In this article, we extend the Bayesian nonparametric regression method Gaussian Process Regression to the analysis of longitudinal panel data. We call this new approach Gaussian Process Panel Modeling (GPPM). GPPM provides great flexibility because of the large number of models it can represent. It allows classical statistical inference as well as machine learning inspired predictive modeling. GPPM offers frequentist and Bayesian inference without the need to resort to Markov chain Monte Carlo-based approximations, which makes (...) the approach exact and fast. GPPMs are defined using the kernel-language, which can express many traditional modeling approaches for longitudinal data, such as linear structural equation models, multilevel models, or state-space models but also various commonly used machine learning approaches. As a result, GPPM is uniquely able to represent hybrid models combining traditional parametric longitudinal models and nonparametric machine learning models. In the present paper, we introduce GPPM and illustrate its utility through theoretical arguments as well as simulated and empirical data. (shrink)

To explore the role of the social adaptability of different groups in human capital in an uncertain environment, this paper empirically tests the relationship between social adaptation and human capital by using a panel quantile regression model with the life satisfaction of 35 cities in China as an indicator. By selecting Nelder Mead and adaptive MCMC estimation methods, this paper finds that the higher the level of social adaptation is, the more obvious the promotion effect on the level (...) of human capital; for eastern cities, social adaptation can promote the improvement of the human capital level; for central or western cities, the relationship between social adaptation and human capital is not obvious and depends on the method selected for estimation. To enhance the social adaptability of individuals, the government leadership should actively create conditions, carry out targeted social adaptation training and active adaptation skills exchange for different social groups in order to strengthen social adaptability and enhance human capital. At the same time, policymakers need to consider the differences between the eastern, central and western regions and choose the most suitable human capital improvement action framework for the region to manage environmental uncertainty and make regional human capital sustainable competitive in the era of uncertainty. (shrink)

Since the “The Belt and Road” initiative was put forward in 2013, China's foreign investment growth rate has been greatly accelerated. In The Belt and Road context, many scholars used models to analyze the relationship between foreign direct investment, trade flows, and import and export trade. From literature reviews, it is found that previous scholars do not conform to reality and cannot be studied dynamically. Therefore, this study used the panel data of China's foreign direct investment and import (...) and export trade in 40 countries along “The Belt and Road” from 2010 to 2019 to focuses on the impact of foreign direct investment on trade flows, import trade and export trade. Regression analysis was carried out with the trade gravity model and Tinbergen's trade gravity model. In addition to model variables and arguments, the following control variables were adopted: exchange rate, natural resource rents, labor force population, differences in consumer ability, trade openness, and trade agreement signing. The results found that: Foreign direct investment has a positive correlation with trade flow and import and export trade. The labor force population has a negative correlation with trade flow, and import and export trade. The expansion of China's economic scale can increase the scale of China's import and export trade, at the same time, the gap in consumption levels between the two countries will restrict the degree of import and export trade. The possession of natural resources can also promote the development of trade. (shrink)

Following Brunswik (1952), social judgement theorists have long relied on regression models to describe both an individual's judgements and the environment about which such judgements are made. However, social judgement theory is not synonymous with these compensatory, static, structural models. We compared the characterisations of physicians' judgements using a regression model with that of a non-compensatory process model (called fast and frugal). We found that both models fit the data equally well. Both models suggest (...) that physicians use few cues, that they disagree among themselves, and that their stated cue use is discrepant with the models' stated cue use. However, the fast and frugal model is easier to convey to physicians and is also more psychologically plausible. Implications for how social judgement theorists conceptualise the process of vicarious functioning are discussed. (shrink)

Cross-disciplinary research (CDR) is an increasingly important part of the contemporary research 'landscape'. Despite its growing importance there remain a large number of barriers to successful CDR and many of these barriers are poorly understood. In particular there are challenges at the conceptual and communicative levels that have received relatively little attention. In this paper it is argued that these challenges are appropriate topics of analysis for philosophers. Appropriate methodologies for such an inquiry are considered and the case is made (...) that agent based models (ABM’s) are an appropriate and under utilized resource. Current research using an ABM approach to philosophical issues in CDR is then described. (shrink)

