Negli ultimi anni, la gestione illegale dei rifiuti ha attirato l’attenzione pubblica e dell’accademia. A causa delle sue conseguenze negative non solo per l’ambiente, ma anche per la salute pubblica e la crescita economica, gli esperti hanno cominciato ad esplorare le dinamiche del fenomeno e le possibilità di prevenzione. Alcuni studi hanno evidenziato l’esistenza di diversi fattori che possono determinare la gestione illegale dei rifiuti, ma pochi di essi hanno empiricamente testato la validità dei fattori stessi. Di conseguenza, si avverte la necessità di produrre nuova conoscenza sull’argomento. Il presente studio consiste in un’analisi esplorativa di fattori socio-economici, fattori di policy e di performance, e fattori criminali che influenzano la gestione illegale dei rifiuti in Italia. Dopo aver identificato le determinanti considerate rilevanti dalla letteratura, l’obiettivo è quello di testarle empiricamente. Per prima cosa, grazie all’unicità di un dataset creato sul contesto italiano, nello studio si indaga quantitativamente l’effetto di diversi fattori sul fenomeno attraverso un’analisi econometrica. Successivamente, lo studio prosegue con un’analisi “crime script” al fine di esplorare quali fattori suggeriti dalla letteratura e testati nella parte quantitativa emergono anche da casi studio e come effettivamente intervengono nel ciclo dei rifiuti italiano. I risultati indicano che la gestione illegale dei rifiuti è determinata da: i) uno scarso sviluppo economico e demografico, un alto livello d’istruzione nel territorio, la presenza di turisti; ii) l'inefficienza della normativa ambientale, delle forze dell’ordine e delle prestazioni sui rifiuti; iii) la presenza di criminalità organizzata e la diffusione di crimini economici e fiscali. Prendendo spunto da questi risultati, lo studio non solo aumenta la conoscenza sul fenomeno, ma è anche in grado di avanzare alcuni suggerimenti di policy per contrastare efficacemente le condotte illegali legate alla gestione dei rifiuti. / In the last several decades, illegal waste management (IWM) has attracted great academic and public attention. Due to its negative consequences not only for the environment, but also for public health and economic growth, scholars started to be interested in the dynamics of IWM and in how to prevent it. Some studies stressed the existence of different factors that can determine the phenomenon, but very few of them have empirically tested their validity. Consequently, developing new research on the topic is still necessary. The present study conducts an explorative analysis of the socio-economic, policy and performance-driven and criminal factors influencing IWM in Italy. After the identification of the most relevant determinants according to the literature, the objective is to empirically test them. First, thanks to a unique dataset focused on the Italian context, the study quantitatively investigates the effect of different factors on the phenomenon through an econometric analysis. Second, the study realises a crime script analysis to explore which factors suggested by the literature and tested in the quantitative part emerge also in concrete case studies and how they effectively intervene in the Italian waste cycle. Results indicate that IWM is determined by: i) a low level of economic development and population density, a high level of education and tourists’ presence; ii) inefficiency in environmental regulation, enforcement and waste performances; iii) the presence of organised crime and the diffusion of economic and fiscal crimes. According to these findings, the study not only deepens the knowledge of the phenomenon, but it is also able to provide some policy suggestions to efficiently hinder illegal conducts related to waste management.

