Starting from version 15, Stata allows users to manage data and fit regressions accounting for spatial relationships through the sp commands. Spatial regressions can be estimated using the spregress, spxtregress, and spivregress commands. These commands allow users to fit spatial autoregressive models in cross-sectional and panel data. However, they are designed to estimate regressions with continuous dependent variables. Although binary spatial regressions are important in applied econometrics, they cannot be estimated in Stata. Therefore, I introduce spatbinary, a Stata command that allows users to fit spatial logit and probit models.
We study inter-regional migration flows due to hospital admissions and focus on the impact of migration beaten paths. We estimate a gravity model applied to Italian data for the period 2010-2016. We find that beaten paths have a positive effect on inter-regional patient flows, with estimated elasticity equal to +0.32%. Therefore, family and social ties among people living in the region of origin and destination for hospital admissions may explain the concentration of health migration (HM) in some regions. Moreover, the beaten path effect is stronger for private hospitals. We also find that HM patients are more sensible to hospital quality than those admitted in local hospitals.
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