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This article assesses specific components of the agricultural sector as it impacts the growth of a developing nation’s economy. A mathematical model is developed with the aid of logistic growth model for the variables and the resulting model is solved numerically using a higher derivative block method to forecast the data for the agricultural components for years 2020- 2025, thus utilizing data ranging from 1981-2025. Econometric analysis was carried out using ARDL bound test method and the findings indicate the existence of short and long run relationship between food production, livestock, and economic growth. In addition, the Pairwise Granger causality showed the causality movement and ascertain the positive link among the variables. Therefore, this research suggests the need to encourage food production and livestock as components of agriculture through precise economic policies and funding for progress in the country’s economic outlook.

This paper describes the early genesis and later evolution of the Italian automotive industry employing the traditional agglomeration approach combined with Klepper's spinoff theory. It highlights the key role played by the Turin car cluster from the late 19th century. We provide the first comprehensive database of Italian automobile companies from 1894 until 2015, based on original archival research. We use historical analysis and econometric models to identify the factors contributing to the creation and success of the automotive industry in Turin. More specifically, we investigate agglomeration economies and the part played by spinoffs and institutional factors with a special emphasis on the role of local education. Our model confirms the existence of a spinoff effect and, especially, the positive effect of inherited technical skills embedded in pilots. We find support for positive agglomeration effects at the regional level, technological complementarities with aeronautics, a metropolitan cluster effect, and importance of local education. Basic and technical education seem to be particularly important initial institutional preconditions for further technical learning and scientific advancement and would make an interesting research topic. [ABSTRACT FROM AUTHOR]

Urban ecological environment resilience is an important characteristic that should be possessed in the process of urban development. It is conducive to coping with the challenges of multiple risks and disturbances such as climate change, resolving chronic pressure, improving the ability to resist disaster risk, self-adjustment, and recovery, to maintain the structure and function stability of the urban system. The digital economy is a new economic form caused by the new technological revolution, which may effectively promote economic ecology and ecological economization. We clarify the elements of the digital economic system, construct the coupling evaluation index system of "digital infrastructure-industrial digitization-digital industrialization," and establish the coupling degree model to analyze the characteristics of the integration interaction, coordination, and self-organization of the digital economy subsystem. Based on emergency management theory, adaptive management concept, and resilient city theory, an evaluation index system is constructed from four levels of prevention, resistance, adaptation, and recovery to measure urban ecological resilience. Taking 278 cities in China from 2011 to 2021 as the research object, we established a spatial econometric model to explore the dynamic mechanism of digital economy system composition and coupling coordination to enhance urban resilience and summarize the theoretical model form. Based on this, we further propose countermeasures and suggestions for improving urban ecological resilience by using a digital economic system. [ABSTRACT FROM AUTHOR]

Realizing the coordination between the economic and environmental systems through a green growth model is an important goal for China to enter the high-quality development stage. Meanwhile, financial technology (fintech) is rapidly developing in China. To explore the relationship between the two, this research uses panel data from 276 cities in China from 2011 to 2022 and empirically tests through constructing econometric models and machine learning algorithms. The empirical result shows that fintech has an impact on green growth. Specifically, there is a U-shaped relationship between fintech and green growth, meaning that before a certain stage, fintech may have a certain inhibitory effect on green growth. After fintech exceeds a certain development level, it will promote the improvement of green growth. Further mediation tests show that innovation plays a mediating role in the impact of fintech on green growth. Additionally, this research also conducts consistency tests based on different criteria including the location, scale, and financial development level of cities. Based on the research findings, policy suggestions are proposed in this paper to promote the development of fintech and stimulate the growth of the green economy. Overall, our research sheds more light on the fintech-green growth linkage and provides new insights into comprehending the role of fintech in advancing towards a low-carbon economy. [ABSTRACT FROM AUTHOR]

Contemporary development policy concentrates predominantly on reducing noticeable economic differences in a spatial system, and an important role in this respect is played by EU Cohesion Policy. Owing to the considerable scale of financial exposure of Cohesion Policy, the assessment of effectiveness of the implemented measures and their greater reliance on evidence are of major significance. Despite numerous attempts to empirically verify the effects of EU funds spending, the problem remains unresolved, and the results of recent studies lead frequently to ambiguous conclusions. The article aims to verify the β-convergence process in EU regions in the years 2007–2015 allowing for the impact of the received EU funds and the spatial effects determining economic growth. In the research, use was made of a convergence approach consisting in the regression modelling of per capita GDP growth. Spatial econometrics methods were applied, by adding variables determining spatial interactions that can influence the economic growth rate to the specification of the estimated models. The estimated econometric models show that in the years 2007–2015 EU funds positively affected economic growth. At the same time, the process of reducing economic disparities between EU regions was observed. Moreover, the existence of spatial effects for a dependent variable was confirmed. The results also show that the value of the EU funds received in the surrounding area generally did not translate into the dynamics of growth in a given location. The research presented is one of the few in which spatial interaction was verified by using weights matrices based on contiguity, distance, flows and affiliation. [ABSTRACT FROM AUTHOR]

Accurately predicting future carbon emissions is of great significance for the government to scientifically promote carbon emission reduction policies. Among the current technologies for forecasting carbon emissions, the most prominent ones are econometric models and deep learning, but few works have systematically compared and analyzed the forecasting performance of the methods. Therefore, the paper makes a comparison for deep learning model, machine learning model, and the econometric model to demonstrate whether deep learning is an efficient method for carbon emission prediction research. In model mechanism, neural network for deep learning refers to an information processing model established by simulating biological neural system, and the model can be further extended through bionic characteristics. So the paper further optimizes the model from the perspective of bionics and proposes an innovative deep learning model based on the memory behavior mechanism of group creatures. Comparison results show that the prediction accuracy of the heuristic neural network is higher than that of the econometric model. Through in-depth analysis, the heuristic neural network is more suitable for predicting future carbon emissions, while the econometric model is more suitable for clarifying the impact of influencing factors on carbon emissions. [ABSTRACT FROM AUTHOR]

