Malmquist Data Envelopment Analysis Research Articles

The influence of agricultural trade liberalization on agricultural total factor productivity growth: Empirical evidence from ASEAN countries

Agriculture is a way of life in the ASEAN region due to the fact that eight out of ASEAN member states rely on agriculture for food and income. Therefore, the present study empirically explores the impact of agricultural trade liberalization on agricultural total factor productivity (ATFP) growth in ASEAN countries using panel data from 2002–2021. By deploying the data envelopment analysis-Malmquist productivity index (DEA-Malmquist) approach in the first stage and the pooled mean group (PMG) panel autoregressive distributed lag (ARDL) model in the second stage, the findings reveal that ATFP in the ASEAN countries demonstrated moderate growth over the study period, with an average mean value of 1.028. This signifies that the average growth in ATFP in the region was 2.8% across the countries and over a period of time. This suggests that the ASEAN countries have become increasingly dependent on technological innovation in their agricultural sectors in recent years. Agricultural trade liberalization positively contributed to ATFP growth in the short term, whereas the impact was not statistically significant in the long term. These consequences are highly significant and should not be disregarded since trade liberalization plays a crucial role in determining agricultural competitiveness.

Empowering sustainable development: revolutionizing agricultural green total factor productivity through rural digitalization

IntroductionThe rapid advancement of digital technology has catalyzed the emergence of “digital villages,” presenting both new opportunities and challenges for the digital transformation of agriculture. This transformation has become a core driving force behind sustainable agricultural development. This paper utilizes panel data from 30 provincial-level administrative regions of mainland China (excluding Tibet) for the period 2011 to 2020 to explore how digital rural development (DRD) impacts agricultural green total factor productivity (AGTFP) and examines its underlying mechanisms.MethodsRegarding variable measurement, the study employs the entropy weighting method to quantify the DRD and uses an extended Data Envelopment Analysis-Malmquist model to assess AGTFP. For the research approach, the study constructs a two-way fixed effects model to evaluate the impact of DRD on AGTFP and explores the underlying mechanisms. Specifically, the study conducts benchmark regression analysis, endogeneity tests, robustness tests, mechanism examinations, and heterogeneity tests.Results(1) DRD significantly enhances AGTFP, a conclusion that remains robust following the province-year clustering robust standard error estimates, winsorization tests, and excluding COVID-19-affected years. (2) Mechanistic analysis indicates that DRD primarily boosts AGTFP by promoting the development of digital inclusive finance (DIF) and facilitating the integration of primary, secondary, and tertiary industries (IPST). (3) Heterogeneity tests show that the positive impact of DRD on AGTFP is more pronounced in regions with higher levels of informatization and central regions of China.DiscussionTo harness DRD’s potential more effectively in fostering sustainable agricultural development, policymakers should concentrate on DIF and the IPST. Additionally, the government should pay close attention to disparities in informatization levels and resource allocation across regions and develop targeted strategies accordingly. Fundamentally, this study aims to elucidate China’s efficacy in rural digitalization and the green transformation of agriculture to an international audience. It seeks to provide insights and guidance on formulating more scientific, efficient, and sustainable strategies for agricultural development worldwide, thereby promoting green agricultural growth and supporting rural revitalization.

Monitoring sustainable waste management in OECD countries: A Malmquist productivity approach

Sustainable waste management (SWM) practices are crucial for tackling environmental concerns under a circular economy (CE) scope. Thirty-one OECD countries have been evaluated on their national SWM performance via Malmquist data envelopment analysis (DEA) methodology from 2000 to 2021. The Malmquist (MPI) and Malmquist-Luenberger (MLPI) productivity indices are compared based on their total factor productivity (TFP) and its elements (i.e., efficiency and technological changes). Both models have the gross domestic product (GDP) as a desirable output, but MLPI also incorporates municipal solid waste (MSW) generation and greenhouse gases (GHGs, i.e. CO2 and CH4) as undesirable outputs. MLPI has a lower average performance (1.74%) than MPI (4.42%), meaning that the incorporation of waste and GHGs has diminished an OECD country’s SWM performance by 2.5 times on average. In essence, it is innovation that drives TFP in the selected countries. Overall, the synergistic implementation of SWM practices, Malmquist DEA-based methodology, and CE principles is crucial for advancing sustainable development and achieving a circular and resilient future. The manuscript also offers policy implications regarding the need of financial incentives, vocational training, and fostering stakeholder encouragement in the waste sectors to implement CE solutions.