The research performed throughout this dissertation aims at implementing quantitative methods in order to assess economic and technological performances of firms, i.e. it tries to assess the impacts of the determinants of technological activity on the results of this activity. For this purpose, a representative sample of the most important R&D firms in the world is constituted. The micro-economic nature of the analysis, as well as its international dimension are two main features of this research at the empirical level.<p><p>The second chapter illustrates the importance of R&D investments, patenting activities and other measures of technological activities performed by firms over the last 10 years.<p><p>The third chapter describes the main features as well as the construction of the database. The raw data sample consists of comparable detailed micro-level data on 2676 large manufacturing firms from several countries. These firms have reported important R&D expenditures over the period 1980-1994.<p><p>The fourth chapter explores the dynamic structure of the patent-R&D relationship by considering the number of patent applications as a function of present and lagged levels of R&D expenditures. R&D spillovers as well as technological and geographical opportunities are taken into account as additional determinants in order to explain patenting behaviours. The estimates are based on recently developed econometric techniques that deal with the discrete non-negative nature of the dependent patent variable as well as the simultaneity that can arise between the R&D decisions and patenting. The results show evidence of a rather contemporaneous impact of R&D activities on patenting. As far as R&D spillovers are concerned, these externalities have a significantly higher impact on patenting than own R&D. Furthermore, these effects appear to take more time, three years on average, to show up in patents.<p><p>The fifth chapter explores the contribution of own stock of R&D capital to productivity performance of firms. To this end the usual productivity residual methodology is implemented. The empirical section presents a first set of results which replicate the analysis of previous studies and tries to assess the robustness of the findings with regard to the above issues. Then, further results, based on different sub samples of the data set, investigate to what extent the R&D contribution on productivity differs across firms of different industries and geographic areas or between small and large firms and low and high-tech firms. The last section explores more carefully the simultaneity issue. On the whole, the estimates indicate that R&D has a positive impact on productivity performances. Yet, this contribution is far from being homogeneous across the different dimensions of data or according to the various assumptions retained in the productivity model.<p><p>The last empirical chapter goes deeper into the analysis of firms' productivity increases, by considering besides own R&D activities the impact of technological spillovers. The chapter begins by surveying the alternative ways proposed in the literature in order to asses the effect of R&D spillovers on productivity. The main findings reported by some studies at the micro level are then outlined. Then, the framework to formalize technological externalities and other technological determinants is exposed. This framework is based on a positioning of firms into a technological space using their patent distribution across technological fields. The question of whether the externalities generated by the technological and geographic neighbours are different on the recipient's productivity is also addressed by splitting the spillover variable into a local and national component. Then, alternative measures of technological proximity are examined. Some interesting observations emerge from the empirical results. First, the impact of spillovers on productivity increases is positive and much more important than the contribution of own R&D. Second, spillover effects are not the same according to whether they emanate from firms specialized in similar technological fields or firms more distant in the technological space. Finally, the magnitude and direction of these effects are radically different within and between the pillars of the Triad. While European firms do not appear to particularly benefit from both national and international sources of spillovers, US firms are mainly receptive to their national stock and Japanese firms take advantage from the international stock.<p> / Doctorat en sciences économiques, Orientation économie / info:eu-repo/semantics/nonPublished