Digital economy has become a "new engine" that driving global economic growth. Nevertheless, numerous controversies persist regarding whether and how digital economy can facilitate the development of emerging industries. Thus, this paper investigates how digital economy affects creative industries development in China and whether innovation efficiency mediates this relationship. Drawing upon a panel data set containing 29 Chinese provinces from 2012 to 2019, an econometric model is constructed for empirical analysis. We find that digital economy significantly promotes creative industries development, and innovation efficiency plays a partial mediating role between digital economy and creative industries development. According to the influence mechanism, the digital economy of various regions could promote the creative industries development by improving the innovation efficiency. Finally, relevant suggestions were put forward from the expanding application paths, improving regional innovation efficiency, and creating an innovative environment. [ABSTRACT FROM AUTHOR]

The metallurgical sector, a key component of European industries, plays a crucial role both in advanced economies and in the infrastructure development of emerging nations. As environmental concerns increasingly influence global trade policies, the introduction of Carbon Border Adjustment Mechanisms (CBAMs) becomes a pivotal factor, especially those affecting the steel industry. This study focuses on assessing the impact of CBAM implementation on the steel industry's evolution until 2034, utilizing a combination of econometric models and scenario analysis. [ABSTRACT FROM AUTHOR]

This paper addresses the confluence of two issues associated with transportation construction: cost overruns, which represent a threat to cost savings, and project bundling, which represents an opportunity for cost savings. The paper hypothesizes that there exists a significant relationship between project bundling practice and the occurrence propensity and severity of cost overrun, and that the nature of this relationship varies across the different project types. Recognizing that current literature lacks adequate evidence regarding the nature of this specific relationship, this paper addresses the question using econometric modeling techniques. The study objective is to quantitatively assess project bundling impacts on the propensity and magnitude of cost overrun and how these impacts vary by project type. The methods used include binary logit and logistic regression specifications. The data were from a highway construction contract database involving 8,782 projects (consisting of 36 project types grouped into six work categories) and 5,180 contracts. The paper finds that, generally, project bundling does affect cost overruns, and that there exists variation of this relationship across the different types of projects. The outcomes of this study can help guide agencies on which project types could be bundled to reduce the occurrence propensity or magnitude of cost overrun as well as the bundling combinations for which cost savings may not be realized. As bundling practices become increasingly prevalent, public agencies interested in adjusting their financial estimates to accommodate possible cost overruns can replicate the study methodology provided in this paper. With the bundling and cost overrun relationship functions that could be developed using their local data, agencies can carry out, in a more reliable manner, the task of contract contingency sum estimation for bundled projects. [ABSTRACT FROM AUTHOR]

In this article, we introduce a package, ddml, for double/debiased machine learning in Stata. Estimators of causal parameters for five different econometric models are supported, allowing for flexible estimation of causal effects of endogenous variables in settings with unknown functional forms or many exogenous variables. ddml is compatible with many existing supervised machine learning programs in Stata. We recommend using double/debiased machine learning in combination with stacking estimation, which combines multiple machine learners into a final predictor. We provide Monte Carlo evidence to support our recommendation. [ABSTRACT FROM AUTHOR]

Purpose: The growth of lending from nontraditional lenders may pose challenges for official US Department of Agriculture (USDA) farm sector debt estimates, but it is difficult to find data to assess official estimates. The purpose of this study is to examine whether debt provided by nontraditional lenders is accurately accounted for in official estimates. Design/methodology/approach: We compare traditional and nontraditional lending data from farm equipment lien collateral values and the USDA Agricultural Resource Management Survey (ARMS). After analyzing trends in equipment lending implied by farm equipment lien data and ARMS, we estimate whether changes in farm equipment lien values predict changes in equipment debt reported in ARMS and whether lender type influences that relationship. Findings: We find that credit provided by nontraditional lenders is likely underreported in ARMS. Our econometric model shows that equipment debt volumes for nontraditional lenders are consistently lower than traditional loan volumes in ARMS across a variety of model specifications. We also find that an increase in lien values for nontraditional lenders is less likely to predict an increase in ARMS equipment debt volumes than an increase for traditional lenders. Practical implications: Official farm sector debt estimates may not fully account for nontraditional lenders. Originality/value: This study demonstrates how the growth of nontraditional lending poses challenges for estimating US farm sector debt. We evaluate farm sector debt estimates and advance knowledge of the role of nontraditional lenders in farm equipment credit provision. The farm equipment lien dataset provides a rich source of novel data for research on local and national equipment debt and investment. [ABSTRACT FROM AUTHOR]

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The main purpose of this study is to analyze how different macroeconomic factors have influenced the financial development of countries in transition as well as the effect of these factors on the volatility of the banking sector in these countries. Secondary data acquired and processed from yearly statistics reports of the World Bank, the International Monetary Fund, the OECD, the European Central Bank, Eurostat, and others were used to develop this research. In the analytical section of the literature review, numerous scientific papers by various authors that have examined the issue of financial development in the economies of various countries have been reviewed. This research covers data from 2005 to 2020, which will be examined using panel and time series econometric models. Based on the study's econometric findings, we can conclude that non-performing loans, natural resources, market liberalization, GDP per capita, inflation, and interest rates have a statistically significant impact on the financial development of transition countries by determining the amount of broad money and the volume of bank loans and deposits in these countries' financial systems. The empirical findings of this study will serve as good scientific and empirical evidence for future studies in the field of financial development and economic growth, as well as practical evidence for governments around the world in the form of strategic macroeconomic policies that will positively affect the financial development of countries in transition. In relation to essential conclusions, this research offers true and consistent findings. The studied period (2005-2020) offers a compelling time frame for making sound findings and recommendations. [ABSTRACT FROM AUTHOR]

