scholarly journals Data envelopment analysis, truncated regression and double-bootstrap for panel data with application to Chinese banking

2018 ◽  
Vol 265 (2) ◽  
pp. 748-764 ◽  
Author(s):  
Kai Du ◽  
Andrew C. Worthington ◽  
Valentin Zelenyuk
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Data Envelopment ◽  
Truncated Regression ◽  
Double Bootstrap ◽  
Chinese Banking

scholarly journals Operational Efficiency of Mexican Water Utilities: Results of a Double-Bootstrap Data Envelopment Analysis

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 553 ◽  
Author(s):  
Jose Humberto Ablanedo-Rosas ◽  
Aaron Guerrero Campanur ◽  
Elias Olivares-Benitez ◽  
Jacqueline Y. Sánchez-García ◽  
Juan Enrique Nuñez-Ríos
Keyword(s):  
Distribution Function ◽  
Data Envelopment Analysis ◽  
Research Method ◽  
Operational Efficiency ◽  
Water Utilities ◽  
Data Envelopment ◽  
Truncated Regression ◽  
Context Variable ◽  
Context Variables ◽  
Double Bootstrap

The objective of this paper is to estimate the operational efficiency of Mexican water utilities and identify the context variables that impact their efficiency. In particular, a bootstrap data envelopment analysis (DEA) and a bootstrap truncated regression analysis are combined in a two-stage research method. In the first stage, an input-oriented DEA model is used to determine bootstrap efficiency scores. Then, the corrected distribution function of the efficiency scores is used to estimate a truncated regression which is aimed to identify the significant influential context variables. Three categorical and two continuous context variables are considered in the analysis. Results show that only one context variable has a significant impact on the water utilities efficiency scores. Managerial recommendations are drawn from the analysis. It is suggested that water utilities continue or implement wastewater treatment, persist in decreasing and controlling leakage across the distribution network, and maximizing sewer coverage.

scholarly journals Using a two-stage data envelopment analysis to estimate the efficiency of county hospitals in China: a panel data study

The Lancet ◽  
2015 ◽  
Vol 386 ◽  
pp. S64 ◽  
Author(s):  
Zhao-Hui Cheng ◽  
Hong-Bing Tao ◽  
Miao Cai ◽  
Hai-Feng Lin ◽  
Xiao-Jun Lin ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Data Envelopment ◽  
Two Stage

Systematic Review: Penilaian Efisiensi Rumah Sakit dengan Metode Data Envelopment Analysis (DEA)

2021 ◽  
Vol 10 (04) ◽  
pp. 258-268
Author(s):  
Pantri Widyastuti ◽  
Atik Nurwahyuni
Keyword(s):  
Systematic Review ◽  
Data Envelopment Analysis ◽  
Hospital Efficiency ◽  
Data Envelopment ◽  
Truncated Regression ◽  
Tobit Regression

Dalam sistem kesehatan yang berkembang saat ini, efisiensi merupakan hal yang utama. Pengukuran efisiensi bermanfaat untuk pemerintah maupun swasta untuk dapat mengambil keputusan yang berhubungan dengan tinggi rendahnya biaya perawatan di rumah sakit. Penelitian ini bertujuan untuk mengkaji metode Data Envelopment Analysis (DEA) yang digunakan dalam berbagai penelitian dalam pengukuran efisiensi rumah sakit. Desain penelitian yang digunakan adalah dengan sistematic review dengan metode PRISMA tanpa meta analisis. Sumber data didapatkan dari Proquest, Sciencedirect dan Pubmed pada tahun 2019 hingga 2020. Pencarian data dilakukan pada bulan Oktober 2020 dengan kata kunci Hospital Efficiency dan Data Envelopment Analysis. Hasilnya adalah penilaian efisiensi rumah sakit menggunakan metode DEA lebih banyak dilakukan dengan analisa dua tahap menggunakan tobit regression atau truncated regression. Perhitungan index malmquist juga banyak digunakan setelah perhitungan DEA dilakukan untuk melihat efisiensi rumah sakit dalam periode waktu tertentu.

scholarly journals Investigating the link between medical urgency and hospital efficiency – Insights from the German hospital market

