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Constant or decreasing returns and increasing returns to scale are two kinds of mechanism in economic growth. The goal of supply-side structural reform is to promote the establishment of the mechanism with increasing returns to scale. The paper aims to discuss this issue.

This paper argues that the overall economic structure of the developing economy has been divided into the sector of constant or decreasing returns to scale and the sector of increasing returns to scale due to the dual economic structure. Among them, the supply-side structural reform is mainly to reduce the sector of decreasing returns to scale and increase the sector of increasing returns to scale. Based on the hypothesis of such two-sector economic structure in the supply side of developing economies and on the industrial data, this paper empirically tests the returns to scale of China’s supply structure. The result suggests that so far the sector of constant or decreasing returns to scale dominates the supply structure of China’s economic growth, which results in the state of decreasing returns to scale in China’s overall economy.

Therefore, to realize the long-term sustained growth and transformation of the development pattern of China’s economy, the authors must carry out the supply-side structural reform, vigorously develop the modern industrial sectors characterized by modern knowledge and technology, and promote the development of an innovation-driven economy.

Besides, the authors must accelerate the transformation from traditional industrial sectors to modern industrial sectors, actively promote China’s industrial structure toward rationalization and high gradation, as well as build a modern industrial system so as to facilitate the formation of the mechanism of increasing returns to scale and accelerate the transformation of the driving force of China’s economic growth.

The purpose of this study is to provide models to analyze the efficiency of programs and efficiency of fundraising to apply the models to non-profit organizations (NPOs) in Korea and to draw out improvement points of inefficiency using data envelopment analysis (DEA).

Using DEA, this study analyzed the program efficiency and fundraising efficiency of 22 Korean NPOs in the field of humanitarian assistance.

Of 22 NPOs, 15 were identified as being efficient in the program efficiency and 7 of 15 NPOs were found efficient in the fundraising efficiency. In all, four organizations were found efficient in both the program and the fundraising efficiency. Using CCR and BCC model, this study proposed the cause of inefficiency and state of returns of scale.

This study presents non-profit efficiency evaluation models regarding program efficiency and fundraising efficiency. This study provides the inefficient DMUs with their reference set of efficient DMUs to improve efficiency and the cause of inefficiency, whether the inefficiency is because of the pure technical inefficiency or the scale inefficiency. This study also indicates the state of variable returns to scale to propose the way of improving inefficiency by controlling the scale of inputs. The methods and the results of this study can serve as a model for researchers and practitioners to follow when evaluating efficiency in the NPOs.

This study has the value of performing the empirical studies of efficiency analysis of Korean NPOs and providing non-profits with the model of efficiency analysis in programs and fundraising activities and basis for establishing strategies to improve both efficiencies.

The purpose of this paper is to investigate whether real estate investment trusts (REITs) have any significant cost-efficiency advantages over real estate operating companies (REOCs).

The data for listed companies were extracted from the Bloomberg terminal. The authors analyzed financial ratios and conducted a non-parametric data envelope analysis (DEA) for 534 firms in the USA, Canada and some EU member states.

The results suggest that REITs were much more cost-efficient than REOCs by all the parameters in the DEA model during the entire three-year period under consideration. Although the debt-to-equity levels were similar, REOCs were more relying on short-term than long-term maturities, which made them more vulnerable against market corrections or shocks. Being larger in asset size did not necessarily guarantee greater economies of scale. Both – the cases of increasing economies of scale and diseconomies – were detected. The time period 2015–2017 showed the general trend of decreasing efficiency.

Very few papers on the topic of REITs have attempted to find out whether a different firm structure displays any differences in efficiency. Because the question of REITs and sustainable growth of the real estate market has become a prominent issue, this research can help EU countries to consider the option of adopting a REIT system. If this system were successfully implemented, the EU member states could benefit from a more sustainable and more rapid growth of their real estate markets.

The paper provides a detailed historical account of Douglass C. North's early intellectual contributions and analytical developments in pursuing a Grand Theory for why some countries are rich and others poor.

The author approaches the discussion using a theoretical and historical reconstruction based on published and unpublished materials.

The systematic, continuous and profound attempt to answer the Smithian social coordination problem shaped North's journey from being a young serious Marxist to becoming one of the founders of New Institutional Economics. In the process, he was converted in the early 1950s into a rigid neoclassical economist, being one of the leaders in promoting New Economic History. The success of the cliometric revolution exposed the frailties of the movement itself, namely, the limitations of neoclassical economic theory to explain economic growth and social change. Incorporating transaction costs, the institutional framework in which property rights and contracts are measured, defined and enforced assumes a prominent role in explaining economic performance.