The aim of the study was to assess and analyse the shadowing of the national economy, as well as to determine the impact of its shadowing on the reduction of tax revenues to the state budget. The research employed general scientific, economic, and mathematical assessment methods, such as regression analysis, correlation analysis, interval forecasts, as well as econometric models of Lacko's household electricity approach, as well as modelling of the shadowing level using the multiple indicators-multiple-causes (MIMIC) model. The calculations and analysis gave grounds to determine the level of the shadow economy of Ukraine based on the state statistics of shadowing trends. There is currently an upward trend estimated to be 37.1% of GDP in 2022 compared with international data, which indicates a rapid growth of the shadow economy from 27% in 2019 to 44% in 2022. An alternative method of assessing the shadowing level was proposed in order to level the peculiarities of calculating the level of shadowing of the economy. According to the calculations, it was 43% in 2022. It was determined that the rapid growth of the shadow economy is an indicator of a reduction in tax revenues, especially in 2022 — by almost 8%. The correlation analysis proved that the increased shadowing level is an indicator of the reduction of tax revenues to the budget of Ukraine. The conducted analysis has certain limitations, therefore it is advisable to further test the hypothesis regarding the indicator of tax revenue reduction on a larger range of data and to apply an alternative method of assessing the shadowing level for developed economies, such as the countries of the European Union (EU). [ABSTRACT FROM AUTHOR]

Purpose: Drawing on social network and information diffusion theories, the authors study the impact of the structural characteristics of a seller's local social network on her promotion effectiveness in social commerce. Design/methodology/approach: The authors define a local social network as one formed by a focal seller, her directly connected users and all links among these users. Using data from a large social commerce website in China, the authors build econometric models to investigate how the density, grouping and centralization of local social networks affect the number of likes received by products posted by sellers. Findings: Local social networks with low density, grouping and centralization are associated with more likes on sellers' posted products. The negative effects of grouping and centralization are reduced when density is high. Originality/value: The paper deepens the understanding of the determinants of social commerce success from a network structure perspective. In particular, it draws attention to the role of sellers' local social networks, forming a foundation for future research on social commerce. [ABSTRACT FROM AUTHOR]

This paper addresses the substitution elasticity of capital on the labour force in the context of the development of intellectualization. Given the substitution of capital for labour, China's benchmark listed logistics companies are compared with an American company to discuss the evolution of capital – labour substitution. A large-scale intellectualization process began in 2017, and based on a variable elasticity of substitution, this paper creates an econometric model of substitution elasticity between capital and labour and its evolution between 2017 and 2021. The American logistics company UPS maintains a relatively high level of substitution elasticity, and Chinese logistics companies are quickly catching up. The substitution elasticity of capital on labour in Chinese enterprises trends upward year after year. In 2021, the capital – labour substitution elasticity of logistics enterprises in both countries showed considerable growth. The calculation model of substitution elasticity presented in this paper can be extended to different regions and industries to measure intelligent development levels and the relationship between capital and the labour force. [ABSTRACT FROM AUTHOR]

In this paper, we provide empirical evidence that real wage rigidity is not a major cause of unemployment volatility. We argue that there is a disconnect between the theoretical and empirical literatures on this topic. While theoretical studies define real wage rigidity as the response of wages to changes in unemployment following productivity shocks, the empirical literature measures real wage rigidity as the estimated semi‐elasticity of wages with respect to unemployment, averaged over all shocks. We show that averaging over shocks gives a biased measure of real wage rigidity, as the impact of other shocks confounds the response to productivity shocks. Our results indicate that the estimated semi‐elasticity with respect to productivity shocks is twice as large as the estimated semi‐elasticity averaged over all shocks. This implies that one cannot attribute unemployment volatility to real wage rigidity. [ABSTRACT FROM AUTHOR]

This paper presents a simple and tractable equilibrium model, where collateralized credit emerges under limited commitment. We show that even if there is no time variation in fundamentals, credit trade can fluctuate endogenously over time. In our theory, credit fragilities are associated with endogenous fluctuations in trade probabilities, collateral values, and lending volumes. [ABSTRACT FROM AUTHOR]

Conducting experiments can be time consuming and expensive, and may not always be reasonable. Therefore, empirical research often derives structural parameters based on observational data and reduced‐form econometric models. The state‐contingent approach presents a consistent conceptual framework for analyzing producer decisions under uncertainty. However, application of this structural modeling approach has been hampered by data constraints, particularly the lack of information for mapping producers' stochastic outputs onto a set of the states of nature representing different uncertain events. Consistent mapping of uncertainty is particularly critical in the context of multiple output production where weather shocks often have different effects across crops and in microeconometric analyses when unobserved farm heterogeneity may confound the effect of uncertainty. Our study demonstrates how the application of reduced‐form approaches can overcome constraints of structural econometric modeling associated with the lack of relevant data and presents an approach for identifying states of nature in the context of multiple output production using reduced‐form econometric models of crop yield. In an empirical application based on Hungarian farm accountancy data, we demonstrate that the proposed approach allows a consistent mapping of production uncertainty in crop farming, utilizes panel data structure, and controls for potential endogeneity due to unobserved farm heterogeneity. We anticipate the presented approach to be useful for developing further the state‐contingent approach and to stimulate further studies combining the strengths of structural approaches and reduced‐form models. [ABSTRACT FROM AUTHOR]

