at 0.47%, unemployment rate at 0.5%, and structural balance at 0.8%. One of the data sets reveals that structural balance is reduced by 0.39% in election years. The visual analysis clearly demonstrates that there exist political business cycles in Belgium, Bulgaria, Czech Republic, Denmark, Germany, Poland and Sweden. Political budget cycles have been proven to exist in Belgium, France, Cyprus, Malta, Germany, Slovakia and the United Kingdom.

Dans cette thèse, nous nous sommes focalisés sur le modèle statistique linéaire à effets mixtes. Nous nous sommes d'abord intéressés à l'estimation consistante des paramètres du modèle dans sa version multidimensionnelle, puis à de la sélection d'effets fixes en dimension un. En ce qui concerne l'estimation des paramètres du modèle linéaire à effets mixtes multidimensionnel, nous avons proposé des estimateurs du maximum de vraisemblance par utilisation de l'algorithme EM, mais avec des expressions plus générales que celles de la littérature classique, permettant d'analyser non seulement des données longitudinales multivariées mais aussi des données multidimensionnelles multi-niveaux. Ici, en s'appuyant sur ces EM-estimateurs, nous avons introduit un test de rapport de vraisemblance permettant de tester la significativité globale des corrélations entre les effets aléatoires de deux dimensions du modèle. Ce qui permettrait de construire un modèle multidimensionnel plus parcimonieux en terme de paramètres de variance des effets aléatoires, par une procédure de selection pas-à-pas ascendante. Cette démarche a été suscitée par le fait que la dimension du vecteur de tous les effets aléatoires du modèle peut très rapidement croitre avec le nombre de variables à analyser, entrainant facilement des problèmes numériques dans l'optimisation du critère choisi (ML ou REML). Nous avons ensuite proposé une procédure d'estimation consistante des paramètres du modèle qui passe par la résolution d'un problème de moindres carrés pénalisés pour fournir une expression explicite de la déviance à minimiser. La procédure de sélection d'effets fixes proposée ici est de type adaptive ridge itérative et permet d'approximer les performances de sélection d'une pénalité de type L0 de la vraisemblance des paramètres du modèle. Nos résultats ont été appuyés par des études de simulation à plusieurs niveaux, mais aussi par l'analyse de plusieurs jeux de données réelles. / This thesis focuses on the statistical linear mixed-effects model, where we have been interested in its multivariate version's parameters estimation but also in the unidimensional selection of fixed effects. Concerning the parameters estimation of the multivariate linear mixed-effects model, we have first introduced more general expressions of the EM algorithm-based estimators which fit the multivariate longitudinal data analysis framework but also the framework of the multivariate multilevel data analysis. Since the dimensionality of the total vector of random effects in the multivariate model can grow with the number of the outcome variables leading often to computational problems in the likelihood optimization, we introduced a likelihood ratio test for testing the global effect of the correlations between the random effects of two dimensions of the model. This bivariate correlation test is intended to help in constructing a more parsimonious model regarding the variance components of the random effects, using a stepwise procedure. Secondly, we have introduced another estimation procedure that yields to consistent estimates for all the model parameters. This procedure is based on the Cholesky factorization of the random effects covariance matrix and the resolution of a preliminary penalized means square problem, and leads to an explicite expression of the profiled deviance of the model. For selecting fixed effects in the one dimensional mixed-effects model, we introduce an iterative adaptive ridge procedure for approximating sL0 penalty selection performances. All the results in this manuscript have been accompanied by extensive simulation studies along with real data analysis examples.

MCom (Economics) / Department of Economics / The need to realise steady economic growth, measured in this research by Gross Domestic Product (GDP), has ignited a plethora of studies about the contributors of economic growth and their optimal levels. Government expenditure is one contributor to economic growth. From a theoretical standpoint, optimal government size is depicted by an inverted U-curve known as the Armey curve which is hypothesised between the relationship of government size and economic growth. Empirical literature provides evidence that optimal government size is between 20-30 percent a share of GDP. However, little has been done to investigate the optimal level of isolated components of government spending that maximizes economic growth. One component of government spending that has gained limelight over the past decade is that of social assistance. Defined as public expenditure spent as cash and food transfers to the poor, this research uses social assistance expenditure to assess its optimal level that maximizes growth. This is important because some policymakers are concerned about the ballooning budgets directed at social assistance, and argue that the scarce resources need to be transferred to other social services sectors such as health and education. Basing on the panel-data accessed from the World Bank, this research uses the quadratic equation model to determine the optimal level of social assistance for African and Organisation for Economic Co-Operation and Development (OECD) countries covering the period 2009-15. The finding is that the optimal level of social assistance spending for African and OECD countries is 3.2 percent of GDP and 29.4 percent of GDP respectively. The study also finds that both African and OECD countries operate below the optimal levels and it is suggested that they need to increase social assistance spending in order to realize positive contributions to economic growth. / NRF