In the early 1970s, North adopted a naive theory of institutions and property rights still grounded in neoclassical assumptions. Institutional and organizational analysis is modeled as a social maximizing efficient equilibrium outcome. However, the increasing tension between the neoclassical theoretical apparatus and its failure to account for contrasting political and institutional structures, diverging economic paths and social change propelled the modification of its assumptions and progressive conceptual innovation. In the later 1970s and early 1980s, North abandoned the efficiency view and gradually became more critical of the objective rationality postulate. In this intellectual movement, North's avant-garde research program contributed significantly to the creation of New Institutional Economics.

This paper aims to identify the scale efficiency of dry ports in Brazil and its main technological drivers.

This paper uses the Data Envelopment Analysis (DEA) model in two stages. The first stage of the DEA was used to measure the efficiency of the dry ports. In the second stage, the Bootstrap Truncated Regression (BTR) was applied to explore the relationship between efficiency and the factors analyzed. The inputs, outputs and contextual variables for this analysis were extracted from the secondary database provided by Revista Tecnologística.

In the first analysis stage, a high level of idleness was verified in the operations. The contextual variables in the second stage were significant: Certification, Warehouse Management System (WMS), barcode and Radio Frequency Identification (RFID). Results corroborate the positive impact of Information Technology (IT) coordination processes on logistics performance.

Results show that dry ports operate below their technical and operational capacity and that the sector's lack of regulation in Brazil can facilitate and encourage the use of ports and marine terminals by importers and exporters.

Application of two-stage DEA measures efficiency as a sectoral benchmarking tool.

The purpose of this paper is to examine the influence of export logistics components: shipment arrangements, timely delivery, customs quality, trade infrastructure, and tracking and tracing on export competitiveness of firms in the East African Community (EAC).

The study adopted the Structural Gravity Model and the Poisson pseudo-maximum likelihood (PPML). PPML a nonlinear estimation method was applied in STATA on a balanced panel data for the period of 2007–2018. Data were obtained from World Bank International Trade Centre (ITC), World Bank Logistics Performance Index (LPI) and World Bank development indicators.

Results show that timely delivery and tracking and tracing of exports are positive and significant predictors of export competitiveness in EAC countries. Conversely, shipment arrangements, customs quality and trade infrastructure have no influence on export competitiveness.

The results of this study show that export logistics components of shipment arrangements, customs quality and trade infrastructure do not matter at the present in improving export competitiveness in the EAC. There is a need to examine the intricate nature of the EAC economy to further this study's findings.

The EAC partner states should embrace deep integration by removing the behind the border trade barriers in addition to other trade restrictions, to create a common economic space among member states. This will further shrink the delivery time and the tracking and tracing of exports hence improving the competitiveness of EAC exports within the region and outside. Also, common and harmonized trade policies and regulations should be implemented through mutual recognition agreements where countries agree to recognize one another's conformity assessments.

This study explains the complex dynamic interactions of export logistics factors in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-dominated countries with less harmonization in their trade policies.

This paper aims to understand the most efficient hotel system and why efficiency varies across years and between the two differing types of hotel businesses in Turkey.

A data envelopment analysis (DEA) analysis was used to characterise the efficiency of all-inclusive (AI) and bed and breakfast (B&B) hotel businesses with one output (total revenue) and three inputs (labour, food and capital costs). The Malmquist approach is then used to discern changes in total efficiency (TTE) and intertemporal shifts in the efficiency frontier (technological change (Tch)).

The results reveal that the AI hotel operates at 100% efficiency in the summer and year-round. The B&B hotel business operates at 89.6% with variable constant returns to scale during the summer and with 100% efficiency. The results of the Malmquist approach indicate that the total factor productivity grew in the years 2015, 2016, 2018 and 2019, while the other years were marked by inefficiency. Such increases were due to technical efficiency change (TEch) and Tch, which means that managerial and allocative efficiency (AE) were barely achieved. Slight differences were noted in the two time periods (all year and summer), suggesting that the scale of hotel businesses is prepared to operate all year round, and this calls for strategies to mitigate seasonality.

