Search results

The objective of this paper is to investigate the direction of the causal relationship between dividend policy (DP) and earnings management (EM).

This research utilizes the panel data analysis to investigate the causal relationship between EM and DP. It provides empirical insights based on a sample of 280 French nonfinancial companies listed on the CAC All-Tradable index during the period of 2008–2015. The study initiates with a Granger causality examination on the unbalanced panel data and employs a dynamic panel approach with the generalized method of moments (GMM). It further estimates the empirical models simultaneously using the three-stage least squares (3SLS) method and the iterative triple least squares (iterative 3SLS) method.

The estimation of our various empirical models confirms the presence of a bidirectional causal relationship between DP and EM.

Our study highlights the prevalence of EM in the French context, particularly within DP. It underscores the need for regulatory bodies, the Ministry of Finance, external auditors and stock exchange organizers to prioritize governance mechanisms for improving the quality of financial information disclosed by companies.

This research is, to the best of our knowledge, the first is to extensively investigate the reciprocal causal relationship between DP and EM in France. Previous studies have not placed a significant emphasis on exploring this bidirectional link between these two variables.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.

Reducing and eradicating global poverty features as a primary objective of the sustainable development goals (SDGs) for 2030. Since over half a century, the World Bank has disbursed loans amounting to billions of US dollars to assist countries to alleviate poverty. However, the path to zero poverty is often impaired with conflicts, social unrest and, most commonly, economic crisis. In this chapter, we examine the inter-linkage between various forms of economic crises, poverty and government expenditure for a set of 127 countries from 1985 to 2010. Using a simultaneous equation model, we test the direct effect of a financial crisis on the incidence of poverty and its indirect effect through the immediate decrease in government expenditure. Contrary to previous studies, our findings suggest that crises have no direct impact on poverty. We find a similar effect for currency, inflation and debt crisis. However, there is evidence that poverty increases indirectly due to a fall in government expenditure. Our results are robust for non-advanced and advanced economies and alternate estimation technique using factor analysis.

We investigate the role of fiscal policy, through several measures of government revenues and expenditures and redistribution, on disposable and market income inequality and economic growth as well as the interaction between inequality and growth for 31 European countries from 1995 to 2019.

We use a simultaneous equations model to assess the linkage between economic growth, inequalities and fiscal policy variables.

(1) While disposable income inequality has a negative effect on all fiscal policy variables, market income inequality has a mixed effects; (2) for Eastern European countries, public consumption and direct taxation positively influence economic growth; conversely, for Western European countries, the effects are negative; (3) disposable and market income inequality have a positive effect on growth for Eastern European countries, and a negative influence on growth for Western European countries; (4) growth contributes to the increase of disposable and market income inequality for Eastern European countries; for Western European countries, the effects are opposite; and (5) fiscal policy allows for the attenuation of disposable income inequality.

The different results between the role of market and disposable income inequality levels lead us to suggest tax progressivity as an important feature to consider when analyse the trivariate relationship between inequalities, fiscal policy and growth. Furthermore, there are different dynamics between inequality and growth, and the role of fiscal policy, on both Eastern and Western European countries.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The COVID-19 pandemic, a sudden and disruptive external shock to the USA and global economy, profoundly affected various operations. Thus, it becomes imperative to investigate the repercussions of this pandemic on the US housing market. This study investigates the impact of the COVID-19 pandemic on a crucial facet of the real estate market: the Time on the Market (TOM). Therefore, this study aims to ascertain the net effect of this unprecedented event after controlling for economic influences and real estate market variations.

Monthly time series data were collected for the period of January 2010 through December 2022 for statistical analysis. Given the temporal nature of the data, we conducted the Durbin–Watson test on the OLS residuals to ascertain the presence of autocorrelation. Subsequently, we used the generalized regression model to mitigate any identified issues of autocorrelation. However, it is important to note that the response variable derived from count data (specifically, the median number of months), which may not conform to the normality assumption associated with standard regression models. To better accommodate this, we opted to use Poisson regression as an alternative approach. Additionally, recognizing the possibility of overdispersion in the count data, we also explored the application of the negative binomial model as a means to address this concern, if present.

This study’s findings offer an insightful perspective on the housing market’s resilience in the face of COVID-19 external shock, aligning with previous research outcomes. Although TOM showed a decrease of around 10 days with standard regression and 27% with Poisson regression during the COVID-19 pandemic, it is noteworthy that this reduction lacked statistical significance in both models. As such, the impact of COVID-19 on TOM, and consequently on the housing market, appears less dramatic than initially anticipated.

This research deepens our understanding of the complex lead–lag relationships between key factors, ultimately facilitating an early indication of housing price movements. It extends the existing literature by scrutinizing the impact of the COVID-19 pandemic on the TOM. From a pragmatic viewpoint, this research carries valuable implications for real estate professionals and policymakers. It equips them with the tools to assess the prevailing conditions of the real estate market and to prepare for potential shifts in market dynamics. Specifically, both investors and policymakers are urged to remain vigilant in monitoring changes in the inventory of houses for sale. This vigilant approach can serve as an early warning system for upcoming market changes, helping stakeholders make well-informed decisions.

