Search Results (36)

The COVID-19 pandemic triggered unprecedented economic disruptions, raising critical questions about the resilience and adaptability of macroeconomic productivity across countries. This study examines the impact of COVID-19 on macroeconomic efficiency and productivity dynamics in 37 OECD countries using quarterly data from 2018Q1 to 2024Q4. By employing a Slack-Based Measure Data Envelopment Analysis (SBM-DEA) and the Malmquist Productivity Index (MPI), we decompose total factor productivity (TFP) into efficiency change (EC) and technological change (TC) across three periods: pre-pandemic, during-pandemic, and post-pandemic. Our framework incorporates both desirable (GDP) and undesirable outputs (inflation, unemployment, housing price inflation, and interest rate distortions), offering a multidimensional view of macroeconomic efficiency. Results show broad but uneven productivity gains, with technological progress proving more resilient than efficiency during the pandemic. Post-COVID recovery trajectories diverged, reflecting differences in structural adaptability and innovation capacity. Regression analysis reveals that stringent lockdowns in 2020 were associated with lower productivity in 2023–2024, while more adaptive policies in 2021 supported long-term technological gains. These findings highlight the importance of aligning crisis response with forward-looking economic strategies and demonstrate the value of DEA-based methods for evaluating macroeconomic performance beyond GDP. Full article

This paper explores the optimization of the bullwhip effect (BWE) and inventory costs considering price information symmetry in an omnichannel environment, offering novel insights into managing supply chain dynamics. We examine the pick-up lead time in the “buy online and pick up in store” (BOPS) channel as a critical operational factor, analyzing how the interaction with the ordering lead time affects omnichannel supply chain performance. The research highlights the impacts of the BOPS strategy on demand and inventory information, developing a comparative examination of the BWE and inventory expenses within various supply chain contexts. We discover that the interplay between ordering lead time and pick-up lead time significantly affects both inventory costs and the BWE of omnichannel retailers, with these impacts presenting an inverse relationship. While numerous studies have validated that product returns can restrain the information distortion in supply chains, our findings reveal that this relationship holds true in omnichannel retail only within specific supply chain contexts. This comprehensive approach offers valuable insights for omnichannel supply chain managers seeking to optimize the BOPS strategy and improve overall operational efficiency. Full article

This study investigates the relationship between overconfidence and meme stock valuation, drawing on panel data from 28 meme stocks listed from 2019 to 2024. The analysis incorporates key financial indicators, including Tobin’s Q ratio, market capitalization, return on assets, leverage, and volatility. A range of overconfidence proxies is employed, including changes in trading volume, turnover rate, changes in outstanding shares, and alternative measures of excessive trading. We observe a significant positive relationship between overconfidence (as measured by changes in trading volume) and firm valuation, suggesting that investor biases contribute to notable pricing distortions. Leverage has a significant negative relationship with firm valuation. In contrast, market capitalization has a significant positive relationship with firm valuation, implying that meme stock investors respond to both speculative sentiment and traditional firm fundamentals. Robustness checks using alternative proxies reveal that turnover rate and changes in the number of shares are negatively related to valuation. This shows the complex dynamics of meme stocks, where psychological factors intersect with firm-specific indicators. However, results from a dynamic panel model estimated using the Dynamic System Generalized Method of Moments (GMM) show that the turnover rate has a significantly positive relationship with firm valuation. These results offer valuable insights into the pricing behavior of meme stocks, revealing how investor sentiment impacts periodic valuation adjustments in speculative markets. Full article

The distortion of energy prices has become an important obstacle to the high-quality development of China’s economy. Moreover, energy price distortions are not merely a domestic issue. They may trigger carbon leakage by diverting emissions-intensive production to countries with cheaper energy. Although the existing literature has extensively examined the effects of energy price distortions, two significant research gaps remain. First, most studies treat energy price distortions merely as an influencing factor, lacking a systematic analysis that places it at the core. Second, the spatial correlation characteristics of energy price distortions are often overlooked. This study measures the degree of energy price distortions across Chinese provinces from 2000 to 2022 and employs methods such as the Global Moran’s I, Local Moran’s I, and kernel density estimation to systematically analyze the spatial correlation, spatial distribution of coordination indices, and dynamic evolution patterns of these distortions. The results reveal that: (1) the overall degree of energy price distortions in China exhibited a trend of rising first and then declining, with significant regional disparities; (2) the regional gap followed an “expansion-contraction” trajectory; (3) there is notable spatial autocorrelation, with high-distortion areas concentrated in Northeast China, the middle reaches of the Yellow River, and Northwest China; and (4) the dynamic evolution suggests that distortion levels in high- and medium-value regions may continue to decline, while those in low-value regions may increase. This study fills a critical gap in the systematic spatial analysis of energy price distortions and provides new empirical evidence and policy insights for advancing market-oriented reforms in energy markets. Full article

