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In this paper, the DEA-Malmquist model is used to evaluate static and dynamic efficiency of 30 civil airports in China from 2016 to 2021. The developments of airports in different regions are discussed from these aspects: the impact of local government policies and COVID-19 on changes of airports' total factor productivity, and differences in operating efficiency of airports in different regions. The results show that: there are great differences in the operating efficiency of airports in China, and the low pure technical efficiency is the main reason leading to the low technological efficiency. The stagnation of total factor productivity of Chinese airports is mainly influenced by technological progress, and its negative effect drags down the stable technical efficiency and scale efficiency. There are obvious differences in operating efficiency among different regions. Airports in the Yangtze River Delta and Pearl River Delta have the highest efficiency, the Chengdu-Chongqing region has the fastest development, and the northeast region has the slowest development and the lowest operating efficiency. © 2022 ACM.
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The energy consumption structure is now shifting from using fossil fuels to using renewable energy. Thus it is crucial to research if decreasing energy use can genuinely boost green total factor productivity (GTFP) and how to reconcile the link between reducing energy consumption and GTFP using various technical approaches. This study proposes the two-way influence of Artificial Intelligence (AI) and natural resources market on GTFP. It comprehensively evaluates the effects of AI on green economic development using the instance of China and a pertinent mathematical model. We found that the impacts of AI on carbon intensity vary between industries and developmental phases. Compared to the 11th Five-Year Plan, AI significantly reduced carbon intensity during the 12th. Compared to capital-intensive businesses, labor- and technology-intensive industries likely have a more significant drop in carbon intensity due to AI and natural resources market.
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Recent transformations following the global financial crisis of 2009, COVID-19 pandemic, supply chains disruptions and newest shocks have radically reshaped global production landscape and challenged comparative benefits of global production networks (GPN) vs global value chains (GVC) paradigms in international production analysis. The study tests the hypothesis that GPN concept allows for a better identification of structural shifts in international production structures while revealing regional patterns of cooperation. In the first section, the main methodological constraints of GVC paradigm are specified. Additionally, the reasons for the application of network-based approach to international production are outlined. The second section dissects the EU automotive manufacturing to support the theoretical propositions. While comparing GVC and GPN quantitative toolkits, the possible trade-off has been reached which is to calculate network indicators (transitivity, centrality, etc.) on the inter-country input-output tables. As a result, the hypothesis was confirmed. Specifically, betweenness centrality metric suggests that Czechia and Slovakia have immediately favoured a positive effect of the entry into the EU, whereas neither of GVC indicators reveals such a shift. Simultaneously, 2008 crisis is depicted via GVC indicators, whilst network metrics suggest no structural changes in the production system. These results corroborate to our theoretical juxtaposition of GVC/GPN approaches. The methodological cohesion of two sets of indicators further advances the views on European regional core-periphery integration and automotive production networks dynamics. At the same time, the findings may contribute to the reassessment of regional integration developments in Europe, as well as in Latin America and Eurasia. © González G. H., Sapir E. V., Vasilchenko A. D. Text. 2023.
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Bu çalışmanın amacı, en büyük uluslararası stratejik havayolu işbirliği olarak kabul edilen Star Alliance grubuna üye olan 15 havayolu işletmesinin 2016-2019 dönemine ait finansal etkinlik ve verimlilik analizinin Malmquist Total Factor Productivity yöntemi ile incelenmesidir. Bunun yanı sıra Star Alliance grubuna üye olan havayolu işletmelerinin Covid-19 salgını öncesi son dönem finansal etkinlik ve verimliliklerinin değerlendirilmesidir. Analiz sonucunda söz konusu havayolu işletmelerinin 2016-2017 döneminde TPEC ve TFP değerlerinin arttığı, TEC değerlerinin ise azaldığı tespit edilmiştir. 2017-2018 döneminde ise tam tersi bir durumun yaşandığı görülmüştür. 2018-2019 döneminde ilgili havayolu işletmelerinin tümünün TEC, TPEC ve TFP değerlerinde azalışların olduğu tespit edilmiştir. Havayolu işletmelerinin ilgili dönemde ortalama TEC, TPEC ve TFP değerlerinin tüm dönem boyunca azaldığı görülmüştür. Air Canada ve Turkish Airlines işletmelerinin tüm dönem boyunca etkinlik ve verimlilik değerlerini arttırdığı tespit edilirken Air New Zealand'ın TFP değerlerinin ve Asiana Airlines'ın ise TPEC değerlerinin tüm dönem boyunca azaldığı tespit edilmiştir.Alternate abstract: The purpose of this study is to analyze the financial efficiency of 15 airlines that are members of the Star Alliance which is considered the largest international strategic airline network for the period 2016-2019 using the Malmquist Total Factor Efficiency method. In addition, other purposes include the comparison of the change in technical efficiency (TE), technological change (TD) and total factor productivity (TFP) values of airlines that are members of the Star Alliance As a result of the analysis, it was found that the average technological change and total factor productivity values of the airlines in question increased in the period 2016-2017, and the average technical efficiency values decreased. On the other hand, in the period 2017-2018, the opposite situation was observed. In the period 2018-2019, technical efficiency, technological change and total factor productivity values decreased. It was found that the technical efficiency values of the Air Canada and Turkish Airlines increased during the entire period, while the average technical efficiency, technological change and total factor productivity values of the Air New Zealand, Asiana Airlines, Avianca, Lufthansa and Thai Airways decreased.
