Econometric Model Research Articles

The role of thematic specialization in international scientific collaboration: the case of Chagas research

Abstract Scientists are increasingly specializing to manage the vast knowledge bases within their domains, making knowledge-sharing strategies essential for scientific progress. Among these strategies, international scientific collaboration has increased markedly. We analyse the role of thematic specialization as one of its drivers in the context of Chagas research, a neglected disease. Since there is a strong research tradition in Latin America on this topic, it is an interesting context to study centre-periphery research dynamics. Using a novel bibliometric database on Chagas research during 1990–2019, we exploit its panel structure in a gravity-type econometric model. The findings show that the degree of specialization and its proximity among partners boost joint publications. Both effects are particularly important for country dyads involving authors from the periphery linked to authors in the centre. This paper is the first empirical study to assess the role of specialization as a driver of international scientific collaboration.

Causal impact evaluation of occupational safety policies on firms’ default using machine learning uplift modelling

It is often undermined that occupational safety policies do not only displace a direct effect on work well-being, but also an indirect effect on firms’ economic performances. In such context, econometric models dominated the scenes of causality until recently while Machine Learning models were seen with skepticism. With the rise of complex datasets, an ever-increasing need for automated algorithms capable to handle complex non-linear relationships between variables has brought to uncover the power of Machine Learning for causality. In this paper, we carry out an evaluation of a public aid-scheme implemented in Italy and oriented to support investment of small and medium enterprises (SMEs) in occupational safety and health (OSH) for assessing the impact on the survival of corporations. A comparison of thirteen models is performed and the Individual Treatment Effect (ITE) estimated and validated based on the AUUC and Qini score for which best values of 0.064 and 0.407, respectively, are obtained based on the Light Gradient Boosting Machine (LightGBM). An additional in-depth statistical analysis also revealed that the best beneficiaries of the policy intervention are those firms that experience performance issues in the period just before the interventions and for which the increased liquidity brought by the policy may have prevented default.

Spatiotemporal variations and influencing factors of methane emissions from livestock in China: A spatial econometric analysis

In recent years, China has been implementing policies to improve the livestock industry in response to the global trend toward green and low-carbon development. These policies include the establishment of demonstration zones for high-standard agriculture, the relocation of farms to the north, etc. This study aims to investigate the impact of changes in the spatial structure of the livestock industry on methane emissions. It used panel data from 31 provinces in China from 2001 to 2021 and applied the IPCC methodology to quantify methane emissions at both the national and provincial levels. In addition, a spatial econometric model was used to analyze the impact of changes in the spatial structure of the livestock industry on methane emissions. The results show that methane from livestock in China decreased from 13.85 million tons in 2001 to 11.82 million tons in 2021. In addition, methane emissions from livestock in China show a significant spatial gradient and correlation. The Southwest has the highest methane emissions, accounting for 24 % of the total emissions. After controlling for spatial correlation and other factors in the model, it was found that the spatial structure of the livestock industry has a different influence on methane emissions both in the province and in neighboring provinces. To improve methane emission efficiency in the future, policies such as establishing functional zones for livestock farming, strengthening technological innovation and sharing for green development in agriculture, and promoting the optimization of agricultural and rural management structures should be implemented.

Unlocking Economic Resilience: A New Methodological Approach and Empirical Examination under Digital Transformation

Economic resilience is crucial for urban sustainability as it ensures stability and growth in the face of external shocks, promotes social cohesion and inclusivity, fosters environmental sustainability, and enhances cities’ adaptability to future challenges. This study expands the conventional perspective on economic resilience beyond the context of shocks, focusing on the inherent resilience of regional economic systems. A novel method for quantifying economic resilience is introduced, emphasizing system sensitivity and adaptability. Using Chinese prefecture-level city data and an econometric model, we empirically examine how Fintech, a major digital transition in current urban systems, affects economic resilience. The findings reveal that Fintech has a substantial positive effect on economic resilience, primarily through the upgrading of industrial structures and technological innovation. Furthermore, there is significant regional heterogeneity in the impact of Fintech on economic resilience, with more pronounced contributions in the east, central, and western regions of China, as opposed to the northeast. Additionally, the impact of Fintech on economic resilience is more substantial in large-scale cities. The promotion of economic resilience through digital transformation serves as a potent risk prevention measure. Understanding the role of economic resilience in urban systems holds valuable implications for countries worldwide.