The performance of machine learning models depends on several factors, including data normalization, which can significantly improve its accuracy. There are many standardization techniques, and none is universally suitable; the choice depends on the characteristics of the problem, the predictive task, and the needs of the model used. This study analyzes how normalization techniques influence the outcomes of real estate price regression models using machine learning to uncover complex relationships between urban and economic factors. Six normalization techniques are employed to assess how they affect the estimation of relationships between property value and factors like social degradation, resident population, per capita income, green spaces, building conditions, and degraded neighborhood presence. The study’s findings underscore the pivotal role of normalization in shaping the perception of variables, accentuating critical thresholds, or distorting anticipated functional relationships. The work is the first application of a methodological approach to define the best technique on the basis of two criteria: statistical reliability and empirical evidence of the functional relationships obtainable with each standardization technique. Notably, the study underscores the potential of machine-learning-based regression to circumvent the limitations of conventional models, thereby yielding more robust and interpretable results. Full article

This paper proposes a novel robustness framework for studying the unit elasticity of intertemporal substitution (EIS) assumption based on the Perron-Frobenius sieve estimation model by Christensen, 2017. The sieve nonparametric decomposition is a central model that connects key strands of the long run risk literature and recovers the stochastic discount factor (SDF) under the unit EIS assumption. I generate various economies based on Epstein–Zin preferences to simulate scenarios where the EIS deviates from unity. Then, I study the main estimation mechanism of the decomposition as well as the time discount factor and the risk aversion parameter estimation surface. The results demonstrate the robustness of estimating the average yield, change of measure, and preference parameters but also reveal an “absorption effect” arising from the unit EIS assumption. The findings highlight that asset pricing models assuming a unit EIS produce distorted parameter estimates, caution researchers about the potential under- or over-estimation of risk aversion, and provide insight into trends of misestimation when interpreting the results. I also identify an additional source of failure from a consumption component, which demonstrates a more general limit of the consumption-based capital asset pricing model and the structure used to estimate relevant preference parameters. Full article

This paper examines the financial health risks that private sector water, sanitation, and hygiene (WASH) businesses in rural Vietnam face. It investigates the challenges faced by water operators and sanitation suppliers involved in donor-funded development projects aimed at supporting poor and vulnerable households. Through surveys and focus group discussions with 15 suppliers who worked in public–private partnerships, this research examines the financial risk factors affecting water and sanitation suppliers and their impact on financial viability through two case studies. For water operators, the risks primarily involve infrastructure management, operational costs, and revenue instability. In the sanitation sector, risks center around fluctuating material prices, limited business expansion capital, and household affordability. This study highlights the dual role of government and donor subsidies, which enhance service accessibility but potentially distort market dynamics. It also underscores the need for targeted financial and policy interventions, including better access to microfinance, regulatory improvements, and human resource development. The findings aim to inform strategies for government, donors, and private sector actors in similar WASH development contexts to enhance financial sustainability, ensuring inclusive WASH services in underserved areas. This paper contributes to policy discussions by proposing mechanisms to balance public–private collaboration while fostering market resilience and equitable access to WASH services in emerging economies similar to that of Vietnam. Full article

Road freight transport is the key driver of the European economy and society; thus, distortion of its operation would have negative influence on growth and well-being. For that reason, implementation of European policies, including transport decarbonization, should be comprehensively evaluated from an environmental, social and economic perspective. In that case, introduction of electric trucks will create a mutual impact on the market and on haulage companies. The main research problem is to assess the future impact of decarbonization on the international road freight transport market structure on the supply side and the competitiveness of companies operating there. Today, a number of small and medium companies, to a great extent from Eastern Europe, render transportation services, creating a competitive structure with high flexibility, accessibility and low prices. Shifting towards electric trucks, with significantly higher upfront costs, will redefine the market structure, eliminating the small carriers and activating horizontal integration. The key objective of this research is to identify the main factors and challenges related to electric truck implementation and define crucial areas of its impact on future market structure. The research shows that the improvement of environmental performance requires low- or zero-emission trucks, where the battery technology is a leading solution. Thus, fleet renewal needs additional financial support from the public side. Different measures are available in European countries, so the level of support is not equal from a competitiveness perspective. Battery truck selling, as well as sustainable strategies, refer mostly to huge transport companies. On the other hand, the case of Polish truckers shows that the economic viability of SMEs is poor; thus, the introduction of BET would be beyond its reach. The research findings could be treated as recommendations for market regulators (EC), where the tempo of implementation, as well as availability of public support programs, should be rethinking. As a result, the costs of the transition will be covered by citizens, as customers, in the prices of products and transport service, or as taxpayers, in public support programs, mainly consumed by large market stakeholders. Full article

