Econometric Model Research Articles

Social capital, government guidance and contract choice in agricultural land transfer.

This study explores the impact of farm households' social capital characteristics and local government policies on the selection of farmland transfer contracts in China's rural industrial revitalization context. Utilizing field research data from 1,979 households in ethnic areas of Hunan Province, this paper constructs an econometric model to assess how farm households' social capital and local governments' involvement in rural industrial revitalization influence farmland transfer contract selections. The findings indicate that, lacking government program support, farmers' social capital significantly affects contract type and duration, but not the rent. Specifically, farmers possessing extensive social capital prefer verbal and short-term contracts (coefficients of 0.525 and 0.643, significant at the 5% level), whereas their influence on rent (coefficient of 2.418, significant at the 5% level) manifests under government program support. These results challenge the conventional theory of farmland transfer contracts and offer substantial empirical support for the development of local government policies in rural industrial revitalization, underlining the critical role of government guidance and social capital in enhancing farmland transfer.

Identification of returns to new technologies under distinctive econometric approaches

ABSTRACT This article uses distinctive econometric models to investigate returns to new technologies and whether adoption decisions can be explained by comparative advantage. I consider three models: static and dynamic panel models of homogeneous returns to fertilizer, as well as the correlated random coefficient model of heterogeneous returns. I discuss both the benefits of using each of the models and their interpretations. I then use data from cocoa farming in Ghana to identify the returns to fertilizer by means of these three models. The estimated average returns in different models are positive, high and strongly significant statistically. I also find evidence of heterogeneous returns to new technology based on comparative advantage. My overall results suggest that adoption of new agricultural technologies may not necessarily benefit all farmers, and the adoption decision may be crop-specific, context-specific or technology-specific.

Make Lectures Match How We Learn: The Nonlinear Teaching Approach to Economics

This paper proposes a nonlinear teaching approach, based on learning theories in cognitive psychology, with a special focus on large-cohort economics modules. The fundamental rationale is to match the features of teaching with the nature of learning. This approach was implemented in an undergraduate economics module, which received qualitative feedback and quantitative evaluation. Formal econometric models with both binary and continuous treatment effects were developed and estimated to quantify the effects of the proposed approach. Evidence shows that the nonlinear teaching approach significantly improves the effectiveness and efficiency of the learning-teaching process but does not promote student attendance.

FDI impact: catalyzing digital capabilities in host nations

Purpose This study aims to investigate the impact of foreign direct investment (FDI) on national digital capability, specifically differentiating the impact between FDI greenfield and mergers and acquisitions (M&A). The research also investigates factors shaping digital capabilities, encompassing government transparency and absorptive capability, while exploring the mediating influence of absorptive capability in the FDI–digital capability relationship. Design/methodology/approach An econometric model has been developed to examine the interrelationship between national digital capability, FDI inflows, national absorptive capability and government transparency. The data set encompasses 55 countries over a period of nine years (2013–2021). National digital capability data is derived from the well-established index published by the World Competitive Centre (WCC). The sources of the explanatory variables align with standard practices, drawing from reputable institutions (UNCTAD and the World Bank, among others). Findings The findings reveal a significant positive impact of FDI, particularly in greenfield investments, on national digital capability. Government transparency and research and development (R&D) investment are crucial factors contributing to digital capabilities. Additionally, the absorptive capacity, reflected by R&D investment, also emerges as a potential moderating factor, influencing the impact of FDI inflows on digital capabilities. Practical implications The results recommend that policymakers and stakeholders should carefully consider the role of FDI, especially in greenfield investments, as a catalyst for enhancing national digital capability. The findings also underscore the significance of promoting government transparency and directing investments towards R&D to nurture digital capabilities. Moreover, understanding the mediating role of absorptive capability can inform strategies aimed at optimizing the impact of FDI on digital capabilities. Originality/value This study contributes uniquely to the existing literature by being the first to systematically explore the influence of FDI on national digital capability. Furthermore, it presents innovative empirical findings on the role of absorptive capability in enhancing the FDI impact on national digital capability, an area that remains relatively uncharted in current literature.

