scholarly journals Research on Total Factor Productivity Measurement and Influencing Factors of Digital Economy Enterprises

2021 ◽  
Vol 187 ◽  
pp. 390-395
Author(s):  
Jinfang Tian ◽  
Yiran Liu
Keyword(s):  
Total Factor Productivity ◽  
Influencing Factors ◽  
Digital Economy ◽  
Factor Productivity ◽  
Productivity Measurement

Digital economy: An innovation driver for total factor productivity

2022 ◽  
Vol 139 ◽  
pp. 303-311
Author(s):  
Wenrong Pan ◽  
Tao Xie ◽  
Zhuwang Wang ◽  
Lisha Ma
Keyword(s):  
Total Factor Productivity ◽  
Digital Economy ◽  
Factor Productivity

scholarly journals Measuring Total Factor Productivity: Accounting for cross country differences in income per capita // Мјерење укупне факторске продуктивности: обрачун међудржавних разлика у БДП-у по раднику

2011 ◽  
Vol 11 (19) ◽  
pp. 75
Author(s):  
Stevo Pucar ◽  
Zoran Borovic
Keyword(s):  
Total Factor Productivity ◽  
Factor Productivity ◽  
Productivity Measurement ◽  
Production Factors ◽  
First Pass ◽  
Development Accounting ◽  
Proximate Determinants ◽  
Country Differences ◽  
Cross Country ◽  
The Cross

Summary: Why are some countries so much richer than others? Why do some countries produce so much more output per worker than others? Influential works by Klenow & Rodriguez-Clare (1997), Hall and Jones (1999), and Parente & Prescott (2000), among others, have argued that most of the cross country differences in output per worker is explained by differences in total factor productivity. Total factor productivity measurement enables researchers to determine the contribution of supply-side production factors to economic growth. Development Accounting is a first-pass attempt at organizing the answer around two proximate determinants: factors of production and efficiency. It answers the question “how much of the cross-country income variance can be attributed to differences in (physical and human) capital, and how much to differences in the efficiency with which capital is used’’?In this article, we will outline framework for growth accounting to account for cross-country difference in income of Republic of Srpska, Republic of Croatia and Republic of Serbia. The current consensus is that differences in income per worker across countries do not arise primarly from differences in quantities in capital or labour, but rather from differences in efficiency with which are these factors used. We find that total factor productivity is very important for the growth of output per worker, but only in cases of Serbia and Croatia. In case of Srpska the most important factor for the growth of output per worker is growth of capital.Резиме: Зашто су неке земље толико богатије од других? Зашто неке земље остварују много већи обим производње по раднику од других? Утицајни радови Klenow и Rodriguez-Clare (1997), Hall и Jones (1999), и Parente и Prescott (2000), између осталих, тврдили су да је највећи број међудржавних разлика у обиму производње по раднику резултат разлика у Укупној Факторској Продуктивности. Мјерење Укупне Факторске Продуктивности омогућава истраживачима да утврде допринос фактора на страни понуде привредном расту. Развој ‘’рачуноводства раста’’ представља први покушаја анализирања двије сродне детерминанте раста: фактори производње и ефикасности.  Ова анализа даје одговор на питање “колико су међудржавне разлике у оствареном БДП-у резултат међудржавних разлика у (физичком и људском) капиталу, а колико су резултат разлика у ефикасности којом се капитал користи’’?У овом раду ћемо приказати оквир за “рачуноводство раста’’ који ће се примјенити за обрачун међудржавних разлика у БДП-у по раднику за Републику Српску, Републику Хрватску и Републику Србију. Тренутни консензус међу ауторима је да разлике у БДП-у по раднику између земаља не настају првенствено због разлика у количинама капитала или рада, него због разлика у ефикасности са којом се ови фактори користе. Анализом смо дошли до закључка да је Укупна Факторска Продуктивност веома важна за раст производње по раднику, али само у случајевима Србије и Хрватске. У случају Српске најважнији фактор за раст производње по раднику је раст техничко-технолошке опремљености рада капиталом.

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.

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.

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