Measuring CIS Health Systems Using the Stochastic Frontier Analysis (SFA)

Abstract

resigned its full membership status in 2005 and changed its CIS standing to observer member. Drawing on the methods of Least Squares and Maximum Likelihood Estimation of Stochastic Frontier Analysis, we made estimations to find efficiency scores in the health sector by using the data for the period from 2010 to 2015 of the countries that are members of the Commonwealth of Independent States. In the study, we used various factors as dependent and independent variables, including the number of doctors, medical personnel and hospital beds per ten thousand people, the life expectancy at birth, under-five mortality rate and the incidence of tuberculosis per one hundred thousand people. We have found that such factor as the number of doctors effectively influences reduction of the under-five mortality rates and the incidence of tuberculosis, while the factor "the number of hospital beds" is not very effective. Finally, for the CIS countries we made recommendations to popularize the health insurance, to change the old Soviet form of health management in public hospitals, and to implement liberal socio-economic policies.

Efficiency measurement and spatial spillover effect of provincial health systems in China: Based on the two-stage network DEA model

“…Currently, most studies use parametric and non-parametric methods to evaluate health system efficiency. Among them, non-parametric methods are represented by Data Envelopment Analysis (DEA) ( 3 , 5 ), and parametric methods are mainly represented by Stochastic Frontier Analysis (SFA) ( 6 , 7 ). Some scholars have combined the two to study health care efficiency ( 8 ), but SFA may lead to biased measurements due to its dependence on the functional form and the distribution of random errors ( 9 ).…”

Growth Models and Influencing Mechanisms of Total Factor Productivity in China’s National High-Tech Zones

“…Regarding factors influencing different TFP growth models, existing literature has mainly focused on TFP influencing factors and its decomposition structure; yet, it has not fully illuminated the reasons behind different TFP growth models [31,32]. Additionally, existing studies have examined factors such as economic development, scientific and technological innovation, industrial structure, capital accumulation, foreign cooperation, and energy structure [33,34].…”

Performance Evaluation of Public Hospitals with Data Envelopment Analysis (DEA) Method

“…Different studies evaluate the efficiency of businesses providing services in the health sector by using the DEA technique (Yeşilyurt and Salamov, 2017;Kutlar and Salamov, 2018;Kohl et al, 2019;Yeşilyurt and Selamzade, 2020;Pereira et al, 2021;Durur et al, 2022;Chiu et al, 2022). Some of the studies in which the performance evaluation of hospitals was carried out using DEA can be seen in Table 1.…”