Factor Productivity Change Research Articles

Productivity in the Insurance Company: A Meta-Regression Analysis

Purpose –This study attempts to examine the studies using Malmquist Productivity Index (MPI) in calculating the productivity of insurance sector and to identify the factors influencing their total factor productivity change through meta-regression analysis. Design/Methodology/Approach –This study employed 29 empirical articles which were published between 1977-2023 and which met the inclusion criteria. Findings –The results on the meta-regression analysis revealed that the publication year of the studies and the income groups of the countries did not have an impact on the total factor productivity. The number of observations, number of variables, and branch of theinsurance company were statistically significant on the estimation of total factor productivity change.Discussion –In this study, a meta-regression analysis systematically highlights the effects of methodological assumptions on the productivity of insurance companies and synthesize empirical findings. The study serves to make the literature more accessible to researchers who will use the MPI method for measuring the productivity of insurance companies. This study is expected to guide managers in enhancing the productivity of insurance companies, considering national requirements, making and implementing legal regulations, and identifying policies

Equity and efficiency of health resource allocation in the Chengdu-Chongqing Economic Circle of China.

This study aimed to evaluate the fairness and efficiency of health resource allocation (HRAE) in Chengdu-Chongqing Economic Circle after the new healthcare reform. This study also aimed to identify existing problems, providing empirical evidence for the government to formulate regional health plans scientifically and reasonably. The fairness of health resource allocation was analyzed using the Gini coefficient, Theil index, and agglomeration degree from population and geographical area perspectives. The three-stage data envelopment analysis and the Malmquist productivity index were used to analyze HRAE from static and dynamic perspectives. The Gini coefficient for population allocation in Chengdu-Chongqing Economic Circle was 0.066-0.283, and the Gini coefficient for geographical area allocation was 0.297-0.469. The contribution rate within a region was greater than that between regions, and health resources were mainly concentrated in economically developed core areas. The overall fairness of Chengdu Economic Circle was relatively better than that of Chongqing Economic Circle. Moreover, the adjusted mean technical efficiency was 0.806, indicating room for HRAE improvement in Chengdu-Chongqing Economic Circle. Stochastic Frontier Analysis found that different environmental variables have varying degrees of impact on HRAE. The adjusted mean total factor productivity change (Tfpch) was 1.027, indicating an overall upward trend in HRAE since the new healthcare reform. However, scale efficiency change (Sech) (0.997) limited the improvement of Tfpch. The fairness of health resources allocated by population was better than that allocated by geographical area. The unfairness of health resources mainly stemmed from intra-regional differences, with considerable health resources concentrated in core areas. Over the past 13 years, HRAE has improved but exhibited spatial heterogeneity and Sech-hindered productivity improvement. The study recommends strengthening regional cooperation and sharing to promote the integrated and high-quality development of the health and well-being in Chengdu-Chongqing Economic Circle.

Efficiency evaluation, regional technological heterogeneity and determinant of total factor productivity change in China’s healthcare system

Enhancing efficiency and productivity in countries’ healthcare systems is a global challenge. The Chinese government invested huge resources to improve the efficiency and productivity of the healthcare system across the country. To assess the success of the mission above, this research utilized DEA-SBM Meta frontier analysis alongside the Malmquist Productivity Index. These methodologies were employed to gauge Efficiency, production technology heterogeneity, and productivity of healthcare systems change across 31 mainland Chinese provinces and four distinct geographical regions throughout the study period spanning from 1997 to 2022. Results revealed that the mean efficiency score of China’s healthcare system is 0.7672. It indicates a growth potential of 23.28 percent in the operational efficiency of healthcare systems. The eastern region’s efficiency level (0.86917) is higher among all four regions. Zhejiang, Shandong, and Guangdong are the top three healthcare-efficiency performers. The technology gap ratio indicates that eastern regions witnessed a high TGR (0.9909), showing the country’s attainment of superior healthcare technologies. Beijing, Guangdong, Shanghai, Tianjin, and Zhejiang witnessed higher TGR values among all 31 mainland Chinese provinces. The total factor productivity index of the healthcare system witnessed a slight growth of 0.33%, with an average MI score of 1.0033. Efficiency change (EC) was found to be the main determinant of TFPC as technology change TC is less than EC. Moreover, the MI score of the Western region (1.033) is higher than the corresponding Eastern, northeastern, and central regions. Guizhou, Anhui, and Yunnan were found to be the top three performers in TFPC growth. Finally, the Kruskal–Wallis test confirmed the statistically significant difference among 4 Chinese regions for the healthcare system’s efficiency, TFPC, and TGR.

