Essays on regulatory risk issues

Most studies around that try to verify the existence of regulatory risk look mainly at developed countries. Looking at regulatory risk in emerging market regulated sectors is no less important to improving and increasing investment in those markets. This thesis comprises three papers comprising regulatory risk issues. In the first Paper I check whether CAPM betas capture information on regulatory risk by using a two-step procedure. In the first step I run Kalman Filter estimates and then use these estimated betas as inputs in a Random-Effect panel data model. I find evidence of regulatory risk in electricity, telecommunications and all regulated sectors in Brazil. I find further evidence that regulatory changes in the country either do not reduce or even increase the betas of the regulated sectors, going in the opposite direction to the buffering hypothesis as proposed by Peltzman (1976). In the second Paper I check whether CAPM alphas say something about regulatory risk. I investigate a methodology similar to those used by some regulatory agencies around the world like the Brazilian Electricity Regulatory Agency (ANEEL) that incorporates a specific component of regulatory risk in setting tariffs for regulated sectors. I find using SUR estimates negative and significant alphas for all regulated sectors especially the electricity and telecommunications sectors. This runs in the face of theory that predicts alphas that are not statistically different from zero. I suspect that the significant alphas are related to misspecifications in the traditional CAPM that fail to capture true regulatory risk factors. On of the reasons is that CAPM does not consider factors that are proven to have significant effects on asset pricing, such as Fama and French size (ME) and price-to-book value (ME/BE). In the third Paper, I use two additional factors as controls in the estimation of alphas, and the results are similar. Nevertheless, I find evidence that the negative alphas may be the result of the regulated sectors premiums associated with the three Fama and French factors, particularly the market risk premium. When taken together, ME and ME/BE regulated sectors diminish the statistical significance of market factors premiums, especially for the electricity sector. This show how important is the inclusion of these factors, which unfortunately is scarce in emerging markets like Brazil.

Bootstrap for panel data models with an application to the evaluation of public policies

Le but de cette thèse est d étendre la théorie du bootstrap aux modèles de données de panel. Les données de panel s obtiennent en observant plusieurs unités statistiques sur plusieurs périodes de temps. Leur double dimension individuelle et temporelle permet de contrôler l 'hétérogénéité non observable entre individus et entre les périodes de temps et donc de faire des études plus riches que les séries chronologiques ou les données en coupe instantanée. L 'avantage du bootstrap est de permettre d obtenir une inférence plus précise que celle avec la théorie asymptotique classique ou une inférence impossible en cas de paramètre de nuisance. La méthode consiste à tirer des échantillons aléatoires qui ressemblent le plus possible à l échantillon d analyse. L 'objet statitstique d intérêt est estimé sur chacun de ses échantillons aléatoires et on utilise l ensemble des valeurs estimées pour faire de l inférence. Il existe dans la littérature certaines application du bootstrap aux données de panels sans justi cation théorique rigoureuse ou sous de fortes hypothèses. Cette thèse propose une méthode de bootstrap plus appropriée aux données de panels. Les trois chapitres analysent sa validité et son application. Le premier chapitre postule un modèle simple avec un seul paramètre et s 'attaque aux propriétés théoriques de l estimateur de la moyenne. Nous montrons que le double rééchantillonnage que nous proposons et qui tient compte à la fois de la dimension individuelle et la dimension temporelle est valide avec ces modèles. Le rééchantillonnage seulement dans la dimension individuelle n est pas valide en présence d hétérogénéité temporelle. Le ré-échantillonnage dans la dimension temporelle n est pas valide en présence d'hétérogénéité individuelle. Le deuxième chapitre étend le précédent au modèle panel de régression. linéaire. Trois types de régresseurs sont considérés : les caractéristiques individuelles, les caractéristiques temporelles et les régresseurs qui évoluent dans le temps et par individu. En utilisant un modèle à erreurs composées doubles, l'estimateur des moindres carrés ordinaires et la méthode de bootstrap des résidus, on montre que le rééchantillonnage dans la seule dimension individuelle est valide pour l'inférence sur les coe¢ cients associés aux régresseurs qui changent uniquement par individu. Le rééchantillonnage dans la dimen- sion temporelle est valide seulement pour le sous vecteur des paramètres associés aux régresseurs qui évoluent uniquement dans le temps. Le double rééchantillonnage est quand à lui est valide pour faire de l inférence pour tout le vecteur des paramètres. Le troisième chapitre re-examine l exercice de l estimateur de différence en di¤érence de Bertrand, Duflo et Mullainathan (2004). Cet estimateur est couramment utilisé dans la littérature pour évaluer l impact de certaines poli- tiques publiques. L exercice empirique utilise des données de panel provenant du Current Population Survey sur le salaire des femmes dans les 50 états des Etats-Unis d Amérique de 1979 à 1999. Des variables de pseudo-interventions publiques au niveau des états sont générées et on s attend à ce que les tests arrivent à la conclusion qu il n y a pas d e¤et de ces politiques placebos sur le salaire des femmes. Bertrand, Du o et Mullainathan (2004) montre que la non-prise en compte de l hétérogénéité et de la dépendance temporelle entraîne d importantes distorsions de niveau de test lorsqu'on évalue l'impact de politiques publiques en utilisant des données de panel. Une des solutions préconisées est d utiliser la méthode de bootstrap. La méthode de double ré-échantillonnage développée dans cette thèse permet de corriger le problème de niveau de test et donc d'évaluer correctement l'impact des politiques publiques.