This paper investigates how responses of US macroeconomic activities to monetary policy shocks depend on the state of broker–dealer leverage. The analysis makes use of leverage data to create an indicator series that distinguishes above and below-trend leverage states for the economy which is then integrated into switching econometric models. Using state-dependent local projection methods, we find that during the below-trend leverage state, monetary policy affects the economy in a traditional fashion. However, during the above-trend leverage state, expansionary monetary policy is problematic for stimulating the economy. Additionally, during the above-trend leverage state, we find that a policy rate cut raises counterparty risks in financial markets, which in part accounts for the weaker effectiveness of the monetary policy. These findings are robust to several alternative modeling specifications and suggest that monetary policy authorities should monitor the leverage cycle when determining their policy stance on macroeconomic stability. [ABSTRACT FROM AUTHOR]

Facial recognition payment technology (FR) has the potential to disrupt the offline retailing industry by automating the payment process. However, some firms that adopted FR payment technology have experienced only moderate success, and many customers have expressed frustration using FR payment technology. By utilizing data sets from three retail chains, we find that customers are less likely to use FR payment technology during self-checkouts when more customers are in line behind them, waiting and watching (the social presence effect), and when more preceding customers use the other payment technology (the herding effect). These findings imply that (1) the design of FR technology can be improved to alleviate the social presence effect (such as adding a privacy screen filter or beautify the appearance of the consumer's image), and (2) monetary incentives may be used to attract more users by leveraging the herding effect. Although facial recognition (FR) payment technology can be more convenient for customers, it is still not consistently used by many customers in retail. Using transaction data from three retail chains, we develop econometric models and an estimation strategy for examining the social presence and herding effects that affect FR payment technology use. Our key findings are as follows: (1) Customers are less likely to use FR payment technology when more customers are in line behind them, waiting and potentially watching—the social presence effect. (2) Customers are more likely to use FR payment technology when more preceding customers use FR payment technology—the herding effect. (3) Customers with more experience using FR payment technology are subject to a weaker social presence effect. The marginal social presence effect can result in a 4.75% reduction in the probability of the focal customer using FR payment technology, and the potential social presence effect can be as high as 48.42%. When the focal customer has one additional experience in using FR payment technology, the social presence effect is reduced by 7.79%. The herding effect can result in a 20.90% increase in the probability of the focal customer using FR payment technology. Theoretical and managerial implications are discussed. History: Rajiv Kohli served as the senior editor for this article. Funding: Y. Rong's work was supported by the National Natural Science Foundation of China [Grants 72025201 and 72221001]. X. Tian's work was supported by the National Natural Science Foundation of China [Grants 72172145 and 71932002], the Beijing Natural Science Foundation [Grant 9212020], and the Fundamental Research Funds for the Central Universities. J. Gao's work was supported by the National Natural Science Foundation of China [Grant 72232001]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2023.1205. [ABSTRACT FROM AUTHOR]

Purpose: The study aims to estimate the impact of the vigilant board independence (BIND) dimension that potentially neutralises the unfair influence of chief executive officer duality (CEODU) on Indian public banks' performance. Design/methodology/approach: The study takes into account the fixed-effects model to investigate the potential moderating effect of BIND in the relationship between CEODU and Indian bank performance. The econometric model is also robust against heteroscedasticity, serial correlation and cross-section dependence issues to ensure that the model is free from such biases. The study also addresses the major issue of endogeneity via vector autoregression and performs the analysis by considering one period lag of the explanatory variables. Findings: The findings demonstrate that CEODU does not always lead to a negative outcome on the performance until or unless the board is monitored by the effective presence of outside directors. Research limitations/implications: The regulatory bodies consider the results to strengthen board capital where CEODU can benefit a business entity if vigilance BIND is present at or above a threshold point. Originality/value: The study evaluated an under-researched role of BIND as a moderator that undermines the negative influence of CEODU on the performance of Indian banks. The study also establishes that the CEO's contribution to performance increases when the number of outside directors is at or above a certain threshold. [ABSTRACT FROM AUTHOR]

This special issue brings together five methodological contributions and responses to the 16th World Conference of the Spatial Econometric Association held in Warsaw, Poland, in June 2022. Each paper presents a new technique to provide better answers to the different stages of spatial econometric modelling. The first paper develops a method for determining principal components that capture both spatial and temporal dependence. The second paper provides five statistics to measure the extent to which spatial lags are collectively or individually significant in a spatial Durbin model. The third paper provides a roadmap to guide scholars through the specification search to explore spatial dependence underlying regions' resilience. The fourth paper employs the Akaike information criterion to determine the best performing k-nearest neighbour matrix in a spatial econometric model. The fifth paper explores a methodology to determine the best performing spatial weight matrix based on a set of covariates rather than geographical distance-based measures. [ABSTRACT FROM AUTHOR]

Spatial GARCH models, like all other spatial econometric models, require the definition of a suitable weight matrix. This matrix implies a certain structure for spatial interactions. GARCH-type models are often applied to financial data because the conditional variance, which can be translated as financial risks, is easy to interpret. However, when it comes to instantaneous/spatial interactions, the proximity between observations has to be determined. Thus, we introduce an estimation procedure for spatial GARCH models under unknown locations employing the proximity in a covariate space. We use one-year stock returns of companies listed in the Dow Jones Global Titans 50 index as an empirical illustration. Financial stability is most relevant for determining similar firms concerning stock return volatility. [ABSTRACT FROM AUTHOR]

We use the Akaike information criterion (AIC) to assess the quality of non-nested spatial econometric models with a different number of nearest neighbours (knn) included in the spatial weight matrix W. This is of particular importance in two cases: when estimating the model on geolocated point data without explicit guidance on the optimal value of knn; and when dealing with different spatial patterns and a unique W generalizes diverse spatial structures. By minimizing AIC for a set of competing models, one can find the optimal knn that guarantees the best fit. We show through simulation and empirical analysis that AIC is a non-linear function of knn and reaches its minimum for a given knn. We provide practical evidence that misspecified W may result in a 20% bias of model parameters. [ABSTRACT FROM AUTHOR]

