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The main objective of this research is to investigate if there is a long-term relationship between “terrorism” and sustainable “economic growth” in Big Ten Countries.

The data was tested via Panel ARDL Analysis. The growth rate (GR) is the dependent variable, and the “Global Terror Index (GTI)” is the independent variable as the terror indicator. The ratio of Foreign Direct Investment (FDI) to the Gross Domestic Product (GDP), and the ratio of External Balance (EB) to Gross Domestic Product (GDP) are included in the model as the control variables due to their effect on the growth rate. A Panel ARDL analysis is conducted to examine the existence of long-term co-integration between terror and the economy. The planning of the study, the formation of its theoretical and conceptual framework, and the literature research were carried out in 2 months, and the collection of data, the creation of the methodology and the analysis of the analyzes were carried out in 2 months, the interpretation of the findings and the development of policy recommendations were carried out within a period of 1 month. The entire study was completed in a total of 5 months.

Results showed that “Terror” has a negative impact on “Growth Rate” in the long term while “External Balance” and “Foreign Direct Investment” positively affect the Growth Rate. The coefficients for the short term are not statistically significant.

The sample is only limited to Big Ten including China, India, Indonesia, South Korea, Argentina, Brazil, Mexico, Turkey, Poland and South Africa. The period for annual data collection covers the years between 2002 and 2019 and due to the unavailability of data.

Considering the risks and the mutual negative effect that turns into a vicious circle between terrorism and the economy, it is necessary to eliminate the problems that cause terrorism in the mentioned countries, on the one hand, and to develop policies that will improve economic performance on the other.

Trustful law enforcement bodies have to be established and supported by all technological means to prevent terror. The conditions causing terror have to be investigated carefully and the problems causing terror or internal conflict have to be solved. International cooperation against terrorism has to be strengthened and partnerships, information, experience sharing have to be supported at the maximum levels.

It is certain that terror might have a negative influence on the performance of economies. But the limited number of studies within this vein and the small size of their sample groups mostly including single-country case studies require conducting a study by using a larger sample group of countries. Big Ten here represents at least half of the population of the world and different regions of the Globe.

The purpose of this paper is to explore a new grey relational analysis model to measure the coupling relationship between the indicators for the water environment status assessment. Meanwhile, the model deals with the problem that the changing of indicator order may result in the changing of the degree of grey relation.

The binary index submatrix of the sample matrix is given first. Then the product of the matrix and its own transpose is used to measure the characteristics of the index and the coupling relationship between the indicators. Thirdly, the grey relational coefficient is defined based on the matrix norm, and a grey coupling relational analysis model is proposed.

The paper provides a novel grey relational analysis model based on the norm of matrix. The properties, normalization, symmetry, relational order invariance to the multiplicative, are studied. The paper also shows that the model performs very well on the water environment status assessment in the eight cities along the Yangtze River.

The model in this paper has supplemented and improved the grey relational analysis theory for panel data.

This study aims to explore the interplay between CO2 emissions, financial development (FD) and foreign direct investment (FDI) in Asia-Pacific and Oceania. It also aims to understand short- and long-term impacts, emphasizing the role of FDI, FD and FD’s moderating effect on the FDI–CO2 relationship.

Using a 21-year panel data set (2000–2020) from 44 countries, the study employs the pooled mean group-autoregressive distributed lag (PMG-ARDL) model supplemented by the Dumitrescu–Hurlin panel causality test. This method assesses the complex dynamics and offers a robust analysis of short- and long-term effects in the Asia-Pacific and Oceanian context.

Long-term results indicate that FDI coupled with FD and FD’s moderating effect on FDI significantly contributes to CO2 emissions. Short-term relationships are more complex and lack statistical significance. FD positively moderates the FDI–CO2 relationship in the long run.

For investors, policymakers and stakeholders in Asia-Pacific and Oceania, the study highlights the importance of considering environmental impacts in investment decisions. The insights into the role of FDI and FD help craft policies and strategies for environmental sustainability.

Socially, this research emphasizes the necessity of a balanced approach to economic development, considering the potential long-term environmental consequences. Policymakers and stakeholders may use these findings to guide discussions and actions to achieve sustainable and socially responsible development in this dynamic region.

The findings contribute original insights into the essential relationships among FDI, FD and CO2 emissions in a diverse region like Asia-Pacific, enhancing the understanding of environmental implications in regions experiencing rapid economic growth.

Existing research has predominantly concentrated on examining the factors that impact consumer decisions through the lens of potential consumer motivations, neglecting the sentiment mechanisms that propel guest behavioral intentions. This study endeavors to systematically analyze the underlying mechanisms governing how negative reviews exert an influence on potential consumer decisions.

This paper constructs an “Aspect-based sentiment accumulation” index, a negative or positive affect load, reflecting the degree of consumer sentiment based on affect infusion model and aspect-based sentiment analysis. Initially, it verifies the causal relationship between aspect-based negative load and consumer decisions using ordinary least squares regression. Then, it analyzes the threshold effects of negative affect load on positive affect load and the threshold effects of positive affect load on negative affect load using a panel threshold regression model.

Aspect-based negative reviews significantly impact consumers’ decisions. Negative affect load and positive affect load exhibit threshold effects on each other, with threshold values varying according to the overall volume of reviews. As the total number of reviews increases, the impact of negative affect load diminishes. The threshold effects for positive affect load showed a predominantly U-shaped course of change. Hosts respond promptly and enthusiastically with detailed, lengthy text, which can aid in mitigating the impact of negative reviews.