2020 ◽  
Vol 23 (4) ◽  
pp. 649-660
Author(s):  
Annika Maren Schneider ◽  
Eva-Maria Oppel ◽  
Jonas Schreyögg
Keyword(s):  
Healthcare Expenditure ◽  
Negative Relationship ◽  
Hospital Efficiency ◽  
Data Envelopment ◽  
Truncated Regression ◽  
Hospital Market ◽  
Double Bootstrap ◽  
Linear Effects ◽  
The Relationship ◽  
Urgency Score

AbstractWith hospital budgets remaining tight and healthcare expenditure rising due to demographic change and advances in technology, hospitals continue to face calls to contain costs and allocate their resources more efficiently. In this context, efficiency has emerged as an increasingly important way for hospitals to withstand competitive pressures in the hospital market. Doing so, however, can be challenging given unpredictable fluctuations in demand, a prime example of which are emergencies, i.e. urgent medical cases. The link between medical urgency and hospitals’ efficiency, however, has been neglected in the literature to date. This study therefore aims to investigate the relationship between hospitals’ urgency characteristics and their efficiency. Our analyses are based on 4094 observations from 1428 hospitals throughout Germany for the years 2015, 2016, and 2017. We calculate an average urgency score for each hospital based on all cases treated in that hospital per year and also investigate the within-hospital dispersion of medical urgency. To analyze the association of these urgency measures with hospitals’ efficiency we use a two-stage double bootstrap data envelopment analysis approach with truncated regression. We find a negative relationship between the urgency score and hospital efficiency. When testing for non-linear effects, the results reveal a u-shaped association, indicating that having either a high or low overall urgency score is beneficial in terms of efficiency. Finally, our results reveal that higher within-hospital urgency dispersion is negatively related to efficiency.

scholarly journals Efficiency of Two-Stage Technological Innovation in High Patent-Intensive Industries That Considers Time Lag: Research Based on the SBM-NDEA Model

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Junzhong Zou ◽  
Wei Chen ◽  
Nan Peng ◽  
Xuan Wei
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Technological Innovation ◽  
Time Lag ◽  
Innovation Process ◽  
Indicator System ◽  
Data Envelopment ◽  
Input Output ◽  
Two Stages ◽  
Output Indicator

Considering time lag and accumulation of inputs and outputs, this paper adopts the superefficiency data envelopment analysis (DEA) model to study the technological innovation efficiency of high patent-intensive industries using panel data from 2007 to 2017. Given the characteristics and the actual circumstances of the industries, the innovation process is divided into two stages, and an input-output indicator system is established. The results show that the overall innovation efficiency level of high patent-intensive industries in China is increasing. However, the R&D achievements in technology are not quickly applied or sufficiently transformed.

scholarly journals Data Envelopment Analysis (DEA) for Measuring the Efficiency of the Hotel Industry in Ecuador

2020 ◽  
Vol 12 (4) ◽  
pp. 1590 ◽  
Author(s):  
Angel Higuerey ◽  
Christian Viñan-Merecí ◽  
Zulema Malo-Montoya ◽  
Valentín-Alejandro Martínez-Fernández
Keyword(s):  
Data Envelopment Analysis ◽  
Panel Data ◽  
Hotel Industry ◽  
Geographic Location ◽  
Data Envelopment ◽  
Market Position ◽  
Tourist Attractions ◽  
The Third ◽  
Factors Of Production ◽  
Efficiency And Productivity

The level of contribution of the hotel industry depends on different factors of production that they use in the provision of their services The way they use these factors of production will allow them to act efficiently, in order to improve profitability and market position. Ecuador, in recent years, has directed public policies betting on the development of this industry. In this sense, this research seeks to measure the efficiency and productivity of the Ecuadorian hotel industry. For this purpose, a significant sample has been selected; it consists of 147 businesses that provided hotel services during the period 2013–2017. These businesses are classified according to their quality and geographic location. This information has been useful to make a balanced panel data with one output (Revenue) and three inputs (Total_personnel, the non-current assets, and Consumption) by using the Data Envelopment Analysis (DEA). The results, which proved to be solid and accurate, indicate that the most efficient businesses are the ones in the third class, whereas those hotels located in zones with tourist attractions and activities have a better optimization of those resources. This situation has an effect on the significant improvement of their productivity.

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