Health Expenditure and Income in the United States

This paper investigates the long-run economic relationship between health care expenditure and income in the US at a State level. Using a panel of 49 US States followed over the period 1980-2004, we study the non-stationarity and cointegration between health spending and income, ultimately measuring income elasticity of health care. The tests we adopt allow us to explicitly control for cross-section dependence and unobserved heterogeneity. Specifically, in our regression equations we assume that the error is the sum of a multifactor structure and a spatial autoregressive process, which capture global shocks and local spill overs in health expenditure. Our results suggest that health care is a necessity rather than a luxury, with an elasticity much smaller than that estimated in other US studies. Further, we observe a significant spatial spill over, though with a smaller intensity than that detected in other studies on spatial concentration of US health spending. Our broad perspective of cross section dependence as well as the methods used to capture it give new insights on the debate over the relationship between health spending and income.Health expenditure; income elasticity; cross section dependence; panels

GMM estimation of spatial panels

We consider Generalized Method of Moments (GMM) estimation of a regression model with spatially correlated errors. We propose some new moment conditions, and derive the asymptotic distribution of the GMM based on them. The analysis is supported by a small Monte Carlo exercise.Generalized Method of Moments, spatial econometrics

Large Panels with Common Factors and Spatial Correlations

This paper considers the statistical analysis of large panel data sets where even after condi-tioning on common observed effects the cross section units might remain dependently distrib-uted. This could arise when the cross section units are subject to unobserved common effects and/or if there are spill over effects due to spatial or other forms of local dependencies. The paper provides an overview of the literature on cross section dependence, introduces the con-cepts of time-specific weak and strong cross section dependence and shows that the commonly used spatial models are examples of weak cross section dependence. It is then established that the Common Correlated Effects (CCE) estimator of panel data model with a multifactor error structure, recently advanced by Pesaran (2006), continues to provide consistent estimates of the slope coefficient, even in the presence of spatial error processes. Small sample properties of the CCE estimator under various patterns of cross section dependence, including spatial forms, are investigated by Monte Carlo experiments. Results show that the CCE approach works well in the presence of weak and/or strong cross sectionally correlated errors. We also explore the role of certain characteristics of spatial processes in determining the performance of CCE estimators, such as the form and intensity of spatial dependence, and the sparseness of the spatial weight matrix.panels, Common Correlated Effects, strong and weak cross section dependence

Medical Technology and the Production of Health Care

This paper investigates the factors that determine differences across OECD countries inhealth outcomes, using data on life expectancy at age 65, over the period 1960 to 2007. We estimate a production function where life expectancy depends on health and social spending, lifestyle variables, and medical innovation. Our first set of regressions includes a set of observed medical technologies by country. Our second set of regressions proxy technology using a spatial process. The paper also tests whether in the long-run countries tend to achieve similar levels of health outcomes. Our results show that health spending has a significant and mild effect on health out- comes, even after controlling for medical innovation. However, its short-run adjustments do not seem to have an impact on health care productivity. Spatial spill overs in life expectancy are significant and point to the existence of interdependence across countries in technology adoption. Furthermore, nations with initial low levels of life expectancy tend to catch up with those with longer-lived populations

Weak and strong cross section dependence and estimation of large panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double- indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects. JEL Classification: C10, C31, C33Panels, Strong and Weak Cross Section Dependence, Weak and Strong Factors

Social Interaction in Patients'�Hospital Choice: Evidences from Italy

In this paper we study the influence of social interaction on patients' hospital choice and its relationship with quality delivered by hospitals, using Italian data. We explore the impact on individual choices of a set of variables such as travel distance, individual- and hospital-specific characteristics, as well as a variable capturing the effect of the neighbourhood. The richness of our data allows us to disentangle contextual effects from the influence of information sharing on patients' hospital choices. We then use this framework to assess how such interaction is related to clinical hospital quality. Results show that network effect plays an important role in hospital choices, although it is less relevant for larger hospitals. Another empirical finding is the existence of a negative relationship between the degree of interaction among individuals and the quality delivered by hospitals. The absence of a source of information on the quality of hospitals accessible to all individuals, such as guidelines or star ratings, exacerbates the importance of information gathered locally in hospital choices, which may result in a lower degree of competition among hospitals and lower quality.health care, social interaction, quality

Weak and Strong Cross Section Dependence and Estimation of Large Panels

This paper introduces the concepts of time-specific weak and strong cross section dependence. A double-indexed process is said to be cross sectionally weakly dependent at a given point in time, t, if its weighted average along the cross section dimension (N) converges to its expectation in quadratic mean, as N is increased without bounds for all weights that satisfy certain ‘granularity’ conditions. Relationship with the notions of weak and strong common factors is investigated and an application to the estimation of panel data models with an infinite number of weak factors and a finite number of strong factors is also considered. The paper concludes with a set of Monte Carlo experiments where the small sample properties of estimators based on principal components and CCE estimators are investigated and compared under various assumptions on the nature of the unobserved common effects.panels, strong and weak cross section dependence, weak and strong factors

A computationally efficient correlated mixed probit model for credit risk inference

Mixed probit models are widely applied in many fields where prediction of a binary response is of interest. Typically, the random effects are assumed to be independent but this is seldom so for many real applications. In the credit risk application that is considered in the paper, random effects are present at the level of industrial sectors and they are expected to be correlated because of interfirm credit links inducing dependences in the firms’ risk to default. Unfortunately, existing inferential procedures for correlated mixed probit models are computationally very intensive already for a moderate number of effects. Borrowing from the literature on large network inference, we propose an efficient expectation–maximization algorithm for unconstrained and penalized likelihood estimation and derive the asymptotic standard errors of the estimates. An extensive simulation study shows that the approach proposed enjoys substantial computational gains relative to standard Monte Carlo approaches, while still providing accurate parameter estimates. Using data on nearly 64000 accounts for small and medium-sized enterprises in the UK in 2013 across 13 industrial sectors, we find that accounting for network effects via a correlated mixed probit model increases significantly the default prediction power of the model compared with conventional default prediction models, making efficient inferential procedures for these models particularly useful in this field

Forecasting Loan Default in Europe with Machine Learning

We use a dataset of 12 million residential mortgages to investigate the loan default behavior in several European countries. We model the default occurrence as a function of borrower characteristics, loan-specific variables, and local economic conditions. We compare the performance of a set of machine learning algorithms relative to the logistic regression, finding that they perform significantly better in providing predictions. The most important variables in explaining loan default are the interest rate and the local economic characteristics. The existence of relevant geographical heterogeneity in the variable importance points at the need for regionally tailored risk-assessment policies in Europe