Essays in Applied Health Economics: Evidence on Health and Health Care in Italy and UK

This thesis is the result of my experience as a PhD student taking part in the Joint Doctoral Programme at the University of York and the University of Bologna. In my thesis I deal with topics that are of particular interest in Italy and in Great Britain. Chapter 2 focuses on the empirical test of the existence of the relationship between technological profiles and market structure claimed by Sutton’s theory (1991, 1998) in the specific economic framework of hospital care services provided by the Italian National Health Service (NHS). In order to test the empirical predictions by Sutton, we identify the relevant markets for hospital care services in Italy in terms of both product and geographic dimensions. In particular, the Elzinga and Hogarty (1978) approach has been applied to data on patients’ flows across Italian Provinces in order to derive the geographic dimension of each market. Our results provide evidence in favour of the empirical predictions of Sutton. Chapter 3 deals with the patient mobility in the Italian NHS. To analyse the determinants of patient mobility across Local Health Authorities, we estimate gravity equations in multiplicative form using a Poisson pseudo maximum likelihood method, as proposed by Santos-Silva and Tenreyro (2006). In particular, we focus on the scale effect played by the size of the pool of enrolees. In most of the cases our results are consistent with the predictions of the gravity model. Chapter 4 considers the effects of contractual and working conditions on selfassessed health and psychological well-being (derived from the General Health Questionnaire) using the British Household Panel Survey (BHPS). We consider two branches of the literature. One suggests that “atypical” contractual conditions have a significant impact on health while the other suggests that health is damaged by adverse working conditions. The main objective of our paper is to combine the two branches of the literature to assess the distinct effects of contractual and working conditions on health. The results suggest that both sets of conditions have some influence on health and psychological well-being of employees.

Modelling biogeochemical cycles in forest ecosystems: a Bayesian approach

Forest models are tools for explaining and predicting the dynamics of forest ecosystems. They simulate forest behavior by integrating information on the underlying processes in trees, soil and atmosphere. Bayesian calibration is the application of probability theory to parameter estimation. It is a method, applicable to all models, that quantifies output uncertainty and identifies key parameters and variables. This study aims at testing the Bayesian procedure for calibration to different types of forest models, to evaluate their performances and the uncertainties associated with them. In particular,we aimed at 1) applying a Bayesian framework to calibrate forest models and test their performances in different biomes and different environmental conditions, 2) identifying and solve structure-related issues in simple models, and 3) identifying the advantages of additional information made available when calibrating forest models with a Bayesian approach. We applied the Bayesian framework to calibrate the Prelued model on eight Italian eddy-covariance sites in Chapter 2. The ability of Prelued to reproduce the estimated Gross Primary Productivity (GPP) was tested over contrasting natural vegetation types that represented a wide range of climatic and environmental conditions. The issues related to Prelued's multiplicative structure were the main topic of Chapter 3: several different MCMC-based procedures were applied within a Bayesian framework to calibrate the model, and their performances were compared. A more complex model was applied in Chapter 4, focusing on the application of the physiology-based model HYDRALL to the forest ecosystem of Lavarone (IT) to evaluate the importance of additional information in the calibration procedure and their impact on model performances, model uncertainties, and parameter estimation. Overall, the Bayesian technique proved to be an excellent and versatile tool to successfully calibrate forest models of different structure and complexity, on different kind and number of variables and with a different number of parameters involved.