Efficiency of global airlines incorporating sustainability objectives: A Malmquist-DEA approach

Despite the impact of the COVID-19 pandemic, airline managers are still expected to look beyond financial and operational metrics to include sustainability goals that address key environmental, social, and governance aspects of the airline business. While there is a considerable body of literature addressing airline efficiency in terms of environmental performance, studies that integrate the various dimensions of sustainability are notably scarce. This paper aims to fill this gap by estimating technical efficiency using a sample of 34 airline groups between 2019 and 2022, with a Malmquist-Data Envelopment Analysis (DEA) methodology that includes financial, operational, and sustainability scores (Environmental and Social). Our results show a productivity loss between 10% and 14% during the COVID-19 period, with a better performance once the carriers are credited for their sustainability achievements. Using a second-stage bootstrapped truncated regression, we confirm the positive impact of hubbing operations and also find significant differences across geographical regions.

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.

Strategic assessment of sustainable production in the international automobile sector using efficiency analysis and decision-making models

In the context of escalating global environmental concerns, the automotive industry faces pressing imperatives to align its growth with sustainable practices. This urgency, coupled with the need for a holistic evaluation framework, sets the stage for this study. Motivated by the gaps in quantitatively assessing sustainability, the study aims to discern and quantify the sustainability efforts of leading automotive manufacturers. The objective is to establish a robust, quantifiable link between operational efficiency and sustainability criteria that informs both business strategy and environmental stewardship. To achieve this, an innovative methodology combining the Malmquist Data Envelopment Analysis (Malmquist DEA) framework and the Ordinal Priority Approach (OPA) is employed. The approach analyzes efficiency changes, technological progress, and the industry's adoption of sustainable practices over a five-year period, concluding in the pre-pandemic year of 2020. The findings reveal a notable emphasis on productivity and market exposure, identifying them as pivotal to sustainable manufacturing. The OPA provides a weighted analysis of sustainability criteria based on expert evaluations, emphasizing the integration of environmental initiatives with core business strategies. The study's contribution is twofold: it introduces a novel, quantifiable approach to evaluating sustainability in the automotive industry, and it offers strategic insights for companies seeking to elevate their sustainability profile effectively. However, the reliance on pre-pandemic data and expert opinions marks the limitations of the study, indicating the necessity for further research. Future studies should aim to expand the temporal scope to include the latest industry shifts and to diversify the pool of sustainability criteria, capturing a broader spectrum of environmental and social dimensions. This continued research will enhance the industry's understanding of sustainable manufacturing in the evolving global context.

The heterogeneity impact of factor market distortions on GTFP: from the view of resource-based cities.

Improving the efficiency of factor allocation, breaking the resource curse, and achieving high-quality economic development are an urgent concern in resource-based cities. Using the panel data of 116 prefecture-level resource-based cities in China from 2005 to 2020, this paper constructs the data envelopment analysis-Malmquist index model to measure the level of high-quality economic development; to construct a resource misallocation growth accounting model based on the total production function to measure the distortion coefficient, the threshold panel model is employed to explore the threshold effect of factor market distortion on high-quality economic development. The results yielded three important findings: (1) The green total factor productivity of resource-based cities in China presents spatial heterogeneity and type heterogeneity. (2) Factor market distortion gradient standards are proposed innovatively, and the factor market distortion level shows the spatial heterogeneity and type heterogeneity. (3) The influence of factor market distortion on high-quality economic development in resource-based cities presents a double threshold effect. According to the above research conclusions, this paper gives several policy recommendations to promote the factor marketization allocation and high-quality economic development of resource-based cities.

ANSPs in Turbulent Times - Uncovering the Impact of Demand Shocks on Efficiency Using the Malmquist Index

Global and local shocks such as COVID-19 or the Russian attack on Ukraine have influenced European air transport, and other shocks will appear in the future. The subsequent traffic shifts affect one bottleneck candidate of the system: the Air Navigation Service Providers (ANSP). In this paper, we apply productivity metrics and Malmquist Data Envelopment Analysis to examine how the ANSP related performance of the air transport system has changed between 2008 and 2020. By considering different levels of granularity we demonstrate the tremendous influence that shocks may have on demand figures and, subsequently, on performance metrics. As an example we show that the Corona pandemic had disparate impacts on various units, resulting in notable efficiency losses in 2020, however, with a strong variation between ANSPs. The findings of our study can be utilized in a subsequent root-cause analysis to quantify the impacts of both endogenous and exogenous factors on performance. This in turn will provide valuable insights for policymakers and industry stakeholders in managing air transport during and after such shocks, and to create a more resilient air traffic management system.