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In the context of the outbreak of the COVID-19 pandemic and China's "digital power” strategy, the realization of a green shift of manufacturing has become a necessary condition to promote the economy, and the digital factor has increasingly become a new driving force. The DEA-Malmquist index and entropy method were used to measure the manufacturing green total factor productivity (GTFP) and the level of digital economy level from 2011 to 2018, respectively. This study then explored the impact of digital economy on manufacturing GTFP based on the system generalized method of moments (GMM) model, as well as the adjustment effects of talent aggregation and financial scale according to the moderating model. This research came to four conclusions. (1) The digital economy can significantly improve the manufacturing GTFP of China, and the influence shows the characteristic of a "marginal increase”;(2) notably, the perspective of manufacturing GTFP decomposition indicates that the digital economy exerts a significant positive effect on manufacturing technical efficiency during the current period but obviously hinders technical progress;(3) interestingly, a mechanistic test showed that the two dimensions of innovation environment—talent aggregation (0.385) and financial scale (0.359)—play critical moderating roles in the influencing process;and (4) the influence has evident regional heterogeneity—it is significantly positive in the east and negative in the central region and west. Finally, corresponding policy suggestions are suggested. © 2022 ERP Environment and John Wiley & Sons Ltd.
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This paper examines the way government spending affected productivity and its decomposition before and during the COVID-19 outbreak. Using panel data from 158 economies, the research shows that spending on health care increases productivity, while spending on the military slows down productivity and its decompositions. These effects are even greater in the context of COVID-19, showing that spending on health care and avoiding conflict and military escalation will be important for future economies to grow in a sustainable way.
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Most cross-country empirical studies on economic growth ignore ambiguity. We argue that ambiguity should matter a priori, given the open-endedness of growth theories. Moreover, ambiguity has received a lot of attention since the great financial crisis (GFC) of 2008, and more recently after COVID-19 and the war in East Europe. Using annual data from the Penn World Table covering 12 East Asian economies from 1954 to 2019, we show that beyond economic factors documented in the current debate about the East Asian miracle, economic agents' confidence shocks about the global economy (one dimension of ambiguity) have a significant negative impact on total factor productivity (TFP) growth. However, correlation uncertainty (another dimension of ambiguity) is low in the region given the persistent high level of connectedness of its productivity network. The empirical results are policy-relevant given that ambiguity, unlike rational expectations, implies that economic agents' beliefs are not policy invariant.
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Bu çalışmanın amacı, kovid-19 salgının Borsa Ístanbul (BÍST)'da işlem gören ve Ímalat alt sektörlerinde faaliyet gösteren işletmelerin finansal performansı üzerindeki etkilerinin incelenmesidir. Bu doğrultuda imalat alt sektörlerinde faaliyet gösteren işletmelerin kovid-19 salgını öncesi ve kovid-19 salgını dönemine ait, finansal açıdan verimlilik ve etkinlik değişim değerleri Malmquist Toplam Faktör Verimlilik Endeksi (MTFV) yöntemiyle incelenmiştir. Çalışmada elde edilen bulgular, kovid-19 salgının imalat alt sektörlerinde yer alan işletmelerin finansal performansı üzerinde olumsuz etkilerinin olduğunu göstermektedir. Aynı zamanda bulgular, kovid-19 salgının en fazla ana metal sanayi, gıda içecek ve tütün, metal eşya ve makine alt sektörlerini etkilediğini göstermektedir.Alternate :The aim of this study is to investigate the effects of COVID-19 pandemic on the financial performance of companies operating in manufacturing subsectors and listed on the Stock Exchange Istanbul. In this direction, the financial efficiency and productivity change values of manufacturing companies before and during the Covid-19 pandemic were examined using the Malmquist Total Factor Productivity Index (MTFP) method. The findings obtained in the study indicate that the COVID-19 pandemic has negative effects on the financial performance of manufacturing companies. At the same time, the findings show that the COVID-19 pandemic has most affected the main metal industry, food, drink, and tobacco, metal and metals products and machinery industry subsectors.