Drivers of fund performance: a panel data analysis

The principle aim of this research is to elucidate the factors driving the total rate of return of non-listed funds using a panel data analytical framework. In line with previous results, we find that core funds exhibit lower yet more stable returns than value-added and, in particular, opportunistic funds, both cross-sectionally and over time. After taking into account overall market exposure, as measured by weighted market returns, the excess returns of value-added and opportunity funds are likely to stem from: high leverage, high exposure to development, active asset management and investment in specialized property sectors. A random effects estimation of the panel data model largely confirms the findings obtained from the fixed effects model. Again, the country and sector property effect shows the strongest significance in explaining total returns. The stock market variable is negative which hints at switching effects between competing asset classes. For opportunity funds, on average, the returns attributable to gearing are three times higher than those for value added funds and over five times higher than for core funds. Overall, there is relatively strong evidence indicating that country and sector allocation, style, gearing and fund size combinations impact on the performance of unlisted real estate funds.

A Panel Data Investigation of Real Exchange Rate Misalignment and Growth

The paper investigates the role of real exchange rate misalignment on long-run growth for a set of ninety countries using time series data from 1980 to 2004. We first estimate a panel data model (using fixed and random effects) for the real exchange rate, with different model specifications, in order to produce estimates of the equilibrium real exchange rate and this is then used to construct measures of real exchange rate misalignment. We also provide an alternative set of estimates of real exchange rate misalignment using panel cointegration methods. The variables used in our real exchange rate models are: real per capita GDP; net foreign assets; terms of trade and government consumption. The results for the two-step System GMM panel growth models indicate that the coefficients for real exchange rate misalignment are positive for different model specification and samples, which means that a more depreciated (appreciated) real exchange rate helps (harms) long-run growth. The estimated coefficients are higher for developing and emerging countries.

Causes of subcontracting: Evidence from panel data on construction firms

This paper examines factors explaining subcontracting decisions in the construction industry. Rather than the more common cross-sectional analyses, we use panel data to evaluate the influence of all relevant variables. We design and use a new index of the closeness to small numbers situations to estimate the extent of hold-up problems. Results show that as specificity grows, firms tend to subcontract less. The opposite happens when output heterogeneity and the use of intangible assets and capabilities increase. Neither temporary shortage of capacity nor geographical dispersion of activities seem to affect the extent of subcontracting. Finally, proxies for uncertainty do not show any clear effect.