Agricultural expenditure and agricultural total factor productivity growth in South Africa

<abstract> <p>The study investigates the impact of government agricultural expenditure on agricultural total factor productivity (TFP) growth in South Africa. South Africa subscribes to the African Union-inspired Comprehensive Africa Agriculture Development Programme (CAADP), established in 2003 to exterminate hunger and lessen absolute poverty. CAADP requires governments to allot at least 10% of government expenditure to agriculture and achieve an average 6% annual growth in agricultural GDP through doubling agricultural productivity. Despite launching CAADP in 2011, South Africa is off-track. Our analysis follows a two-step procedure using data for the period 1986 to 2018. Firstly, using input and output data, we estimate agricultural TFP indices using the growth accounting (GA) and the Malmquist-Data Envelopment Analysis (DEA) methods. Secondly, we use the Autoregressive Distributed Lag econometric technique to estimate the agricultural TFP impact of government expenditure. Estimates from the GA approach proved more reliable. We find that government agricultural expenditure has a significant positive effect on agricultural TFP growth of 4% and 18.5% in the short-run and long-run, suggesting high and increasing marginal gains. Estimations on weather variables reveal that a 1% increase in average temperatures and rainfall would increase TFP by 2.7% and 1.4% respectively. We recommend that South Africa fully implements the CAADP. Also, given significantly positive estimates of imports and exports, we call for increased agricultural trade liberalization biased towards export promotion and more intra-Africa agricultural trade within the AfCFTA framework.</p> </abstract>

A nonparametric analysis of climate change nexus on agricultural productivity in Africa: implications on food security

Abstract Earlier research largely ignored the effects of climate change on the growth of agricultural total factor productivity (TFP) in Africa. This study shows how climate inputs impact TFP growth in addition to other productivity growth indicators and metrics, as well as how they can impact overall input efficiency as productivity drivers. We use a panel of 42 African nations from 1999 to 2019 and a nonparametric data envelopment analysis-Malmquist technique. The non-parametric analysis revealed that the average growth rate of the non-climate-induced TFP estimates was 1.9%, while the average growth rate of the climate-induced TFP estimates was 2.4%. Accounting for temperature and precipitation separately, TFP grew by 2.3% on average. This growth rate (2.3%) is slightly less than the combined effect of temperature and precipitation (2.4%) but higher than the typical TFP growth rate (1.9%) that ignores climate variables, indicating that TFP growth in African agriculture risks being underestimated when climate inputs are ignored. We also find the distribution of the climate effects to vary across regions. In northern Africa, for example, the temperature-induced TFP growth rates were negative due to rising temperature in the region. Evidence from the decomposed TFP estimates indicates that climate variables also influence productivity determinants. However, technology improvement is fundamental to mitigating the effects of extreme weather inputs on TFP growth in Africa's agriculture. As a result, a few policy suggestions are provided to help policymakers deal with the effects of climate change on TFP growth in Africa's agriculture and ensure food security. The study advocated for a reevaluation of the climate–agriculture effect in order to fully comprehend the role of climate factors and their contributions to agricultural TFP growth in Africa.

Green finance and energy efficiency: Dynamic study of the spatial externality of institutional support in a digital economy by using hidden Markov chain

The development of a green finance system can be important for the control of energy consumption. This research focuses on the effects of the spatial externality of institutional support from the green finance systems on the energy efficiency of Chinese cities. The Malmquist data envelopment analysis (DEA) model is used to analyze the efficiency of energy consumption. The spatial Durbin model is used to analyze the effects of the spatial externality of green finance on energy efficiency. The results show that green finance has an inhibitory impact on energy consumption. The interaction of the policy of green finance and the development of the digital economy also has a negative spatial externality effect on energy consumption in cities. In addition, the control of energy consumption can be further enhanced due to negative spatial autoregressive relationships with cities. The control of energy consumption in cities with large population can be further strengthened through the positive interaction between gross domestic product (GDP) and green finance. Cluster analysis is performed to analyze the characteristics of cities, and the stationary probability in the switch of clusters is forecasted by the Markov chain. Hidden Markov Chain Modeling (HMM) is applied to analyze the probability of city clusters based on hidden connection between institutional support for digital economy and development of green finance with energy consumption. This study can provide policy makers with a systematic comprehension of the impact of spatial externalities on energy efficiency by green finance system in the cross-functional digital economy.

