Using data envelopment analysis, we examine the efficiency of Canada's universal health care system by considering a set of labour (physicians) and capital (beds) inputs, which produce a level of care (measured in terms of health quality and quantity) in a given region. Data from 2013–2015 were collected from the Canadian Institute for Health Information regarding inputs and from the Canadian Community Health Survey and Statistics Canada regarding our output variables, health utility (quality) and life expectancy (quantity). We posit that variation in efficiency scores across Canada is the result of regional heterogeneity regarding socioeconomic and demographic disparities. Regressing efficiency scores on such covariates suggests that regional unemployment and an older population are quite impactful and associated with less efficient health care production. Moreover, regional variation indicates the Atlantic provinces (Newfoundland, Prince Edward Island, Nova Scotia, New Brunswick) are quite inefficient, have poorer economic prospects, and tend to have an older population than the rest of Canada. Oaxaca-Blinder decompositions suggest that the latter two factors explain about one-third of this efficiency gap. Based on our two-stage semi-parametric analysis, we recommend Canada adjust their transfer payments to reflect these disparities, thereby potentially reducing inequality in regional efficiency.

We examined the effect of using input and output quantities as compared with costs and revenues when estimating farm-level efficiency scores and ranking. We used farm-level data from the 2015 Ethiopia Rural Socioeconomic Survey (ERSS) where production inputs and outputs in quantities as well as monetary units could be distinguished. Average technical efficiency scores of 72.2% and 68.6%, respectively, were found for analysis based on quantities and on costs and revenues. Efficiency ranking differed significantly. Results suggest that type of data compilation introduces bias to the efficiency assessment and that conclusions may be unclear, which complicates policy advice.

This study sought to investigate whether animal health and welfare state and changes were associated with technical efficiency in a sample of 34 Austrian dairy farms. Health and welfare were assessed twice using the Welfare Quality® (WQ) assessment protocol for dairy cattle. Following a baseline welfare assessment, farm-specific health and welfare planning was conducted on the farms. This included the identification, selection and implementation of measures in housing and management that aimed at improving health and welfare states. One year after implementation, farms were reassessed to evaluate changes in health and welfare states and consequences for farms’ technical efficiency were analysed using the Malmquist index. Our results indicated that farms with a higher health state (WQ principle score ‘Good health’) achieved higher technical efficiencies. However, we could not show that changes in the welfare state within a one-year period affected technical efficiency: across all farms, technical efficiency remained stable and Malmquist indices (indicating efficiency and technological change) could not be explained by the different welfare scores. Nevertheless, our study showed data envelopment analysis to be a valuable method for analysing the relationship between animal welfare and farm success and our results indicate substantial potential synergies between these two aspects.

A future sustainable dietary pattern for Japanese is yet undefined. This study aimed to explore more sustainable Japanese diets that are nutritious, affordable and with low greenhouse gas emissions (GHGE) and particular emphasis on cultural acceptability. A newly developed data envelopment analysis (DEA) diet model was applied to 4-d dietary record data among 184 healthy Japanese men and 185 women volunteers aged 21–69 years. Alternative diets were calculated as the linear combinations of observed diets. Firstly, for each individual, four modelled diets were calculated that maximised cultural acceptability (i.e. minimise dietary change from observed diet), maximised nutritional quality assessed by the Nutrient-Rich Food Index (NRF), minimised monetary diet costs or minimised diet-related GHGE. The final modelled diet combined all four indicators. In the first four models, the largest improvement was obtained for each targeted indicator separately, while relatively small improvements or unwanted changes were observed for other indicator. When all indicators were aimed to optimise, the NRF score and diet-related GHGE were improved by 8–13 % with the lower monetary cost than observed diets, although the percentage improvement was a bit smaller than the separate models. The final modelled diets demanded increased intakes for whole grains, fruits, milk/cream/yogurt, legumes/nuts, and decreased intakes for red and processed meat, sugar/confectioneries, alcoholic and sweetened beverages, and seasonings in both sexes. In conclusion, more sustainable dietary patterns considering several indicators are possible for Japanese, while total improvement is moderate due to trade-offs between indicators and methodological limitation of DEA diet model.

U.S. dairy production has been consolidating into large-scale confinement operations. Large numbers of small- to medium-scale dairies have disappeared in the last two decades, and many more are disappearing. This article analyzes small- to medium-scale dairy operations in Maryland during 1995–2009 for changes in technology and efficiency through a novel two-stage DEA approach to examine productivity changes. Conventional confinement dairy operations and management-intensive grazing dairies are analyzed separately. The results show that both dairy systems have become more productive on the technological frontiers, yet the rate of technical change for graziers was less than half the rate for confinement.

In this study we use data envelopment analysis (DEA) and an extension of DEA called value efficiency analysis (VEA) to explore the “production” of quality of life within counties in the mid-Atlantic region and the extent to which production frontiers and efficiency differ between rural and urban counties. These methods allow us to identify counties that are inefficient in their quality of life production, and to rank (using DEA) those counties according to their distance from a performance standard established by other observed counties, or (using VEA) by a single unit designated as “most preferred.”

Technical efficiency for cotton growers is examined using both stochastic (SFA) and nonstochastic (DEA) production function approaches. The empirical application uses farm-level data from four counties in west Texas. While efficiency scores for the individual farms differed between SFA and DEA, the mean efficiency scores are invariant of the method of estimation under the assumption of constant returns to scale. On average, irrigated farms are 80% and nonirrigated farms are 70% efficient. Findings show that in Texas, the irrigated farms, on average, could reduce their expenditures on other inputs by 10%, and the nonirrigated farms could reduce their expenditures on machinery and labor by 12% and 13%, respectively, while producing the same level of output.

