scholarly journals Measuring Total Factor Productivity of China Provincial Non-Life Insurance Market: A DEA-Malmquist Index Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fangping Yu ◽  
Hang Chen ◽  
Jiaqi Luo ◽  
Haibo Kuang
Keyword(s):  
Total Factor Productivity ◽  
Life Insurance ◽  
Technological Progress ◽  
Insurance Industry ◽  
Malmquist Index ◽  
Insurance Market ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Index Method ◽  
Life Insurance Industry

The unbalanced economic development results in the difference in operating efficiency of the non-life insurance industry in China’s provinces; based on the DEA-Malmquist index method, this paper investigates the provincial differences, dynamic change characteristics, and causes of non-life insurance productivity in 31 provinces of China from 2004 to 2017. The results show that in the sample period, there are significant differences between provinces and regions in China’s non-life insurance efficiency, which generally shows the echelon spatial characteristics of “strong in the west and weak in the east”. Technological progress in the western region promotes the rapid growth of total factor productivity, while the low efficiency of technological progress in the eastern region restrains the improvement of total factor productivity. The overall total factor productivity of China’s provincial non-life insurance industry is on the rise, mainly due to the improvement of pure technical efficiency and scale efficiency, while technological progress has an inhibiting effect on the contrary. These conclusions are of reference value for relevant stakeholders in China’s provincial non-life insurance market to formulate development strategies and business strategies.

Total factor productivity of the non-life insurers in India: Malmquist index with a new decomposition

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ashiq Mohd Ilyas ◽  
S. Rajasekaran
Keyword(s):  
Total Factor Productivity ◽  
Life Insurance ◽  
Economies Of Scale ◽  
Negative Impact ◽  
Malmquist Index ◽  
Scale Efficiency ◽  
Factor Productivity ◽  
Decomposition Approach ◽  
Content Type ◽  
Insurance Sector

PurposeThis paper aims to measure the change and the sources of change in total factor productivity (TFP) of the Indian non-life insurance sector over the period 2005–2016.Design/methodology/approachThis study employs the bootstrapped Malmquist index (MI) to assess the changes in the TFP and adopts a decomposition approach proposed by Balk and Zofío (2018). Moreover, it utilises truncated regression to identify the determinants of the TFP. In addition, it employs Wilcoxon-W test and t-test to scrutinise the difference between the state-owned and the private insurers in terms of variations in TFP and its various components.FindingsThe results divulge a miniature improvement in TFP of the insurance sector, which is primarily attributable to the improvement in scale efficiency (economies of scale). The results also reveal that there are no significant TFP differences across the ownership. However, private insurers have better scale efficiency and lower input-mix efficiency than state-owned insurers. In addition, the results unveil that size, diversification and reinsurance have a negative impact on the TFP, while age has a positive impact on it.Practical implicationsThe results may help the policymakers to frame new consolidation policies. Moreover, the findings may guide the decision-makers of the Indian non-life insurance companies to abate inefficiency and improve TFP.Originality/valueThis study estimates bias-corrected changes in TFP and efficiency in the non-life insurance sector. Moreover, it adopts an elaborated decomposition of the MI to identify the true sources of change in the TFP.

scholarly journals Dynamic Analysis of Total Factor Productivity (TFP) of Traditional Chinese Medicine (TCM) Hospitals in Hubei Province from 2013 to 2019

2021 ◽  
Vol 292 ◽  
pp. 02069
Author(s):  
Tang Cheng ◽  
MA Jun ◽  
Chen Li
Keyword(s):  
Total Factor Productivity ◽  
Malmquist Index ◽  
Hubei Province ◽  
Scale Efficiency ◽  
Productivity Change ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Index Method ◽  
Mean Values ◽  
The Mean

Objectives: To study the changes of TFP in TCM hospitals in Hubei Province from 2013 to 2019, so as to provide policy basis for improving service efficiency and promoting the development of TCM. Methods: The statistical reports of 90 public TCM hospitals in Hubei Province from 2013 to 2019 were collected using the Hubei Provincial Comprehensive Statistical Information System for TCM, and the TFP of TCM hospitals were dynamically analysed using the DEA-Malmquist index method. Results: From 2013 to 2019, the Total Factor Productivity Change (TFPCH) index of TCM Hospitals in Hubei Province increased by 0.8%; the Efficiency Change (EFFCH) index increased by 0.5%; the Technical Change (TECH) index increased by 0.3%; the Pure Efficiency Change (PECH) index increased by 0.2% and the Scale Efficiency Change (SECH) index increased by 0.4%. The mean values of the TFPCH index in the eastern, central, and western regions of Hubei Province were 1.001, 1.011, and 1.013, respectively, and the mean values of that in the secondary and tertiary hospitals were 1.004 and 1.024, respectively. Conclusions: The TFP of TCM hospitals in Hubei Province tends to improve on the whole. There are differences in the TFPCH index of TCM hospitals in different regions and levels. The stage development characteristics of core cities play a leading role and are worth learning.