Measuring efficiency of banks in Saudi Arabia: A data envelopment analysis approach

The current study investigates the efficiency of banks in Saudi Arabia in terms of technical change and change in total productivity. The study considers ten banks listed on Tadawul as a sample from 2016 to 2021. The Malmquist Data Envelopment Analysis (Malmquist DEA) model is employed to measure banks’ efficiency. Customer deposits and balances with other banks and financial institutions are inputs, while the operating profit and net income are outputs to measure efficiency. The results of efficiency report that most of the Saudi Arabian banks are considerably efficient, while some are marginally efficient. The technical change report results show that Saudi Arabia’s banks are enthusiastic about adopting new technologies that lead to their growth. Further, the results of the change in total productivity show great dynamism among the Saudi Arabian banks to become more productive, which ultimately leads to a more remarkable performance. The study results demonstrate the good performance of Saudi Arabian banks; however, very few banks are marginally efficient in terms of efficiency change. Therefore, the study supports the established hypothesis that there is a significant change in the technical efficiency and total productivity of Saudi Arabian banks. AcknowledgmentThe author(s) acknowledge that the Deanship of Scientific Research supports the current project at Prince Sattam Bin Abdulaziz University under research project number 2022/02/20810.

A dynamic performance evaluation of distress prediction models

AbstractSo far, the dominant comparative studies of competing distress prediction models (DPMs) have been restricted to the use of static evaluation frameworks and as such overlooked their performance over time. This study fills this gap by proposing a Malmquist Data Envelopment Analysis (DEA)‐based multi‐period performance evaluation framework for assessing competing static and dynamic statistical DPMs and using it to address a variety of research questions. Our findings suggest that (1) dynamic models developed under duration‐dependent frameworks outperform both dynamic models developed under duration‐independent frameworks and static models; (2) models fed with financial accounting (FA), market variables (MV), and macroeconomic information (MI) features outperform those fed with either MVMI or FA, regardless of the frameworks under which they are developed; (3) shorter training horizons seem to enhance the aggregate performance of both static and dynamic models.

Impact of Carbon Trading System on Green Economic Growth in China

Whether China’s economy can maintain sustainable growth has been debated both in China and internationally, and the most representative critique has been summarized in the “Krugman Query”. Faced with such doubts, how to achieve a “win-win” for economic growth and environmental protection has become one of the central objectives of local government work while striving for the new vision of development. Taking China’s carbon trading pilot policy as an example, and based on panel data of 30 provincial administrative regions in China from 2001 to 2018, this paper uses the Data Envelopment Analysis-Malmquist index model and the Propensity Score Matching-Difference in Difference method to measure the level of green economic growth from two aspects: green development mode and economic growth effect, and further explore the impact of China’s carbon trading system on green economic growth. The results show that the implementation of the carbon trading system promoted both the green development level and economic growth of pilot cities, and positively affected green total factor productivity, refuting the “Krugman Query”. Finally, the study puts forward a series of recommendations in strengthening environmental regulation, improving green technology innovation, and developing low-carbon industries.

Modeling the convergence analysis of sustainable production and consumption in terms of ecological footprints and human development index in Belt and Road Initiative countries

Announced in 2013, China's 6-trillion-dollar multi-decadal Belt and Road Initiative (BRI) is a massive infrastructure project that would stretch from East Asia to Europe. But skeptics worry that BRI is likely to have a sizeable ecological footprint that may worsen the sustainable production and consumption of its signatories. Sustainable growth convergence for the BRI countries therefore warrants scrutiny. Here we incorporated two comprehensive indicators namely ecological footprint and human development index into the total factor productivity framework. The novel sustainable total factor productivity (STFP) index is developed to comprehensively measure sustainable growth across 62 BRI countries for the period 1990 to 2019. We employed the Data Envelopment Analysis-Malmquist approach and the Phillips and Sul (2007) club convergence test to estimate STFP changes and identify its convergence pattern. The primary results showed that: (1) the average sustainable growth for a full sample was below unity indicating that these countries placed less emphasis on sustainable growth. (2) 29 of the 62 BRI countries had positive, sustainable productivity growth and achieved sustainable growth. (3) the high-income countries and the Middle Eastern Europe achieved sustainable growth. (4) results derived from the convergence test led to the rejection of full sample convergence and to the formation of two convergent clubs. (5) although the speed of convergence varied by income level and geographic location, the highest speed was noted in the low-income countries and the Middle Eastern Europe. To accelerate sustainable growth in line with the SDGs, BRI policy makers need to prioritize joint efforts to build comprehensive sustainable growth mechanisms and provide longstanding solutions for mounting ecological footprint.

