Efficiency of Social Sector Expenditure in India by Purohit Brijesh C.;

Author:Purohit, Brijesh C.; [Brijesh C. Purohit]
Language: eng
Format: epub
ISBN: 9781317663683
Publisher: Taylor & Francis Group

A hospital’s score will be 1 if it is scale-efficient or less than 1 if otherwise. Further, for scale-inefficient hospitals, characteristics of inefficiency are also identified by economies of scale or diseconomies of scale. An input-based measure of congestion efficiency (CE) is defined by the study, which is a ratio of the technical efficiency (TE) measure under strong disposability of inputs (TE (VRS, S)) to the TE measure under weak input disposability technology (TE (VRS, W)).

A sample size of 32 hospitals was determined based on the budget. This would encompass about 30 per cent of the first- and second-level hospitals. Further, the survey was to be conducted in five provinces (Central, Copperbelt, Eastern, Southern and Central) of the nine provinces of Zambia to ensure there was contiguity between the study sites. Two hospitals were removed due to incomplete data, leaving a sample of 30. This sample was composed of eighteen (60%) government-owned hospitals, eight (27%) church mission hospitals and four (13%) private hospitals. In terms of geographical location, 16 (53%) hospitals were based in rural areas. The four input variables included were total nonlabour cost (x1), number of medical doctors (x2), number of nursing and other clinical staff (x3) and the number of nonclinical staff (x4). It was confirmed during the field visits that there was a good degree of substitutability among clinical staff, especially in rural hospitals because of staff shortages. The input x1 was a composite including running costs, administration, allowances, overhead costs and capital costs. To estimate x1, all capital and equipment costs were annualized using a lifespan of 30 years for buildings and 10 years for vehicles and equipment, and a discount rate of 5 per cent. These rates have been applied in studies in Africa. Hospital service, which supposedly improves patients’ health, was used as an intermediate-level output. However, quality adjustment in outputs could not be done in this study due to nonavailability of the necessary data on case-mix and quality. This implied that the case-mix and severity patterns were assumed to be constant across the sample hospitals.

According to the results, Zambian hospitals could have attained their output levels with about 30 per cent less resources, suggesting potential for better service coverage. For instance, if inefficiency is eliminated or minimized, the extra resources could be invested in a range of operational areas such as better quality patient care, new technology, expansion of service profile, staff training in needed specialties or improved staff welfare. Hospitals could also finance part of their debt stock.

Further, decomposing of technical efficiency clearly emphasized the specific role of scale and input congestion in contributing to hospital inefficiency. In particular, unsuitable hospital scale of operation or size and low productivity of some inputs reinforce each other to make Zambian hospitals technically inefficient at delivering services. In this case, strategies such as hospital mergers or downgrades may help bring down costs and improve overall efficiency in the hospital industry. It is possible that some hospitals may be using more of some resources only because they have been historically overfunded or overstaffed relative to their outputs.

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