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The main idea of this thesis is to study three different issues, economic, political, or financial, related to three different public infrastructure sectors, transport, water and sewerage, and electricity, by using three different methodological approaches. In the first chapter, I make creative use of a non-parametric technique traditionally used to measure the relative efficiency of a set of similar firms, data envelopment analysis, to identify the most likely objective, economic vs. political, behind a specific policy. In the second chapter I empirically investigate the effects of the increasing private financial ownership of the water and sewerage utilities in England and Wales on key outcome variables such as leverage levels and consumer bills. Finally, in the third chapter, I evaluate an equity-aimed policy introduced in the electricity sector in Spain in 2009 by measuring the effect of its introduction on the probability of a household of being energy poor.Chapter One – Unbundling political and economic rationality: a non-parametric approach tested on transport infrastructure in SpainThis paper suggests a simple quantitative method to assess the extent to which public investment decisions are dominated by political or economic motivations. The true motivation can be identified by modeling each policy goal as the focus of the optimization anchoring a data envelopment analysis of the efficiency of the observed implementation. In other words, we rank performance based on how far observed behavior is under each possible goal, and the goal for which the distance is smaller reveals the specific motivation of the investment or any policy decision for that matter. Traditionally, data envelopment analysis is used to measure the relative efficiency of a set of firms having a similar productive structure. In this case, each firm corresponds to a different policy year, the policy being the determinant of the investment made.The approach is tested on Spain’s land transport infrastructure policy since it is argued by many observers to be driven more by political than economic concerns, resulting in a mismatch between capacity investment and traffic demand. History has shown that when the source of financing has been private, the network has been developed in areas with high demand, i.e. the Northern and Mediterranean corridors. When the source has been public, the network has been developed following a radial pattern, converging from a to Madrid. The method clearly shows that public investments in land transport infrastructure have generally been more consistent with a political objective – the centralization of economic power – than with an economic objective – maximizing mobility –.Chapter Two – On the effects of the private financial ownership of regulated utilities: lessons from the UK water sectorThis paper analyzes the quantitative impact of the growing role of non-traditional financial actors in the financing structure and consumer pricing of regulated private utilities. The focus is on the water sector in England and Wales, where the effect of the firms’ corporate financing and ownership strategies on key outcome variables may have been underestimated. The sector was privatized in 1989, year in which the 10 regional monopolies became 10 water and sewerage companies, listed and publicly traded on UK Stock Exchanges. Since then, six of the ten have been de-listed, bought-out by private equity – investment and infrastructure funds. I make use of this variation in ownership to measure the effect on leverage levels and consumer bills.I develop a theoretical framework allowing me to derive two hypotheses: first, the buyout of a company increases its leverage level, and second, the buyout of a company increases the consumer bill through higher leverage levels. The empirical analysis is based on two sequential steps: a staggered difference-in-differences estimation shows that private equity buyouts increase the leverage levels of water utilities. An instrumental variable and two-stage least squares estimation then show that these higher leverage levels increase the average consumer bills of bought-out utilities more than if they had not been bought-out. The estimated impact of the private equity buyouts in the sector in England and Wales on the annual average consumer bill ranges from 13.5 to 32.6 GBP, for a sample average bill of about 427 GBP.Chapter Three – Understanding the effectiveness of the electricity social rate in reducing energy poverty in SpainThis paper analyzes the causal impact of the introduction of a social subsidy, the bono social de electricidad, in Spain's electricity market in 2009. The measure was introduced following the surge in energy poverty, increasing particularly after the financial crisis. Using data from the family budget survey from 2006 to 2017, we evaluate the social policy in its fight against energy poverty.We proceed in two steps. First, we use a difference-in-differences approach to measure such a causal impact and to analyze how the introduction of the measure directly affected eligible households. We find that the introduction of the subsidy has reduced the likelihood of energy poverty for the eligible households. Therefore, the bono social de electricidad has reached its equity objective of increasing affordability of electricity. The second step aims at understanding how specifically the introduction of the subsidy affects consumers. We find that, in reaction to lower effective prices, households do not increase their consumption of electricity, resulting in lower total electricity expenditure. We are therefore able to show that this policy did not induce a change in the consumption behavior and that the increased affordability entirely resulted in a decrease of expenditure in electricity, Doctorat en Sciences économiques et de gestion, info:eu-repo/semantics/nonPublished