The purpose of this study is to examine the relationship between the productive structure and the size of the informal economy in Latin American countries.

The study employs econometric techniques for panel data covering the period from 2002 to 2017 and considering 17 Latin American countries. The evidence presented is based on the informal economy data generated by Medina and Schneider (2018) who estimate the size of the informal economy using a structural equation model and the share of manufacturing in total employment as a measure of the size of the manufacturing sector. Also, the study addresses the possible endogeneity bias in the relationship studied and makes the conclusions more robust, thus avoiding spurious correlations that weaken the findings.

The results indicate that most industrialized Latin American countries are associated with a smaller size of the informal economy.

The findings have important policy implications, as they suggest that Latin American economies need to switch the structure of the economy toward more sophisticated productive structures if they want to reduce the size of the informal economy. Thus, more efforts should be deployed to policies to diversify and upgrade economies.

The study contributes to the literature on the informal economy by connecting the country’s productive structure and informality. Specifically, the results show that the productive structure of countries is a plausible explanation for the size of the informal economy.

The textile industry holds immense significance in the economy of any nation, particularly in the production of synthetic yarn and fabrics. Consequently, the pursuit of acquiring high-quality products at a reduced cost has become a significant concern for countries. The primary objective of this research is to leverage data mining and data intelligence techniques to enhance and refine the production process of texturized yarn by developing an intelligent operating guide that enables the adjustment of production process parameters in the texturized yarn manufacturing process, based on the specifications of raw materials.

This research undertook a systematic literature review to explore the various factors that influence yarn quality. Data mining techniques, including deep learning, K-nearest neighbor (KNN), decision tree, Naïve Bayes, support vector machine and VOTE, were employed to identify the most crucial factors. Subsequently, an executive and dynamic guide was developed utilizing data intelligence tools such as Power BI (Business Intelligence). The proposed model was then applied to the production process of a textile company in Iran 2020 to 2021.

The results of this research highlight that the production process parameters exert a more significant influence on texturized yarn quality than the characteristics of raw materials. The executive production guide was designed by selecting the optimal combination of production process parameters, namely draw ratio, D/Y and primary temperature, with the incorporation of limiting indexes derived from the raw material characteristics to predict tenacity and elongation.

This paper contributes by introducing a novel method for creating a dynamic guide. An intelligent and dynamic guide for tenacity and elongation in texturized yarn production was proposed, boasting an approximate accuracy rate of 80%. This developed guide is dynamic and seamlessly integrated with the production database. It undergoes regular updates every three months, incorporating the selected features of the process and raw materials, their respective thresholds, and the predicted levels of elongation and tenacity.

In this chapter, we consider the possibility that a firm may use costly resources to improve its technical efficiency. Results from static analyses imply that technical efficiency is determined by the configuration of factor prices. A dynamic model of the firm is developed under the assumption that managerial skill contributes to technical efficiency. Dynamic analysis shows that the firm can never be technically efficient if it maximizes profits, the steady state is always inefficient, and it is locally stable. In terms of empirical analysis, we show how likelihood-based methods can be used to uncover, in a semi-non-parametric manner, important features of the inefficiency-management relationship using a flexible functional form accounting for the endogeneity of inputs in a production function. Managerial compensation can also be identified and estimated using the new techniques. The new empirical methodology is applied in a data set previously analyzed by Bloom and van Reenen (2007) on managerial practices of manufacturing firms in the UK, US, France and Germany.

While today's customer steadily adapts to various modes of shopping, their beliefs around fluency through each shopping channel, and personal factors such as income level, can impact their intention to patronage or purchase from omnichannel department stores. Hence, this study analysed the customers of omnichannel fashion department stores, using perceived fluency and income as indirect factors that help understand customers' patronage intention and purchase intention.

The overarching framework for this research is the theory of reasoned action, in which patronage and purchase intentions represent the specific likelihood-of-performance behaviours. A Seemingly Unrelated Regression model was empirically used to analyse the relationships between generational cohorts, income, and perceived channel fluency and the behaviours that lead to patronage intention and purchase intention. Researchers conducted a survey among 552 omnichannel fashion department store consumers to examine today's retail environment.

The results of this study suggest that (1) consumers between the ages of 50 and 69 years, including older Generation X and younger Baby Boomers, who earn between $60,000 and $79,999 in annual salary show a significantly positive relationship with both patronage and purchase intentions through perceived fluency and (2) consumers between the ages of 38 and 49 years, including older Millennials and younger Generation X, who earn between $80,000 and $99,999 in annual salary show a significantly positive relationship with purchase intention through perceived fluency

This study analyses correlations between a generational cohort, perceived fluency as moderated by income and the relationship between these variables and customers' patronage and purchase intentions, which has not been studied before.