Estimating economies of scale and scope with flexible technology

The final publication is available at Springer via http://dx.doi.org/10.1007/s11123-016-0467-1Economies of scope are typically modelled and estimated using a cost function that is common to all firms in an industry irrespective of their type, e.g. whether they specialize in a single output or produce multiple outputs. Instead, we estimate a flexible technology model that allows for type-specific technologies and show how it can be estimated using linear parametric forms including the translog. A common technology remains a special case of our model and is testable econometrically. Our sample, of publicly owned US electric utilities, does not support a common technology for integrated and specialized firms. Our empirical results therefore suggest that assuming a common technology might bias estimates of economies of scale and scope. Thus, how we model the production technology clearly influences the policy conclusions we draw from its characteristics

Multi-product costs and standby capacity derived from queuing theory: the case of Belgian hospitals

Empirical hospital cost function studies can be divided into two categories: studies estimating traditional multi-product cost functions and studies including demand uncertainty (assuming that hospitals provide standby capacity to cope with uncertain demand and stressing that the relationship between the uncertain demand, excess capacity and costs should be investigated). Most studies include (the inverse of) the occupancy rate in a relatively basic cost function. The first contribution of this paper is to incorporate an indicator of reserve capacity into a genuine multi-product cost function. The second contribution is to propose an alternative indicator to proxy the reserve margin. The often used occupancy rate has an important shortcoming: the same occupancy rate can hide different turnaway probabilities and waiting times, obscuring the true degree of reservation quality. Since turnaway probabilities and waiting times are typical queuing theory indicators, an indicator for average waiting time (derived from queuing theory) is incorporated into a proper multi-product cost function to capture the degree of standby capacity into a proper multi-product cost function. The study uses 1997 data on Belgian general care hospitals to estimate a multi-product cost function and calculate cost elasticities, marginal costs and the degree of economies of scale. The results further show that providing standby capacity has a significant impact on total costs.

Hospital performance in a noncompetitive environment

In this article, we perform a quantitative analysis of the performance of hospital services in a nonmarket environment based on a very complete data set. Evaluating efficiency in a nonmarket system is important since the absence of market mechanisms could yield large inefficiencies. In fact, we show that hospital performance varies greatly across institutions. Specifically, our results suggest that hospitals with high levels of quasi-fixed factors are less efficient than other institutions. In general, hospitals performed very differently even after controlling for differences in services, hospital and patient characteristics. All in all, increased efficiencies could lead to potential savings, for the Quebec government, in the hundreds of millions of dollars.

Measurement of technical and allocative efficiencies using a CES cost frontier: a benchmarking study of Japanese transmission-distribution electricity

CES production frontier, Technical efficiency, Allocative efficiency, Benchmarking, Transmission-distribution electricity,

The scale efficiency of hospitals: One size does not fit all.

This paper analyses the productive efficiency of 141 public hospitals from 1998 to 2004 in two Canadian provinces; one a small province with a few small cities and a generally more rural population and the other a large province that is more urban in nature, with a population who mainly live in large cities. The relative efficiencies of the hospitals, the changes in productivity during this time period, and the relationship between efficiency and the size or scale of the hospitals are investigated using data envelopment analysis. The models for the production of health care use case mix adjusted hospital discharges as the output, and nursing hours as inputs. We find clear differences between the two provinces. Making use of 'own' and 'meta' technical efficiency frontiers, we demonstrate that efficient units in the larger and more urban province are larger than non-efficient units in that province. However, efficient hospitals in the smaller and more rural province are smaller than non-efficient hospitals in that province. Overall, efficient hospitals in the larger more urban province are larger than efficient hospitals in the smaller more rural province. This has interesting policy implications-different hospitals may have different optimal sizes, or different efficient modes of operation, depending on location, the population they serve, and the policies their respective provincial governments wish to implement. In addition, there are lessons to be learned by comparing the hospitals across the two provinces, since the inefficient hospitals in the small rural province predominantly use hospitals from the large urban province as benchmarks, such that substantially larger improvement potential can be identified by inter-provincial rather than intra-provincial benchmarking analysis

Measuring performance in the presence of stochastic demand for hospital services: an analysis of Belgian general care hospitals

Since demand for hospital services is subject to substantial variability, the relationship between uncertain demand, excess capacity, hospital costs and performance should be investigated thoroughly. In this paper a waiting time indicator to proxy hospital standby capacity is incorporated into a multi-product translog cost function for Belgian general care hospitals. The indicator is derived from queuing theory and improves on the conventionally used (inverse of the) occupancy rate. The multi-product stochastic frontier specification allows calculation of cost elasticities and marginal cost of seven hospital departments, as well as the degree of economies of scale and scope and enables identification of differences in efficiency. Copyright Springer Science+Business Media, LLC 2007Hospital costs, Stochastic demand, Efficiency, Productivity, Stochastic frontier analysis, Econometrics, Queuing theory, Multi-product cost function, C01, C13, C21, D24, H51, l11, l12,