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The relevance of the article is due to the search for the possibility of a COVID-19 post-pandemic recovery of the Russian economy and a return to long-term sustainable growth in total factor productivity (TFP), taking into account the recognition of the priority of the environmental aspect of development. The purpose of the study is to develop an original scientific hypothesis, according to which, in the context of planetary manifestations of large-scale environmental challenges, on the one hand, and unprecedented external sanctions pressure on the Russian Federation, on the other hand. At first, environmental investment should become the main condition and a powerful factor in the long-term sustainable growth of TFP, as well as a radical transformation of the "pro-crisis" Russian economic model in accordance with the principles of the global ESG agenda. Environmental investments are positioned as responsible, in fact, and transformative in terms of their functional role in the economy. An econometric model has been constructed that reflects the dependence of per capita GDP growth rates on the volume of investments in fixed assets directed in the Russian Federation for environmental protection and rational use of natural resources, which has the form of a system of dynamic economic regressions with a distributed lag of a polynomial structure. Such a model can be used to assess the long-term and short-term responses of economic growth indicators from green investment. The methodological basis for the development of the aforementioned model was the methods of correlation, linear and nonlinear regression, factorial and dispersion analysis, the generalized least squares method, the method of instrumental variables. The main restrictions on environmental investment in modern Russia are established and proposals are made to stimulate it. © The Author(s) Publisher: University of Guilan,.
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The interconnectedness of sectors displays the demand for inputs and supply as a level of output in any economy. This paper addresses the Total Factor Productivity (TFP) in Kazakhstan sectors by using input-output tables during 2012-2017. The change in total sectoral production was separated into two parts: the changes in technical coefficients of intermediate inputs and the change in value-added inputs, respectively. The main findings have identified a changing pattern in sectoral performance. At the same time, the result justified that various sectors such as;petroleum, manufacturing, construction, and food processing sectors have shown increased productivity. The country highly depends on extractive industries but still has better manufacturing value-added performance. The study suggests that to combat challenges like COVID-19 and climate change, it is vital to develop human capital and diversity. With diversification and innovative measures, an economy can attain sustainable economic growth in the long term.
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Growing differentiation among developing countries, declining capital flows and remittances, uncertain external aid, weakening global architecture, and rising China are reviewed. In 2021, developed countries, led by the United States, had begun a recovery. Considerable progress was achieved in developing countries prior to the COVID-19 pandemic in reducing poverty;infant and child mortality, stunting, wasting, anemia;increasing food security and nutrition;and improving gender empowerment. Impacts of the pandemic on the poverty-food security-nutrition-health nexus and implications for action are described. Agricultural total factor productivity growth across regions and countries shows huge differences in aggregate productivity growth performance. Countries with low growth also lagged in structural transformation. Premature deindustrialization in developing countries peaks at earlier levels of per capita GDP than for industrialized countries. All farm sizes can achieve productivity growth and success, but smallholders require the functioning of factor and product markets, with strong public policy. Productivity growth measures have not included changes in the quality or quantity of natural resources, but that is changing. Overall, the issue of low financial flows to developing countries needs to be addressed, and available resources need to be used strategically to leverage greater public and private investments to food and agriculture. Substantial investments are needed in human and institutional capital and physical infrastructure for new technologies. The G20's contribution to the global architecture for food and agriculture has not met its potential relative to a promising early start. For 54 industrial and emerging countries monitored by the Organisation for Economic Co-operation and Development, changes in their agricultural policies offer scope for improvement in the overall policy environment and investment climate at the global level, including release of valuable resources for building better. © Uma Lele, Manmohan Agarwal, Brian C. Baldwin, and Sambuddha Goswami 2021.