Private policing is generally used to curb crime in certain neighbourhoods. The impact of an increase in these services on the city as a whole is unclear, as crime could shift from protected to unprotected targets, causing greater social harm. In practice, estimating this effect at the city level encounters at least two problems. First, the simultaneity between the demand for private policing and criminal activity requires appropriate instrumental variables to mitigate endogeneity biases. Second, the dependent and endogenous variables can have an excessive number of zeros at the same time, requiring the use of more specialized econometric models. In this paper, I combine Clotfelter's theoretical model for private policing and crime with Amemiya's econometric model for simultaneously censored outcomes, linking structural and reduced forms and discussing identification issues. Then, a regular Bitobit is modified to estimate reduced forms with many zeros outcomes in a set of copulas to improve censoring specifications with endogeneity. Finally, I conduct an empirical study examining an exclusive database from Brazil to discuss the copula approach, and the hypothesis that more private policing reduces crime at the city level is not rejected. [ABSTRACT FROM AUTHOR]

This paper empirically investigates corruption-related disclosure in the banking industry, aiming to shed light on the reasons why financial institutions disclose corruption-related information in their annual financial reports. Using a total sample of 88 banks from the GIPSI countries during the period 2011-2019, our results reveal that, on average, banks involved in corruption issues disclose less on corruption-related matters than banks not involved in any corruption scandal. Moreover, banks not involved in corruption cases disclose even more information after other banks' corruption events become public. These basic relationships, however, are shaped by the characteristics of each particular country in terms of control of corruption and the specific regulation on non-traditional banking activities. Our results are robust to different specifications of econometric models, to alternative empirical methods accounting for potential reverse causality and sample selection concerns and to the inclusion of internal corporate governance mechanisms. [ABSTRACT FROM AUTHOR]

Non-fungible tokens (NFTs) are unique digital assets that exist on a blockchain and have provided new revenue streams for creators. This research investigates NFT market inefficiencies to identify claimed cyclic behavior and cryptocurrency influences on NFT prices. The research found that while linear models are not useful in modeling NFT price series, models that extract periodic behavior can provide explanations and predictions of price behavior. The investigation of autocycles in cryptocurrency and NFT markets did not support the existence of Elliott Wave behavior in any of these blockchain enabled assets. Rather NFT price behavior is strongly tied to the underlying asset and its community of fans. These fans commit to periodic bouts of idiosyncratic trading which cools for a while, and then restarts. The research found no evidence supporting whole market effects across the full price series of individual NFTs. The research strongly supports prior findings that the offsetting movements significantly influence NFT prices and trading volume in Bitcoin and Ether. The research found NFT markets exhibit characteristics resembling a social media platform rather than more traditional asset markets like stock exchanges. It found that traditional linear econometric models cannot predict or explain NFT price series, only that NFT price and volume were weakly correlated. Fractal models consistent with Elliott wave theory do explain some of NFT price behavior, but are not consistent or stable over time. This research confirmed prior research findings that Bitcoin and Ether price movements are correlated with general NFT price and volume series in periods of between 24 and 48 h, with significant numbers of trades into and out of cryptocurrencies at 2 and 8 h. [ABSTRACT FROM AUTHOR]

Background: A rail passenger delay compensation scheme aiming at improving attractiveness of rail services and providing minimum customer service standards for delayed passengers operates in the European Union and Great Britain. British rail passengers are eligible to claim 50% of fare for delays of more than 30 min and 100% for delays of over 1 h. The scheme rules were chosen arbitrarily and are homogeneous across all ticket types and journey lengths. As longer journeys are usually more expensive and subjected to longer delays, long distance operators are likely to see more passengers being eligible to claim compensation. This, combined with higher engagement rates due to differences in sensitivity to lateness or opportunity cost of not claiming compensation is likely to have an impact on the differing revenue burden for operating companies. Objective: Against this background, this study aims to quantify the revenue impact of homogeneity of scheme rules for different types of train operators to advance understanding of the scheme's costs and motivate further research into the economic rationale behind the scheme's provision and design. Methodology: An econometric model was constructed to empirically test the impact of performance levels and train operator characteristics on the compensation payments made to passengers through the 'Delay Repay' scheme in Great Britain. Results: The combined differences in the nature of operation and engagement levels mean that with delay levels and engagement increasing with journey length and fare, short, medium and long distance train operating companies (TOCs) repay on average respectively 0.3%, 0.8% and 1.8% of their ticket revenues, increasing the scheme's proportionate burden on the revenues of long distance operators. Further research is needed to either explain the economic or regulatory reasons behind the differing revenue impact of the scheme on different types of TOCs or suggest how the scheme can be redesigned to take these differences into account. [ABSTRACT FROM AUTHOR]

With the increase in haze pollution in Chinese cities, answering the question of whether using a high-speed rail (HSR) as a green and efficient transportation system can alleviate haze pollution in China has become a research hotspot. This study empirically tests the impact of HSR network construction on haze pollution and its spatial spillover effects. (1) The construction of a HSR would have a mitigation effect on haze pollution in node cities and surrounding cities, and the more developed the HSR is, the more significant that this effect would be. (2) Haze pollution persists for a long time, the haze pollution from the previous year may have a positive promoting effect on the haze pollution in the following year. (3) The use of a HSR reduces haze pollution by replacing traditional road transportation and promoting industrial structure upgrading and technological innovation. (4) The inhibitory effect of HSR use on haze pollution varies due to regional differences and variation in city size. [ABSTRACT FROM AUTHOR]

