La thèse explore l'opportunité d'utiliser des micro-données, sous la forme de données individuelles de firmes, pour dépasser les limites imposées par les données spatialement agrégées généralement utilisées en géographie économique. Le cas d'étude est l'estimation des exportations régionales, y compris les exportations vers d'autres régions du même pays. Prenant la Belgique comme exemple, la thèse présente un nouveau modèle d'estimation de ces exportations qui intègre un modèle gravitaire d'estimation des flux entre lieux de production et lieux de consommation avec les informations contenues dans les tables d'entrée-sortie à l'échelle nationale. Les résultats du modèle confirment l'hypothèse de départ sur l'importance de la consommation locale ou à courte distance du lieu de production.Ces résultats sont analysés devant l'arrière-fond des débats passés et actuels en géographie économique et en politique de développement régional en Europe. Un regard critique est posé sur la notion des politiques « place-based », généralement focalisées sur des politiques de l'offre. Avec le soutien des estimations sortant du modèle l'argument est avancé que la demande locale joue un rôle important pour les économies régionales. Cet argument est renforcé par une revue des débats concernant l'importance de la distance géographique dans les relations économiques.La thèse met également un grand accent sur les questions de méthodes et de données. Elle présente en détail les difficultés liées à l'utilisation de données individuelles, notamment le problème du géocodage. L'utilisation de système d'information géographiques existants dans la construction du modèle est montré, argumentant que de tels systèmes facilitent la vie aux chercheurs en géographie économique dès lors qu'ils utilisent des données massives positionnées dans l'espace réel. L'utilisation de telles données est aussi analysée dans le contexte de la naissance du mouvement du « Big Data » qui pose des questions sur les paradigmes actuels et futurs de la recherche en géographie économique. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

This thesis contains three essays in the field of real-time econometrics, and more particularly<p>forecasting.<p>The issue of using data as available in real-time to forecasters, policymakers or financial<p>markets is an important one which has only recently been taken on board in the empirical<p>literature. Data available and used in real-time are preliminary and differ from ex-post<p>revised data, and given that data revisions may be quite substantial, the use of latest<p>available instead of real-time can substantially affect empirical findings (see, among others,<p>Croushore’s (2011) survey). Furthermore, as variables are released on different dates<p>and with varying degrees of publication lags, in order not to disregard timely information,<p>datasets are characterized by the so-called “ragged-edge”structure problem. Hence, special<p>econometric frameworks, such as developed by Giannone, Reichlin and Small (2008) must<p>be used.<p>The first Chapter, “The impact of macroeconomic news on bond yields: (in)stabilities over<p>time and relative importance”, studies the reaction of U.S. Treasury bond yields to real-time<p>market-based news in the daily flow of macroeconomic releases which provide most of the<p>relevant information on their fundamentals, i.e. the state of the economy and inflation. We<p>find that yields react systematically to a set of news consisting of the soft data, which have<p>very short publication lags, and the most timely hard data, with the employment report<p>being the most important release. However, sub-samples evidence reveals that parameter<p>instability in terms of absolute and relative size of yields response to news, as well as<p>significance, is present. Especially, the often cited dominance to markets of the employment<p>report has been evolving over time, as the size of the yields reaction to it was steadily<p>increasing. Moreover, over the recent crisis period there has been an overall switch in the<p>relative importance of soft and hard data compared to the pre-crisis period, with the latter<p>becoming more important even if less timely, and the scope of hard data to which markets<p>react has increased and is more balanced as less concentrated on the employment report.<p>Markets have become more reactive to news over the recent crisis period, particularly to<p>hard data. This is a consequence of the fact that in periods of high uncertainty (bad state),<p>markets starve for information and attach a higher value to the marginal information content<p>of these news releases.<p>The second and third Chapters focus on the real-time ability of models to now-and-forecast<p>in a data-rich environment. It uses an econometric framework, that can deal with large<p>panels that have a “ragged-edge”structure, and to evaluate the models in real-time, we<p>constructed a database of vintages for US variables reproducing the exact information that<p>was available to a real-time forecaster.