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Accurate measures of productivity growth are an important policy tool but are difficult to obtain in South African circumstances. In this article, we review work on the measurement of farm-level productivity in South Africa since the earliest attempts at multifactor indices in the early 1990s. The focus is on total factor productivity, but single input measures such as labor and land productivity (yields) are also discussed. Measurements using time-series and cross-sectional data are discussed separately, along with measures to explain the effect of climate change. Data deficiencies are also pointed out. The article concludes that international collaboration should be maintained if important issues such as the COVID-19 impact, food security, climate change, and labor market shocks are to be successfully addressed.
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Improving green energy efficiency (GEE) and promoting green economic transformation are important goals for China to achieve sustainable economic development in the post-COVID-19 era. Based on panel data of 27 manufacturing industries in China, this paper uses GMM model and threshold model to study the impact of environmental regulation and technological innovation on green energy efficiency. Our findings show that technological innovation promotes green energy efficiency in both pollution-intensive and clean industries, and its promotion effect is more pronounced in pollution-intensive industries. Environmental regulation not only directly improves the green energy efficiency of polluting industries and clean industries, but also plays a positive intermediary role between technology and green energy efficiency. The impact of technological innovation on GEE has a threshold effect of environmental regulation. When environmental regulation did not cross the threshold, technological innovation does not significantly promote GEE. The promotion effect of technological innovation on GEE will increase with the strengthening of environmental supervision. Therefore, the government should formulate reasonable environmental regulations according to the industry heterogeneity to vigorously promote the green energy efficiency of the manufacturing industry.
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This study examines the impact of the 2003 SARS epidemic on the total factor productivity (TFP) of Chinese industrial enterprises using a difference-in-differences (DID) approach. The results exhibit that SARS significantly reduces TFP by 3.12-5.81%, lasting for three to five years. Further, this impact is heterogeneous across industries. A significantly negative impact is found in labor intensive industries, while capital and technology intensive industries is less affected. Contrarily, a significantly positive impact is observed in those industries necessary for life and production. Mechanism tests show that the impact on TFP is caused by a reduction in labour productivity and a decrease in innovation investment after SARS outbreak. This study highlights the importance of more targeted policy on Covid-19 and similar epidemics both in industrial, national and international level.
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Recent high dropout and low graduation rates in the South African higher education institutions as well as government funding cuts and the economic uncertainty due to COVID-19 pandemic have heightened the urgency for the higher education sector to improve its productivity. However, empirical evidence on the productivity growth of the sector remains unexplored. To address this gap, we applied a Färe-Primont index approach to a panel data of 22 public universities over an 8-year period to measure total factor productivity (TFP) and its components-technological change, technical, scale and mix efficiency changes. We also used a feasible generalised least squares model to assess the determinants of productivity and efficiency growth. The results show that the average TFP of the sector for the study period was 0.631, led by historically advantaged universities (0.894), whilst historically disadvantaged universities had lower average TFP (0.823). During the period, TFP increased by 3.43%, largely driven by scale and mix efficiency changes (5.32%) and technical efficiency change (0.83%), whilst technical change declined by 1.80%. In terms of university types, the comprehensive universities achieved the largest TFP growth (6.13%) followed by traditional universities (4.85%), and technology universities by 1.41%. TFP growth was positively influenced by student graduation rates, quality of academics and academic-student ratios. Therefore, policy considerations to improve the sector's productivity and efficiency should consider investment on research and development, adoption of teaching and research innovations, re-skilling through training and education and aligning admission policies with staffing.
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Seaports are important infrastructures to support international trade. Therefore, it is vital that port efficiency and productivity are continuously evaluated and improved. In this context, the objective of this article is to evaluate both the technical efficiency and the change in productivity of the six most important Tunisian commercial seaports, Bizerte, Rades, Sousse, Sfax, Gabes, and Zarzis, over a period of twelve years from 2005 to 2016. To achieve this objective, the data envelopment analysis (DEA) method is applied. The first output-oriented DEA application is about efficiency evaluation, which, for each seaport, allows the estimation of overall technical efficiency, pure technical efficiency, and scale efficiency. The second application concerns the evolution of the productivity of Tunisian seaports during the study period using the Malmquist DEA-based productivity index. The productivity analysis is performed according to the year (period) and according to each studied seaport. The first output-oriented DEA method provides that the overall technical efficiency in the above-mentioned ports is 69.4% while the pure technical efficiency is 83.3%. Furthermore, the average scale efficiency is about 82.6%, which implies that the decreasing type of returns to scale dominates in this study. Regarding the second DEA application for productivity evolution, the obtained results from the data analysis revealed that it fell by 6.7%, mainly due to the degradation of the technological change (8.3%). The results obtained provide useful basic criteria for establishing efficiency improvement strategies for each studied seaport.