Efficiency analysis is used for assessing links between technical efficiency (TE) of livestock farms and animal diseases. However, previous studies often do not make the link with the allocation of inputs and mainly present average effects that ignore the often huge differences among farms. In this paper, we studied the relationship between exposure to gastrointestinal (GI) nematode infections, the TE and the input allocation on dairy farms. Although the traditional cost allocative efficiency (CAE) indicator adequately measures how a given input allocation differs from the cost-minimising input allocation, they do not represent the unique input allocation of farms. Similar CAE scores may be obtained for farms with different input allocations. Therefore, we propose an adjusted allocative efficiency index (AAEI) to measure the unique input allocation of farms. Combining this AAEI with the TE score allows determining the unique input-output position of each farm. The method is illustrated by estimating efficiency scores using data envelopment analysis (DEA) on a sample of 152 dairy farms in Flanders for which both accountancy and parasitic monitoring data were available. Three groups of farms with a different input-output position can be distinguished based on cluster analysis: (1) technically inefficient farms, with a relatively low use of concentrates per 100 l milk and a high exposure to infection, (2) farms with an intermediate TE, relatively high use of concentrates per 100 l milk and a low exposure to infection, (3) farms with the highest TE, relatively low roughage use per 100 l milk and a relatively high exposure to infection. Correlation analysis indicates for each group how the level of exposure to GI nematodes is associated or not with improved economic performance. The results suggest that improving both the economic performance and exposure to infection seems only of interest for highly TE farms. The findings indicate that current farm recommendations regarding GI nematode infections could be improved by also accounting for the allocation of inputs on the farm.

Technical efficiency measures are calculated for ratite producers using data envelopment analysis. Regression analysis is then used to determine producer characteristics that are likely to lead to higher technical efficiencies. Results indicate that the most technically efficient ratite producers in Louisiana are not producing at the benchmark efficiency level advocated by the industry. Producer experience with other livestock, specialization, and labor are factors likely to lead to higher technical efficiency. These results are expected to hold for most new, alternative livestock enterprises.

National Oceanic and Atmospheric Administration Fisheries has conducted several buyback programs to reduce harvesting capacity in fisheries. These programs have attempted to maximize capacity reduction given a fixed budget. However, restructuring issues have not been considered. We explore the possibility of satisfying three different buyback objectives. We examine the black sea bass trap fishery and determine the number of vessels given different allowable catch levels and objectives of maximizing technical efficiency, capacity utilization, and vessels in the fishery. We find considerable variation in the number of vessels allowed to remain in the fishery given the different objectives.

The present study examines technical efficiency patterns in Ukraine's crop sector for 1991-1996. The economic and policy environment in Ukraine has changed since reform began in 1991. Many policy changes have exerted offsetting economic pressures on efficiency. Enterprise privatization and the liberalization of prices and trade put upward pressure on technical efficiency, whereas start-stop land privatization efforts, unpredictable government intervention, and slow developments in the credit and labor markets put downward pressure on efficiency. We found that technical efficiency appears to have improved slightly over the 1991-1996 period, suggesting that the positive forces had more impact.

The efficiency of public education is examined using a cost indirect output distance function. Efficiency estimates are obtained using data envelopment analysis applied to data from Georgia public schools. Georgia school districts utilize educational budgets with reasonable efficiency, achieving an overall efficiency of 98% with a range of 93%–100%. If all school districts were 100% efficient, outputs could be expanded 2%. This could be achieved by increasing funding $75.46 million state-wide in total for each of the 3 years. From the consumers' (voters') point of view, this result suggests that inefficiency costs Georgia, on average, a total of $226.38 million from 1994 to 1996.

Data envelopment analysis is used to calculate technical, allocative, economic, and scale efficiencies for fields enrolled in the University of Arkansas Rice Research Verification Program. The results reveal most fields have high technical and scale efficiencies, implying inputs are used in minimum levels necessary to achieve given output levels and fields are close to optimal in size. However, most fields exhibit allocative and economic inefficiencies and do not use inputs in the right combinations necessary to achieve cost minimization. Tobit analysis indicated allocative and economic efficiencies could be improved with better variety selection and better irrigation management.

Data envelopment analysis (DEA) has been widely used to measure the performance of theoperational units that convert multiple inputs into multiple outputs. In many real worldscenarios, there are systems that have a two-stage network process with shared inputs usedin both stages of productions. In this paper, the problem of evaluating the efficiency ofa set of specialized and interdependent components that make up a large DMU is considered.In these processes the first stage consists of two parallel components which are connectedserially with the process in the second stage. The paper develops a DEA approach formeasuring efficiency of decision processes which can be divided into two stages. Thisapplication of parallel-series production process involves shared resources and the paperdetermines an optimal split of shared resources among two components.

In the performance measurement using tools such as data envelopment analysis (DEA), datawithout explicit inputs has attracted considerable attention among researchers. In suchstudies the problem of production planning in the next production season is an importantand interesting subject. Because of the uncertain nature of the future, decision makersneed to provide robust procedures in order to examine alternative courses of action andtheir implications. The purpose of this paper is to develop an approach to productionplanning problem in production processes without explicit inputs that typically appears incentralized decision making environment. Application of the proposed approach isillustrated empirically using a real case.

Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res. 180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.