scholarly journals Analysis on the Efficiency of Health Care Service Delivery and Influencing Factors in China: A Study Based on DEA-Malmquist Index and Tobit Model

2020 ◽  
Author(s):  
Kui Chen ◽  
Jun Ye
Keyword(s):  
Health Care ◽  
Health Service ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Influencing Factors ◽  
Health Care Service ◽  
Health Care Expenditure ◽  
Tobit Model ◽  
Utilization Rate ◽  
Factor Productivity

Abstract Objective: To evaluate the changeing trend and influencing factors of medical and health service supply efficiency in 31 provinces of China.Methods: According to the input-output relevant index data of medical and health service in China from 2009 to 2018, data envelopment analysis- Malmquist(DEA-Malmquist) was used to calculate the total factor productivity, technical efficiency, and technical change. Meanwhile, Tobit model to analyzed the main effective factors of medical and health service supply efficiency in ChinaResults: In 2018, 21 provinces including Beijing, Shanghai, Zhejiang and Guangdong were effective in DEA of China's medical and health supply efficiency. Jilin, Heilongjiang, Jiangsu and Shandong were weak DEA effective, while Shanxi, Inner Mongolia, Liaoning, Anhui, Fujian and Xinjiang were not DEA effective. From 2009 to 2018, the total factor productivity of China's medical and health service supply has been decreased steadily, which was mainly affected by technological changes. From the perspective of regions, the technical efficiency and pure technical efficiency of medical and health service supply was the highest in the east, followed by the central and the western region. Associate’s degree or above, gross regional domestic product, and health care expenditure were significantly associated with the increasing of medical and health service supply efficiency.Conclusions: According to their own conditions and constraints, all localities should take targeted measures to strengthen the allocation and management level of medical and health resources, promote technological progress, give full play to the role of education and economic development, increase the expenditure on medical and health care, improve the utilization rate of beds, shorten the average hospitalization days, effectively improve the efficiency of medical and health services supply, and better provide health care for people.

Impact of Determinants of Industrial Development on Employment and Wages in Indian Manufacturing

2020 ◽  
pp. 18-30
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Technical Change ◽  
Positive Impact ◽  
Manufacturing Sector ◽  
Factor Productivity ◽  
Firm Level ◽  
Employment Level ◽  
The Impact ◽  
Level Performance

ABSTRACT The present study was undertaken to explore the evolution of the impact of firm-level performance on employment level and wages in the Indian organized manufacturing sector over the period 1989-90 to 2013-14. One of the major components of the economic reform package was the deregulation and de-licensing in the Indian organized manufacturing sector. The impact of firm-level performance on employment and wages were estimated for Indian organized manufacturing sector in major sub-sectors in India during the period from 1989-90 to 2013-14 of the various variables namely profitability ratio, total factor productivity change, technical change, technical efficiency, openness (export-import), investment intensity, raw material intensity and FECI in total factor productivity index, technical efficiency, and technical change. The study exhibited that all explanatory variables except profitability ratio and technical change cost had a positive impact on the employment level. Out of eight variables, four variables such as net of foreign equity capital, investment intensity, TFPCH, and technical efficiency change showed a positive impact on wages and salary ratio and rest of the four variables such as openness intensity, technology acquisition index, profitability ratio, and technical change had negative impact on wages and salary ratio. In this context, the profit ratio should be distributed as per the marginal rule of economics such as the marginal productivity of labour and capital.

scholarly journals Effectiveness of Public and Co-operative Health Insurance Models in Nepal: A Concurrent Mixed Study

2016 ◽  
Author(s):  
Chhabi Ranabhat ◽  
Chun-Bae Kim ◽  
Myung-Bae Park
Keyword(s):  
Health Care ◽  
Health Insurance ◽  
Health Care Utilization ◽  
Health Care Service ◽  
Large Population ◽  
High Income ◽  
Care Utilization ◽  
Utilization Rate ◽  
Religious Minority ◽  
Mixed Study