Impact of Islamicity on the productivity of conventional and Islamic banks in selected southeast Asian countries

This study provides a new understanding of how bank-specific characteristics, the macroeconomic environment, and Islamicity affect the productivity of conventional banks (CBs) and Islamic banks (IBs) in selected Southeast Asian countries. The data of 153 banks, collected between 2015 and 2020, were used, and the influence of the selected factors was investigated by using panel data approaches, as well as the data envelopment analysis-based Malmquist productivity index (DEA-MPI). In the first-stage analysis, bank productivity was measured by using total factor productivity changes (TFPC), and in the second-stage analysis, panel regression analysis was performed, specifically, multiple panel regression analysis, which employed pooled ordinary least squares, a fixed-effects model, and a random-effects model. Parametric and nonparametric (Mann–Whitney and Kruskall–Wallis) tests found that the IBs were more progressive than the CBs. The findings indicated that unlike the IBs, change in the productivity of the CBs was adversely affected by economic Islamicity, as well as legal and governance Islamicity, which indicated that macroeconomic and legal structures that leaned toward Islamic values adversely affected the CBs. Meanwhile, the IBs were likely to be structured in line with such values and thus can mitigate the adverse impact of various factors. Both groups were negatively affected by human and political rights Islamicity, which indicated the use of a setup that could not benefit from Islamic values. This finding lends credence to the arguments in the literature that IBs mirror CBs in practice to remain competitive in their pricing based on market principles. The IBs were found to benefit from international relations Islamicity, which bodes well for the competitiveness of new players against the ancient setup of CBs in the globalized world.

Bank Productivity: A Meta-Regression Analysis

Purpose: This study aims at examining studies employing the Malmquist Productivity Index (MPI) in calculating banks’ productivity. It also seeks to determine the factors affecting the total factor productivity change of banks through meta-regression analysis. Methodology: On December, 2023, relevant works were systematically reviewed using Web of Science (WoS), Scopus, and Google Scholar. The literature review employed a comprehensive search involving all files with the keywords such as ‘‘productivity” and “bank’’. The research process adhered to the PRISMA guidelines. Findings: Key features of the 35 studies incorporated in the analysis are presented. The samples of 65.71% of the studies are Asian countries. The bank productivity of 45.71% was calculated through the DEA-MPI method. The studies under consideration were sourced from diverse populations. These studies share key similarities in terms of subject and methodology. Random Effects Model was used to test heterogeneity across studies. The common effect size is 19.361 (z= 4.23, 95% CI: [10.384, 28.338]). Inter-study heterogeneity was determined through Cochran Q test and I^2 index (I^2= % 100, df=32.000, Q=141163533.762, p

External shocks and dynamics of resource use patterns: empirical evidence from the Indian manufacturing sector

While promoting manufacturing growth is imperative to boost economic growth, potential adverse consequences on natural resources and ecology have created sustainability-related concerns and challenges. In India, with the reform processes deepening further and the business environment becoming more market-oriented over the years, the adoption of appropriate measures for sustainable manufacturing growth under such conditions would require a deeper investigation of the underlying dynamics, particularly to address the impacts of external shocks. This is especially true when external shocks alter market dynamics and, hence, input is used significantly in the manufacturing sector. Given this backdrop, the present paper examines how the major external shocks to the Indian economy during in the post-reform era have altered the resource use patterns in selected manufacturing industries. The paper uses secondary data collected from the KLEMS database of the Reserve Bank of India for the period from 1980–81 to 2019–20 and applies descriptive statistics and econometric techniques to address the research objective. It is found that energy intensity, vertical integration, capital intensity, and rate of growth of total factor productivity decreased over the decades, whereas labour productivity experienced an increasing trend in most industries. However, the trends are mixed in the case of material intensity. Further, the regression results indicate significant effects of external shocks on resource use, factor productivity, and structural changes. There are also industry-specific differences in the nature and extent of such changes. Hence, a deeper investigation of the underlying factors and impacts at a disaggregate industry level is necessary to draw more robust insights.