<p>The second Chapter, “Real-time nowcasting of GDP: a factor model versus professional<p>forecasters”, performs a fully real-time nowcasting (forecasting) exercise of US real GDP<p>growth using Giannone, Reichlin and Smalls (2008), henceforth (GRS), dynamic factor<p>model (DFM) framework which enables to handle large unbalanced datasets as available<p>in real-time. We track the daily evolution throughout the current and next quarter of the<p>model nowcasting performance. Similarly to GRS’s pseudo real-time results, we find that<p>the precision of the nowcasts increases with information releases. Moreover, the Survey of<p>Professional Forecasters does not carry additional information with respect to the model,<p>suggesting that the often cited superiority of the former, attributable to judgment, is weak<p>over our sample. As one moves forward along the real-time data flow, the continuous<p>updating of the model provides a more precise estimate of current quarter GDP growth and<p>the Survey of Professional Forecasters becomes stale. These results are robust to the recent<p>recession period.<p>The last Chapter, “Real-time forecasting in a data-rich environment”, evaluates the ability<p>of different models, to forecast key real and nominal U.S. monthly macroeconomic variables<p>in a data-rich environment and from the perspective of a real-time forecaster. Among<p>the approaches used to forecast in a data-rich environment, we use pooling of bi-variate<p>forecasts which is an indirect way to exploit large cross-section and the directly pooling of<p>information using a high-dimensional model (DFM and Bayesian VAR). Furthermore forecasts<p>combination schemes are used, to overcome the choice of model specification faced by<p>the practitioner (e.g. which criteria to use to select the parametrization of the model), as<p>we seek for evidence regarding the performance of a model that is robust across specifications/<p>combination schemes. Our findings show that predictability of the real variables is<p>confined over the recent recession/crisis period. This in line with the findings of D’Agostino<p>and Giannone (2012) over an earlier period, that gains in relative performance of models<p>using large datasets over univariate models are driven by downturn periods which are characterized<p>by higher comovements. These results are robust to the combination schemes<p>or models used. A point worth mentioning is that for nowcasting GDP exploiting crosssectional<p>information along the real-time data flow also helps over the end of the great moderation period. Since this is a quarterly aggregate proxying the state of the economy,<p>monthly variables carry information content for GDP. But similarly to the findings for the<p>monthly variables, predictability, as measured by the gains relative to the naive random<p>walk model, is higher during crisis/recession period than during tranquil times. Regarding<p>inflation, results are stable across time, but predictability is mainly found at nowcasting<p>and forecasting one-month ahead, with the BVAR standing out at nowcasting. The results<p>show that the forecasting gains at these short horizons stem mainly from exploiting timely<p>information. The results also show that direct pooling of information using a high dimensional<p>model (DFM or BVAR) which takes into account the cross-correlation between the<p>variables and efficiently deals with the “ragged-edge”structure of the dataset, yields more<p>accurate forecasts than the indirect pooling of bi-variate forecasts/models. / 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.<p><p>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.<p><p>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.<p><p>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.<p><p>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.<p><p>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.<p><p>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.<p><p>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). <p><p>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.<p><p>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.<p><p>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.<p><p>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.<p><p>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.<p><p>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

En France, les prix des EHPAD sont élevés au regard d’une qualité qui semble insuffisante. Des projets de réformes sont en discussion, mais les pouvoirs publics sont face à un dilemme : toute recherche de réduction des coûts risque de dégrader la qualité plus qu’elle ne l’est déjà et toute amélioration de la qualité serait probablement inflationniste.L’objectif de cette thèse est d’étudier si ce dilemme peut être résolu, en analysant en particulier le rôle de la concurrence, réelle et par comparaison, qui pourrait être introduite dans ce secteur. Par une approche micro-économétrique, nous organisons notre analyse autour de trois questions de recherche : (i) que peut-on attendre des réformes proposées de la tarification et de l’augmentation de la capacité des établissements ? (ii) comment le développement du secteur privé lucratif pourrait permettre de réduire les prix et d’améliorer la qualité ? (iii) existe-t-il des contraintes environnementales responsables de la faible qualité des EHPAD? Nous observons, d’une part, que les projets de réforme permettraient de réduire l’inefficacité et