scholarly journals Analysis of heterogeneity and convergence of TFP in culture industry

2021 ◽  
Vol 235 ◽  
pp. 02009
Author(s):  
Yun Li ◽  
Weijun Zhao
Keyword(s):  
Panel Data ◽  
Convergence Rate ◽  
Technological Progress ◽  
Malmquist Index ◽  
Culture Industry ◽  
Evaluation Index System ◽  
Evaluation Index ◽  
Western Region ◽  
Factor Productivity ◽  
Index Method

Based on the panel data of China’s culture and related industries, this paper constructs an evaluation index system of total factor productivity of culture industry, measures the TFP of culture industry by using the global Malmquist index method, The results show that the TFP index of China’s culture industry decreases by 3.1% every year, which is mainly driven by technological progress, There is an obvious trend of σ convergence among China’s culture industry as a whole, the western region and the subdivided industries. The convergence rate of TFP in the whole culture industry is 2.306%.

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.

Some thoughts on the development and structure of the New Zealand life insurance industry

1994 ◽  
Vol 121 (3) ◽  
pp. 573-588 ◽  
Author(s):  
M. B. Adams
Keyword(s):  
New Zealand ◽  
Life Insurance ◽  
Insurance Industry ◽  
Insurance Market ◽  
Distinctive Features ◽  
Life Insurance Industry ◽  
Key Characteristics ◽  
Historical Role ◽  
The Government

AbstractThis paper seeks to explain key characteristics of the New Zealand life insurance industry, in particular the important role played by overseas-controlled mutual companies, and the dearth of regulation relative to other countries. It proposes that the dominance of mutual companies reflects the historical development of the New Zealand life insurance market. It also examines how agency theory may help to explain how the market has come to be dominated by mutual companies, and suggests that the unregulated nature of the life insurance industry may reflect the New Zealand government's historical role of direct intervention in the market through the Government Life Office. Further light on this issue is shed by the economic theory of regulation. This theory suggests that cartelisation and reinsurance may help to explain the existence of the unregulated insurance market in New Zealand. The paper concludes that many socio-economic and historical reasons may account for the distinctive features of the New Zealand life insurance industry. The possibilities are presented in this paper as a stimulus for further insurance markets-based research.

Estimating China’s Future Life Insurance Market

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Jiyue Ma ◽  
Fei Huang ◽  
Aaron Bruhn
Keyword(s):  
Capital Markets ◽  
Life Insurance ◽  
Insurance Industry ◽  
Insurance Market ◽  
Economic Expansion ◽  
Life Insurance Industry ◽  
Specific Factors ◽  
Important Indication

AbstractAfter decades of economic expansion, China is transitioning to meet the insurance needs of its aging and increasingly affluent population. Of particular interest to insurers and reinsurers is China’s life insurance industry, which is likely to be globally significant due to its size and scale of opportunity. The long term nature of life insurance will also see it play a key role in China’s financial and capital markets. By uniquely accounting for demographic, economic and insurance-specific factors, we estimate the long term size of China’s life insurance market, giving an important indication of the scale of its future influence.

scholarly journals TOTAL FACTOR PRODUCTIVITY GROWTH IN CHINA’S CORN FARMING: AN APPLICATION OF GENERALIZED PRODUCTIVITY INDICATOR

2021 ◽  
Vol 22 (5) ◽  
pp. 1189-1208
Author(s):  
Zhiyong Niu ◽  
Yining Zhang ◽  
Tianxiang Li ◽  
Tomas Baležentis ◽  
Dalia Štreimikienė ◽  
...  
Keyword(s):  
Total Factor Productivity ◽  
Technological Progress ◽  
General Setting ◽  
Scale Efficiency ◽  
Production Data ◽  
Factor Productivity ◽  
Efficiency Change ◽  
Corn Production ◽  
Tfp Growth ◽  
Input And Output

Total factor productivity (TFP) growth measures usually focus on a certain direction of optimization and ignore the general setting encompassing the input and output orientations simultaneously. This paper uses the generalized Luenberger-Hicks-Moorsteen (LHM) TFP indicator which is additively complete and can be decomposed by three mutually exclusive elements. The input- and output-oriented analysis is undertaken in order to derive the generalized TFP measured. The paper uses the corn production data from 19 Chinese provinces over the period of 2004–2017. This research is important as China is the second largest corn producer in the world. The TFP growth was observed for Chinese corn farming the rate of 0.56% per year. The technological progress (0.48%) was the major source of the TFP growth, whereas the importance of the technical efficiency change (0.09%) and scale efficiency change (–0.01%) was negligible.

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