The Effect of Technology Transfers on Productivity Increment in Japanese Overseas Manufacturing

Technological innovation and technology transfer play an important role in enhancing the productivity of MNEs, especially the Japanese overseas manufacturing subsidiaries. The purpose of this study is to examine the association between R&D cost, technology purchase, and total factor productivity change and regression models with the survey data of Japanese Ministry of Economy, Trade, and Industry on Overseas Business Activities conducted over 2021-2023. Basic and Trend Surveys were combined to create a sample of 1,798 affiliates across 38 countries with variables such as R&D spending, Technology Transfer payments, capital stock growth, and employment growth. The evidence presented suggests that both technology and R&D payments enhance productivity improvement for the company, and the estimated returns on R&D investments is 0.54 Yen (JPY) for every JPY spent and technology transfer payments 0.76 JPY. The regression results also show a synergistic relationship between technology and R&D payments indicating that affiliates involved in both processes report higher levels of efficiency. In addition, the study also focuses on geographical specialization of technology transfer and R&D in industries including chemicals, pharmaceuticals, and machinery, where major expenditure on R&D is done across the globe and particularly in United States and in Europe.

Impacts of Capital Formation and Investment Sources on Total Factor Productivity: The Example of Nigeria’s Agriculture

The study was designed to analyse the impact of capital formation and investment sources on Nigeria's agricultural productivity from the period 1980 - 2021. The time series data were analysed with Malmquist Data Envelopment Analysis (DEA) and the tobit regression models using DEA, the Total Factor Productivity Change, which quantifies the degree of productivity, was estimated at 1.022, which implies an average productivity progress of 2.2% annually. Again, since the value of technical efficiency change is less than technological change, it implies that the productivity gains are mainly attributable to technological progress instead of efficiency improvements in the periods under the study. The results of tobit regression indicate that domestic capital formation significantly influences agricultural TFP, highlighting the importance of government expenditure on agriculture (GEA) and gross fixed capital formation (GFCF) in enhancing productivity, although with minimal effects. Human capital formation (HCF) also showed positive impacts on Total Factor Productivity TFP, suggesting that improvements in workforce skills and knowledge are crucial for agricultural efficiency. However, FDI exhibited no significant correlation with TFP, implying that local investments are more beneficial than foreign aids in the context of Nigeria's agricultural sector. The findings emphasize the need for robust policy frameworks that align capital disbursements with agricultural production periods to optimize TFP. This study contributes to the discourse on agricultural productivity by providing empirical evidence on the critical role of capital investments in enhancing TFP within developing economies like Nigeria.

Comprehensive multistage approach for measuring the efficiency of logistics processes in the presence of a mismatch between sales and logistics

Frequently, various sectors within the same company often find themselves at odds with one another due to conflicting objectives. For example, the sales and logistics departments frequently fail to synchronize, with sales viewing logistics merely as a cost center. Conversely, sales place demanding delivery tasks on logistics, particularly evident in scenarios involving out-of-stock or non-assortment items. To address this pervasive issue, the primary objective of this paper is to propose a model for assessing the efficiency of logistical processes, distribution centers, and drivers. Hence, it can be asserted that this study, through its proposed model, aims to accomplish several aims: (i) determine the effectiveness of distribution centers (DCs), (ii) analyze how the efficiency of DCs evolves over time, (iii) evaluate the performance of distribution drivers, and (iv) rank proficient drivers for bonus allocation based on their performance. To achieve these objectives, the suggested model relies on the application of the CI-DEA-Malmquist Index-DEA-MEREC-ADAM methodology. Specifically, the model is delineated into three distinct phases. The initial phase entails employing the CI-DEA and Malmquist index methodologies to condense the multitude of parameters considered in the study by utilizing the CI-DEA approach. Additionally, Malmquist analysis is applied to observe the multi factor productivity change in the efficiency of DCs and to identify the most efficient DC across all time periods. Following the identification of the most efficient DC, the subsequent phase focuses on selecting drivers from that center, followed by applying the DEA method to isolate only the most efficient drivers, who are then further evaluated and ranked accordingly. Through the application of these methodologies, it was determined that out of a total of 10 drivers, only four were deemed efficient. In the final phase of the model, these drivers were assessed based on seven criteria for ranking purposes. During this phase, the MEREC method was utilized to ascertain the criteria weights, while the ADAM method was employed for driver ranking. The findings revealed that the second driver ranked the highest and thus should be granted the highest bonus.