Background: Health insurance (HI) run by government is providing health care service to large population. Due to poor accountability, participation and sustainability, cooperative health insurance is becoming more popular and effective in low and middle income and some high-income countries too. In Nepal, there are public and cooperative HI is in practice. The aim of this study is to compare the effectiveness of public (government) and cooperative HI in relation to benefit packages, population coverage, inclusiveness, health care utilization, and promptness for treatment in these two health insurance models in Nepal. Method: This is an institution based concurrent mixed study consists of qualitative and quantitative variables from public and cooperative groups. We included all public HI operated by government hospitals and cooperatives groups those purchased hospital service in contract. Two separate study tools were applied to access the effectiveness of insurance models. The key questions were asked for the representatives of government and private health insurance. The numeric information consisted of in quantitative data and subjective response was included in qualitative approach. Descriptive statistics and Mean Whitney U test was applied in numeric data and qualitative information were analyzed by inductive approach Results: The study revealed that new enrolment was not increased, health care utilization rate was increased and the benefit package was almost same in both groups. The overall inclusiveness was higher for the government HI, but enrolment from the religious minority, proportion of negotiated amount during treatment were significantly higher (p<0.05). During illness, the response time to reach hospital was significantly faster in cooperative health insurance than government health insurance. Qualitative findings showed that level of participation, accountability, transparency and recording system was better in cooperative health insurance than public. Conclusion: Cooperative HI could be more sustainable and accountable to the community for all; low, middle and high-income countries.

scholarly journals A Study on the Efficiency of Tourism Poverty Alleviation in Ethnic Regions Based on the Staged DEA Model

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianchun Yang ◽  
Ying Wu ◽  
Jialian Wang ◽  
Chengcheng Wan ◽  
Qian Wu
Keyword(s):  
Ethnic Minority ◽  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Poverty Alleviation ◽  
Malmquist Index ◽  
Great Difficulty ◽  
Local Economy ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Two Stages

Poverty alleviation through tourism is an important way for China to achieve targeted poverty alleviation and win the battle of poverty alleviation. As a region with deep poverty and great difficulty in poverty alleviation, whether tourism development has injected key impetus into ethnic minority areas needs to be tested by both qualitative analysis and quantitative measurement. This paper takes eight ethnic provinces (regions) in China as an example to conduct an empirical study. Based on the Data Envelopment Analysis (DEA)-BCC model and Malmquist index, it evaluates the tourism investment and tourism poverty alleviation efficiency of the ethnic regions in the two stages of tourism poverty alleviation, and analyzes them by classification. The results of the study show: (1) The pure technical efficiency in the first stage is relatively high, but the total factor productivity of each region is declining; (2) The pure technical efficiency in the second stage is also relatively high, but the scale efficiency is low, and the change rate of total factor productivity of the provinces in China has increased significantly; (3) The “double high” type includes Guangxi, Inner Mongolia, and Guizhou, and the “double low” type includes Qinghai, Yunnan, Tibet, Xinjiang, and Ningxia. The results of the study generally show that tourism poverty alleviation has brought about the improvement of the living standards of residents and the development of local economy, but the efficiency of tourism poverty alleviation needs to be improved. On this basis, the article puts forward corresponding improvement measures, in order to further help the ethnic minority areas get rid of poverty in a comprehensive way by promoting the efficient and sustainable development of tourism.

scholarly journals Agricultural basic industry subjects productivity development examination in the region Trnava (SR) by adopting the Malmquist indexes

2010 ◽  
Vol 56 (No. 3) ◽  
pp. 108-115
Author(s):  
P. Bielik ◽  
D. Hupková ◽  
M. Vadovič ◽  
V. Benda
Keyword(s):  
Technical Efficiency ◽  
Total Factor Productivity ◽  
Future Trend ◽  
Factor Productivity ◽  
Technological Changes ◽  
Efficiency Change ◽  
Basic Industry ◽  
Factors Influencing ◽  
Two Factors ◽  
Malmquist Indexes

Analysis of the productivity and efficiency development could be used to asses the trend and factors influencing this process. The main goal of this paper is estimation of the Total Factor Productivity (TFP) development of agricultural farms in the Trnava region in the period 2002–2006. Results of this analysis could be used to detect the trend in the TFP development. The results of the analysis confirmed there is no evident trend in the average TFP indicators. This could be explained by the variation of technical efficiency change and technological changes during this period. These two factors represent the components of the TFP indicator. According to the present development of the TFP indicator, it is not possible to unambiguously forecast the future trend.