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Fiber flows in global paper and pulp industry. A panel data approach.

The purpose of this thesis is to study factors that explain the bilateral fiber trade flows. This is done by analyzing bilateral trade flows during 1990-2006. It will be studied also, whether there are differences between fiber types. This thesis uses a gravity model approach to study the trade flows. Gravity model is mostly used to study the aggregate data between trading countries. In this thesis the gravity model is applied to single fibers. This model is then applied to panel data set. Results from the regression show clearly that there are benefits in studying different fibers in separate. The effects differ considerably from each other. Furthermore, this thesis speaks for the existence of Linder’s effect in certain fiber types.

An update on EMU sovereign yield spread drivers in times of crisis: A panel data analysis

We empirically investigate the determinants of EMU sovereign bond yield spreads with respect to the German bund. Using panel data techniques, we examine the role of a wide set of potential drivers. To our knowledge, this paper presents one of the most exhaustive compilations of the variables used in the literature to study the behaviour of sovereign yield spreads and, in particular, to gauge the effect on these spreads of changes in market sentiment and risk aversion. We use a sample of both central and peripheral countries from January 1999 to December 2012 and assess whether there were significant changes after the outbreak of the euro area debt crisis. Our results suggest that the rise in sovereign risk in central countries can only be partially explained by the evolution of local macroeconomic variables in those countries.

An update on EMU sovereign yield spread drivers in times of crisis: A panel data analysis

We empirically investigate the determinants of EMU sovereign bond yield spreads with respect to the German bund. Using panel data techniques, we examine the role of a wide set of potential drivers. To our knowledge, this paper presents one of the most exhaustive compilations of the variables used in the literature to study the behaviour of sovereign yield spreads and, in particular, to gauge the effect on these spreads of changes in market sentiment and risk aversion. We use a sample of both central and peripheral countries from January 1999 to December 2012 and assess whether there were significant changes after the outbreak of the euro area debt crisis. Our results suggest that the rise in sovereign risk in central countries can only be partially explained by the evolution of local macroeconomic variables in those countries.

A panel data analysis of FDI and informal labor markets

The objective of this paper is to examine whether informal labor markets affect the flows of Foreign Direct Investment (FDI), and also whether this effect is similar in developed and developing countries. With this aim, different public data sources, such as the World Bank (WB), and the United Nations Conference on Trade and Development (UNCTAD) are used, and panel econometric models are estimated for a sample of 65 countries over a 14 year period (1996-2009). In addition, this paper uses a dynamic model as an extension of the analysis to establish whether such an effect exists and what its indicators and significance may be.

Bt cotton and production risk: Panel data evidence

The farm-level success of Bt-cotton in developing countries is well documented. However, the literature has only recently begun to recognise the importance of accounting for the effects of the technology on production risk, in addition to the mean effect estimated by previous studies. The risk effects of the technology are likely very important to smallholder farmers in the developing world due to their risk-aversion. We advance the emergent literature on Bt-cotton and production risk by using panel data methods to control for possible endogeneity of Bt-adoption. We estimate two models, the first a fixed-effects version of the Just and Pope model with additive individual and time effects, and the second a variation of the model in which inputs and variety choice are allowed to affect the variance of the time effect and its correlation with the idiosyncratic error. The models are applied to panel data on smallholder cotton production in India and South Africa. Our results suggest a risk-reducing effect of Bt-cotton in India, but an inconclusive picture in South Africa.

Does terrorism threaten human rights? Evidence from panel data

Using panel data for 111 countries over the period 1982–2002, we employ two indexes that cover a wide range of human rights to empirically analyze whether and to what extent terrorism affects human rights. According to our results,terrorism significantly, but not dramatically, diminishes governments’ respect for basic human rights such as the absence of extrajudicial killings, political imprisonment, and torture. The result is robust to how we measure terrorist attacks, to the method of estimation, and to the choice of countries in our sample. However, we find no effect of terrorism on empowerment rights.