The asymmetric effects of global and national factors on agricultural production negatively affect the sustainability of agriculture in Turkey. This study seeks to explore those impacts on wheat prices by focusing on key input prices such as diesel, fertilizers, and substitute barley prices and wheat production. Unlike studies that use crude oil prices as agricultural input parameters, this study aims to address the lack of behavioural variables in time series studies by considering diesel and fertilizer prices. The Vector Autoregressive (VAR) model analysis examines the effect of barley prices as a substitute for wheat, while the Granger causality analysis is conducted to assess the causal relationships between variables. Additionally, unlike previous studies that primarily focus on causality between variables or the effects of lagged values, this study investigates the dual effects of explanatory variables. Furthermore, impulse response functions are utilized to analyse the dynamic interactions among the variables and to identify symmetric and asymmetric relationships. Granger causality analysis indicates that wheat production in Türkiye is influenced by wheat prices; however, production does not impact prices. Wheat prices are not market-driven, and price interventions aim to ensure agricultural sustainability. The absence of causality between the wheat production amount and its price emerged bilaterally as barley price/wheat production/barley price. An analysis of wheat price responses to shocks in fertilizer and diesel prices reveals an asymmetric pattern. Wheat prices reacted more strongly to negative shocks, while their response to positive shocks was more moderate. These findings indicate the existence of asymmetric relationships between wheat prices and these two agricultural inputs, underscoring the asymmetric nature of price transmission in agricultural markets. They also highlight the policy requirements associated with ensuring food price stability and sustainable agricultural practices as well as a crucial lesson: policymakers in developing countries should prioritize structural reforms over interventionist policies that distort market signals. Full article

As one of the most dynamic natural elements, water resources play a vital role in both ecological and economic activities. The rational allocation of water resources is essential for the sustainable development of human society. Using data from prefecture-level cities in the Yangtze River Economic Belt from 2013 to 2022, this study employs the Cobb–Douglas (C-D) production function to measure the degree of water resource allocation distortion across provinces and cities. Additionally, a panel data model is applied to analyze the influencing factors. The key findings are as follows: The issue of excessive water resource allocation is widespread in the Yangtze River Economic Belt, with the highest degree of distortion observed in the middle reaches, where the average distortion index reaches 1.43—significantly higher than that in the upstream (1.15) and downstream (1.20) regions. Water resource endowment, the South-to-North Water Diversion Project, and rising water prices contribute significantly to improving water resource allocation, whereas increased industrial water use efficiency and excessive government intervention exacerbate allocation distortions. Regionally, the influencing factors exhibit broadly similar effects across provinces. In sectoral analysis, only the estimated coefficients of water resource policies show directional differences, whereas other factors have no significant impact on allocation distortion. By identifying the extent and causes of water resource misallocation, this study provides empirical evidence to support region-specific water management strategies, aiming to enhance resource efficiency and promote sustainable development. Full article

The inefficient allocation of resources in agricultural production not only affects the quality of agricultural development and the efficiency of resource utilization but also represents a pivotal issue that constrains the sustainable progress of agriculture. Considering the urgent societal need for the optimization and advancement of industries, investigating the issue of resource misallocation within agricultural production and its specific losses on AGTFP is profoundly important in advancing the pursuit of high-quality and sustainable agricultural development. This study employs the Cobb–Douglas function and the theory of price distortion to establish a model for quantifying losses in Agricultural Green Total Factor Productivity (AGTFP). Drawing on provincial panel data from North China spanning the years 2006 to 2022, we analyze the characteristics of resource allocation and the corresponding losses in AGTFP. The findings suggest that AGTFP in North China has been gradually rising, accompanied by notable regional disparities in both the level of AGTFP and its growth rate. Nevertheless, due to the varying effects of distorted agricultural input factors, there exists different resource misallocation across North China. Despite some improvement in resource misallocation, this improvement has not been significant. Consequently, there is a loss of AGTFP in the North China region. If resource misallocation is effectively addressed, AGTFP losses could be reduced by at least 29%. It is anticipated that over the course of the next decade, AGTFP will rise and resource misallocation and AGTFP losses will diminish slightly, and it is crucial to step up efforts to enhance resource allocation. By ensuring adequate agricultural funding, enhancing agricultural efficiency, and optimizing energy inputs, it is possible to mitigate resource misallocation, thereby effectively diminishing AGTFP losses and fostering the sustainable advancement of agriculture. Full article

