This contribution is the first attempt to systematically review all empirical surveys that so far have been made available in the broad field of efficiency and productivity analysis using frontier estimation methodologies. We provide a systematic bibliometric review on the many empirical surveys in the field of efficiency and productivity analysis, the most relevant concepts, areas, overlaps, and potentials to explore from its introduction to the most recent surveys. We combine the United Nations' International Standard Industrial Classification (ISIC) taxonomy for the economic activity with the Journal of Economic Literature (JEL) classification system to classify the empirical surveys and to identify the current gaps in the literature. In addition to the most relevant/generic potential areas for applications (according to the United Nation's ISIC), this methodology provides a cluster analysis with the most relevant concepts that have been considered so far (according to the JEL codes). This overview brings an interesting guide for future work to develop the whole field.

Empirical Surveys of Frontier Applications: A Meta-Review

The ongoing outbreak of SARS-CoV-2 has been deeply impacting health systems worldwide. In this context, it is pivotal to measure the efficiency of different nations' response to the pandemic, whose insights can be used by governments and health authorities worldwide to improve their national COVID-19 strategies. Hence, we propose a network Data Envelopment Analysis (DEA) to estimate the efficiencies of fifty-five countries in the current crisis, including the thirty-seven Organisation for Economic Co-operation and Development (OECD) member countries, six OECD prospective members, four OECD key partners, and eight other countries. The network DEA model is designed as a general series structure with five single-division stages - population, contagion, triage, hospitalisation, and intensive care unit admission -, and considers an output maximisation orientation, denoting a social perspective, and an input minimisation orientation, denoting a financial perspective. It includes inputs related to health costs, desirable and undesirable intermediate products related to the use of personal protective equipment and infected population, respectively, and desirable and undesirable outputs regarding COVID-19 recoveries and deaths, respectively. To the best of the authors' knowledge, this is the first study proposing a cross-country efficiency measurement using a network DEA within the context of the COVID-19 crisis. The study concludes that Estonia, Iceland, Latvia, Luxembourg, the Netherlands, and New Zealand are the countries exhibiting higher mean system efficiencies. Their national COVID-19 strategies should be studied, adapted, and used by countries exhibiting worse performances. In addition, the observation of countries with large populations presenting worse mean efficiency scores is statistically significant. 

https://doi.org/10.1016/j.eswa.2022.118362

A network Data Envelopment Analysis to estimate nations’ efficiency in the fight against SARS-CoV-2

The nonparametric assessment of police efficiency and effectiveness is challenging due to the stochastic nature of criminal behavior and the subjective dependence on multiple decision criteria, leading to different prospects depending on the regulation, necessity, or organizational objective. There is a trade-off between sustainable efficiency and effectiveness in many police performance assessments, because many departments can be crime-specialized or cannot reproduce good results effectively on more severe or complex occurrences. This study aims to provide a non-compensatory ranking classification combining Conditional Frontier Analysis with the PROMETHEE II methodology for the multidimensional efficiency and effectiveness analysis of police. The results on Pernambuco (Brazil) Police departments offer interesting perspectives for public administrations concerning prioritizations of units based on the mitigation of resources and strategic objectives.

https://www.mdpi.com/2071-1050/13/8/4251/pdf

Multicriteria Ranking for the Efficient and Effective Assessment of Police Departments

/pdf/the-core-of-healthcare-efficiency-a-comprehensive-2a5r6gzv.pdf

The Core of Healthcare Efficiency: A Comprehensive Bibliometric Review on Frontier Analysis of Hospitals

https://doi.org/10.1016/j.cie.2022.108491

Emergency medical resource allocation among hospitals with non-regressive production technology: A DEA-based approach

Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

/pdf/a-dea-based-complexity-of-needs-approach-for-hospital-beds-289dosunup.pdf

Késsia T. C. Nepomuceno

Papers

A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak.