Purpose: From the perspective of cause and effect, the operational processes of property insurance companies can be considered as historical events. The purpose of this study is to measure the operating efficiency of China's property insurance industry, explore the determinants that affect technical efficiency and outline the path to achieving high-quality development. Design/methodology/approach: We chose 44 Chinese property insurance companies as research objects. The data were obtained from the Chinese Insurance Yearbook and China Statistical Yearbook 2015–2017. First, the data envelopment analysis (DEA) method was used to calculate the technical efficiency of property insurance companies. Then, Tobit regression and quantile regression were adopted to explore the influencing factors of technical efficiency. Finally, the fuzzy-set qualitative comparative analysis (fsQCA) method was employed to summarize the path to improving the operating efficiency of property insurance companies. Findings: The empirical results in the first stage suggested that the operation efficiency of China's property insurance industry was technically inefficient, and the scale efficiency was relatively better than the pure technical efficiency. In the second stage, we observed that the drivers for firm size, reinsurance rate, claim ratio and equity restriction were important determinants of an insurance firm's efficiency. Research limitations/implications: We also put forward four applicable, targeted and proven ways to improve the technical efficiency of property insurance companies. These configurations are verified by cases of existing property insurance companies, which can provide practical references for the insurance industry. Originality/value: Our research enriches the insurance literature and efficiency methods, particularly regarding the specific paths of improving the technical efficiency. The relationship between elements and results is analyzed from a systematic perspective, and the research results are not only more consistent with what logic might imply but also more instructive for the improvement of reality.

Purpose: This paper aims to measure the operating efficiency of China’s basic pension insurance from 2014 to 2019 in 31 provinces. Methods: The three-stage DEA model was used to evaluate the operating efficiency of basic pension insurance in 31 provinces. Results: On the whole, the operating efficiency of China’s basic pension insurance was at a high level, but there was still room for improvement. GDP, urbanization level and scale of government public expenditure had a positive impact on the operating efficiency of regional basic pension insurance. In contrast, the old-age dependency ratio had a significant negative effect. There were noticeable regional differences in the operating efficiency of China’s basic pension insurance, which showed a pattern of Central (0.742) >Eastern (0.689) >Western (0.505) after removing the influence of environmental variables. Conclusion: This study systematically analyzes the impact of external environmental regulations on China’s basic pension insurance’s operating efficiency and provides decision-making references for further improving the operating efficiency of China’s basic pension insurance. In order to further optimize the allocation of basic endowment insurance, this paper proposes the following countermeasures and suggestions: (1) broaden investment channels and improve the investment efficiency; (2) increase supervision of social security departments and strengthen information disclosure; (3) unify the management of social basic pension insurance.

Purpose: This paper aims to measure the technical efficiency of China’s medical and health institutions from 2012 to 2017 and outline the path to achieve high-quality development. Methods: The DEA-Malmquist was used to evaluate the total factor productivity of medical and health institutions in 31 provinces. A fuzzy set Qualitative Comparative Analysis (fsQCA) was used for configuration analysis of determinants affecting technical efficiency. Results: The average total factor productivity (TFP) of those institutions was 0.965, namely TFP declined averagely by 3.5% annually. The efficiency change and the technical change were 0.998 and 0.967, respectively. The realization paths of high technical efficiency are composed of high fatality rate and high financial allocation-led, high population density and high GDP-led. Low dependency ratio and low financial allocation-led, low fatality rate and low financial allocation-led are the main reasons for low technical efficiency. Conclusion: Due to advanced medical technology and economic development, major cities like Beijing, Shanghai, and Guangdong have attracted a large number of high-level health personnel, achieving long-term and stable health business growth. Hubei, Anhui, and Sichuan also have made rapid development of health care through appropriate financial subsidies and policy supports. The technical changes in Qinghai, Yunnan, and Inner Mongolia are higher than the national average, but the operation and management level of the medical and health institutions is relatively weak. Henan, Jiangxi, and Heilongjiang have a prominent performance in the efficiency change, but the technical change is weaker than the national average.