The city of Kruja (Albania)contains three types of dwellings that date back to different periods of time: the historic ones, the socialist ones, the modern ones. This paper has to deal only with the historic building's typology. The questionnaire that is applied will be considered for the development of mathematical regression based on specific data for this category. Variation between the relevant variables of the questionnaire is fairly or inverse-linked with a certain percentage of influence. The aim of this (...) study is to have better insights into the important role of building occupancy, people's lifestyle, economic level, and the quality of life of the residents in the inner medieval citadel, the physics characteristics, and the energy efficiency of the historical dwellings. The variables such as the number of residents, quality of life, the surface of the dwellings, heating instruments and time spent in the dwelling, current living conditions, monthly bills, level of satisfaction, restoration, and building improvements, interact with each other with probabilities with different positive or negative percentages. (shrink)

The price of copper is unstable but it is considered an important indicator of the global economy. Changes in the price of copper point to higher global growth or an impending recession. In this work, the forecasting of the spot prices of copper from the New York Commodity Exchange is studied using a machine learning method, support vector regression coupled with different model schemas (recursive, direct and hybrid multi-step). Using these techniques, three different time series analyses are built and (...) its performance are compared. The numerical results show that the hybrid direct-recursive obtains the best results. (shrink)

Today, business corporations across the globe are moving beyond the short-term myopic goal of profit maximization to long-term sustainability goals involving environmental, social and corporate governance goals. This is due to the growing realization that ESG factors constitute a significant source of risk for the business and can affect their financial returns. Academic research has shown that improved ESG performance has lowered risk and enhanced financial performance but results seemed to vary widely across countries. Regrettably, this subject remains largely un-researched (...) in the context of emerging economies, including India. This paper attempts to fill this much needed gap in sustainability literature in one of the largest emerging market economies, India. It empirically examines the impact of environmental, social and corporate governance performance of companies on their financial performance, in India, using panel regression models. The findings of the study indicate that good corporate ESG performance enhances financial performance. The findings of this study have important implications for investors, corporate management as well as policymakers and regulators. (shrink)

This study investigates how individuals’ life satisfaction and depression are affected by the dissolution of a steady non-cohabiting intimate relationship. Previous studies have focused more on the consequences of divorce and less on the influence of non-cohabiting relationships on the well-being of the individual. The data for this study were taken from pairfam, a large-scale German panel survey, and were used to estimate fixed-effects panel regression models and impact functions to identify the overall effect of dissolution (...) and trajectories after separation. The study sample comprised 2,631 individuals who were observed over the course of 11,219 partnership years. Based on the results of this study, three main findings were reported. First, the dissolution of a non-cohabiting relationship led to a significant decline in mental health and life satisfaction. Second, the trajectories after dissolution suggest that the decline was only temporary, showing readjustment after 1 year. Third, gender differences were identified, suggesting worse consequences for men who experienced a significant decline in both dimensions and did not readjust in life satisfaction until several years after the dissolution. For women, decreases were only found for life satisfaction, but quick readjustments were observed. (shrink)

This research paper aims to investigate. the impact of inflation. on financial sector performance in Western. Balkan. countries. The topic was chosen considering the financial sector’s fundamental role and its. impact on sustainable economic growth. This impact is measured through the effect that macroeconomic determinants of financial. performance such. as inflation, GDP. growth, general government final consumption expenditure, trade, and the lending interest rate have on credit to private sector as a share of GDP and broad definition of money as (...) a ratio of GDP, which were used to proxy financial sector development. The data are provided by the World Bank as well as the Central. Banks. of the respective countries, for the period 2002–2021. The research methodology consists of different panel regression models such as OLS, OLS robust, Fixed effects, Random effects, and Generalized method of moments (GMM). Findings indicate that domestic credit to the private sector and inflation are positively correlated, whereas broad money and inflation are negatively correlated. (shrink)

Academic frauds, dishonesty and cheating are pervasive in Pakistan, but thus far less systematic research has been undertaken on the effectiveness of the policies designed for countering academic dishonesty. Generally, the success of a policy depends on a good design and appropriate implementation. The design aspect of the policies to counter academic dishonesty in Pakistan is studied elsewhere, the purpose of this article is to empirically examine the implementation stages of the same policies. In doing so, the study utilizes a (...) panel data set comprising of 60 cross-sections and 06 time series observations (2012–2017). Results of the rigorous panel regression models shows that leniency in the prescribed punishments and an exhaustive belief on the efficacy of traditional examination surveillance methods are counterproductive. The study however failed to substantiate the direct relationship of discrimination and academic dishonesty in the sample. It is therefore concluded that prescribed punishments must be awarded in letter and spirit and that there is need for coordination at different stages of the implementation. Further, the study also recommends that traditional forms of examination surveillance must be augmented using modern technology. (shrink)