An evaluation of the regional heterogeneity of museums' operational efficiency and productivity changes across Chinese provinces

Museums are critical in safeguarding cultural heritage and cultivating community educational opportunities. This research aims to evaluate operational efficiency (OE), the impact of technological change on total factor productivity change (TFPC), and the regional heterogeneity of museum performance in three regions and 31 provinces across China. To this end, the study employed DEA-SBM and the Malmquist Productivity Index to gauge OE, TFPC, and determinants of TFPC (efficiency change or emerging technologies change) across 31 provinces for 2012–2021. Results reveal that the average OE of the Chinese Museum is 0.8394. It shows a 16.06 % growth potential in the operational efficiency of Chinese Museums. Further, the OE of Chinese Museums declined over the study period from 0.8965 in 2012 to 0.8088 in 2021. Beijing, Fujian, and Hunan are ranked top with a Museum's OE Score of 1. The average MI score of Chinese Museums is 0.9744, and technology change is the main determinant of Decline in productivity change as EC = 0.9992 is greater than TC = 0.9846. The MI of Liaoning, Shanghai, Ningxia, Jiangxi, Chongqing, Sichuan, Guangdong, and Tianjin is over 1, indicating growth in total factor productivity over the study period. The eastern region of China shows higher operational efficiency and total factor productivity scores of museums than the central and western regions. The results of the Kruskal-Wallis test proved that a statistically significant difference exists among different regions of China for the OE, MI, EC, and TC of museums.

Can performance-based financing improve efficiency of health centers in Ethiopia? A Malmquist Productivity Index analysis

IntroductionThe Ethiopian government has introduced several healthcare financing reforms intending to improve efficiency. Piloting implementation of performance-based financing is one of these actions. The purpose of this research is to assess the efficiency of healthcare facilities that have implemented performance-based financing compared to those that have not.MethodsEfficiency was measured using a nonparametric data envelopment analysis and the Malmquist Productivity Index technique. Total factor productivity change, technical change, and technological change are compared across eight sampled healthcare facilities that are implementing performance-based financing and eight that are not in Ethiopia.ResultsHealth facilities implementing performance-based financing have a mean technical efficiency score of 64%, allowing for a potential 36% reduction in inputs without affecting outputs. Their scale efficiency is 88%, indicating a potential 12% increase in total outputs without expanding facilities. In contrast, facilities not implementing performance-based financing have a mean technical efficiency score of 62%, with a potential for 38% input reduction without affecting outputs. Their scale efficiency is 87%, suggesting a potential 13% increase in total outputs without scaling up facilities. Among the 16 healthcare facilities observed, seven experienced a decline in the mean total productivity, while one remained stagnant. The remaining eight facilities witnessed an increase in productivity. The healthcare facilities implementing performance-based financing showed a 1.3% decrease in mean total productivity during the observed period. Among them, five showed an increase and three showed a decrease in the total factor of productivity. The mean total factor of productivity of all healthcare facilities not implementing performance-based financing remained stagnant over the three-year period (2019–2021), with four showing an increase and four showing a decrease in total productivity.ConclusionsThe study concludes that implementing performance-based financing did not improve productivity levels among healthcare facilities over three years. In fact, productivity decreased among the facilities implementing performance-based financing, while those not implementing it remained stagnant. This shows health facilities that implement performance-based financing tend to utilize more resources for similar outputs, contradicting the anticipated efficiency improvement.

Digitalization as a Factor of Production in China and the Impact on Total Factor Productivity (TFP)

In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect of digitalization on total factor productivity change (TFPC) is explained by comparing TFPC with and without digitalization. Findings reveal that digitalization’s integration into economic growth displays a U-shaped trajectory, with initial productivity setbacks transitioning to long-term benefits as industries adapt. The periodic complementarity and substitution between digitalization and labor, along with a weak substitution relationship with capital, illustrate that, as a production factor, digitalization dynamically interacts with other factors, both complementing and substituting them. This dynamic interplay highlights the intricate role that digitalization plays within the production function. Furthermore, digitalization has played a crucial role in China’s TFP growth, which also highlights the lack of other technological progress. Meanwhile, the pace of digital transformation presents scalability challenges, evident in the fluctuating scale efficiency change (SEC). Policymakers are advised to address these early stage challenges through supportive measures, ensuring smoother digital transitions. Concurrently, industries should embrace this non-linear transformation, emphasizing adaptability to maximize digitalization’s long-term advantages.

