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Waiting time for non-emergency medical care in developing countries is rarely of immediate concern to policy makers that prioritize provision of basic health services. However, waiting time as a measure of health system responsiveness is important because longer waiting times worsen health outcomes and affect utilization of services. Studies that assess socio-economic inequalities in waiting time provide evidence from developed countries such as England and the United States; evidence from developing countries is lacking. In this paper, we assess the relationship between social class i.e. caste of an individual and waiting time at health facilities—a client orientation dimension of responsiveness. We use household level data from two rounds of the Indian Human Development Survey with a sample size of 27,251 households in each wave (2005 and 2012) and find that lower social class is associated with higher waiting time. This relationship is significant for individuals that visited a male provider but not so for those that visited a female provider. Further, caste is positively related to higher waiting time only if visiting a private facility; for individuals visiting a government facility the relationship between waiting time and caste is not significant. In general, caste related inequality in waiting time has worsened over time. The results are robust to different specifications and the inclusion of several confounders.

Despite generous universal social health insurance with little formal restrictions of outpatient utilisation, Austria exhibits high rates of avoidable hospitalisations, which indicate the inefficient provision of primary healthcare and might be a consequence of the strict regulatory split between the Austrian inpatient and outpatient sector. This paper exploits the considerable regional variations in acute and chronic avoidable hospitalisations in Austria to investigate whether those inefficiencies in primary care are rather related to regional healthcare supply or to population characteristics. To explicitly account for interregional dependencies, spatial panel data methods are applied to a comprehensive administrative dataset of all hospitalisations from 2008 to 2013 in the 117 Austrian districts. The initial selection of relevant covariates is based on Bayesian model averaging. The results of the analysis show that supply-side variables, such as the number of general practitioners, are significantly associated with decreased chronic and acute avoidable hospitalisations, whereas characteristics of the regional population, such as the share of population with university education or long-term unemployed, are less relevant. Furthermore, the spatial error term indicates that there are significant spatial dependencies between unobserved characteristics, such as practice style or patients' utilization behaviour. Not accounting for those would result in omitted variable bias.

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This paper estimates a theory-guided gravity equation of regional patient flows. In our model, a patient's choice to consult a physician in a particular region depends on a measure of spatial accessibility that accounts for the exact locations of both patients and physicians. Introducing this concept in a spatial economics model, we derive an augmented gravity-type equation and show that our measure of accessibility performs better in explaining patient flows than bilateral distance. We conduct a rich set of counterfactual simulations, illustrating that the effects of physicians' market exits on patient mobility crucially depend on their exact locations.