Digital finance and the energy transition: Evidence from Chinese prefecture-level cities

The increasing digitalisation of the financial sector can potentially steer cities towards carbon neutrality, especially through digital finance. We conduct a panel data analysis of Chinese prefecture-level cities from 2014 to 2020 using a spatial econometric model to explore the effect of digital finance on energy transition. Further, we explore the spatial spillover effect and its moderating mechanism. Our results confirm a significant positive correlation between digital finance and energy transition efficiency. Moreover, digital finance is found to engender positive spatial spillover effects on energy transition in neighbouring cities. Finally, the analysis of the moderating effect reveals that digital economy policies can amplify the positive correlation between digital finance and energy transition. This finding highlights the importance and positive role of digital finance in realising energy transition.

Analysis of Synergistic Effect of Energy Efficiency Improvement and Vocational Competence Development

INTRODUCTION: Growing global energy demand and constant competition for energy resources have made energy efficiency a focus of international attention. At the same time, occupational competency development is crucial for sustained individual and overall economic growth.OBJECTIVES: This study explores the synergistic effects of energy efficiency improvement and occupational competence development to inform policy-making and business strategies.METHODS: A literature review summarises relevant theories and research progress on energy efficiency improvement and occupational capability development. Then, the relationship between energy efficiency improvement and vocational ability development was analyzed in depth using econometric models and a large amount of empirical data.RESULTS: There is a significant positive association between energy efficiency improvement and vocational ability development. Specifically, improving energy efficiency not only reduces the cost of energy consumption and enhances productivity but also helps promote technological innovation and industrial upgrading, which in turn enhances the level of occupational ability of workers. In turn, improving occupational ability further encourages technological innovation and productivity in enterprises, forming a virtuous circle and promoting the sustainable development of the economy.CONCLUSION: This study reveals the synergistic effect between energy efficiency improvement and vocational competence development, emphasizing the critical role of energy efficiency improvement and vocational competence development in promoting economic growth and achieving sustainable development.

Behavioural determinants of the healthcare providers in Ukraine: Focus on competition context

The development of the healthcare system is an important task for every country, given that long-term economic and social development is absolutely impossible without a healthy nation. Therefore, improving public health and ensuring that the maximum number of people have access to quality healthcare services is one of the state's strategic priorities. In this regard, the authors of the paper set a goal to establish how the reform of the healthcare system in Ukraine has led to changes in the behavioural determinants of healthcare providers and recipients in the domestic healthcare market. The paper uses such research methods as comparison, visualization, graphical data visualization tools, and statistical methods of data analysis. The study revealed that Ukrainian citizens got a chance to freely choose their physicians by signing declarations with them, which led to increased competition among physicians and other doctors and healthcare facilities; at the same time, the number of declarations signed with private healthcare facilities increased significantly. Furthermore, the developed econometric model allows us to identify statistically significant relationships between the amount of state funding and lifestyle and environmental indicators, which will allow us to improve the mechanisms and amount of funding in the sector, improving the health of the nation in the new competitive environment.