As to avenues for future research, the limitations of this study are threefold. First, the hotel businesses are not parallel in terms of the duration of their service offerings. Future research may consider including an AI hotel business that is in operation for the whole year. Second, businesses in Turkey are sceptical about sharing their data as it is considered confidential. However, to better generalise the results and encourage hoteliers to consider the positive outcomes of such analysis, the number of observations could be increased by considering more hotel businesses in both categories. Third, a mixture of data representing businesses operating in various countries may reflect if the efficiency scores vary internationally.

Overall, AI hotel businesses are more attractive but less efficient than B&B. Furthermore, the external crisis impacts the efficiency of hotel businesses meaning that hotel managers could keep on exploring AI, perhaps educating their hosts not to waste or not offer huge quantities. Hotel managers may also need to enlarge their seasonal activities to ensure more efficiency.

Despite the intentions of AI hotel businesses to increase their profitability with a lower level of service quality, this study shows that the AI hotel business is very attractive but not so efficient due to the higher propensity of guests to consume food and beverages in excess that compromises the definition of efficiency as zero waste. AI is very attractive for family groups or those seeking the pleasure of relaxation at seaside resorts and is also very popular in Turkey. On the other hand, the B&B hotel business is more efficient but less attractive.

The contributions of this paper are threefold. First, the authors analysed the efficiency and inefficiency of hotel businesses within nine years of operations. During this period, Turkey experienced first a tourism boom (2011–2014) followed by stagnation and subsequently a sharp decline due to political instability resulting in an (in)direct impact on tourism (2015–2019). Second, the authors compared the efficiency and inefficiency of AI and B&B hotel businesses. Third, the authors examined the effects of hotel management factors to ensure efficiency.

The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

The purpose of this paper is to examine the influence of innovation indicators: Internet usage, patent rights, innovation in exporting countries and innovation in the importing country on the export competitiveness of firms in the East African Community (EAC).

The study adopted the structural gravity model and the Poisson Pseudo Maximum Likelihood a nonlinear estimation method that was applied in STATA on balanced panel data from 2007 to 2018. Data were obtained from World Bank International Trade Center and World Bank development indicators.

Results show that innovation in the importing country, innovation in the exporting country and patent rights of exports are positive and significant predictors of export competitiveness in developing countries. While Internet usage is an insignificant predictor in the EAC.

There is a need to examine the complicated nature of the EAC economy to further this study's findings.

Exporting countries need to take deeper reforms as regards structural transformation to enable firms to integrate into the Global Value Chains (GVCs) to enable them to increase their productivity by reviewing the existing policies to match the changes in the market.

This study explains the complex dynamic interactions of technological innovation indicators in the EAC using quantitative data and that this interaction has an effect on the export competitiveness in import-oriented countries with less harmonization in their trade policies.

This paper aims to promote the higher quality development of high-tech enterprises in China. While science and technology have greatly promoted human civilization, resources have been excessively consumed and the environment has been sharply polluted. Therefore, it is particularly important for current enterprises to make use of scientific and technological innovation to maximize the benefits of mankind, minimize the loss of nature, and promote the sustainable development of our country.

By using DEA-Banker-Charnes-Cooper (BCC) model and DEA-Malmquist model, this paper comprehensively examines the innovation efficiency of high-tech enterprises from both static and dynamic perspectives, and conducts a provincial comparative study with the panel data of ten representative provinces from 2011 to 2020.

The research findings are as follows: the rapid number increase of high-tech enterprises in most provinces (cities) is accompanied by an ineffective input–output efficiency; the quality of high-tech enterprises needs to comprehensively examine both input–output efficiency and total factor productivity; and there is not a positive correlation between element investment and innovation performance.

Because the DEA model used in this paper assumes that the improvement direction of invalid units is to ensure that the input ratio of various production factors remains unchanged but sometimes the proportion of scientific and technological activities personnel and the total research and development investment is not constant. In the future, the nonradial DEA model can be considered for further research. Due to historical data statistics, more provinces, cities and longer panel data are difficult to obtain. The samples studied in this paper mainly refer to the provinces and cities that ranked first in the number of national high-tech enterprises in 2020. Limited by the number of samples, DEA analysis failed to select more input and output indicators. In the future, with the accumulation of statistical data, the existing efficiency analysis will be further optimized.

Aiming at the misunderstanding of emphasizing quantity and neglecting quality in the cultivation of high-tech enterprises, this paper comprehensively uses DEA-BCC model and DEA Malmquist index decomposition method to make a comprehensive comparative study on the development of high-tech enterprises in ten representative provinces (cities) from two aspects of static efficiency evaluation and dynamic efficiency evaluation.