donc éventuellement les prix des EHPAD, mais au détriment de leur qualité. D’autre part, nous constatons que l’essor des EHPAD lucratifs s’accompagne d’une augmentation des tarifs et d’une dégradation de la qualité de la prise en charge, qui pourraient être modérées par une structure de marché plus concurrentielle. Enfin, les EHPAD sont confrontés à des difficultés locales de fidélisation du personnel soignant qui affectent leur qualité et qui ne semblent pas pouvoir être résolues par un ajustement des salaires. Ces résultats peuvent servir de repères à la mise en place d’une politique publique adaptée. / High prices and insufficient quality of care are observed in nursing homes in France. Reforms are currently under discussion, but governments are facing a dilemma : any measure of price cut is likely to affect quality and any improvement in quality would probably be inflationary. This work analyzes if this dilemma can be solved by focusing more particularly on the potential effect of real and yardstick competition that could be introduced in this long term care sector. Using a micro-econometric framework, we organize this analysis into three research issues : (i) What might be expected from the pricing reform and the increase in the number of beds in nursing homes currently proposed ? (ii) How the development of for-profit nursing homes could reduce prices and improve quality ? (iii) Are there local difficulties responsible for the poor quality of nursing homes ? Several implications for public policy may be involved. Using cost frontier estimates, we demonstrate that the reform plans would reduce inefficiency and nursing home prices, but at the expense of their quality. The rise of for-profit nursing homes leads to high prices and a deterioration of the quality of care which could be tempered however by a more competitive market structure. Nursing homes face local difficulties in nursing staff retention, affecting their quality and which do not seem to be solved by adjusting wages.

La prévision macroéconomique à court terme est un exercice aussi complexe qu’essentiel pour la définition de la politique économique et monétaire. Les crises financières récentes ainsi que les récessions qu’ont endurées et qu’endurent aujourd’hui encore, en ce début d’année 2014, nombre de pays parmi les plus riches, témoignent de la difficulté d’anticiper les fluctuations économiques, même à des horizons proches. Les recherches effectuées dans le cadre de la thèse de doctorat qui est présentée dans ce manuscrit se sont attachées à étudier, analyser et développer des modélisations pour la prévision de croissance économique. L’ensemble d’informations à partir duquel construire une méthodologie prédictive est vaste mais également hétérogène. Celle-ci doit en effet concilier le mélange des fréquences d’échantillonnage des données et la parcimonie nécessaire à son estimation. Nous évoquons à cet effet dans un premier chapitre les éléments économétriques fondamentaux de la modélisation multi-fréquentielle. Le deuxième chapitre illustre l’apport prédictif macroéconomique que constitue l’utilisation de la volatilité des variables financières en période de retournement conjoncturel. Le troisième chapitre s’étend ensuite sur l’inférence bayésienne et nous présentons par ce biais un travail empirique issu de l’adjonction d’une volatilité stochastique à notre modèle. Enfin, le quatrième chapitre propose une étude des techniques de sélection de variables à fréquence multiple dans l’optique d’améliorer la capacité prédictive de nos modélisations. Diverses méthodologies sont à cet égard développées, leurs aptitudes empiriques sont comparées, et certains faits stylisés sont esquissés. / Economic downturn and recession that many countries experienced in the wake of the global financial crisis demonstrate how important but difficult it is to forecast macroeconomic fluctuations, especially within a short time horizon. The doctoral dissertation studies, analyses and develops models for economic growth forecasting. The set of information coming from economic activity is vast and disparate. In fact, time series coming from real and financial economy do not have the same characteristics, both in terms of sampling frequency and predictive power. Therefore short-term forecasting models should both allow the use of mixed-frequency data and parsimony. The first chapter is dedicated to time series econometrics within a mixed-frequency framework. The second chapter contains two empirical works that sheds light on macro-financial linkages by assessing the leading role of the daily financial volatility in macroeconomic prediction during the Great Recession. The third chapter extends mixed-frequency model into a Bayesian framework and presents an empirical study using a stochastic volatility augmented mixed data sampling model. The fourth chapter focuses on variable selection techniques in mixed-frequency models for short-term forecasting. We address the selection issue by developing mixed-frequency-based dimension reduction techniques in a cross-validation procedure that allows automatic in-sample selection based on recent forecasting performances. Our model succeeds in constructing an objective variable selection with broad applicability.