scholarly journals Economic Efficiency and Its Influencing Factors on Urban Agglomeration—An Analysis Based on China’s Top 10 Urban Agglomerations

2019 ◽  
Vol 11 (19) ◽  
pp. 5380 ◽  
Author(s):  
Junwei Ma ◽  
Jianhua Wang ◽  
Philip Szmedra
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Economic Efficiency ◽  
Policy Implications ◽  
Factor Productivity ◽  
Urban Agglomerations ◽  
Capital Productivity ◽  
Industrial Specialization ◽  
New Findings ◽  
The Impact

Economic efficiency is the key issue of sustainable development in urban agglomerations. To date, more attention has been paid to the estimates of productivity gains from urban agglomerations. Differing from the previous studies, this paper focuses on the influencing factors and mechanisms of the economic efficiency of urban agglomerations, and check the effects of three different externalities (industrial specialization, industrial diversity and industrial competition) on the economic efficiency of urban agglomerations. The selected samples are multiple urban agglomerations, and the economic efficiency of urban agglomerations includes single factor productivity and total factor productivity. China’s top 10 urban agglomerations are selected as the case study and their differences in economic efficiency are portrayed comparatively. Firstly, a theoretical analysis framework for three different externalities effect mechanisms on the economic efficiency of urban agglomerations is incorporated. Secondly, economic efficiency measurement index system composes of labor productivity, capital productivity, land productivity and total factor productivity, and the impact of various factors on the economic efficiency of urban agglomerations is tested. The results confirm some phenomena (MAR externality, Jacobs externality and Porter externality) discussed or mentioned in the literature and some new findings regarding the urban agglomerations, derive policy implications for improving economic efficiency and enhancing the sustainability of urban agglomerations, and suggest some potentials for improving the limitations of the research.

scholarly journals Technological Bias and Its Influencing Factors in Sustainable Development of China’s Transportation

2020 ◽  
Vol 12 (14) ◽  
pp. 5704
Author(s):  
Shuai Zhang ◽  
Xiaoman Zhao ◽  
Changwei Yuan ◽  
Xiu Wang
Keyword(s):  
Sustainable Development ◽  
Technological Change ◽  
Total Factor Productivity ◽  
Influencing Factors ◽  
Technological Progress ◽  
Transportation Industry ◽  
Factor Productivity ◽  
The Sustainable Development ◽  
Biased Technological Change ◽  
Technological Bias

The bias of technological progress, particularly relating to energy saving and carbon emissions reduction, plays a significant role in the sustainable development of transportation, and has not yet received sufficient attention. The objectives of this paper were to examine the bias of technological change (BTC), input-biased technological change (IBTC), and output-biased technological change (OBTC), and their influencing factors in the sustainable development of China’s regional transportation industry from 2005 to 2017. A slack-based measure (SBM) Malmquist productivity index was adopted to measure the BTC, IBTC, and OBTC by decomposing green total factor productivity. The results revealed that: (1) Continuous technological bias progress and input-biased technological progress existed in China’s transportation development from 2005 to 2017, making an important contribution to green total factor productivity. The output-biased technological change was close to 1, indicating a slight impact on the sustainable development of the transportation industry; (2) The bias of technological progress in eastern regions was slightly greater than that in central regions, and obviously greater than that in western regions. Moreover, different provinces experienced different types of technological bias change, with four major types observed during the research period; (3) The input-biased technology of a majority of provinces tended to invest more capital relative to labor, using more capital comparing to energy, and consume more energy relative to labor, while the output-biased technology of most provinces tended to produce desirable outputs (value added in transportation) and reduce the byproduct of CO2 relatively; (4) Average years of education, green patents in transportation, industrial scale, and local government fiscal expenditure in transportation significantly contributed to promoting the bias of technological progress, which was inhibited by the R&D investment. This study provides further insight into the improvement of sustainable development for China’s transportation, thereby helping to guide the government to promote green-biased technological progress and optimize the allocation of resources.

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