Wilson and Jadlow (1982) examined the relative efficiency of for-profit and non-profit hospitals in the provision of nuclear medicine services. The mode of evaluation employed by Wilson and Jadlow was the non-parametric DEA methodology. This analysis revealed how close each type of hospital came to producing the maximum possible amount of output given its choice of input mix. In addition to comparing non-profit and for-profit private entities, they also evaluated public facilities. They discovered that in terms of technical efficiency (maximum output for given inputs), government providers performed worse than private non-profits, which performed worse than for-profits. The average efficiency scores were 83.3 %, 87.2 % and 92.7 % for public, private non-profit and for-profit hospitals respectively. They found that for-profit nuclear medicine services were significantly closer to maximum production than non-profits were. The authors conclude that institutional changes in the direction of profit incentives are likely to improve the performance of hospital care provision.
A study along similar lines by Nyman and Bricker (1989) employed the DEA to investigate the impact of profit incentives on the technical efficiency in the production of nursing home care. The evidence in their study suggested that for-profit nursing homes had significantly higher scores than non-profit homes. According to their study, for-profit nursing homes used about 4.5 % fewer labor resources per patient day than non-profit homes. Both studies support the property rights hypothesis that for-profit homes are inherently more efficient than non-profit ones. The authors identified the following crucial factors as key drivers of efficiency:
i) The status of firm. For-profit nursing homes have explicit reasons for minimizing costs, hence they have reasons for wanting to produce efficiently.
ii) The reimbursement policy associated with the clients. If nurses are reimbursed simply for costs incurred and given a certain return on capital independent of present period behaviour,
58 there is no reason to minimize costs even for profit maximizing firms. This is because in such a case profit cannot be raised by minimizing costs.
iii) Occupancy rate. The occupancy rate is the number in the home on a certain day divided by
the actual number of beds. It is assumed that nursing homes tend to staff 100% occupancy rates, then the degree to which the firm’s actual occupancy rate is less than this target occupancy rate will have an effect on the firms staffing hours per patient day.
iv) Patient case mix has an effect on resource use. It is important to hold constant this factor if
meaningful inferences are to be used.
v) Quality of care is another dimension of output that needs to be held constant. Greater quality
care requires more inputs per unit of output, to the extent that higher quality service providers may have lower efficiency scores not because they are less efficient but because they provide better services.
vi) Finally the authors identified the need to introduce competitive dynamics as a remedy to the market failure in health sector, a problem normally created by the presence of oligopolistic and monopolistic structures
Vincenzo and Dino (2006) investigated the levels of efficiency by adopting the Data Envelopment Analysis non-parametric method. A large sample of 85 hospitals in Italy divided into 61 public and 24 private (7 non-profit and 17 for-profit) was considered. The author’s model consisted of three outputs namely:
Total care discharges.
Number of days of treatment in hospital known as inpatient days. Number of treatments provided by emergency services.
The following five inputs were considered in the model. Number of physicians
Number of Nurses
Number of other employers.
Number of hospital beds as a proxy for capital Total admissions as a proxy for hospital demand.
59 They distinguished between three components of technical inefficiency of hospitals: internal inefficiency attributable to the responsibility of hospital management, external inefficiency that could be due to past health care policy decisions and to exogenous demand and scale inefficiency which is due to under or over sizing of hospitals with respect to their actual activity levels. Their study revealed that non-profit private hospitals exhibited a level of total inefficiency higher than public hospitals. Private for-profit hospitals produced the least total inefficiency scores. However, both non-profit and for-profit hospitals were characterized by higher scale inefficiency than public hospitals. Under the VRS returns to scale model, the results indicated that 23 of all public hospitals exhibited decreasing returns to scale (DRS), 42 hospitals of which 80% were private hospitals exhibited increasing returns to scale (IRS) and finally 20 hospitals exhibited constant returns to scale (CRS). It was concluded that the problem of scale inefficiency mainly characterized the private hospital sector with many hospitals small in relation to their output levels. Private for-profit hospitals had the highest score of total efficiency than the public and the non-profit hospitals.
Fizel and Nunnikhoven (1992) examined nursing home efficiency in the state of Michigan. Their study attempted to determine the technical efficiency of for-profit and non-profit nursing homes to shed some light on the debate about the validity of the theory of property rights. The study examined a sample of 163 nursing homes classified into 104 for-profit homes and 59 non-profit homes using the non-parametric techniques of Data Envelopment Analysis. A regression analysis was estimated to identify some factors that may be associated with (in)-efficiency such as quality variations, ownership status and competition. Inpatient days and intermediate care patients were used as outputs whereas number of hours of registered nurses, aides and orderlies were captured as inputs. The number of beds was not included in the model. The DEA results computed an overall efficiency score of 0.68 and 0.48 for for-profit and non-profit homes respectively. The for-profit nursing homes used 20 % fewer resources than the non-profit homes. Using a property rights framework, the authors theorized that since for-profit homes have exclusive rights to income generated, with the resulting incentive to meter input productivity and rewards. Given the threat of take-overs, an incentive existed to produce efficiently. On the other hand, in a non- profit home the owner’s rights to income are attenuated (and ultimately non-transferable). The
60 study highlighted the need to exercise caution when interpreting efficiency scores derived where the quality dimension of output has been assumed to be constant. This is because higher quality can translate in a lower efficiency score due to the additional resources required to improve quality.
Mills and Liu (1998) applied the DEA methodology to 62 private and public general hospitals in Korea. The hospitals were assumed to use the following inputs; beds, doctors, registered nurses, nursing aides, pharmacists, technicians and administrative staff to produce 16 outputs. The outputs were basically the various health specialties provided by the hospitals. Among other health specialties the outputs included internal medicine, pediatrics, general surgery and gynaecology. The DEA results showed that public hospitals were more scale inefficient than private hospitals. The average inefficiency scores were 23 % and 32.6 % for public and private hospitals respectively. The comparison between hospitals size and the overall technical efficiency scores showed a positive relationship between hospital size and efficiency scores. The optimal hospital size was estimated using the number of beds as a proxy for hospital size.