Countries striving to improve their HSE should aim to impact population behavior and welfare rather than only ensure adequate medical care. In addition, they may consider avoiding specific institutional arrangements, namely gatekeeping and the presence of multiple insurers. Finally, the ambiguous association found between socioeconomic and environmental indicators, and a country's HSE necessitates caution when interpreting different ranking techniques in a cross-country efficiency evaluation and needs further exploration.

Although the measurement of efficiency and productivity in the tourism industry at the micro level has been the subject of considerable research in recent years, there has been little research at the macro level. Using data envelopment analysis, this paper analyses the efficiency of the tourism sector in 105 countries, including 34 developed and 71 developing countries. It finds that globalization and accessibility are critical for the efficiency of the tourism sector in developing countries and that labour productivity may be a good proxy for the efficiency of the aggregate tourism industry.

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This paper presents a two‐stage model for fully ranking organizational units where each unit has multiple inputs and outputs. In the first stage, the Data Envelopment Analysis (DEA) is run for each pair of units separately. In the second stage, the pairwise evaluation matrix generated in the first stage is utilized to rank scale the units via the Analytical Hierarchical Process (AHP). The consistency of this AHP/DEA evaluation can be tested statistically. Its goodness of fit with the DEA classification (to efficient/inefficient) can also be tested using non‐parametric tests. Both DEA and AHP are commonly used in practice. Both have limitations. The hybrid model AHP/DEA takes the best of both models, by avoiding the pitfalls of each. The nonaxiomatic utility theory limitations of AHP are irrelevant here: since we are working with given inputs and outputs of units, no subjective assessment of a decision maker evaluation is involved. AHP/DEA ranking does not replace the DEA classification model, rather it furthers the analysis by providing full ranking in the DEA context for all units, efficient and inefficient.

A new measure for evaluating and ranking police stations by performance and by improvement trends over time is presented. The measure, which is identified as DEA/MI, combines data envelopment analysis (DEA) methodology with the Malmquist Index (MI). The DEA component measures the relative efficiency of the DMUs, while the MI component measures the improvement or decrease in efficiency of a DMU over a specified period of time. A case study of all 13 police stations in the south of Israel shows that according to the DEA/MI measure a police station with relatively low efficiency can be ranked high if it has a high improvement index.