La dynamique d'ajustement structurel dans le secteur laitier en France est l'une des plus fortes de tous les secteurs agricoles avec des rythmes particulièrement élevés de disparition des exploitations et de croissance de la taille moyenne par exploitation. Cette dynamique est hétérogène dans l'espace, les régions les plus touchées sont celles où la densité laitière est faible à l'origine, celles qui résistent sont celles où la densité est élevée et où un tissu industriel est bien développé. Ces mouvements ont eu lieu malgré une politique agricole qui a cherché, au travers de multiples instruments (quota laitier, soutien des prix, aides directes…), à limiter ces mouvements et à maintenir la production laitière sur une grande partie du territoire français. Les modifications à venir de ces instruments risquent de modifier le paysage laitier jusqu'ici connu, et ainsi d'affecter la localisation et la structure des exploitations laitières. Dans ce contexte, l'objectif principal de cette thèse est d'analyser les déterminants de la croissance et de la localisation des exploitations laitières, d'identifier quels sont ceux qui renforcent la croissance et l'agglomération des exploitations et ceux qui ont tendance à limiter cette croissance et à disperser les exploitations et la production. Pour ce faire, nous avons dans un premier temps, estimé, en utilisant la méthode de chaînes de Markov, l'impact de certains facteurs économiques et politiques, sur les changements de taille des exploitations laitières. Dans un deuxième temps, à l'aide des méthodes d'économétrie spatiale, nous avons introduit une dimension spatiale à cette analyse afin d'appréhender les différences régionales (départementales) et de détecter d'éventuels effets d'agglomération. Dans un troisième temps, nous avons intégré de manière originale un modèle dynamique spatial récursif au modèle de Markov non-stationnaire afin de mesurer la distribution de la taille des exploitations selon la localisation en prenant en compte les interactions entre localisations. Ces différentes méthodes ont permis de montrer que les externalités positives liées à l'agglomération des exploitations laitières sont des facteurs prépondérants dans la détermination non seulement de la localisation mais aussi de la taille des exploitations laitières. Les externalités pécuniaires et les relations marchandes d'amont et d'aval ainsi que les prix des inputs et des outputs sont tout aussi importants dans la détermination de ces dynamiques. Les politiques agricoles, ici considérées au travers des aides directes du premier et second pilier, ont un impact assez faible dans l'ajustement structurel des exploitations laitières, seules les dotations à l'installation des jeunes s'avèrent très significatives et positivement liées à la localisation et la croissance des exploitations laitières. Les réglementations environnementales ont un effet plutôt dispersif des exploitations laitières et ceci particulièrement pour les grandes. Les activités concurrentes de l'activité laitière ont également un effet négatif sur la localisation des exploitations laitières mais cet effet s'estompe avec l'augmentation de la taille des exploitations. / Structural change in French dairy sector is one of the most important in agriculture with high rates of decreasing in the number of farms and increasing average farm size. This structural change is heterogeneous in space; the regions the most affected are those which are not traditional dairy producing. The regions which resist are the traditional dairy ones where dairying is highly developed. Agricultural policy instruments (dairy quota, price support, direct payments…) have affected those changes while trying to maintain the dairy production on a large part of France. The modifications to come of those instruments could modify the dairy farm location and structure. The aim of this thesis is to analyze the determinants of dairy farm growth and location, to identify which are those they foster growth and agglomeration of dairy farms and those they tend to slow down this growth and disperse dairy farms. To do so, we firstly estimate, using the non stationary Markov model, the impact of some economic and policy factors on the size farm distribution. Secondly, by means of the methods of spatial econometrics, we introduce a spatial dimension in this analysis to deal with regional differences and detect a possible effect of agglomeration externalities. Thirdly, we integrate a spatial dynamic recursive component to the non stationary Markov model. This allows us to model the effects of factors influencing the number, the size and the location of the dairy farms and to take account of interaction between locations. Those different methods allow us to show that agglomeration externalities are very important in the determination of the farm location as well as the growth of farm size. Pecuniary externalities and forward and backward linkages as well as the market prices are also determinant factors affecting farm structure and location. Agricultural policies, namely second pillar direct payments have a rather low impact in the structural adjustment of dairy farms. However subsidies to installation of young farmers are highly significant and positively related to farm growth and location. Environmental stringency seems to negatively affect dairy farm location and especially medium and large sized ones. Other livestock activities seem to compete with dairy farms especially smaller ones.