Denna studie undersöker huruvida studier på fristående gymnasieskola påverkar sannolikheten att påbörja vidareutbildning på högskola eller universitet. Detta studeras genom användande av paneldata över åren 2008-2021 för samtliga 290 kommuner i Sverige. För att analysera datan används metoden multipel regressionsanalys både med och utan fixa effekter för år och kommuner. De prediktorer som hålls konstanta i studien för att mäta effekten av andel elever i friskola på övergång till vidare studier är grundskolebetyg, unga i ekonomiskt utsatta hushåll, utländsk bakgrund och lågutbildade föräldrar. Resultatet visar inledningsvis, vid både enkel linjär regression och vid hänsyn till årsfixa effekter, en statistisk signifikant positiv effekt på övergång till högskola och universitet. Med det sagt försvinner både det positiva sambandet och den statistiska signifikansen när även kommunfixa effekter inkluderas. / This study examines whether the proportion of students in private upper secondary school affects the proportion of students at college or university. This is studied using panel data over the years 2008-2021 regarding all 290 municipalities in Sweden. The data is used for the multiple regression analysis method, both with and without fixed effects regarding years and municipalities. The predictors used in the study to measure the effect of the proportion of students in independent schools are primary school grades, youth in economically disadvantaged households, foreign backgrounds, and low-educated parents. The results show that the proportion of students in private upper secondary schools initially, using simple linear regression and yearly fixed effects, has a statistically significant positive effect on further studies. With that said, the positive effect fades away when the fixed effects for municipalities are added. In addition, the result is no longer statistically significant.

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Thesis (M. Com. (Economics)) -- University of Limpopo, 2021 / The main aim of the study is to analyze determinants of sovereign credit ratings (SCRs) for Southern African Development Community (SADC) countries, namely Angola, Botswana, Mozambique, South Africa and Zambia. The analysis is based on the SCRs given by Standard and Poor’s (S&P). The selected explanatory variables are gross domestic product (GDP) per capita, inflation, external debt, foreign direct investment (FDI) inflows and control of corruption for the period 1990-2016, based on annual data. The panel root test results, namely IPS, LLC, ADF Fisher and PP Fisher, show that GDP per capita, external debt and FDI are stationary at 5% level of significance. The Hausman test results indicates that the identified explanatory variable explains 80% of SCRs. The model observed a positive relationship between SCR, inflation and control of corruption. It also observed a negative correlation between SCR, GDP per capita, external debt and FDI. The Pedroni residual cointegration test results indicate that there is no long-run relationship among variables and no autocorrelation as shown by serial correlation LM test results. The study recommends that the selected member states of SADC develop strategic plans for reducing budget deficits. This will help countries to manage their debts, especially foreign currency denoted debt and to attract foreign investment. Keywords: Sovereign credit ratings, fixed effects model, random effects, Hausman test.

This thesis consists of an introductory part and two self-contained chapters related to the supply of volunteers to the Swedish Armed Forces. Chapter [I] represents the first effort to explore the relationship between civilian labour market conditions and the supply of labour to the military in the all-volunteer environment that Sweden entered after the abolishment of the peacetime draft in 2010. The effect of civilian unemployment on the rate of applications from individuals aged 18 to 25 to initiate basic military training is investigated using panel data on Swedish counties for the years 2011 through 2015. A linear fixed-effects model is estimated to investigate the relationship, while controlling for a range of socio-demographic covariates and unobserved heterogeneity on the regional level, as well as aggregate trends on the national level. The results indicate a positive and statistically significant relationship between the unemployment rate and the application rate. The results are robust to non-linear form specifications, as well as allowing the civilian unemployment rate to be endogenous. As such, the results suggest that the civilian labour market environment in Sweden can give rise to non-trivial fluctuations in the supply of applications to initiate basic military training within the Swedish Armed Forces. Chapter [II] studies how local labour market conditions influence the quality composition of those who volunteer for military service in Sweden. A fixed-effects regression model is estimated on a panel data set containing IQ scores for those who applied for military basic training across Swedish municipalities during the period 2010 to 2016. The main finding is that low civilian employment rates at the local level tend to increase the mean IQ score of those who volunteer for military service, whereas the opposite is true if employment rates in the civilian labour market move in a more favourable direction. As such, the results suggest that the negative impact of a strong civilian economy on recruitment volumes is reinforced by a deterioration in recruit quality.

This thesis examines the effectiveness of macroprudential policies on reducing housing price growth in the international database of 56 countries with the use of GMM and fixed effects between 2000 and 2017. The macroprudential index is added to the dynamic panel data model where the housing price index is regressed on housing price determinants as the economic growth or unemployment rate. The analysis is also conducted on the sample of countries with a higher market share of owners with a mortgage as there is a higher opportunity to control the housing market through the credit channel. Nevertheless, results show that we do not have enough evidence to state that macroprudential policies curb house price booms. Contrarily, the effect seems to work in the opposite direction which is probably caused by a reverse causality between the growth of real estate prices and the implementation of macroprudential tools. The debt-to-income restriction is the only tool that decreases housing price growth according to the fixed effects model. Detailed counterfactual analysis of the Czech market proposes only a slight impact of the loan-to-value measure on the apartment price development according to one out of four predictions. 1

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