Climate adaptation investments: Short-term shocks and long-term effects of temperature variation on air conditioning adoption

Air conditioning (AC) has gradually become the main way for households to adapt to climate change. Using a climate econometric model with two-way fixed effects, we systematically examine the short-term shocks and long-term effects of temperature variation on AC ownership in urban and rural households in the Yangtze River Delta region of China. When considering short-term shocks of temperature variation, our results indicate that cooling demand is the main driver of growth in AC ownership while heating demand and intra-seasonal temperature variance are not. In terms of the long-term effect, we find mean summer temperature has a positive lagged effect, with the cumulative effect being larger for rural households. The analysis of heterogeneity shows that while the greater elasticity of air conditioning ownership to temperature variation for rural residents indicates a faster diffusion of air conditioning during 2001–2020, the larger income elasticity (greater than 1) suggests that air conditioning is still a luxury item for them. Our findings not only provide empirical evidence for studying the pattern of climate adaptation investments in response to temperature variation, but also offer policy guidelines for households to better adapt to climate change.

Measuring Impact of CAMELS Model on Financial Performance of Indian Commercial Banks

The purpose of this study is to examine the effects of CAMELS components on the financial performance of Indian commercial banks. To fulfill the objectives of the study,secondary data were collectedfor the fiscal year 2016 to 2021 from four public sector and four private sector banks based on their market capitalization. To analyse the data two econometric models are constructed using return on assets (ROA) and return on equity (ROE) as proxies for commercial banks’ financial performance as dependent variables and six CAMELS’s key indicators (capital adequacy, asset quality, management efficiency, earning quality, liquidity, and sensitivity to market risk)as independent variables. To determine the extent to which the independent variables have an effect on the dependent variable, panel ordinal least square regression with their assumption has been used. The findings of the study revealed that the financial performance of Indian banking sector as evaluated by ROA and ROE is statistically and significantly affected by capital adequacy, liquidity and sensitivity to market risk, whereas asset quality, managerial efficiency and earning quality is found to have insignificant impact on the financial performance of Indian commercial banks. In order to perform better, it is suggested that the commercial banks should focus more on the variables that have a substantial impact on their financial performance. Keywords: Random effect, ROA, ROE, liquidity, sensitivity to market risk, CAMELS.

Endogenous Growth Theory: The Influence of Health Budget Allocation and Financial Sector Growth on the Indonesian Economy

This study delves into the application of Endogenous Growth Theory to the Indonesian context, focusing on the interplay between health budget allocation and the growth of the financial sector in shaping the country's economy. Endogenous Growth Theory posits that economic growth can be internally driven by factors such as human capital accumulation, technological progress, and institutional development. Through empirical analysis, this research examines how the allocation of resources to the health sector and the expansion of the financial industry impact Indonesia's economic performance. By employing quantitative methods and econometric models, the study elucidates the extent to which these factors contribute to economic growth and development. The findings shed light on the importance of targeted budgetary allocations and financial sector policies in fostering sustained and inclusive economic progress in Indonesia

Analyzing the transition from two-vehicle collisions to chain reaction crashes: A hybrid approach using random parameters logit model, interpretable machine learning, and clustering

Chain reaction crashes (CRC) begin with a two-vehicle collision and rapidly intensify as more vehicles get directly involved. CRCs result in more extensive damage compared to two-vehicle crashes and understanding the progression of a two-vehicle collision into a CRC can unveil preventive strategies that have received less attention. In this study, to align with recent research direction and overcome the limitations of econometric and machine learning (ML) modelling, a hybrid approach is adopted. Moreover, to tackle the existing challenges in crash analysis, addressing unobserved heterogeneity in ML, and exploring random parameter effects and interactions more precisely, a new approach is proposed. To achieve this, a hybrid random parameter logit model and interpretable ML, joint with prior latent class clustering is implemented. Notably, this is the first attempt at using a clustering with hybrid modeling. The significant risk factors, their critical values, distinct effects, and interactions are interpreted using both marginal effects and the SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes crash, traffic, and geometric data from eleven suburban freeways in Iran collected over a 5-year period. The overall results indicate an increased risk of CRC in congested traffic, higher traffic variation, and on horizontal curves combined with longitudinal slopes. Some parameters exhibit distinct or fluctuating effects, which are discussed across different conditions or considering interactions. For instance, during nighttime, heightened congestion on 2-lane freeways, increased traffic variation in less congested conditions, and adverse weather combined with horizontal curves and slopes pose risks. During daytime, increased traffic variation within highly congested sections, higher proportion of heavy vehicle traffic in moderately congested sections, and two lanes in each direction coupled with curves, elevate the levels of risk. The results of this study provide a better understanding of risk factors impact across different conditions, which are usable for policy makers.

