The determinants of individual health care expenditures in the Italian region of Friuli Venezia Giulia: evidence from a hierarchical spatial model estimation

Luca Grassetti () and Laura Rizzi
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Luca Grassetti: University of Udine
Laura Rizzi: University of Udine

Empirical Economics, 2019, vol. 56, issue 3, No 9, 987-1009

Abstract: Abstract This work investigates the determinants of health care expenditures, such as drug prescriptions, inpatient care, and outpatient care, of the resident population of the Region of Friuli Venezia Giulia (Italy). The phenomenon of interest is examined here by considering a cross-sectional register-based dataset on individual expenditures exhibiting a cross-classified hierarchical structure. In fact, patients (about 1,000,000) are grouped by general practitioners and municipalities. Does the evidence in individual data analyses support the results of the micro- and macroeconomic literature? The adoption of disaggregated data allows us to disentangle the role of the micro- and macroeconomic determinants of the expenditures. Moreover, the degree of interdependence between neighbouring municipalities is measured by accounting for the spatial correlation in the error convolution. A feasible two-stage Heckit method has to be adapted to encompass the zero-inflation issue, to consider the hierarchical structure of data and to study the spatial diffusion process of the expenditures in the sample selection model framework. The main results on the determinants of health care expenditures at the macro-level are confirmed in our analysis on disaggregated data. On the contrary, however, the substitution effect, which is typically observed in aggregated data, has not been confirmed by the present research. Moreover, the selection process appears to be relevant in drug prescriptions and outpatient care expenditures and a significant spatial correlation in both the selection and the outcome equations emerges from the structure of error components.

Keywords: Health care expenditures; Health econometrics; INLA; Multilevel models; Sample selection; Spatial econometrics (search for similar items in EconPapers)
JEL-codes: C01 C11 C21 H75 I11 I18 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00181-017-1372-9

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