Cognitive neuroscience research has found that individuals from different cultures have different neural responses and emotional perceptions. Differences in executives’ perception of external pay gaps in different cultures can affect their work attitudes and behavior. In this study, we explore the direct relationship between executive compensation fairness and executive innovation motivation. We also investigate the moderating effects of Confucian culture and western culture between executive compensation fairness and executive innovation motivation. Data were collected from the Chinese listed firms from 2011 (...) to 2019 and test the relationship using the fixed-effect panel regression models. The results demonstrate that executive compensation fairness positively influences the executive innovation motivation. This effect is more pronounced in Confucian culture regions, while Western culture weakens this effect. The findings of this study confirmed that executive compensation fairness, provide a cross-cultural comparison for compensation research, validate the findings of cultural neuroscience, and provide useful insights into the research of common prosperity. To improve the corporate compensation structure, it is necessary to consider the relative pay equity with firms in the same region and the influence of corporate culture. (shrink)

Kruja is a middle-range city located in the center of Albania. The city of Kruja dates back to its existence from the V-VI century and extends to the city around the VI and IX centuries. It becomes the first capital of Albania in the XI-th century, specifically in 1190. This paper is going to deal with only two groups of buildings that are an integral part of the historical city of Kruja, the historical dwellings (XVIII century) and the socialist ones (...) (1960-1985). This paper aims to analyze through linear regressions the relation between two parameters: moisture content and temperature of the indoor walls and floors of the historic and socialist buildings in the city of Kruja. Three types of historic dwelling and three types of socialist apartments have been analyzed, relatively uniformly oriented. The measurements with specific instruments are done on the same day, for three hours. The degree of reactivity is different for both groups of buildings. The historical buildings are more sensitive to temperature rise. They respond invers to it (as the temperature rise, the level of the moisture content decreases), with a higher coefficient than socialist ones, and appear to be more efficient in reducing the level of the moisture, despite the average moisture content being higher. This paper takes into consideration also the analysis of the materials of the construction for both categories. If the level of moisture is reduced, the buildings will be improved and the quality of life indicators of the inhabitance will be improved too. (shrink)

Compared with traditional statistics, only a few social scientists employ Bayesian analyses. The existing software programs for implementing Bayesian analyses such as OpenBUGS, WinBUGS, JAGS, and rstanarm can be daunting given that their complex computer codes involve a steep learning curve. In contrast, this paper introduces a new open software for implementing Bayesian network modelling and analysis: the bayesvl R package. The package aims at providing an intuitive gateway for beginners of Bayesian statistics to construct and analyse mathematical models (...) in social sciences... (shrink)

The authenticity of the company’s accounting information is an important guarantee for the effective operation of the capital market. Accounting fraud is the tampering and distortion of the company’s public disclosure information. The continuous outbreak of fraud cases has dealt a heavy blow to the confidence of investors, shaken the credit foundation of the capital market, and hindered the healthy and stable development of the capital market. Therefore, it is of great theoretical and practical significance to carry out the research (...) on the identification and governance of accounting fraud. Traditionally, accounting fraud identification is mostly based on linear thinking to build the fraud identification model. However, more and more studies show that fraud has typical nonlinear characteristics, and the multiobjective of fraud means also determines the limitations of using the linear model for identification. Considering that the traditional identification methods may have the defects of model setting error and insufficient information extraction, this paper constructs the support vector machine and logistic regression model to identify accounting fraud. The support vector machine is used to improve the learning ability and generalization ability of unknown phenomena, and the explanatory power of each variable to the whole model is identified by the logistic regression model. This paper breaks through the linear constraint hypothesis and explores the model setting form which is more suitable for the law of corporate fraud behaviour to extract the fraud identification information more fully and provide more powerful support for investors to effectively identify fraud. (shrink)