Ce mémoire de thèse porte sur la statistique inférentielle des données spatiales et/ou fonctionnelles. En effet, nous nous sommes intéressés à l’estimation de paramètres inconnus de certains modèles à partir d’échantillons obtenus par un processus d’échantillonnage aléatoire ou non (stratifié), composés de variables indépendantes ou spatialement dépendantes.La spécificité des méthodes proposées réside dans le fait qu’elles tiennent compte de la nature de l’échantillon étudié (échantillon stratifié ou composé de données spatiales dépendantes).Tout d’abord, nous étudions des données à valeurs dans un espace de dimension infinie ou dites ”données fonctionnelles”. Dans un premier temps, nous étudions les modèles de choix binaires fonctionnels dans un contexte d’échantillonnage par stratification endogène (échantillonnage Cas-Témoin ou échantillonnage basé sur le choix). La spécificité de cette étude réside sur le fait que la méthode proposée prend en considération le schéma d’échantillonnage. Nous décrivons une fonction de vraisemblance conditionnelle sous l’échantillonnage considérée et une stratégie de réduction de dimension afin d’introduire une estimation du modèle par vraisemblance conditionnelle. Nous étudions les propriétés asymptotiques des estimateurs proposées ainsi que leurs applications à des données simulées et réelles. Nous nous sommes ensuite intéressés à un modèle linéaire fonctionnel spatial auto-régressif. La particularité du modèle réside dans la nature fonctionnelle de la variable explicative et la structure de la dépendance spatiale des variables de l’échantillon considéré. La procédure d’estimation que nous proposons consiste à réduire la dimension infinie de la variable explicative fonctionnelle et à maximiser une quasi-vraisemblance associée au modèle. Nous établissons la consistance, la normalité asymptotique et les performances numériques des estimateurs proposés.Dans la deuxième partie du mémoire, nous abordons des problèmes de régression et prédiction de variables dépendantes à valeurs réelles. Nous commençons par généraliser la méthode de k-plus proches voisins (k-nearest neighbors; k-NN) afin de prédire un processus spatial en des sites non-observés, en présence de co-variables spatiaux. La spécificité du prédicteur proposé est qu’il tient compte d’une hétérogénéité au niveau de la co-variable utilisée. Nous établissons la convergence presque complète avec vitesse du prédicteur et donnons des résultats numériques à l’aide de données simulées et environnementales.Nous généralisons ensuite le modèle probit partiellement linéaire pour données indépendantes à des données spatiales. Nous utilisons un processus spatial linéaire pour modéliser les perturbations du processus considéré, permettant ainsi plus de flexibilité et d’englober plusieurs types de dépendances spatiales. Nous proposons une approche d’estimation semi paramétrique basée sur une vraisemblance pondérée et la méthode des moments généralisées et en étudions les propriétés asymptotiques et performances numériques. Une étude sur la détection des facteurs de risque de cancer VADS (voies aéro-digestives supérieures)dans la région Nord de France à l’aide de modèles spatiaux à choix binaire termine notre contribution. / This thesis is about statistical inference for spatial and/or functional data. Indeed, weare interested in estimation of unknown parameters of some models from random or nonrandom(stratified) samples composed of independent or spatially dependent variables.The specificity of the proposed methods lies in the fact that they take into considerationthe considered sample nature (stratified or spatial sample).We begin by studying data valued in a space of infinite dimension or so-called ”functionaldata”. First, we study a functional binary choice model explored in a case-controlor choice-based sample design context. The specificity of this study is that the proposedmethod takes into account the sampling scheme. We describe a conditional likelihoodfunction under the sampling distribution and a reduction of dimension strategy to definea feasible conditional maximum likelihood estimator of the model. Asymptotic propertiesof the proposed estimates as well as their application to simulated and real data are given.Secondly, we explore a functional linear autoregressive spatial model whose particularityis on the functional nature of the explanatory variable and the structure of the spatialdependence. The estimation procedure consists of reducing the infinite dimension of thefunctional variable and maximizing a quasi-likelihood function. We establish the consistencyand asymptotic normality of the estimator. The usefulness of the methodology isillustrated via simulations and an application to some real data.In the second part of the thesis, we address some estimation and prediction problemsof real random spatial variables. We start by generalizing the k-nearest neighbors method,namely k-NN, to predict a spatial process at non-observed locations using some covariates.The specificity of the proposed k-NN predictor lies in the fact that it is flexible and allowsa number of heterogeneity in the covariate. We establish the almost complete convergencewith rates of the spatial predictor whose performance is ensured by an application oversimulated and environmental data. In addition, we generalize the partially linear probitmodel of independent data to the spatial case. We use a linear process for disturbancesallowing various spatial dependencies and propose a semiparametric estimation approachbased on weighted likelihood and generalized method of moments methods. We establishthe consistency and asymptotic distribution of the proposed estimators and investigate thefinite sample performance of the estimators on simulated data. We end by an applicationof spatial binary choice models to identify UADT (Upper aerodigestive tract) cancer riskfactors in the north region of France which displays the highest rates of such cancerincidence and mortality of the country.