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Korea has recorded negative real GDP growth only three times in the last 60 years: the oil crisis in 1980, the financial crisis in 1998, and the COVID-19 crisis in 2020. While the economic recession for the first time in 22 years may be attributed to COVID-19, it is more noteworthy is that the growth rate of the Korean economy has been continuously declining. To find counter measures for the decline in the economic growth rate, it is necessary to analyze the cause of the decline in the growth rate. The factors of economic growth can generally be divided into changes in input factors such as labor and capital and productivity. We analyzed the relative contribution of each input factor from 1982 to 2020. Our result suggests that the contributions of capital and labor to economic growth are decreasing over time, and the contribution of TFP is gradually increasing. This study is employing annual time series data to provide up-to-date estimates of TFP and exploring the determinants of TFP to help detect future growth engines for the long-run sustainable development in Korea. © 2022 IEEE.
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Industry agglomeration has become a prominent feature of tourism industry development in developed and developing countries and regions in the world. According to the literature analysis, the development of industrial agglomeration has both agglomeration effect and congestion effect. This paper constructs a theoretical and empirical analysis framework for the impact of tourism industry agglomeration on the total factor productivity of Chinese urban agglomerations, and analyze the moderating effect of the public epidemic on this impact. From results of empirical analysis, a U-shaped relationship exists between tourism industry agglomeration and the total factor productivity of Chinese urban agglomerations. The public epidemic positively moderated (enhanced) the negative effect (congestion effect) of tourism industry agglomeration on total factor productivity, and negatively moderated (weakened) the positive effect (agglomeration effect) of tourism industry agglomeration on total factor productivity.
Subject(s)
COVID-19 , Epidemics , Tourism , China/epidemiology , Humans , IndustryABSTRACT
Based on provincial panel data for the past 15 years in China, the SBM-ML index method was used to measure agricultural productivity under the environmental-constraint perspective with agricultural surface source pollution as the non-desired output. A dynamic panel regression model was used to empirically analyze the factors influencing agricultural productivity to provide a reference for formulating policies to alleviate the conflict between economic development and environmental pollution. The results show that the green total factor productivity of Chinese agriculture exhibits a slow, incremental trend year by year. The growth of green total factor productivity in agriculture mainly comes from the increase in the rate of green technological progress. In terms of geographical disparity, the eastern, central, and western regions show a high-to-low gradient of agricultural green total factor productivity. Agricultural green total factor productivity showed a significant positive spatial correlation in some years. As for the influencing factors, foreign trade in agricultural products is conducive to enhancing green total factor productivity in agriculture, whereas foreign direct investment in agriculture and agricultural technology input inhibit the growth of green total factor productivity in agriculture. This research also found a significant U-shaped relationship between environmental management inputs and green total factor productivity in agriculture. Accordingly, suggestions are provided to optimize the international trade structure of agricultural products, selectively introduce high-quality green foreign investment projects, drive the efficiency of R&D investment through digital technology, and increase investment in special funds for agricultural pollution control.
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The digital economy is an important engine to promote sustainable economic growth. Exploring the mechanism by which the digital economy promotes economic development, industrial upgrading and environmental improvement is an issue worth studying. This paper takes China as an example for study and uses the data of 286 cities from 2011 to 2019. In the empirical analysis, the direction distance function (DDF) and the Global Malmquist-Luenberger (GML) productivity index methods are used to measure the green total factor productivity (GTFP), while Tobit, quantile regression, impulse response function and intermediary effect models are used to study the relationship among digital economy development, industrial structure upgrading and GTFP. The results show that: (1) The digital economy can significantly improve China's GTFP; however, there are clear regional differences. (2) The higher the GTFP, the greater the promotion effect of the digital economy on the city's GTFP. (3) From a dynamic long-term perspective, the digital economy has indeed positively promoted China's GTFP. (4) The upgrading of industrial structures is an intermediary transmission mechanism for the digital economy to promote GTFP. This paper provides a good reference for driving green economic growth and promoting the environment.