Professional football clubs invest vast amounts of money in the recruitment of players. This article presents the latest advances in statistical modeling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain up to 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices is thus highly relevant on a global scale. Full article

Increasing export technology complexity could effectively enhance export competitiveness. High-tech exports generally show lower resource consumption and environmental pollution, thus promoting sustainable economic development. However, immature factor markets could lead to factor price distortions. In fact, factor price distortions hinder improvements in export technology complexity. Thus, this study measures the degree of factor price distortions in various regions of China. Empirical methods such as regression model analysis and heterogeneity analysis are used. We reveal the mechanism of how factor price distortions affect export technology complexity. The conclusions are as follows: (1) Factor price distortions suppress the enhancement of export technology complexity. As the degree of factor price distortions increases, export technology complexity decreases. (2) Factor price distortions show significant regional heterogeneity in the suppression of export technology complexity. The impact gradually decreases from west to east. (3) Factor price distortions could hinder improvements in export technology complexity by weakening the positive effects of the FDI and trade openness. However, with the continuous advancements in market-oriented reforms, this inhibitory effect will gradually diminish. Studying the impact of factor price distortions on the sophistication of export technology significantly enhances economic competitiveness. It also improves resource allocation and further promotes the sustainability of economic development and green development. Furthermore, the logic and principles behind the impact of factor price distortions on export technology complexity can provide valuable insights for our consideration of sustainability in the workplace. Full article

Haze pollution not only has negative impact on public health and air quality, but also has restricted China’s industrial upgrading and high-quality development, and Chinese urban agglomerations are one of the areas hardest hit of haze pollution. In the process of China’s economic transformation, local governments will adopt local protectionism, leading to market segmentation. This is a phenomenon that refers to the distortion of resource allocation by local governments for the sake of vested interests and the existence of segmentation in commodity markets. This behavior is considered to be one of the important factors causing haze pollution. As Yangtze River Delta urban agglomerations are considered to be one of the earliest, fastest growing, and most mature for market integration in China, there is a lack of empirical testing on the impact of market segmentation on haze pollution in this urban agglomeration. Based on urban panel data from the period of 1998–2018 and the market segmentation index calculated by the relative price method, we use the dynamic spatial Durbin model and generalized space two-stage least squares method to explore the effect of market segmentation in urban agglomeration on haze pollution; the results are as follows: (1) Market segmentation significantly exacerbates haze pollution, in other words, haze pollution will increase by 2.14% when market segmentation increases by 1%. (2) Cities with a high degree of market segmentation and high levels of haze pollution have the potential to reduce pollution through market integration in the future. (3) Market segmentation in surrounding regions also has a significant worsening effect on haze pollution in the region. The indirect effect of market segmentation is 3.67 times that direct effect, indicating that the spatial spillover effect of market segmentation on haze pollution is greater than its own impact. (4) Mechanism analysis finds that it will aggravate haze pollution by hindering economic scale, industrial structure, and technological progress when the degree of market segmentation is high. Full article

This study examines the relationship between CO₂ emissions and economic factors in the hospitality industry, covering the period from January 2000 to December 2021. The research findings reveal an association between prices, fiscal and monetary factors, and wages in the hospitality industry. CO₂ emissions contribute to environmental degradation and are among the external variables. To test the validity of their hypotheses, the researchers employ the principal components analysis method, using two main components and new regressors to explain most of the variances in a sample of 18 variables. The study employs monthly time series data to explore the links between the variables in the hospitality industry. The study results reveal a connection between shocks during the analysed period and increased CO₂ emissions. The period under review includes Slovenia’s European accession in 2004, adoption of the Euro in 2007, the financial crises in 2008/2009, economic expansion in the previous decade, and, most recently, the COVID-19 pandemic and the beginning of energy distortions. The study’s primary application involves processing 264 monthly observations, which provide an essential time series vector. The discussion section delves into the country’s sustainable tourism development concept, aligning with the Sustainable Development Goals (SDGs) Key Action 12. Full article