Background: A linchpin to realizing the internal circulation (referring to the domestic cycle of production, distribution and consumption) is reducing residents’ saving rate and expanding the domestic needs. However, rural residents in China demonstrate a strong propensity to save money. Methods: In light of practical characteristics of urban-rural health integration promoted in different places, the three-phase data (from 2014 to 2018) and the dual difference-in-differences model of the China Labor-force Dynamics Survey (CLDS) are used to empirically investigate the impact of urban-rural health insurance integration on rural household savings. Results: Research reveals that urban-rural health integration can reduce the health risks and medical risks facing rural households, thus weakening the motivation of precautionary savings. The analysis of heterogeneity reveals that the integration of urban-rural health insurance significantly influences the savings rates of households headed by older individuals, particularly women, with lower levels of educational attainment. Besides, the single-tier health insurance system can have a more significant impact, whereas the multi-tier insurance system may not significantly affect the savings rate. Conclusion: Based on the aforesaid research conclusions, this article believes that in order to reduce the savings rate of rural households and expand consumption, the health insurance system should be further improved.

Background: To analyze the regional disparities, dynamic evolution, and influencing factors of medical resource allocation efficiency in TCM hospitals across China from 2016 to 2022, providing references for optimizing resource allocation in TCM hospitals. Methods: The study employed a super-efficiency Slack-Based Measure (SBM) model considering undesirable outputs to assess regional equity in efficiency, utilized the Dagum Gini coefficient to measure regional disparities in efficiency, and applied kernel density estimation and spatial econometric models to analyze the dynamic evolution and spatial spillover effects of medical resource allocation efficiency in TCM hospitals. Results: In 17 provinces, the efficiency is higher than the average value of 0.839, and in 8 provinces, the average value has exceeded 1. The regional pattern of efficiency shows a gradient characteristic of "high in the east and stable in the west, with the Northeast lagging behind." There is a significant spatial difference in the efficiency of resource allocation. The overall difference in the allocation of resources for traditional Chinese medicine (TCM) hospitals shows a fluctuating upward trend. The contribution rate of regional differences reaches 53.45%, which is the dominant factor. The largest regional differences are found within the central region, while the gaps between the eastern and central regions continue to widen, and those between the western and northeastern regions tend to become more balanced. The most significant interregional differences are observed between the central and western regions. The efficiency of resource allocation for TCM hospitals is on the rise, with the kernel density curve shifting to the right. The main peak height first decreases and then increases, while the width first expands and then contracts. The absolute difference first increases and then decreases. The rightward convergence of the tail indicates that there are efficient hospitals, but the gaps are narrowing. The multi-peak distribution reveals a multi-level differentiation pattern with the coexistence of low-efficiency and high-efficiency clusters. Per capita GDP, urbanization level, aging rate, population density, and the number of graduates from higher medical colleges can promote efficiency improvement. Population density and the proportion of TCM physicians have a positive spatial spillover effect on efficiency, while per capita GDP has a negative spatial spillover effect. Conclusion: The efficiency of medical resource allocation in traditional Chinese medicine (TCM) hospitals is steadily improving, and the regional differences are continuously narrowing. The degree of efficiency multi-polarization is becoming more moderate, and the development of regional equilibrium is being achieved. Both internal and external environmental factors jointly influence the improvement of medical resource allocation efficiency in TCM hospitals. It is recommended to take measures such as technological empowerment, institutional constraints, financial support, and talent absorption to enhance the efficiency of medical resource allocation in TCM hospitals and bridge the regional gaps.

This paper aims to assess the competitiveness of 68 high-growth enterprises listed on China's STAR market and explore the combination of antecedent conditions that led to their listing using fuzzy set qualitative comparative analysis. The interpretive structure model was utilized to identify the factors influencing their competitiveness, and the listing index weight for the STAR market was determined using the analytic hierarchy process. The competitiveness of the listed enterprises was found to be promising, with new energy, next-generation IT, and high-end equipment manufacturing being the most prominent fields. However, energy conservation and environmental protection exhibited relatively weak listed competitiveness. The listing of these enterprises was the result of multiple factors rather than a single factor. The listing paths of high-growth enterprises in China were classified into three types: well-operated with high technical competence and innovation-led, high-profit with low growth and innovation, and large-scale, high-profit, and innovation-led.