if this message does not display correctly, click here | Table of Contents Vincenzo Atella, University of Rome, Tor Vergata - Centre for International Studies on Economic Growth (CEIS), Department of Economics and Finance, University of Rome, Tor Vergata - Faculty of Economics
Federico Belotti, University of Rome, Tor Vergata - Faculty of Economics
Valentina Conti, University of Rome, Tor Vergata
Claudio Alberto Cricelli, Simg
Joanna Kopinska, University of Rome, Tor Vergata - Faculty of Economics, University of Rome, Tor Vergata - Centre for International Studies on Economic Growth (CEIS)
Andrea Piano Mortari, CEIS Tor Vergata Leonardo Becchetti, University of Rome, Tor Vergata - Faculty of Economics
Rocco Ciciretti, University of Rome II - Department of Economics and Finance
Ambrogio Dalo, University of Rome, Tor Vergata - Department of Economics and Finance Luisa Corrado, University of Rome, Tor Vergata - Faculty of Economics
Bernard Fingleton, University of Cambridge - Department of Land Economy | |
CEIS: CENTRE FOR ECONOMIC & INTERNATIONAL STUDIES
Vincenzo Atella - Director "Modeling Public Health Care Expenditure Using Patient Level Data: Empirical Evidence from Italy" 
CEIS Working Paper No. 367 VINCENZO ATELLA, University of Rome, Tor Vergata - Centre for International Studies on Economic Growth (CEIS), Department of Economics and Finance, University of Rome, Tor Vergata - Faculty of Economics
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FEDERICO BELOTTI, University of Rome, Tor Vergata - Faculty of Economics
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VALENTINA CONTI, University of Rome, Tor Vergata
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CLAUDIO ALBERTO CRICELLI, Simg
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JOANNA KOPINSKA, University of Rome, Tor Vergata - Faculty of Economics, University of Rome, Tor Vergata - Centre for International Studies on Economic Growth (CEIS)
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ANDREA PIANO MORTARI, CEIS Tor Vergata
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In this work we present some results obtained with a unique database of patient level data collected through GPs. The availability of such data opens new scenarios and paradigms for the planning and management of the health care system and for policy impact evaluation studies. The data set, representative of the Italian population, contains detailed information on prescribed drugs, laboratory tests, outpatient visits and hospitalizations of more than 2 millions patients, managed by 900 GPs overtime. This pool of registers has produced a stock of information on about 25 millions of medical diagnosis, 100 millions of laboratory and diagnostic tests, 10 millions of blood pressure measurements and 50 millions of drug prescriptions. Using this novel data set we analyze the expenditures of the Italian NHS over time, across age and geographical areas for the period from 2004 to 2011. "Fishing the Corporate Social Responsibility Risk Factors"
CEIS Working Paper No. 368 LEONARDO BECCHETTI, University of Rome, Tor Vergata - Faculty of Economics
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ROCCO CICIRETTI, University of Rome II - Department of Economics and Finance
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AMBROGIO DALO, University of Rome, Tor Vergata - Department of Economics and Finance
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Corporate Social Responsibility (CSR) is an increasingly relevant aspect in the current economic and financial scenario. A typical argument in the literature is that CSR reduces the risk of conflicts with stakeholders. In this paper we test whether: i) CSR risk factors are uncorrelated with those traditionally considered, ii) exist a pricing anomaly related to a social responsible behavior that could be captured by CSR risk factors, and iii) a multifactor model that includes a CSR risk factor performs better in explaining the cross section of expected returns. Our finding documents that CSR risk factors are uncorrelated with those traditionally used by asset pricing literature. We find that large stocks are more exposed than small stocks to this source of risk and that, even if the pricing anomaly is still significant, an asset pricing model augmented with a CSR risk factor performs better than standard models in explaining the cross section of stock returns. "The W Matrix in Network and Spatial Econometrics: Issues Relating to Specification and Estimation"
CEIS Working Paper No. 369 LUISA CORRADO, University of Rome, Tor Vergata - Faculty of Economics
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BERNARD FINGLETON, University of Cambridge - Department of Land Economy
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Network and spatial econometric models commonly embody a so-called W matrix which defines the connectivity between nodes of a network. The reason for the existence of W is that it facilitates parsimonious parametrization of inter-nodal interaction which would otherwise be very difficult to achieve from a practical modelling perspective. The problem considered in this paper is the effect of misspecifying W. The paper demonstrates the effect in the context of two types of model, the dynamic spatial autoregressive panel model and the multilevel spatial autoregressive panel model, both of which include W as part of the model specification and use W in estimation. Monte Carlo results are presented showing the impact on bias and RMSE of misspecification of W. The paper highlights the need for careful attention to the correct structure of W in spatial econometric and network modelling. | | ^top
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