Introduction: Tobacco, an important commercial crop, plays a crucial role in farmers' incomes and livelihoods to a sizable population and contributes significant exchange earnings to the Indian economy. Currently, India is the second-largest tobacco producer after China, with a production of 758 million kg (13% of global production) and exports of 190 million kg of tobacco (9% of global tobacco export volume). However, there are uncertainties surrounding the tobacco sector, such as growing public health and environmental issues associated with tobacco production and consumption and changing national and international tobacco-related policy regimes. In this context, the current study investigates the determinants of tobacco exports and geographical shifts in export destinations over the years. Methods: The statistical models employed are co-integration, and vector error-correlation models to test the short-run and long-run dynamics relationship between tobacco exports and the explanatory variables, and the Markov chain approach to find out geographical shifts in export destinations. Results and discussion: The econometric model estimated the relationship between the tobacco export volume with domestic production, export price, and global demand for Indian tobacco, and investigated the geographical shift in export destinations of tobacco in the context of changing global and national policy regimes on the sector. The econometric modeling framework confirms that there exists a statistically significant relationship between Indian tobacco export demand, domestic production, export price, and world demand for Indian tobacco. The geographical shift was evident in major export destinations during the post-WHO-FCTC (Framework Convention on Tobacco Control) regime. The model findings direct that India should take advantage of the export price, and global demand for tobacco as India ratified WHO-FCTC; there is no scope for horizontal expansion of the area under tobacco. This modeling framework aids as a tool to direct and explore the possible options with a greater emphasis on export-centric farming system in tobacco production by augmenting crop compliance and quality to meet the standards of international markets. [ABSTRACT FROM AUTHOR]

• The 80 km/h speed limit is associated with a significant reduction in fatalities. • Estimated benefits are between 300 and 350 lives saved. • Estimated effects vary between counties, illustrating the potential impact depending on the local context. • The dynamics of the measure evolve over time. Introduction : Speeding is considered to be a major contributor to road fatalities and injuries worldwide. Inappropriate speeding behavior is associated with a high casualty burden. It could be responsible for at least 30% of road accidents. Method : In 2018, the French authorities decided to introduce a new speed limit. They lowered the speed limit to 80 km/h on the unseparated interurban network. The aim was to reduce the number of fatalities and injuries and to implement some measures in line with international commitments. This paper uses different econometric models applied to time series for different groups of counties. Results : The results show a significant positive contribution of the new speed limit. The estimated number of lives saved is between 300 and 350. The overall reduction in the number of fatalities is 10%. The results also show a differentiated impact according to the local context and the different dynamics at play. Conclusions and Practical Applications : The results of this paper are in line with the scientific literature on speed limit reductions. They represent a validation of a debated public decision, while at the same time consolidating the body of knowledge on the subject, helping the decision-maker to adopt an appropriate measure to improve road safety performance. [ABSTRACT FROM AUTHOR]

Optimal sampling period selection for high-frequency data is at the core of financial instruments based on algorithmic trading. The unique features of such data, absent in data measured at lower frequencies, raise significant challenges to their statistical analysis and econometric modelling, especially in the case of heavy-tailed data exhibiting outliers and rare events much more frequently. To address this problem, this paper proposes a new methodology for optimal sampling period selection, which better adapts to heavy-tailed statistics of high-frequency financial data. In particular, the novel concept of the degree of impulsiveness (DoI) is introduced first based on alpha-stable distributions, as an alternative source of information for characterising a broad range of impulsive behaviours. Then, a DoI-based generalised volatility signature plot is defined, which is further employed for determining the optimal sampling period. The performance of our method is evaluated in the case of risk quantification for high-frequency indexes, demonstrating a significantly improved accuracy when compared against the well-established volatility-based approach. [ABSTRACT FROM AUTHOR]

According to the information content of financial reports, this research aims to investigate the effect of Voluntary Disclosure (VD) on the quality of information, the quality of decision and the behavior of shareholders. The population of the research is the firms listed in Tehran Stock Exchange. Based on the screening, 138 firms were selected between 2013 and 2021. The required data was extracted through the website of the stock exchange organization. The collected data were analyzed through econometric models and EViews software. According to the results, there is a significant relationship between VD, disclosure quality, decision quality and shareholders' behavior. Therefore, the amount of stock transactions can be a function of the amount of disclosure characteristics and the quality of the decision. By focusing on the shareholders, the results can provide a basis for making managers' decisions to meet their information needs and improve the transparency of the financial market. [ABSTRACT FROM AUTHOR]

Morocco has integrated the human development paradigm into their country-wide planning goals. The 2004 Moroccan census measured HDI for every commune in Morocco, the smallest spatial unit for which data are available. However, when the 2014 Moroccan census was released HDI values were omitted. To measure the change in HDI over the 10-year period, this article uses spatial econometric models along with the RandomForest algorithm in order to make out-of-sample predictions for 2014 HDI scores at the Moroccan commune level. The article concludes that in certain situations RandomForest can outperform spatial econometric models, and discusses the benefits this approach. [ABSTRACT FROM AUTHOR]

Purpose: The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP. Design/methodology/approach: This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis. Findings: The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity. Practical implications: The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state. Originality/value: The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions. [ABSTRACT FROM AUTHOR]