Environmental policy stringency and carbon leakages: a case for carbon border adjustment mechanism in the European Union

AbstractThis study examines the link between the strictness of environmental policies and carbon leakage in the European Union (EU). It utilizes an econometric model to analyse how carbon leakage is influenced by environmental policies and other factors. A comprehensive dataset spanning from 1995 to 2020 for 20 EU member nations is employed. This study is ground-breaking, as it is the first to comprehensively assess the effect of aggregated environmental policies on carbon leakages in the EU. This study employs a range of econometric techniques to ensure the reliability of its findings, including the continuously updated fully modified approach, bias-adjusted ordinary least squares method, and bootstrap panel causality testing. The findings confirm that stringent environmental policies cause greater carbon leakage by increasing the quantity of foreign carbon emissions embodied in EU’s domestic final demand. Specifically, carbon leakage increases within the range 0.051–0.111% as environmental policy stringency rises by 1%. This outcome confirms that direct carbon leakage occurs through the international trade channel as domestic carbon emissions reduction is continuously being offset by greater emissions abroad. Country-specific reactions captured through causality tests further reveal that the predictive powers between environmental policy stringency and carbon leakage is widespread among the sampled EU countries. Thus, our conclusion is that stringent environmental policies put the region at a disadvantage in the international markets. The main recommendation therefore is that ample justification exists for the introduction of carbon border adjustment mechanism, as the positives associated with its imposition are likely to outweigh the negatives.

Evolution Model and Driving Mechanism of Urban Logistics Land: Evidence from the Yangtze River Delta

Logistics land is the spatial carrier for the development of logistics enterprises. Its evolution mode and driving mechanism determine the level of high-quality development of the logistics industry, and serve as an important basis for urban planning and territorial spatial planning. This study introduced a Boston consulting group (BCG) matrix and geographically weighted regression (GWR) spatial econometric models to carry out empirical research on the Yangtze River Delta (YRD), in an effort to provide scientific information for evidence-based decision-making by governments and enterprises. The scale and ratio of logistics land (LLS and LLR) in the YRD showed significant spatial heterogeneity and autocorrelation, cities with large logistics land use converging from clusters to belts from 2000 to 2020, and agglomerations with high logistics land ratio (LLR) migrating from inland to coastal areas. Diversified models of logistics land evolution also emerged, such as high scale–high speed cities, low scale–low speed cities, high scale–low speed cities, and low scale–high speed cities. In addition, the driving mechanism of LLS and LLR was very complex, with a great difference in the intensity, nature and spatial effects of the influence of different factors. The inspiration from empirical case studies is urgent to revise the planning norms and clarify the LLS and LLR control standards for logistics land use. Meanwhile, the synergistic development target of the logistics industry in the new era is changing from the manufacturing industry to the commerce and trade industry; the establishment of planning zoning and the designing of differentiated management policies significantly improve the planning applicability.

Sustainable energy consumption – Empirical evidence of a household in Poland

There is an expected increase in the demand for electricity from households in European countries. The outcome of the growing proportion of renewable energy sources in the energy supply mix should result in improved customer awareness regarding their electricity usage. Additionally, they must assume a more proactive role in overseeing their electricity usage. Innovation and pollution management have become crucial catalysts for promoting sustainable economic development and environmental protection. This is a response to the increasing threats posed by climate change. This study addresses this existing research gap in sustainable energy consumption. The research aims to examine the advantages of replacing traditional sources of heating with renewable ones. The empirical case study of households in Poland serves as an illustration of the theoretical foundation. The purpose of the study entails the construction of a multi-equation econometric model describing the mechanisms of electricity consumption in a specific household. The model hypothesis indicates that the model should take the form of a system of interdependent equations. The following will be described: the monthly volume of electricity consumption, the cost of this energy payment (in PLN), and the price per one MWh in a given month. Monthly time series from September 2015 to June 2023 were used. The resulting time series amounted to a count of 94 statistical observations. The results in the form of a multi-level empirical econometric model indicate that the economic, environmental, and social benefits of using renewable sources of heating can make a significant contribution to reducing fossil fuel consumption and CO2 emissions. In the context of growing environmental awareness and the need to reduce greenhouse gas emissions, heat pumps are becoming an increasingly important element in the discussion of the future of heating. Their role in transforming the energy sector in the direction of sustainability is crucial.