Water quality control helps in the estimation of water bodies and detects the span of pollutants and their effect on the neighboring environment. This is why the water quality of the northern part of Lake Manzala has been studied here from January to March, 2016. This study aims to model and create a program for linear and nonlinear regression of the water elements in Lake Manzala to assess and predict the water quality. Water samples have been extracted from various (...) depths, and physio-chemical properties and heavy metal concentrations have been evaluated. This study has proposed a new algorithm for predicting water quality called “Algorithm of Estimation Regression Model”. On the contrary, in renewable energy applications, statistical modeling and forecasting the solar radiation remains a significant issue with detect to reinforce power management. A new proposed method for forecasting the average Monthly Global Solar Energy in Queensland, Australia, is called, Converting Data Set into Markov Model. It was used to obtain Markov transition probability matrices for three and six states of the solar energy. The proposed forecasting method yielded accurate results with minimal error. (shrink)

Economic burden of private supplementary tutoring is increasing around the world. Demand for private supplementary tutoring, contributing factors, and their effects are investigated in the study. For this purpose, data were collected through questionnaire, and multistage non-probability technique was used. Hurder model was used to find the factors affecting demand for private supplementary tutoring at the higher secondary level. Linear regression model was used to find the economic burden of family and factors affecting the demand for private tutoring. The (...) findings showed that several factors have a significant effect on the demand for private supplementary tutoring, and some of them significantly impact on the economic burden. The study recommends regulating the market of shadow education because of differentiated tuition fee in different tuition centers to cut down the burden of private tutoring for households. (shrink)

Specifying accurate informative prior distributions is a question of carefully selecting studies that comprise the body of comparable background knowledge. Psychological research, however, consists of studies that are being conducted under different circumstances, with different samples and varying instruments. Thus, results of previous studies are heterogeneous, and not all available results can and should contribute equally to an informative prior distribution. This implies a necessary weighting of background information based on the similarity of the previous studies to the focal study (...) at hand. Current approaches to account for heterogeneity by weighting informative prior distributions, such as the power prior and the meta-analytic predictive prior are either not easily accessible or incomplete. To complicate matters further, in the context of Bayesian multiple regression models there are no methods available for quantifying the similarity of a given body of background knowledge to the focal study at hand. Consequently, the purpose of this study is threefold. We first present a novel method to combine the aforementioned sources of heterogeneity in the similarity measure ω. This method is based on a combination of a propensity-score approach to assess the similarity of samples with random- and mixed-effects meta-analytic models to quantify the heterogeneity in outcomes and study characteristics. Second, we show how to use the similarity measure ωas a weight for informative prior distributions for the substantial parameters in Bayesian multiple regression models. Third, we investigate the performance and the behavior of the similarity-weighted informative prior distribution in a comprehensive simulation study, where it is compared to the normalized power prior and the meta-analytic predictive prior. The similarity measure ω and the similarity-weighted informative prior distribution as the primary results of this study provide applied researchers with means to specify accurate informative prior distributions. (shrink)

Infertility is a huge problem nowadays, not only from the medical but also from the social point of view. A key step to improve treatment outcomes is the possibility of effective prediction of treatment result. In a situation when a phenomenon with more than 2 states needs to be explained, e.g. pregnancy, miscarriage, non-pregnancy, the use of multinomial logistic regression is a good solution. The aim of this paper is to select those features that have a significant impact on (...) achieving clinical pregnancy as well as those that determine the occurrence of spontaneous miscarriage. Two multi-factor models were obtained, used in predicting infertility treatment outcomes. One of the models enabled to conclude that the number of follicles and the percentage of retrieved mature oocytes have a significant impact when prediction of treatment outcome is made on the basis of information about oocytes. The other model, built on the basis of information about embryos, showed the significance of the number of fertilized oocytes, the percentage of at least 7-cell embryos on day 3, the percentage of blasts on day 5, and the day of transfer. (shrink)

Predicting suspended sediment load in water resource management requires efficient and reliable predicted models. This study considers the support vector regression method to predict daily suspended sediment load. Since the SVR has unknown parameters, the observer-teacher-learner-based Optimization method is integrated with the SVR model to provide a novel hybrid predictive model. The SVR combined with the genetic algorithm is used as an alternative model. To explore the performance and application of the proposed models, five input combinations of (...) rainfall and discharge data of Cham Siah River catchment are provided. The predictive models are assessed using various numerical and visual indicators. The results indicate that the SVR-OTLBO model offers a higher prediction performance than other models employed in the current study. Specifically, SVR-OTLBO model offers highest Pearson correlation coefficient, Willmott’s Index, ratio of performance to IQ, and modified index of agreement and the lowest relative root mean square error in comparison with SVR-GA and SVR models, respectively. (shrink)

