Abstract
In this paper we explore the geographical scope of hospital competition on quality, using Italian data on over 207,000 patients admitted to 174 hospitals located in the Lombardy region in the years 2008–2014. We propose an economic framework that incorporates both local and global forms of quality competition among hospitals, the latter emerging from periodically released hospital performance rankings. Under this framework, we derive the hospital reaction functions and, accordingly, we characterize the structure of interdependence among hospital qualities. We employ recent methods from the graphical modelling literature to estimate the set of local rivals for each hospital, as well as the degree of global interdependence among hospitals. Consistently with our micro-founded framework, our results show a significant positive degree of short- and long-range dependence, suggesting the existence of forms of local and global competition amongst hospitals with relevant implications for health care policy.
Highlights
In recent years, several central and local governments in Western countries have implemented pro-competition reforms in the health care sector with the view that, when prices are regulated, an increase in competition amongst providers would lead to improvements in the quality of health care, which would translate into better health outcomes
There is a large debate in the literature over the effect of hospital competition on the quality of health care
We employ for the first time graphical modelling techniques and spatial econometrics to estimate who the competitors are in hospital reaction functions, using data on 6 quality indicators in the Lombardy region in the years between 2008 and 2014
Summary
Several central and local governments in Western countries have implemented pro-competition reforms in the health care sector with the view that, when prices are regulated, an increase in competition amongst providers would lead to improvements in the quality of health care, which would translate into better health outcomes. One common feature of these studies is that they assume that hospitals compete with a pre-specified set of providers within their local market, or catchment area This is typically defined using geographical or travel time distance (see, among others, Propper et al, 2004; Gravelle et al, 2014; Longo et al, 2017). The adoption of graphical modelling techniques gives us the advantage on the one hand of estimating the rivals in the market without relying on some pre-specified assumption on the spatial weights, and on the other hand of allowing for heterogeneity in the local competition network amongst hospitals.
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