Toward a Sustainable Development of E-Commerce in EU: The Role of Education, Internet Infrastructure, Income, and Economic Freedom on E-Commerce Growth

The emergence of e-commerce reshaped the traditional trade models, also playing a significant role in meeting the UN sustainable development goals. According to the UN, sustained growth and social development must include resilient infrastructure, foster innovation, allow for better access to information and communications technology, and universal and affordable internet infrastructure. This study explores a multidimensional analysis of e-commerce development in the EU generated by the following factors: education, internet infrastructure, income, and economic freedom. We use an ARDL econometric model and Eurostat data. Additionally, we analyze the time responsiveness of e-commerce growth to changes in these factors. In the long run, our findings identify a stable and positive relationship between e-commerce and all these factors. However, in the short run, our results illustrate significant dynamics between two variables and e-commerce. Specifically, the level of internet access and the percentage of individuals who use the internet daily exhibit a positive short-run impact on e-commerce sales, with the system absorbing shocks within a short period. This research advocates for targeted policies that support innovation, fair competition, and consumer protection in the digital economy. This research provides valuable guidance for policymakers and stakeholders in improving the institutional framework to promote a sustainable development of e-commerce in the EU.

Investigating the temporal and spatial evolution of bank performance and its impact pathways in the context of financial technology development

Financial technology transforms humans and businesses as globalization and the digital economy accelerate. This affects bank performance. Thus, studying whether financial technology affects bank performance is crucial to enhancing living standards and business development. This article examines the development and interaction between financial technology and bank performance from 2012 to 2021 using standard deviation ellipses, kernel density estimation, Moran's index, and spatial econometric models. The research found that (1) financial technology development improves regional bank performance. In contrast, control variables like economic development, urbanization, tax burden, capital adequacy ratio, net interest margin, and loan-to-deposit ratio also affect bank performance. (2) From 2012 to 2021, Chinese bank performance initially grew, then declined, while financial technology declined slowly and improved rapidly. Financial technology and bank performance development were highest in the eastern coastal regions and lowest in the northwest and northeast. (3) China's financial technology and bank performance had high-high or low-low spatial agglomeration. (4) Financial technology and control variables have a spatial spillover effect on bank performance, so their development in one region can affect neighboring regions. This article provides recommendations for governments and banks based on these findings.

The impact of digital technology deployment on mitigating supply chain disruptions: Evidence from Chinese automotive manufacturers during the COVID-19 crisis

Digital supply chain management provided opportunities to mitigate the effects of supply chain disruptions on business activities. Unfortunately, the real-life impacts of digital technology deployment in mitigating supply chain disruptions and enhancing resilience have not been fully evaluated, and it is unclear which digital technology deployment strategy had the greatest effect on firms' operations. This research seeks to fill this gap using data from the annual reports of 123 Chinese automotive manufacturers during the COVID-19 crisis. We focus on two dimensions of digital technology deployment: amount and category. Metrics describing financial and operational performance are used to estimate firms' overall performance. Additionally, this research presents econometric models to assess the effects of digital technology deployment from the two aforementioned dimensions. We found that the deployment of digital technology assets could mitigate negative effects on firms' performance during COVID-19 crisis, albeit with a time lag. Different categories of digital technology deployment, however, had different effects on firms' financial and operational performance. Digital front-end technologies were more efficient in improving the financial performance of the automotive firms. Meanwhile, digital base technologies were more impactful on the firms’ operational performance. This research contributes to the body of research on digital SCM and supply chain risk management by examining the effect of digital technologies from a dynamic capability perspective.