In this thesis, I apply the sophisticated tools made available by the econometrics of panel data and treatment models to a range of different issues. In the first Chapter, an ECM model is used to test on the existence of financing constraints in firms’ investment and R&D, taken a proxy for the efficiency of market institutions and governance rules in different countries. In the second chapter we test an agency model linking pay-performance contracts of CEOS to the financial situation of a firm by using a UK panel data. In the third chapter I use a sophisticated treatment model to evaluate the effectiveness of Italian public subsidies to R&D. Finally, in the fourth chapter I try to evaluate the efficiency of Italian regional systems of public healthcare by controlling for socio-economic factors and quality of healthcare in a composite model using panel data estimation and efficient frontier techniques.The first Chapter analyzes the investment behavior of a sample of R&D intensive firms which are quoted on the stock market from USA, UK and Japan for the period 1990-1998. By using an error correction model we test the elasticity of investment and R&D to cash flow in these countries to see by which measure different market institutions and corporate governance rules affects the cost of external financing. Contrary to previous studies, we find significant differences in the sensitivity to cash flow of the two types of investment, with R&D expenditure being much less sensitive than ordinary investment. This is not surprising given the more long-term nature of R&D expenditures. For what concerns the comparison between the different systems/countries, the USA stock markets confirms as the most efficient market providing outside financing at a much lower cost compared to other markets, especially for young, smaller firms.The second Chapter is a joint work with Biagio Speciale. It uses the data on a panel of quoted UK firms over the period 1995–2002 to study the effects of financial leverage on managerial compensation. The change in the investors’ expectations that caused the recent collapse of the stock market tech bubble is a perfect example of natural experiment that has been used as a source of plausibly exogenous variation in the firm’s debt. The estimates show that pay-for-performance sensitivity is increasing in financial leverage, with the exception of the 10% most levered firms, giving rise at the end to a non-linear (inverted U-shape) relationship between the two variables. The chapter includes also a theoretical model accounting for this relationship where an higher leverage increases both the expected returns and the expected variance of investment returns: the first effect (determining increased pay-performance sensitivity) prevails for low leverage values and the second effect (determining decreased pay-performance sensitivity) prevails for high leverage values.The third Chapter undertakes an empirical estimation of the additionality of public funding on both the propensity to initiate R&D activity and the intensity of R&D spending of Italian enterprises for the period 1998-2000, using data from the Third Community Innovation Survey and from firms' financial accounts. The chosen methodology (Endogenous Switching Type II-Tobit) takes into account the possibility that decisions about both starting an R&D activity (sample selection effect) and applying for/obtaining public funding (essential heterogeneity) are influenced by private knowledge of enterprises' idiosyncratic propensities in R&D spending. The present analysis shows that both these effects are indeed important and that they contribute to explain most of the additionality found with less sophisticated models.The fourth Chapter investigates the underlying causes of variability of public health expenditure per capita (SSPC henceforth) between Italian regions. A fixed-effect panel data estimate on the SSPC (for the period 1997-2006) is used in the first part of the paper to account for regional differences in terms of physical, demographic, socio-economic characteristics and in terms of other variables that affect demand and supply of health services. In the second part, we take the ‘adjusted’ SSPC and proceed to estimate an "efficient production function" of the quality of health services through Data Envelopment Analysis. This procedure allows us to separate the share of expenditure used for the improvement of the quality from the one that can be traced only to an inefficient use of financial resources. A comparison of regional SSPC after factoring out the socio-economic factors and the quality of healthcare shows that big differences still remain and are even exacerbated, signalling big pockets of inefficiency and correspondingly a huge potential for cost savings. Finally, a preliminary analysis shows a positive correlation between the efficiency of regional public spending in healthcare and the level of social capital., Doctorat en Sciences économiques et de gestion, info:eu-repo/semantics/nonPublished