Au cours des dix dernières années, les prix immobiliers ont augmenté de façon considérable. Selon certains observateurs, cette hausse peut au moins en partie être attribuée à une insuffisance de l’offre, elle-même due à des politiques foncières restrictives mises en place par certaines communes. Cette thèse cherche à comprendre ce qui motive une commune à instaurer de telles politiques de maîtrise de la croissance. Nous nous plaçons dans le cadre de la Nouvelle Economie Politique et modélisons cette décision comme le résultat d’un jeu de pouvoir entre différents intérêts liés au foncier, arbitré par des élus locaux opportunistes. Ce jeu de pouvoir oppose principalement les propriétaires immobiliers et fonciers, qui peuvent trouver des alliés parmi les entreprises locales, qui s’associent au sein de growth ou ideas machines. Dans un premier modèle nous décrivons ce jeu d’influence sous différentes hypothèses concernant l’influence du vote et du lobbying. Nous montrons que la rigueur de la politique implémentée peut sous certaines conditions être liée de façon négative à la part des propriétaires dans la population locale – et ceci malgré le fait que ces derniers sont clairement partisans d’une politique stricte. Par ailleurs, la mobilité des individus rend les décisions des communes interdépendantes et nous parvenons à une solution d’équilibre qui s’apparente directement au modèle spatial autorégressif utilisé en économétrie spatiale. Une étude empirique portant sur la taxe locale d’équipement confirme l’hypothèse d’interactions stratégiques et montre que les choix des élus sont avant tout influencés par les habitants-propriétaires et les ideas machines d’un côté et par les agriculteurs de l’autre. Dans une deuxième analyse, nous étudions si les élus locaux se servent des mesures de maîtrise de la croissance d’une manière stratégique afin de faciliter leur réélection. Nous développons un modèle de vote probabiliste dans lequel la composition de la population est endogène à la politique foncière. Une étude empirique confirme que les choix de zonage des élus locaux sont influencés par la mobilité de l’électorat, même si leur comportement ne peut pas être qualifié de stratégique. / Over the last ten years, real estate prices have risen considerably and accordingly to most observers, this can at least partly be attributed to an insufficient supply, due to local growth control measures. This thesis tries to understand what motivates local authorities implementing such policies. Local politicians are considered as opportunistic and their decision is modelled as the result of a political struggle between different land-related interests. This game for influence mainly opposes the owners of developed and undeveloped land, who find allies amongst local business interests that might form growth or ideas machines. Our first model describes this struggle under different hypotheses concerning the influence of voting and lobbying. We show that the growth controls implemented might under some conditions be less strict the greater the percentage of homeowners in the local population – despite the fact that homeowners favour strict policies. When individuals are mobile, local decisions become interdependent, and under imperfect mobility our theoretically derived equilibrium solution can directly be interpreted as a spatial autoregressive model. Our empirical analysis concerning the “taxe locale d’équipement” confirms our predictions concerning strategic interactions and shows that local decisions are influenced by “homevoters” and ideas machines on the one side, and by the local farmers on the other side. Our second model examines if local politicians use growth control policies strategically in order to modify the local electorate in a manner that facilitates their re-election. Our model is based on probabilistic voting and the composition of the local population is considered as endogenous. Our empirical analysis confirms that zoning decisions are influenced by the mobility of the local electorate, even if we cannot ascertain that politicians are acting strategically.

Dans le présent travail de recherche, nous proposons d’analyser principalement l’effet direct et indirect du changement climatique sur la production agricole et la croissance économique. Pour ce faire, la voie méthodologique retenue dans cette thèse est une voie empirique, s’appuyant à la fois sur l’économétrie spatiale, la technique de cointégration sur données de panel ainsi que le modèle d’équilibre général dynamique multisectoriel / The aim of this research is to analysis both direct and indirect effect of the climate change on the agricultural production and the economic growth. This PhD research we will be based on an empirical methodology, through applying a spacial econometrics and the cointegration technique of a panel data as well as a multisectoral general equilibrium growth model. The first part is devoted to find the effect of the climate change on the agricultural production in a macro spatial level during the period 1980-2012. The main gaol of this first chapter of this PhD is to determine the direct and indirect effect of the weather forecast and the temperature changes in one region compared to the neighbouring regions. The originality of this spacial modelisation is to give an effective spacial effect. The second part of this research is aimed to use a micro spacial analysis to find out the effect of the climate change on the agricultural production in the long term way and for each region rather then all of them together as in the first chapter. In the last part of this PhD research we developed a general and dynamic equilibrium model for the Tunisian economy taking in a count eventual climate change forcast from 2015 to 2030. Aiming to calculate the effect of this change on the agricultural production and the economic aggregate in Tunisa. The resulats show that the climate change forecast in the futur will affect the agricultural production in Tunisia in both the short run and the long term and this is because of the irrigated and non irrigated plantations. The decrease of the production will affect the food industry which will decrease the level of the investment, the employment in this sector as well as the consumption.