At present, the research on the influence mechanism of psychological capital on farmers’ entrepreneurial behavior is relatively mature. However, the relationship between farmers’ entrepreneurial behavior and macroeconomics by entrepreneurial psychological capital is still unclear. Based on this, firstly, this work analyzes the entrepreneurial PsyCap in detail. Secondly, the research hypothesis is put forward and a conceptual model is implemented. A questionnaire is designed to analyze the current situation of farmers’ entrepreneurial PsyCap and entrepreneurial behavior. Finally, a structural equation model is (...) implemented to explore the relationship between farmers’ entrepreneurial behavior and macroeconomics. The path test of the SEM is utilized to obtain the following. Macroeconomic growth has a significant positive impact on entrepreneurial behavior. Macroeconomics can affect farmers’ entrepreneurial behavior to varying degrees by affecting the four entrepreneurial PsyCap of farmers’ subjective cognition, Tenacity, hope and open-mindedness. This indicates that entrepreneurial PsyCap plays an intermediary role between farmers’ entrepreneurial behavior and macroeconomics. The purpose of this work is to explore the relationship among farmers’ entrepreneurial behavior, macroeconomics, and the role of entrepreneurial PsyCap through empirical analysis, thereby providing a theoretical reference for the subsequent country’s optimization of farmers’ entrepreneurial strategies. (shrink)

The life we considered normal was disrupted due to measures taken to limit the spread of the novel coronavirus. Quarantine, isolation, social distancing, and community containment have influenced consumer behavior and contributed to the rapid development of e-commerce. In pandemic times, even those unfamiliar with the online environment have had to adapt and make acquisitions in this new manner. Hence, we focused our research on measuring the perception of consumers on how the restrictive measures imposed to limit the spread of (...) the COVID-19 virus had influenced their decision to buy a product or service from the online environment, given that purchases are highly subjective and influenced by cumulative effects of economic, social, psychological and behavioral factors. Our paper comes with additional insights from the literature. It adds empirical evidence that reveals that the number of transactions and the value per transaction increased during the COVID-19 pandemic and highlights that online purchases will continue as such even after the pandemic. (shrink)

Donors to biobanks are typically asked to give blanket consent, allowing their donation to be used in any research authorized by the biobank. This type of consent ignores the evidence that some donors have moral, religious, or cultural concerns about the future uses of their donations – concerns we call “non-welfare interests”. The nature of non-welfare interests and their effect on willingness to donate to a biobank is not well understood. In order to better undersand the influence of non-welfare interests, (...) we surveyed a national sample of the US population using a probability-based internet panel. Logistic regression models assessed the demographic and attitudinal characteristics associated with participants’ willingness to give consent for unspecified future uses of their donation when presented with 7 research scenarios that raised possible non-welfare interest concerns. Most people had non-welfare interests that significantly affect their willingness to donate to a biobank using blanket consent. Some non-welfare interests are associated with subgroups but others are not. A positive attitude toward biomedical research in general was associated with increased willingness to donate, while concerns about privacy and being African American were associated with decreased willingness. Non-welfare interests matter and can diminish willingness to donate to a biobank. Our data suggest that trust in research promotes willingness to donate. Ignoring non-welfare interests could erode this trust. Donors’ non-welfare interests could be accommodated through greater transparency and easier access to information about the uses of donations. (shrink)

This study uncovers a new finding on the impact of CEO tenure on corporate social and environmental performance (CS&EP) in coastal and non-coastal areas of China using fixed-effect panel data regression models. The Two-Stage Least Squares instrumental panel regression is used to validate the veracity of the empirical results. To this end, we extract data from all non-financial Chinese listed firms for the period of 2009 to 2015. By applying the multivariant framework, the findings of (...) the study exhibit a negative and significant effect of CEO tenure on CS&EP. Moreover, this study shows that firms with head offices in coastal areas of China tend to weaken the negative impact of CEO tenure on CS&EP, indicating that CS&EP is more focused in coastal areas of China than non-coastal ones. The findings suggest that the increase in CEOs’ CS&EP in the early years of their service tenure tends to increase their compensation packages. This study is useful for policymakers to link CS&EP with firm economic practices to attain sustainable development objectives. (shrink)