Tracking Eco-Efficiency Progress of the Countries Denmark, France, Germany, Luxembourg, and the Netherlands Using the Malmquist Data Envelopment Analysis Approach

Tracking Eco-Efficiency Progress of the Countries Denmark, France, Germany, Luxembourg, and the Netherlands Using the Malmquist Data Envelopment Analysis Approach

How Can Information Technology Use Improve Construction Labor Productivity? An Empirical Analysis from China

Labor productivity is a significant indicator to measure the sustainable development potential and competitiveness of the construction industry. Under the background of the integration of global construction industry and information and communication technology (ICT), the pursuit of the growth of construction labor productivity (CLP) requires deepened understanding of how these technological advancements characterized by ICT take effect in the change of CLP as well as what the key factors are that led to the variation of CLP at this stage. The paper aims to investigate the effect of ICT progress on CLP and examine the key factors influencing CPL. Based on the data of 31 regions from the China Construction Industry Statistical Yearbook and the Local Statistical Yearbook during the period 2000–2018, this study proposed new methodology (Cobb–Douglas production function, growth rate model, and Malmquist Data Envelopment Analysis) for measuring the technology progress contribution and identified the key factors affecting the change of CLP. The analysis results illustrate that the information technology progress has a significant contribution to CLP growth, but the contribution rate is decreasing with the growing degree of development of the regional construction industry. Three main factors affecting the further improvement of CLP have been identified: human resources, research and development (R&D) investment, and ICT level. The findings can provide the decision-making reference and the general methodology for the local and international industry practitioners to improve the labor productivity performance of the construction sector.

Mapping destination competitiveness in Indonesia’s Nusa Tenggara Timur (NTT) province: A Malmquist–data envelopment analysis approach

AbstractThis research explores destination competitiveness in the Nusa Tenggara Timur (NTT) Province of Indonesia. Absence of empirical studies related to destination efficiency in Indonesia, and the significant uprise of the tourism sector in NTT, justify the study. We employed a Malmquist–data envelopment analysis (DEA) to assess performances of regions in NTT. Results presented technical efficiency and efficiency change indexes, and further provisions regarding managerial implications. Regional tourism stakeholders therefore should adhere to the new scenario, which may entail radical rethinking regarding their traditional ways of defining, structuring, and distributing tourism products.

Provincial carbon emission performance analysis in China based on a Malmquist data envelopment analysis approach with fixed-sum undesirable outputs

The environmental issue has attracted ever-increasing attention from governments and academics in recent years. Environmental performance analysis is widely considered a crucial and useful tool for effectively protecting the environment and developing a sustainable society. Many analytical techniques have been used to assess carbon emission performance, among which data envelopment analysis (DEA) is prominent. However, few previous DEA-related carbon emission performance studies recognize that the total amount of carbon dioxide emissions is limited to a specific level by authorities; ignoring this fixed-sum requirement may lead to distortions and deviations in empirical results. This paper proposes an alternative Malmquist DEA approach for evaluating the carbon emission performance while considering fixed-sum undesirable outputs. For this purpose, we develop a generalized equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and combine the model with the Malmquist productivity index (MPI). The proposed approach is applied to assess the carbon emission performance of 30 provincial regions in China from 2009 to 2015. Then we provide analytical results and policy suggestions regarding the provincial carbon emission performance in China.

Total Factor Productivity of Listed Companies in Manufacturing Industry: Study Based on Three Major Coastal Economic Zones in China

Su, H., 2020. Total factor productivity of listed companies in manufacturing industry: Study based on three major coastal economic zones in China. In: Liu, X. and Zhao, L. (eds.), Today's Modern Coastal Society: Technical and Sociological Aspects of Coastal Research. Journal of Coastal Research, Special Issue No. 111, pp. 183–186. Coconut Creek (Florida), ISSN 0749-0208.As terrestrial resources become increasingly scarce, human activities extend farther into the ocean. Coastal areas are blessed with rich aquatic resources, as well as the advantage of convenient shipping traffic. The manufacturing industry is a comprehensive symbol of a country's economic strength. Therefore, the improvement of the efficiency of manufacturing industry in the most developed coastal areas is the key of economic operation. This paper uses the Malmquist-Data Envelopment Analysis (DEA) model to analyze the total factor productivity (TFP) of listed manufacturing firms in China's three major coastal economic zones from 2009 to 2018. The result shows that the efficiency of manufacturing industry in China's three major coastal economic zones has maintained a relatively high level, and the growth rate of TFP is on a steady growth trend. A gap occurs between the actual level of manufacturing output and the level of frontier output in the three major coastal economic zones. Therefore, improving technological efficiency is one of the main issues to be faced. In the coming year, technological progress, the introduction of advanced technologies, and institutional innovations will be the main drivers of productivity growth.