What factors affect the destination choice of Jordanian tourists?A panel data analysis

This paper investigates what factors affect the destination choice for Jordanian to 8 countries (Oman, Saudi Arabia, Syria, Tunisia, Yemen, Egypt, Lebanon and Bahrain) using panel data analysis. Number of outbound tourists is represented as dependent variable, which is regressed over five explanatory variables using fixed effect model. The finding of this paper is that tourists from Jordan have weak demand for outbound tourism; Jordanian decision of traveling abroad is determined by the cost of traveling to different places and choosing the cheapest alternative.

Non-durable consumption and real-estate prices in Brazil: panel-data analysis at the state level

Housing is an important component of wealth for a typical household in many countries. The objective of this paper is to investigate the effect of real-estate price variation on welfare, trying to close a gap between the welfare literature in Brazil and that in the U.S., the U.K., and other developed countries. Our first motivation relates to the fact that real estate is probably more important here than elsewhere as a proportion of wealth, which potentially makes the impact of a price change bigger here. Our second motivation relates to the fact that real-estate prices boomed in Brazil in the last five years. Prime real estate in Rio de Janeiro and São Paulo have tripled in value in that period, and a smaller but generalized increase has been observed throughout the country. Third, we have also seen a recent consumption boom in Brazil in the last five years. Indeed, the recent rise of some of the poor to middle-income status is well documented not only for Brazil but for other emerging countries as well. Regarding consumption and real-estate prices in Brazil, one cannot imply causality from correlation, but one can do causal inference with an appropriate structural model and proper inference, or with a proper inference in a reduced-form setup. Our last motivation is related to the complete absence of studies of this kind in Brazil, which makes ours a pioneering study. We assemble a panel-data set for the determinants of non-durable consumption growth by Brazilian states, merging the techniques and ideas in Campbell and Cocco (2007) and in Case, Quigley and Shiller (2005). With appropriate controls, and panel-data methods, we investigate whether house-price variation has a positive effect on non-durable consumption. The results show a non-negligible significant impact of the change in the price of real estate on welfare consumption), although smaller then what Campbell and Cocco have found. Our findings support the view that the channel through which house prices affect consumption is a financial one.

Infant mortality in Brazil, 1980-2000: A spatial panel data analysis

Background: Infant mortality is an important measure of human development, related to the level of welfare of a society. In order to inform public policy, various studies have tried to identify the factors that influence, at an aggregated level, infant mortality. The objective of this paper is to analyze the regional pattern of infant mortality in Brazil, evaluating the effect of infrastructure, socio-economic, and demographic variables to understand its distribution across the country. Methods: Regressions including socio-economic and living conditions variables are conducted in a structure of panel data. More specifically, a spatial panel data model with fixed effects and a spatial error autocorrelation structure is used to help to solve spatial dependence problems. The use of a spatial modeling approach takes into account the potential presence of spillovers between neighboring spatial units. The spatial units considered are Minimum Comparable Areas, defined to provide a consistent definition across Census years. Data are drawn from the 1980, 1991 and 2000 Census of Brazil, and from data collected by the Ministry of Health (DATASUS). In order to identify the influence of health care infrastructure, variables related to the number of public and private hospitals are included. Results: The results indicate that the panel model with spatial effects provides the best fit to the data. The analysis confirms that the provision of health care infrastructure and social policy measures (e. g. improving education attainment) are linked to reduced rates of infant mortality. An original finding concerns the role of spatial effects in the analysis of IMR. Spillover effects associated with health infrastructure and water and sanitation facilities imply that there are regional benefits beyond the unit of analysis. Conclusions: A spatial modeling approach is important to produce reliable estimates in the analysis of panel IMR data. Substantively, this paper contributes to our understanding of the physical and social factors that influence IMR in the case of a developing country.