The dissertation suggests that geographical, institutional, religious and cultural links may be determinants of growth. We address a number of issues in this thesis. The starting point is naturally a study on growth, while the main focus is on the analysis of inequalities between countries with respect to their environment, and also on inequalities within countries.The very first step of the study, presented in Chapter one, is to build such non-physical relations between countries. In this chapter, we present both the choices and methods used to model the institutional and cultural weights matrices. Chapter 1 also presents a comparative study between the different matrices built. The final aim of this chapter is to identify the differences between the geographical, institutional and cultural environment.The following chapter incorporates these innovative new types of matrices in a study on growth. An externality growth model is therefore developed that takes proximities between entities into account, whether geographical, institutional or cultural. The purpose of the chapter is threefold. First, it compares the results obtained from spatial econometrics methods with classical regression, where observations of growth are considered as independent. Second, it examines whether the development of an externality model improves the quality of the estimation. Third, it investigates whether the institutional and cultural types of proximity make sense compared to the geographical one.Chapter 3 narrows the analysis of countries’ dependency with regard to their neighborhood, whether geographical, institutional or religious, and a quintile regression approach allows us to check whether the countries' wealth level matters. Do the poorest countries react in the same way as richer ones regarding the wealth of their geographical, institutional and religious neighbors? The gross impact of neighboring wealth on a country’s wealth is then estimated, and some relative effects of the three matrices combined are also shown, as well as the robustness of the estimates.Finally, Chapter 4 analyzes the dependence of poverty regarding neighborhood. The relative wealth and poverty of the neighborhood are examined as factors that can influence a country’s poverty level. The poverty index used is the proportion of people living on less than one or two dollars a day. The study only considers the developing countries as data for the developed countries on the proportion of this variable is near zero. Once again, the final aim is to check whether a country’s poverty is exacerbated by its geographical, institutional and religious neighborhood poverty or if it takes advantage of neighborhood wealth to manage its own poverty issues., Doctorat en Sciences économiques et de gestion, info:eu-repo/semantics/nonPublished

The thesis contains four essays covering topics in the field of real time econometrics and forecasting.The first Chapter, entitled “An area wide real time data base for the euro area” and coauthored with Domenico Giannone, Jerome Henry and Magda Lalik, describes how we constructed a real time database for the euro area covering more than 200 series regularly published in the European Central Bank Monthly Bulletin, as made available ahead of publication to the Governing Council members before their first meeting of the month.Recent research has emphasised that the data revisions can be large for certain indicators and can have a bearing on the decisions made, as well as affect the assessment of their relevance. It is therefore key to be in a position to reconstruct the historical environment of economic decisions at the time they were made by private agents and policy-makers rather than using the data as they become available some years later. For this purpose, it is necessary to have the information in the form of all the different vintages of data as they were published in real time, the so-called "real-time data" that reflect the economic situation at a given point in time when models are estimated or policy decisions made.We describe the database in details and study the properties of the euro area real-time data flow and data revisions, also providing comparisons with the United States and Japan. We finally illustrate how such revisions can contribute to the uncertainty surrounding key macroeconomic ratios and the NAIRU.The second Chapter entitled “Maximum likelihood estimation of large factor model on datasets with arbitrary pattern of missing data” is based on a joint work with Marta Banbura. It proposes a methodology for the estimation of factor models on large cross-sections with a general pattern of missing data. In contrast to Giannone et al (2008), we can handle datasets that are not only characterised by a 'ragged edge', but can include e.g. mixed frequency or short history indicators. The latter is particularly relevant for the euro area or other young economies, for which many series have been compiled only since recently. We adopt the maximum likelihood approach, which, apart from the flexibility with regard to the pattern of missing data, is also more efficient and allows imposing restrictions on the parameters. It has been shown by Doz et al (2006) to be consistent, robust and computationally feasible also in the case of large cross-sections. To circumvent the computational complexity of a direct likelihood maximisation in the case of large cross-section, Doz