Digital Financial Services (DFS) And Productivity of Indian Banking Sector - Empirical Evidence Using Malmquist Productivity Index and Panel Data Regression

Purpose: The aim of this study is to examine the impact of Digital Financial Services (DFS) on the productivity of banking sector in India. Theoretical framework: This research considered various digital banking services offered by bank and how it affects the actual bank performance in terms of productivity, by adopting a two-stage model i.e., Malmquist Productivity Index (MPI) and panel data regression. Design/Methodology/Approach: The empirical study was based on eight-year balanced panel data from 2012 to 2020. The sample of the study consists of forty-four commercial banks from India. This study is completely based on secondary data collected from the website of the database of the Indian economy and the National Payment Corporation of India (NPCI). To achieve the research goals, a two-stage approach has been used. Initially, Malmquist Productivity Index (MPI) was employed to estimate the total factor productivity changes. In the second phase, panel regression analyses were used to study the impact of Digital Financial Services (DFS) on bank productivity. Findings: The findings show that the Digital Financial Services (DFS) variables such as mobile banking, online banking, Automatic Teller Machines (ATM) and Point of Sale (POS) transactions are significantly improved the productivity of the Indian banking industry. Research, Practical & Social implications: The study addresses the issues such as identifications of factors affecting the productivity of banks including Digital Financial Services (DFS). In the world of digital revolution, it analyses whether bank can retain, continue and enhance their performance by offering modern product and services to their customers. Originality/Value: This article has conducted extensive analyses of Digital Financial Services (DFS) and banks' productivity. The authors also provide suggestions for the policymakers for the future implementation of digital banking services.

Regional efficiency and total factor productivity change for small and medium sorghum producers in Zambia: A slacks-based measure and Malmquist index approach

Zambia's efforts to promote countrywide production of sorghum is backed by a number of apropos factors. It is not only an adaptive response to the climate change challenge, but also a dietary diversity improvement measure, to achieve food and nutrition security. Further, improved sorghum production will supply emerging agro-industries, especially specialty foods, beverages and stock feed manufacturing in the country. However, regional sorghum production efficiencies in Zambia are not well known. This paper attempts to evaluate regional sorghum production technical efficiencies (TE), as well as Total Factor Productivity (TFP) change from 2011 to 2022. To study these aspects, we apply non-parametric Data Envelopment Analysis (DEA) techniques such as the Slacks-Based Measure (SBM), and the Malmquist Productivity Index (MPI), followed by bootstrap OLS regression respectively. Cross-sectional estimates from the SBM model for the 2022 cropping season indicate that average TE for small and medium sorghum farmers in Zambia was 85 percent. Sorghum production could be expanded by 411.90 t by improving TE in five of ten provinces in Zambia. The MPI shows that TFP for sorghum production in Zambia declined by 21 percent, mainly due to deteriorating sorghum production technology. Populations, rurality, agro-ecological zone IIB are the factors associated with EC and TFP decline.

Technical Efficiency Evaluation of Maize Production in Ghana: An Application of Three-Stage Data Envelopment Analysis

Purpose: In Ghana, maize constitutes the primary source of revenue and food for small-scale farmers, and to attain maize self-sufficiency, appropriate production techniques must be employed, coupled with the scaling up of production efficiency to supplement our food output. The objectives of the study are to estimate the technical efficiency and analyze the trend in total factor productivity of maize production. Method: This study focuses on forty-eight (48) cities in six (6) selected regions of Ghana: Northern, Upper East, Upper West, Central, Eastern, and Volta. Secondary data on maize production was obtained from the Ministry of Food and Agriculture from the 2010/11 to 2020/21 cropping seasons. Environmental variables (amount of rainfall and temperature) were sourced from the Ghana Meteorological Agency (GMA) from 2010/11 to 2020/21. Results and Conclusion: The results displayed a fairly high level of TE, and environmental factors significantly influence production efficiency. The study revealed that the TE of maize cultivation was 0.752 and 0.853 for the first and third stages, respectively. From the findings, the efficiency scores attained after the adjustments to the input in the third stage of the DEA model were greater than those in the first stage. Also, the results indicated the eastern region had the highest TE scores, showed an increment in productivity, and serves as the finest region for maize cultivation. Overall, the technical performance of maize cultivation had shown an upward trend in productivity, and the eastern region recorded the highest efficiency performance and technological progress. Implications of the Research: The research highlights the significance of the maize crop to the Ghanaian economy and how environmental factors impact farmers' production efficiency. It also emphasizes the benefits of resource use efficiency and its implied effects on the total factor productivity of maize cultivation. Policy suggestions include sustainable production practices primarily for farmers living in susceptible areas to enhance productivity and improve their food security status. Originality/Value: The gap discovered in the examined literature was that there exists a substantial deficiency in the usage of the three-stage DEA Malmquist model for analyzing technical efficiency and limited studies on the estimation of the total factor productivity (TFP) changes, specifically focusing on maize farming. This paper adds to the body of the existing literature by utilizing a three-stage DEA model to evaluate the TE of maize production. This is entirely limited both in theory for annual crops and methodologically in the Ghanaian literature on efficiency studies.