According to the "experimenter's regress", disputes about the validity of experimental results cannot be closed by objective facts because no conclusive criteria other than the outcome of the experiment itself exist for deciding whether the experimental apparatus was functioning properly or not. Given the frequent characterization of simulations as "computer experiments", one might worry that an analogous regress arises for computer simulations. The present paper analyzes the most likely scenarios where one might expect such a "simulationist's regress" to surface, and, (...) in doing so, discusses analogies and disanalogies between simulation and experimentation. I conclude that, on a properly broadened understanding of robustness, the practice of simulating mathematical models can be seen to have sufficient internal structure to avoid any special susceptibility to regress-like situations. (shrink)

We aim to investigate the effect of corporate environmental responsibility (CER) engagement on investors' reactions. We also explore heterogeneity of this impact among different types of companies and different company's market performance. We use panel data models and quantile regression based on data related to firms listed on the A-share China security market and the final sample consists of 3,776 firm-year observations. The results show that CER engagement has a significant positive impact on investors' investment decisions. Further, (...) investors are more sensitive to CER engagement of high energy-consumption companies and no matter the company is a state-owned or a non-state-owned, CER engagement has a significant positive impact on investors' reactions. CER engagement has a significant positive impact on investors' reactions in all quantiles except one and the promoting effect increases first and then decreases with the growth of corporate market value from lower to upper quantiles. (shrink)

This article empirically examines how the performance of Indian manufacturing corporations is affected by corporate governance practices. The study has used panel data comprising of 76 manufacturing companies listed in BSE, for a consecutive six-year period, from 2015-2016 to 2020-2021. The study has applied panel data regression model to enquire the impact of ownership structure variables; and also board composition variables on firm performance using Tobin's Q and ROA. The findings reveal that ownership structure variables, board size (...) and multiplicity of the board positively affect both ROA and Tobin's Q. While independent board and women director show positive effect on Tobin's Q, the CEO-duality is negatively associated with Tobin's Q. The study suggests policymakers to enhance the multiplicity of directors and gender diversity, to maintain the right proportion of board independency, and to increase the size of board. Policymakers may also restrict the CEO to play dual role.Keywordsboard composition, ownership structure, CEO-duality, Tobin's Q. (shrink)

In the past 20 years, China’s educational advantage has undergone a gender reversal. The average educational level of women is higher than that of men. However, the gender difference in housework is gradually expanding, and women are still the main undertakers of housework. Based on the China Family Panel Studies, this study explores the impact of the educational gap between husband and wife on the inequality of housework division and its mechanism. OLS regression model was used to estimate (...) the impact of marital education gap on household inequality. It is concluded that the higher the education level of the husband is than that of the wife, the greater the gender inequality in housework. This conclusion is significant at the level of 0.01. On this basis, the instrumental variable method was used to overcome the endogenous problems and a more accurate conclusion was reached. Every unit of increase in the education gap between husband and wife would increase the degree of household inequality by 0.281 percentage points. Quantile regression provides strong evidence for the results. When the gender time ratio of housework is in the range of 0.8–0.95, the education gap will have an impact on the gender division of housework. After the robustness test and heterogeneity analysis of the model, an intermediary variable was established to discuss the mechanism of the model. The income disparity and the working time gap were proved to be intermediary variables. This study believes that in modern society, the education gap between husband and wife will affect the inequality of housework division by changing the relative income and relative working time of husband and wife. Although the educational advantages of women in the whole society have not changed their role in the division of housework. However, with the narrowing of the educational gap between husband and wife, the degree of inequality in the division of housework has been alleviated, indicating that the improvement of women’s education level has alleviated the inequality in the division of housework to a certain extent. (shrink)