Diagnostics of the influence of medical and demographic indicators on regional socio-economic systems

The article presents approaches to monitoring and assessing social aspects of regional systems with identification of elements of the health system and demographic components that potentially affect the economic efficiency of functioning of territories. The relevance of the article is determined by objective features of the development of mesoterritories. The analysis of spatial features of the systems’ functioning was conducted on the materials of the Central Federal District. The purpose of the study is to assess the features of the regional genesis using regression analysis and the resulting indicator – gross regional product (hereinafter referred to as GRP), which allows us to give a more reliable characterisation of the mesoterritorial transformation. The subjects were grouped according to the quality of medical and demographic characteristics on the basis of the cluster analysis with further allocation of flagship, catching-up and lagging regions. To substantiate the reliability of the territories’ affiliation to a specific cluster group, a statistical analysis was performed on average values with consideration to identification of the level of medical and demographic development. The conclusion is made about the influence of some factor criteria on the regional space with the construction of a scatter plot. It displays the affiliation to the selected confidence interval and distribution of the elements of the set in the plane between the resulting and factor values, describing the dependence of the GRP on the medical and demographic indicator. These econometric and analytical models can be used by regional governments in the process of strategic planning.

Do Natural Disasters Reduce Loans to the More CO2-Emitting Sectors?

We studied the impact of major floods occurring in Turkey between 2005 and 2020 on lending and the allocation of loans between sectors that differ in their CO2 emissions. Our evidence shows that the floods are not significant determinants of lending or the allocation of loans between sectors, even though CO2 emissions contribute to the reallocation of loans from the more polluting to the less polluting sectors. Indeed, risks and returns of the sector remain the main determinants of lending and of the allocation of loans among sectors. The results are robust to alternative estimation methods and specifications of the econometric models. Since in the period of investigation no environmental regulations were implemented in Turkey, and the Paris Agreement was ratified only at end-2021, the evidence suggests that more stringent regulations and green policies are required to accelerate the green transition in Turkey.

Strategic Integration: Fostering Sustainability in Low-Income Country through Poverty Alleviation, Carbon Efficiency, Energy and Economic Resilience

This study emphasizes the critical importance of strategic integration for sustainable development goals (specifically Goals 1 and 2) in low-income countries (LICs). The research uses econometric models to focus on poverty reduction, economic resilience, energy use, and carbon emissions in Indonesia from 1996 to 2022. The study reveals a long-run co-integration among the estimated parameters, with a significant speed of adjustment at a 1.04% level. ECM models highlight a positive relationship between energy use, poverty, and carbon emissions while indicating a negative association between economic growth and carbon emissions. Robustness checks support these findings, including Fully Modified Ordinary Least Squares (FMOLS) and Canonical Co-Integrating Regression (CCR). Granger causality analysis reveals a unidirectional causality from energy use to carbon emissions and economic growth, along with one-way causation from carbon emissions to economic growth and poverty. Diagnostic tests affirm the model's reliability. The study's outcomes contribute valuable insights to the discourse on strategic integration, offering policymakers and stakeholders guidance in formulating sustainable development strategies tailored to the unique challenges encountered by low-income countries.

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.

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.

Determinants and spatial spillover effects of inbound tourism in Chinese cities

Purpose By using a wide range of macro and micro factors, this paper aims to provide a new assessment of the recent literature on inbound tourism demand models. Design/methodology/approach This study examines the determinants and spatial effects of inbound tourism using Hausman–Taylor and spatial econometric models. Findings Several important factors were identified, including local economic growth, openness to the outside world, regional size, geographic distance, foreign direct investment, level of innovation and average annual temperature. In addition, the study found strong cross-city competition effects on tourism resource endowment and hotel infrastructure. Originality/value Inbound tourism is a crucial link in achieving high-quality economic development. However, previous studies have mainly focused on the analysis of single influencing factors, ignoring the spatial spillover effects of factors.

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.

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.