Natural resources utilization efficiency evaluation, determinant of productivity change, and production technology heterogeneity across developed and developing G20 economies

Efficiently utilizing natural resources (NRs) and enhancing their productivity growth are crucial factors for sustainable economic growth in major global economies. Further production technologies heterogeneity among developed and developed G20 countries is vital to natural resource utilization efficiency (NRUE). To this end, the estimation of NRUE, natural resources total factor productivity, and technological gap ratio (TGR) is worth investigating. This study employed Data Envelopment Analysis (DEA) Super-SBM, Meta-frontier analysis, and the Malmquist productivity index to evaluate NRUE, TGR, and productivity changes among G20 countries from 1995 to 2020. The average Natural Resource utilization Efficiency (NRUE) among G20 countries is 0.7071, indicating an improvement potential of around 29.29% in natural resource conversion. Developed G20 economies have a natural resource utilization efficiency (NRUE) of 0.9528, higher than that of the NRUE (0.506) of developing G20 countries. Developed countries have a TGR of 0.9962, which is significantly higher than the TGR of developing countries at 0.6746, indicating a significant gap in production technology. Total factor productivity growth of 0.8 % was witnessed in developed countries while developing countries demonstrated a decline of 0.6 percent in Total Factor Productivity Change (TFPC) over the study period. Analyzing the decomposition of TFPC reveals technological change as the predominant factor influencing productivity change in G20 economies instead of efficiency changes. A Mann-Whitney U test has proved the existence of significant statistical differences in average NRUE, TGR, and technological change (TC) levels between developed and developing G20 countries.

Financial resources utilization efficiency in sports infrastructure development, determinant of total factor productivity growth and regional production technology heterogeneity in China

Despite China's heavy investment in sports infrastructure development in the last decades, the financial resources utilization efficiency (FRUE), regional technological gap (TGR), and total factor productivity change (TFPC) in sports infrastructure development are undiscovered and worth investigating. To this end, this study employed DEA-SBM, Meta-frontier analysis, and Malmquist productivity index on the data set of 31 Chinese provinces and 3 regions for the years 2014–2021 to gauge the FRUE, TGR, and TFPC in sports infrastructure development. The results indicate that the average FRUE is 0.4859, with a growth potential of 51.41% in financial resource utilization efficiency for sports infrastructure development. Further Eastern region is more efficient as compared to the Central and western regions. Beijing, Shanghai, Tibet, Hainan, and Guangdong are top performers in FRUE. Further, the value of TGR in the East is 0.9787 which is higher than in Central (0.4977), and Western (0.5821) regions. It indicates that the eastern region contains superior production technology in the development of the sports infrastructure in China. Moreover, the average TFPC value is 1.035 witnessed a 3.5% growth, primarily determined by technological change (TC). As TC = 1.0273 is higher than efficiency change EC = 0.997. The eastern region has a higher average TFPC of 1.048, indicating higher total factor productivity growth in sports infrastructure development. Beijing, Liaoning, Tianjin, Shanghai, and Zhejiang are the top performers in the TFP growth over the study period. Finally, the Kruskal-Wallis test proved the statistically significant difference in three regions of China for FRUE, TGR, and TFPC in sports infrastructure development.

Autonomy Acquisition and Performance within Higher Education in Vietnam—A Road to a Sustainable Future?