Spatial distribution and influencing factors of CDC health resources in China: a study based on panel data from 2016-2021.

Measuring the development of Chinese centers for disease control and prevention only by analyzing human resources for health seems incomplete. Moreover, previous studies have focused more on the quantitative changes in healthcare resources and ignored its determinants. Therefore, this study aimed to analyze the allocation of healthcare resources in Chinese centers for disease control and prevention from the perspective of population and spatial distribution, and to further explore the characteristics and influencing factors of the spatial distribution of healthcare resources. Disease control personnel density, disease control and prevention centers density, and health expenditures density were used to represent human, physical, and financial resources for health, respectively. First, health resources were analyzed descriptively. Then, spatial autocorrelation was used to analyze the spatial distribution characteristics of healthcare resources. Finally, we used spatial econometric modeling to explore the influencing factors of healthcare resources. The global Moran index for disease control and prevention centers density decreased from 1.3164 to 0.2662 (p < 0.01), while the global Moran index for disease control personnel density increased from 0.4782 to 0.5067 (p < 0.01), while the global Moran index for health expenditures density was statistically significant only in 2016 (p < 0.1). All three types of healthcare resources showed spatial aggregation. Population density and urbanization have a negative impact on the disease control and prevention centers density. There are direct and indirect effects of disease control personnel density and health expenditures density. Population density and urbanization had significant negative effects on local disease control personnel density. Urbanization has an indirect effect on health expenditures density. There were obvious differences in the spatial distribution of healthcare resources in Chinese centers for disease control and prevention. Social, economic and policy factors can affect healthcare resources. The government should consider the rational allocation of healthcare resources at the macro level.

Weather variability risks slow climate adaptation: An empirical analysis of forestry

The timing of climate adaptation decisions can have substantial consequences for the assessment of climate damages. Since weather variability can create risks for natural resource management that differ across adaptation choices, such variability has the potential to alter the speed of climate adaptation. This paper estimates the effect of weather variability on the timing of adaptation decisions of forest landowners in the Eastern United States. A discrete-choice econometric model of forest management is estimated and used in a bio-economic simulation that shows how variability in cold temperatures can significantly slow the rate of adapting from cold-tolerant natural hardwood forests to cold-sensitive, but highly valuable pine plantations. The range of weather variability in climate projections and across the landscape generates large differences in adaptation timing. Ignoring projected future decreases in weather variability results in a large downward bias in estimating future paths of climate adaptation. Since pine plantations produce fewer non-market ecosystem services than natural hardwood forests, an important source of future conservation uncertainty is the economic response of private forest landowners to changing weather variability.

Ownership Structure and Financial Sustainability of Saudi Listed Firms

This research assesses the impact of ownership structure on financial sustainability. Panel data from 102 Saudi non-financial listed firms covering 2013 to 2022 were analysed using OLS and fixed effects methods. Further, the GMM was employed to check for robustness. The research outcomes reveal the strong and positive effects of institutional ownership and family shareholding on financial sustainability. This positive impact implies that robust and stringent monitoring of family shareholding and institutional investors may neutralise managerial entrenchment, reduce agency costs and pave the way for financial sustainability. However, government ownership appears insignificant, while managerial ownership exerts a strong negative influence on financial sustainability. The negative effect suggests that managerial shareholding may be counterproductive to organisational efficiency. Importantly, the outcomes look consistent using several econometric models. Therefore, the research findings may further shape policymakers’ understanding of how the diverse monitoring strategies of ownership structure influence financial sustainability. Also, the results may serve as an incentive for managers and standard setters to support firms in embracing institutional and family shareholding. The presence of these shareholders may minimise agency conflicts and maximise firm value for sustainable profitability.