et al (2006) propose to use the iterative Expectation-Maximisation (EM) algorithm. Our contribution is to modify the EM steps to the case of missing data and to show how to augment the model in order to account for the serial correlation of the idiosyncratic component. In addition, we derive the link between the unexpected part of a data release and the forecast revision and illustrate how this can be used to understand the sources of the latter in the case of simultaneous releases.We use this methodology for short-term forecasting and backdating of the euro area GDP on the basis of a large panel of monthly and quarterly data.The third Chapter is entitled “Nowcasting Inflation Using High Frequency Data” and it proposes a methodology for nowcasting and forecasting inflation using data with sampling frequency higher than monthly. In particular, this Chapter focuses on the energy component of inflation given the availability of data like the Weekly Oil Bulletin Price Statistics for the euro area, the Weekly Retail Gasoline and Diesel Prices for the US and the daily spot and future prices of crude oil.Although nowcasting inflation is a novel idea, there is a rather long literature focusing on nowcasting GDP. The use of higher frequency indicators in order to Nowcast/Forecast lower frequency indicators had started with monthly data for GDP. GDP is a quarterly variable released with a substantial time delay (e.g. two months after the end of the reference quarter for the euro area GDP). The estimation adopts the methodology described in Chapter 2, modeling the data as a trading day frequency factor model with missing observations in a state space representation. In contrast to other procedures, the methodology proposed models all the data within a unified single framework that allows one to produce forecasts of all the involved variables from a factor model, which, by definition, does not suffer from overparametrisation. Moreover, this offers the possibility to disentangle model-based "news" from each release and then to assess their impact on the forecast revision. The Chapter provides an illustrative example of this procedure, focusing on a specific month.In order to assess the importance of using high frequency data for forecasting inflation this Chapter compares the forecast performance of the univariate models, i.e. random walk and autoregressive process, with the forecast performance of the model that uses weekly and daily data. The provided empirical evidence shows that exploiting high frequency data relative to oil not only let us nowcast and forecast the energy component of inflation with a precision twice better than the proposed benchmarks, but we obtain a similar improvement even for total inflation.The fourth Chapter entitled “The forecasting power of international yield curve linkages”, coauthored with Kleopatra Nikolaou, investigates dependency patterns between the yield curves of Germany and the US, by using an out-of-sample forecast exercise.The motivation for this Chapter stems from the fact that our up to date knowledge on dependency patterns among yields curves of different countries is limited. Looking at the yield curve literature, the empirical evidence to-date informs us of strong contemporaneous interdependencies of yield curves across countries, in line with increased globalization and financial integration. Nevertheless, this yield curve literature does not investigate non-contemporaneous correlations. And yet, clear indication in favour of such dependency patterns is recorded in studies focusing on specific interest rates, which look at the role of certain countries as global players (see Frankel et al. (2004), Chinn and Frankel (2005) and Wang et al. (2007)). Evidence from these studies suggests a leading role for the US. Moreover, dependency patterns recorded in the real business cycles between the US and the euro area (Giannone and Reichlin, 2007) can also rationalize such linkages, to the extent that output affects nominal interest rates.We propose, estimate and forecast (out-of-sample) a novel dynamic factor model for the yield curve, where dynamic information from foreign yield curves is introduced into domestic yield curve forecasts. This is the International Dependency Model (IDM). We want to compare the yield curve forecast under the IDM versus a purely domestic model and a model that allows for contemporaneous common global factors. These models serve as useful comparisons. The domestic model bears direct modeling links with IDM, as it can be seen as a nested model of IDM. The global model bears less direct links in terms of modeling, but, in line with IDM, it is also an international model that serves to highlight the advantages of introducing international information in yield curve forecasts. However, the global model aims to identify contemporaneous linkages in the yield curve of the two countries, whereas the IDM also allows for detecting dependency patterns.Our results that shocks appear to be diffused in a rather asymmetric manner across the two countries. Namely, we find a unidirectional causality effect that runs from the US to Germany. This effect is stronger in the last ten years, where out-of-sample forecasts of Germany using the US information are even more accurate than the random walk forecasts. Our statistical results demonstrate a more independent role for the US., Doctorat en Sciences économiques et de gestion, info:eu-repo/semantics/nonPublished