The Vietnamese Government prioritizes education as a developmental investment within its socioeconomic development programs. Subsequently, Vietnam’s higher education system (HE) is experiencing substantial transformation, emphasizing autonomy, because institutions endowed with self-governance capabilities may allocate societal resources more efficiently for developmental purposes. In this paper, we measured Vietnamese universities’ total factor productivity change (TFPCH) in the autonomy context, using it as a proxy for the sustainable performance of HE institutions. We decomposed TFPCH into Technical Efficiency Change (EFFCH) and Technology Change (TECHCH) and regressed these indices with independent variables to derive their determining factors. Notably, we employed the derived intercept as a proxy for the autonomy context of Vietnam. The DEA found significant advancements in productivity and technology, indicating a positive paradigm shift within Vietnam’s higher education system. The intercepts obtained from these regressions are positive and significant, implying that the autonomous environment supports the sustainable advancement of Vietnam’s higher education system in both components of TFPCH: the catch-up ability (EFFCH) and technological improvement (TECHCH). In addition, we found that investment in vital resources (number of laboratories, research funding, or quality accreditation) improves productivity (TFPCH) via technological improvement. We also observed that private universities experienced higher performance progress than public ones. However, we did not find any significant relationships between the university scale or the location of the main campus and their performance. To further the growth of Vietnam’s higher education system, we propose that the autonomy of institutions continues to be granted.

The Regional Total Factor Productivity Changes of Healthcare Delivery in China: A Bootstrap Malmquist Data Envelopment Analysis.

Improving the productivity of healthcare delivery and optimizing the allocation of regional healthcare resources are crucial for the health providers. The objective of this study is to evaluate the productivity dynamics of healthcare delivery at the regional (provincial) level in China, to provide evidence-based policy implications. After a review of literature, actual number of open beds, number of occupational or assistant doctors, number of registered nurses, and number of other staff were selected as input variables. The number of diagnostic visits and number of discharged inpatients were adopted as the output indicators. The panel data of 31 provinces in mainland China from 2010 to 2019 were extracted from Health Statistics Yearbook. Bootstrap-Malmquist Data Envelopment Analysis (DEA) model was used to measure the total factor productivity changes (TFPC) and its components. During the study period, the analysis of total factor productivity (TFP) in China revealed a declining trend with an average annual decline of 0.9% (ranging from 0.860 to 1.204). For each of the 31 provinces, the annual TFP scores varied from 0.971 to 1.029. On average, technical efficiency changes (TEC) had showed a downward trend from 2010-2011 (0.980) to 2013-2014 (0.982), and then an upward trend in 2014-2015 (1.029) and the following three consecutive years since 2016-2017 (1.000, 1.013, 1.009). Similarly, the trend in technological changes (TC) was consistent with the TEC from 2010-2019, which fluctuated between 0.969 and 1.011 on average per year at the provincial level. Notably, the point of inflection appeared at 2013-2014. Regional healthcare inputs and outputs in mainland China saw an upward trend from 2010 to 2019. However, TFPC, TEC, and TC decreased across all 31 provinces. TFP experienced a declining trend from 2010 to 2014, followed by growth until 2019. This may be related to the new healthcare reform being implemented since 2009, as service efficiency and capacity may undergo a reversal at the beginning of the reform.

Productivity Comparison between Conventional and Islamic Commercial Banks in Indonesia during the COVID-19 Pandemic

This study was conducted to measure and compare the productivity level between conventional and Islamic commercial banks in Indonesia during the COVID-19 pandemic. The number of samples used in this study was 105 banks consisting of 95 conventional banks and 10 Islamic banks. The level of productivity was measured using the Malmquist Productivity Index (MPI) method and Data Envelopment Analysis (DEA) with an intermediation approach, while the productivity of conventional and Islamic commercial banks was compared using normality and different tests. The results showed that the productivity level of the Islamic banks with a Total Factor Productivity Changes (TFPCH) value of 1.001 was driven by technological advances. Meanwhile, the conventional banks were not productive. On the other hand, the results of the different tests showed that there was no significant difference between the productivity level of the conventional and Islamic banks. Conventional banks must enhance innovations in the use of technology in their operational activities to improve their productivity and maintain their high efficiency achievement. Meanwhile, Islamic banks could improve their efficiency in the operational activities so that they would achieve higher productivity and to innovate continually with the use of technology. During the study observation, there was no study comparing between the productivity level of conventional and Islamic commercial banks during the COVID-19 pandemic in Indonesia. Therefore, this research was the first study to discuss the comparison of productivity level between conventional and Islamic commercial banks in Indonesia during the COVID-19 pandemic.