Scientifically measuring the level of environmental governance (EGL) and understanding its spatial convergence has important reference value for ecological governance. In this paper, the global entropy method is applied to measure the EGL of 284 prefecture-level cities in China from 2007 to 2019, which are divided into three major river basins, including the Yellow River, Yangtze River, and Pearl River, to observe the spatial–temporal evolutionary characterization through a standard deviation ellipse model. The coefficient of variation and the spatial econometric model are the tools used to conduct the spatial convergence test. The results are as follows: (1) China's EGL is low overall, though it is fluctuating upward at low magnitude, and the three major river basins follow the ranking: The Pearl River Basin > The Yangtze River Basin > The Yellow River Basin. (2) Spatially, the distribution pattern of China's EGL changes from "scattered and sporadic" to "multipolar core". (3) The center of China's environmental governance was concentrated in the east from 2007 to 2019, and the EGL in the midstream and downstream regions of the three major river basins increased rapidly. (4) Environmental governance in China has significant absolute and conditional β-convergence characteristics, as do the three major basins, while the ranking of convergence speed remains "Yangtze River Basin > Yellow River Basin > Pearl River Basin". Of these, economic development accelerated the convergence rate of environmental governance in China and its three major river basins; financial pressure significantly inhibited the convergence of the EGL of the Yellow River Basin. The improvement of the EGL in the Pearl River Basin was also negatively influenced by the industrial structure. [ABSTRACT FROM AUTHOR]

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Machine learning (ML) is becoming one of the most anticipated methods in predicting consumer demand. However, it is still uncertain how ML methods perform relative to traditional econometric methods under different dataset scales. This study estimates and compares the out-of-sample predictive accuracy of household budget share for organic fresh produce using two parametric models and six ML methods under regular and large sample sizes. Results show that ML method, particularly Logistic Elastic Net, performs better than econometric models under regular sample size. Contrarily, when dealing with big data, econometric models reach to same accuracy level as ML methods whereas random forest presents a possible overfitting problem. This study illustrates the competence of ML methods in demand prediction, but choosing the optimal method needs to consider product specifics, sample sizes, and observable features. [ABSTRACT FROM AUTHOR]

The present study examines the impact of family ownership and control on the internationalization strategies of Portuguese manufacturing firms. The study contributes to the existing literature by providing evidence that different forms of international market presence are asymmetrically influenced by family control and by underscoring the importance of innovative strategies. The analysis includes a sample of 25,533 firms observed from 2018 to 2021. Econometric models address the role of ownership in alternative internationalization endeavors, demonstrating that these firms differ from their non-family counterparts. By comparing the export propensity, intensity, and reach of family businesses to non-family businesses, the research sheds light on the challenges faced by family-owned firms and the significance of structural characteristics such as technological regimes and regional competitive advantages. The findings emphasize the negative impact of family presence on internationalization while highlighting the importance of innovation and ecosystem support. Additionally, the study contributes to the empirical refinement of firm classification by proposing a more reliable segmentation method. It also presents alternative econometric methods to appraise internationalization strategies better. Future research directions are suggested, particularly regarding the use of additional information related to innovation and human capital, offering insights for enhancing the global engagement of family businesses in global markets. This research provides valuable empirical evidence and practical implications for policymakers and practitioners seeking to support the required actions to promote the growth and internationalization of family businesses in the context of the Portuguese manufacturing industry. [ABSTRACT FROM AUTHOR]

Fiscal policy stands as a crucial pillar of economic development through its economic financing function. The regulatory effects of fiscality have been shown to reduce the ripple effects of uncertainties on economic growth within the EU. Unlike the average European economy, the Romanian economy has exhibited particularities concerning economic growth (ranking highly in economic growth among European nations in absolute terms), partly due to a more assertive fiscal policy applied to a consumption-based economy affected by hyperinflation (especially in the last five calendar years). The research issue stems from the premise of the lack of predictability in Romanian fiscal policy and its implications for the business environment. Our aim is to develop an econometric model of the fiscal effects of VAT on the business performance of the construction sector in Romania for the period 2010–2021. The methods employed involve empirical analysis and the development of consolidated industry-level databases followed by econometric modeling using the multiple linear regression method. The results of the research demonstrate that financial independence and solvency promote excessive taxation in emerging markets and developing countries, such as Romania, being correlated with the macroeconomic evolution of the respective state. Additionally, the results indicate that tax pressure can constitute a barrier to the sustainable development of firms, with direct repercussions for consumers. Attractiveness to investors is also affected, remaining a priority for companies. The study's findings will enable the identification of the main impediments and opportunities brought about by VAT taxation on branch-level performance, proving useful for construction sector managers and fiscal policy makers in fostering sustainable industry development and establishing a sustainable fiscal regime to safeguard investors. [ABSTRACT FROM AUTHOR]

The study of the forest coverage rate (FCR) is related to the ecological environment and sustainable development goals (SDGs) of a region. In light of the lack of an organic integration method of "spatiotemporal evolution, correlation analysis, and change prediction" and the lack of a methodology that integrates methods of "remote sensing (RS) and GIS, multi-phase LUCC, and construction of econometric models" in the research methods at present, this study focus on Yunnan, a typical border province located in China with a relatively fragile "innate" ecological environment, as the research area. Based on the interpretation of land use/land cover (LULC) data retrieved from seven periods RS images (1990, 1995, 2000, 2005, 2010, 2015, and 2020), the spatiotemporal evolution of FCR in 129 counties was analyzed. Complementary research methods, such as the spatial econometric model, geographically weighted regression (GWR), and the geographic detector (GD), are used to reveal the influencing factors of FCR. Finally, this study predicts the FCRs of 129 counties in Yunnan from 2025 to 2050. The FCR in Yunnan presents an increasing trend year by year, increasing from 28.96% in 1990 to 49.05% in 2020. In addition, it exhibits spatial agglomeration characteristics with fewer values in the east and more in the west. The analysis of influencing factors show that the increases in the per capita GDP, land utilization rate, and annual average temperature, and the implementation of the Conversion of Cultivated Land into Forest Project (CCFP) will significantly improve the FCR, while the increases in the population density land reclamation rate, the proportion of construction land area, and the proportion of soil erosion land area will significantly reduce the FCR. Furthermore, the FCR is influenced by multiple factors, and the relative factors observed not only show significant spatial differences, but also present complex and diverse patterns, with the additional characteristics of being interwoven and overlapping. This study contributes to expanding and improving the methods and pathways of exploring the spatiotemporal evolution characteristics of FCR in ecologically fragile areas using RS methods, providing a reference for increasing FCR and improving the ecological environment's quality in Yunnan Province and other ecologically fragile areas. [ABSTRACT FROM AUTHOR]