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

Le financement des soins de santé de qualité constitue un défi majeur pour les pays en développement. Malgré les efforts consentis pour améliorer l’offre des services de santé, une frange importante de la population n’a toujours pas accès aux soins de santé. La faible croissance économique, le manque des ressources, la corruption et les contraintes imposées au secteur public peuvent expliquer pourquoi la conception d’un système de financement des soins de santé est complexe. Au cours des deux dernières décennies, il y a eu une baisse de l'utilisation des services de santé après l'introduction du recouvrement des coûts dans les établissements de santé publics. Les personnes les plus touchées par cette politique sont les ménages à faibles revenus notamment dans les zones rurales qui sont le plus souvent vulnérables aux maladies. L'assurance communautaire a été proposée comme une alternative pour améliorer une meilleure accessibilité des ménages à faibles revenus aux soins de santé. L'assurance communautaire apparaît ainsi comme un outil de protection sociale pour un grand nombre de personnes qui, autrement, n'auraient pas une couverture face au risque maladie. Toutefois, un tel système d’assurance maladie ne peut avoir des effets à long terme que s’il existe une forte préférence des ménages pour une telle politique, et un capital social dans les zones rurales. Evaluer les préférences des ménages pour l'assurance communautaire est importante pour la formulation des recommandations de politique économique. Une connaissance adéquate des déterminants de la demande pour l'assurance communautaire est aussi essentielle pour l'élaboration de stratégies visant à accroître l’allocation des ressources, et à améliorer la qualité des services. La présente étude a pour objet d’évaluer les préférences des ménages pour l’assurance communautaire en milieu rural camerounais. L’usage de la méthode d’évaluation contingente suggère que les ménages à faibles revenus sont disposés à payer pour l’assurance communautaire. En outre, le capital social a un effet positif et significatif sur la demande. L’usage des doubles questions binaires pour évaluer des préférences des ménages est incompatible avec les incitations et sujets à un shift effect hétérogène expliqué par les caractéristiques intrinsèques des ménages. Les ménages très certains de leurs réponses ne sont pas sujets aux anomalies comportementales. Enfin, les préférences des ménages sont inter-indépendantes du fait des interactions spatiales expliquées par les normes sociales / The financing of quality healthcare is a major challenge for developing countries. Despite efforts to improve the provision of healthcare services, a significant proportion of the population does not always have access to healthcare services. Low economic growth, lack of economic resources, corruption and constraints on the public sector could explain why the design of a system of financing healthcare is complex. Over the past two decades, there has been a decline in the use of healthcare services after the introduction of cost recovery in public health facilities. Those most affected by this policy are low-income households particularly in rural areas that are most often vulnerable to diseases. The community-based health insurance has been proposed as an alternative to improve better access to low-income households to healthcare services. The community-based health insurance is thus a tool of social protection for many households who otherwise would not have formal insurance. However, such a health insurance scheme can have long-term effects if households have a strong preference for it, and there is social capital in rural areas. Assessing the preferences of households for the community-based health insurance is important for the formulation of policy recommendations. Adequate knowledge on the determinants of demand for the community-based health insurance is essential for developing strategies to increase resource allocation, and improve the quality of services. This study aims at assessing the preferences of households for community-based health insurance in rural areas of Cameroon. The use of contingent valuation method suggests that low-income households are willing to pay for the community-based health insurance. Furthermore, social capital has a positive and significant effect on the demand, and the use of double-bounded dichotomous choice to assess the preferences of households is incentive incompatible. We also found that there is heterogeneous shift effect in preferences anomalies and could be mostly explained by the salient characteristics of households. A striking result is that more certain households are not subjected to preference anomalies. Lastly, there is spatial dependence in the preferences of households explained by social norms