We investigate whether the geographic concentration of non-EU immigrants in the various Italian provinces affects labour tax avoidance practices adopted by firms located in the same provinces, as well as in the neighbouring provinces, and operating in construction and agriculture industries that mostly employ immigrants in Italy. For this purpose, we develop a LTAV proxy based on the financial accounting information of a sample of 993,606 firm-years, disseminated throughout the 108 Italian provinces, over the period 2008–2016. Our results, based on (...) a Spatial Durbin Model panel regression, reveal a statistically significant positive association between the concentration of non-EU immigrants and LTAV at province level, as well as the presence of spillover effects among neighbouring provinces. Our findings are robust to several additional analyses, including instrumental variable estimations. Our study provides empirical support to previous structuralist or marginalization theories holding that socioeconomically marginalized groups, such as non-EU immigrants, are more likely to be involved in labour exploitation practices, which could underlie our LTAV outcomes. Furthermore, it supports the need for tax authorities to strengthen labour inspections, coordinated at national level, especially in those contexts where non-EU immigrants are mostly employed. On the other hand, a greater social integration, assistance, and recognition of rights of immigrants may help to alleviate their situation of weakness that makes them more vulnerable to LTAV practices. Finally, tackling LTAV, associated with the underemployment of immigrants, may prevent its negative effects for society arising from the reduction of public resources to sustain the social welfare and finance public goods and services. (shrink)

PurposeThis study is justified by the economic importance of information on greenhouse gases, as well as the interest in the question of governance structure after the adoption of the objectives of the 2030 Agenda. The problem is also explained by the lack of research that has investigated the relationship between the best governance structure that contributes to achieving sustainability goals, including climate actions (SDG13) and clean energy adoption (SDG7) as part of the 2030 Agenda.Design/methodology/approachThe level of disclosure is measured on (...) the basis of the carbon disclosure score calculated by the carbon disclosure project (CDP). The study sample consists of 387 US companies that voluntarily participated in the CDP survey from 2011 to 2018. The authors use panel data analysis based on multiple regression models.FindingsThe results confirm the influential role of board size, director independence, the presence of women on the board and the presence of an environmental committee on carbon disclosure. In terms of carbon disclosure, the results suggest that a better governance structure is likely to reduce carbon emissions and improve carbon performance practices. Similarly, the analyses show a different representation of the role of corporate governance in high-carbon sectors compared to low-carbon sectors.Research limitations/implicationsThis study has some limitations. First, the sample is only interested in US companies that responded to the CDP questionnaire during the period 2011–2018. Thus, the results cannot be generalized to countries with different governance structures. Second, the data from this study on carbon disclosure, specifically focuses on CDP reporting to determine the carbon disclosure score. In this sense, the findings on information disclosed do not necessarily address disclosures through other media, such as a company’s website or a press release.Originality/valueSustainability and commitment to the sustainable development goals (SDGs) are more likely to exist in companies that have good governance and, in particular, a better board. The research is inspired by the SDGs. The study aims to examine the relationship between carbon disclosure and corporate governance in the context of SDGs. Indeed, this research work contributes to achieving sustainability goals, including climate actions (SDG13) and clean energy adoption (SDG7). (shrink)

BackgroundAddictive behavior of gambling, gaming and internet activity is partly a new research domain and has not been well investigated with regard to sexual minority populations. Although health disparities between sexual minorities and the general population are well documented, there is a lack of inclusion of sexual minorities in both research and clinic. Among lesbian, gay and bisexual populations certain features could be present that play a role for the development of addictive behaviors, such as social isolation and increased risk (...) of other psychiatric problems. The aim of this study was to investigate problem gambling, problem gaming and problematic internet behavior in a European context and if it is affected by sexual orientation status.MethodsAn online web-survey was distributed among web-panels in England, Poland, Switzerland, Italy, Spain, Denmark, and Sweden in 2017–2018.Result10 983 complete answers were collected. 7.1% of the participants had a sexual minority status. Regression models found that there was no difference in gambling, gaming and internet behavior among heterosexual and sexual minority men. Sexual minority women were associated with problematic gambling and gaming behavior, when also controlling for age and nationality. When also controlling for psychological distress, women defining as having another sexual minority status than lesbian and bisexual remained significant for having a problematic gaming behavior.ConclusionAn awareness of female sexual minority perspectives is relevant in facilities treating behavioral addiction as well as in future research in behavioral addiction. More research is needed in problematic gambling and gaming behavior in different sexual minority populations with regard to psychiatric comorbidity and living conditions. An inclusion of sexual minority groups defining as other than gay and bisexual is needed in future research. No significant differences were found between heterosexual and sexual minority men in adjusted analysis in this study. (shrink)