To achieve the triple goals of digesting excess capacity, making effective investments, and accelerating green governance, it is important and necessary to comprehensively examine the intrinsic relationship between media reports, environmental regulation, and enterprise investment inefficiency. To this end, this study employs multiple econometric models to investigate the intrinsic relationship between them by using the data of listed companies in China's A-share heavy-polluting industries between 2010 and 2020. The results of the study are as follows. Firstly, media reports can encourage heavy-polluting enterprises to pay attention to stakeholder demands and significantly ameliorate the enterprise investment inefficiency. In addition, environmental regulation can ameliorate the enterprise investment inefficiency through the "push-back effect" and "compensation effect". Moreover, pollution fees can positively moderate the amelioration effect of media reports on the investment inefficiency of heavy-polluting enterprises, while it fails in terms of environmental protection subsidies. Finally, conclusions and policy implications are provided. [ABSTRACT FROM AUTHOR]

Gambling preferences are analysed using survey data from the wider population. Respondents were confronted with a hypothetical lottery question, in which they were asked to imagine having just won a large prize, and asked how much of this prize they would be willing to invest in a further gamble. We observe the majority of respondents avoiding the gamble altogether. We demonstrate that such behaviour cannot easily be explained by standard models of choice under risk, since it implies implausible degrees of risk aversion. We propose that the observed behaviour can instead be explained in terms of gambling aversion. Since the decision variable takes the form of the number of ‘units’ of the prize that the respondent wishes to invest in the gamble, and since the decision is observed twice for some respondents, we adopt the panel version of the Zero-Inflated Poisson model as an econometric framework. We assume that individual characteristics affect both stages of the decision-making process. We are particularly interested in the effect of gender, and we find that males have a significantly higher probability of participating in the gamble, and are also (conditional on gambling) prepared to gamble significantly larger amounts. [ABSTRACT FROM AUTHOR]

The level of Financial Inclusion in Latin America is characterized. The data table lists countries with their financial and socioeconomic characteristics. The methodological path, in a first stage, integrates the exploratory analysis of main components, the hierarchical classification of the observations and the grouping of countries according to the observed similarity; in a second stage, it proposes an econometric model of ordered response to inform the probability of convergence of the groups of countries furthest behind to the most developed in financial inclusion. It is concluded that the incorporation of unbanked population segments requires the development of commercial strategies for financial services; in a context where regulatory authorities acquire a deeper and more active role. [ABSTRACT FROM AUTHOR]

This study aims to econometrically investigate the relationship between non-performing loans and Euro and USD indices in Turkey. The data set of the study consists of the Turkish banking sector non-performing loan ratio and Euro and American Dollar (USD) indices variables for the period 01/2003-09/2023. Time series analyses were used to test the relationship between the variables. Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests were applied to the data. Since the series became stationary at different levels, the ARDL (Autoregressive Distributed Lag Bound Test) test, one of the cointegration tests, was applied. Afterward, whether there is causality between the variables and if there is a causality relationship, the determination of the direction of causality is mutually tested with the "Granger Causality" method. According to the results of the research, a cointegration relationship was found between the non-performing loan ratios of the Turkish banking system and the Euro and USD indices. In the long run, it is concluded that a 1% increase in the Euro index is associated with a 2.93% decrease in non-performing loan ratios. On the other hand, in the short run, Euro and USD index values have no effect on non-performing loan ratios. According to the results of the Granger causality test, a unidirectional Granger causality relationship was detected between nonperforming loan ratios and the Euro index. A similar unidirectional causality relationship was also found between the Euro index and the USD index. [ABSTRACT FROM AUTHOR]

Entrepreneurship is widely recognized as a key driver of economic growth, wealth creation, and improved living standards in modern economies. However, in Nigeria, its potential to drive development has not been fully realized. This study investigates the impact of entrepreneurship on economic development in Nigeria using annual time series data from 1992 to 2022 and employing ARDL techniques. Self-employment rates were used as a proxy for entrepreneurship, while the contribution of SMEs, private sector credit, and inflation rate were also considered in the model, with the human development index representing economic development. The findings suggest that while the short-term impact of self-employment rates on economic development is positive yet insignificant, its long-term impact is significant. Similarly, the contributions of SMEs and private sector credit are positive but insignificant in the long run, while the inflation rate shows an inverse relationship. These results underscore the need for governments and monetary authorities to formulate and implement effective economic policies that promote entrepreneurship, taking into account macroeconomic variables. Financial institutions, particularly deposit money banks, should also support entrepreneurship by providing start-up funds at reduced or concessionary rates. [ABSTRACT FROM AUTHOR]

With the widespread application of spatial data in fields like econometrics and geographic information science, the methods to enhance the robustness of spatial econometric model estimation and variable selection have become a central focus of research. In the context of the spatial error model (SEM), this paper introduces a variable selection method based on exponential square loss and the adaptive lasso penalty. Due to the non-convex and non-differentiable nature of this proposed method, convex programming is not applicable for its solution. We develop a block coordinate descent algorithm, decompose the exponential square component into the difference of two convex functions, and utilize the CCCP algorithm in combination with parabolic interpolation for optimizing problem-solving. Numerical simulations demonstrate that neglecting the spatial effects of error terms can lead to reduced accuracy in selecting zero coefficients in SEM. The proposed method demonstrates robustness even when noise is present in the observed values and when the spatial weights matrix is inaccurate. Finally, we apply the model to the Boston housing dataset. [ABSTRACT FROM AUTHOR]