Spatial differencing (SD) is a spatial data transformation pioneered by Holmes (1998) increasingly used to estimate causal effects with non-experimental data. Recently, this transformation has been widely used to deal with omitted variable bias …
The classical stochastic frontier panel data models provide no mechanism to disentangle individual time invariant unobserved heterogeneity from inefficiency. Greene (2005a, b) proposed the so-called \"true\" fixed-effects specification that …
Understanding the role that drug adherence has on health outcomes in everyday clinical practice is central for the policy maker. This is particularly true when patients suffer from asymptomatic chronic conditions (e.g., hypertension, …
We investigate the impact of maize-legume intercropping, soil and water conservation practices (SWC), organic fertilizers, inorganic fertilizers and high yielding maize varieties on maize productivity under weather shocks in Tanzania using panel …
Aging and excessive adiposity are both associated with an increased risk of developing multiple chronic diseases, which drive ever increasing health costs. The main aim of this study was to determine the net (non‐estimated) health costs of excessive …
Recent literature on panel data emphasizes the importance of accounting for time-varying unobservable individual effects, which may stem from either omitted individual characteristics or macro-level shocks that affect each individual unit …
Over the last decades spatial econometric models have represented a common tool for measuring spillover effects across different geographical entities (counties, provinces, regions or nations). The aim of this paper is to investigate the issue of …
Using data from 1176 Italian municipalities in 2005, we identify factors associated with the development of e-government services supplied by local public administrations (PAs). We show that the combination of internal competencies and …