Purpose of the article: Most regional macroeconomic processes may not be adequately analyzed without accounting for their spatial nature: regional distances, interactions between neighbors, spill-over effects and interdependencies. This contribution focuses on various factors ruling unemployment dynamics in the Visegrad Group countries and their major economic partners: Germany and Austria. The analysis is performed at the NUTS2 level. Methodology/methods: Spatial econometrics is a unique tool for a broad range of quantitative analyses and evaluations. Spatial econometric models are based on (...) geo-coded data. Spatial econometrics and regional competitiveness paradigms are combined into different types of regression model specifications describing unemployment dynamics. Alternative spatial structures are used for verification of stability in estimated model properties. Scientific aim: We aim to provide a detailed empirical evaluation of spatially determined factors of regional unemployment dynamics, along with insight into the robustness of such approach. Both conceptually and parametrically varying neighbor definitions are used to provide evidence for model evaluation. Findings: We find strong positive spatial dependence patterns in the estimated models, robust against varying neighborhood definitions. Our results strongly support the importance of regional and potentially cross-border cooperation in macroeconomic policies addressing unemployment. The estimated models also underline the importance of using spatial models, by pointing out the bias in OLS-estimated models. Conclusions and limits: Spatial approach to econometric analysis provides important insight and robustness to a broad range of unemployment analyses that may be carried out using regional data. At the same time, it should be noted that this article focuses mostly on the spatial and stability aspects of model estimation, while leaving out other interesting topics such as spill-over effects calculations as based on estimated models. Also, estimations provided in this article might benefit from spatial panel data-based methods - once data availability issues are sorted. (shrink)

Proper cognitive functions are critical to the life of the elderly. With the rapid aging of the population, community support plays an important role in cognitive functioning. This study examines the association between community support and the level of cognitive functioning in the elderly, and the mediating effect of social participation in the relationship. Based on the panel data of China Longitudinal Healthy Longevity Survey in 2005, 2008, 2011, 2014, and 2018, people aged 65 and over are selected as (...) the research object. The panel Logit model is used to analyze the influence of community support on their cognitive functioning. In addition, the stepwise regression and KHB decomposition methods are used to test the influence mechanism of community support on their cognitive function. The benchmark regression results show that there is a significant correlation between community support and cognitive function in the elderly. Daily care has the strongest impact on the cognitive function of the elderly, followed by health care and legal support, while psychological care has the weakest impact on the cognitive function of the elderly. The results of the mediation effect test show that social participation plays a significant intermediary role in the impact of community support on the cognitive function of the elderly, demonstrating that community support can improve the cognitive function of the elderly by promoting the social participation of the elderly. In classified community support, social participation plays a significant intermediary role in the impact of psychological care on cognition. (shrink)

Due to the pandemic of coronavirus disease 2019, taking online courses has become a “new normality” for college students. This study paid particular attention to the role of college students’ attitude toward online courses in shaping their psychological distress during the COVID-19 epidemic in China. Participants were from a national panel survey that has been administered before and during the COVID-19 epidemic. Besides bivariate analysis, a multivariate regression model while adjusting for a lagged dependent variable was estimated to (...) show the association between ATOC and during-COVID distress. We found that respondents from a disadvantaged family background were more likely to have an “unsupportive” ATOC. Moreover, both bivariate and multivariate analyses confirmed that respondents with a “neutral” or “unsupportive” ATOC had greater during-COVID psychological distress, compared to their counterparts with a “supportive” ATOC. Given the persistent spread of the COVID-19 worldwide and the profound onsite-online transition in course delivery in higher education, students’ perceptions and evaluations of the massive online courses should be carefully considered and integrated into curriculum reforms in both present and post COVID-19 situations. (shrink)

We study Compositional Models based on Dirichlet Regression where, given a (vector) covariate x, one considers the response variable, y, to be a positive vector with a conditional Dirichlet distribution, y | X We introduce a new method for estimating the parameters of the Dirichlet Covariate Model given a linear model on X, and also propose a Bayesian model selection approach. We present some numerical results which suggest that our proposals are more stable and robust than traditional approaches.