The study extends the application of the affect infusion model and enriches the conditions for its theoretical scope. It addresses the research gap by focusing on the threshold effects of negative or positive review sentiment on decision-making in sharing accommodations.

The purpose of this paper is to construct a grey incidence model for panel data that can reflect the incidence direction and degree between indicators.

Firstly, this paper introduces the concept of a negative matrix and preprocesses the data of each indicator matrix to eliminate differences in dimensions and magnitudes between indicators. Then a model is constructed to measure the incidence direction and degree between indicators, and the properties of the model are studied. Finally, the model is applied to a practical problem.

The grey-directed incidence degree is 1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a positive linear relationship. This degree is −1 if and only if corresponding elements between the feature indicator matrix and the factor indicator matrix have a negative linear relationship.

The example shows the number of days with good air quality is negatively correlated with the annual average concentration of each pollutant index. PM_2.5, PM₁₀ and O₃ are the main pollutants affecting air quality in northern Henan.

This paper introduces the negative matrix and constructs a model from the holistic perspective to measure the incidence direction and level between indicators. This model can effectively measure the incidence between the feature indicator and factor indicator by integrating information from the point, row, column and matrix.

The purpose of this study is to investigate the impact of pervasive immigrant inflows on GDP productivity growth in selected OECD countries, including Australia, Canada, Germany, Italy, New Zealand and the USA. The study aims to consider patent filing residence and non-residence as well as R&D expenditure to see if large immigrant destination countries can accept many immigrants to generate knowledge and creativity and stimulate economic development.

The study uses OECD and WDI data sets from 2000 to 2019 and employs a fundamental correlation matrix and static panel model to analyze the data. The study examines the impact of residential and non-residential patent applications and R&D expenditure on GDP productivity growth in the selected OECD countries.

The study found an adverse effect for residential patent applications, while non-residential patent application and R&D expenditure variables were strongly linked to GDP productivity. This indicates that to reap the benefits of skilled immigration inflows, the selected OECD countries must devote more resources to research and development and build a knowledge-based economy. This will improve economic efficiency and overall growth.

This paper assists policymakers in comprehending how to effectively utilize immigration inflows in developed and emerging economies in order to construct a future knowledge-based economic system.

The shadow economy (SE) has been a serious issue with varied dimensions in all countries that significantly affect economic growth. Therefore, all countries have made an effort to tackle the SE by pursuing several measures. This study aims to investigate the impact of financial markets (stock and bond) in reducing the SE while considering the role of country risk (political, economic and financial) in N-11 countries.

The study employed first-generation methodological techniques, including a unit root test to identify stationarity in the series, a panel cointegration test and panel autoregressive distributive lag (ARDL) to estimate long-run and short-run relationships. Finally, the Granger causality is applied to determine the direction of the causal relationship.

The study explored that country risk factors are crucial in reducing the size of the SE. Moreover, the significant moderating role of country risk factors in the financial market development and SE nexus suggests that by controlling the country's risk, financial market development can negatively affect the SE.

Due to the availability of data, the study used data, ranging from 1995 to 2015, because the tax burden data is available from 1995 while the maximum data for the SE is available till 2015, using Medina and Schneider's (2019) data estimates for the SE.

The previous studies have focused explicitly on the role of financial institutions' development in the SE. To the best of the author's knowledge, no previous study is attempted to investigate the role of financial markets (bonds and stock) in the size of the SE. Furthermore, previous studies have ignored the important role of country risk factors in the size of the SE. This study investigates the impact of country risk on the SE and the moderating role of country risk in the development of financial markets and the SE nexus.

This study aims to analyze the factors influencing households housing tenure choices in Togo.

The authors applied a rigorous econometric approach, using Harmonized Household Living Conditions Survey (EHCVM) data from 2018 and 2021 to construct a longitudinal panel, and Unified Basic Welfare Indicators Questionnaire (QUIBB) data from 2006, 2011 and 2015 to construct a pseudo-panel.

The study reveals that a household’s life-cycle variables like age of the household head, marital status, household size and place of residence, have a significant influence on homeownership. In addition, households in the highest wealth quartiles and used heads of household are more likely to own their home.

Housing policies focused on improving the financial sustainability of low-income households and reducing the transaction costs associated with property acquisition are essential to promoting homeownership.

As part of the implementation of the Government Roadmap 2020–2025, the government has committed to providing 20,000 affordable social housing units, aiming to significantly boost the supply of decent housing. However, the findings of this study highlight the need for targeted subsidy programs for low-income households, particularly for female-headed households and those living in urban areas. These subsidies could cover part of the cost of purchasing homes. For middle-income households, it is crucial to develop suitable financing mechanisms, such as low-interest mortgages and loan guarantees. Given demographic pressures and the high cost of public housing programs, promoting self-build remains essential. This support should be accompanied by the provision of low-cost building materials and technical training in innovative, sustainable construction methods. Additionally, improved access to employment and land regularization are essential prerequisites for the success of these initiatives.

Research on the determinants of tenure choice is relatively limited in sub-Saharan Africa due to the unavailability of housing survey data. This paper proposes a case study of Togo, whose housing market characteristics correspond to most sub-Saharan African countries. Furthermore, this study applied two methodological approaches commonly used in dynamic analyses, thereby enhancing the robustness of the findings.

The present paper makes an attempt to investigate the determinants that affect FDI inflows distribution among Indian states. Together with traditional determinants, the impact of institutional determinants on state-level FDI inflows distribution in India has been analysed.

The study uses panel data for a period of 20 years (2000–2019) for 17 groups of Indian states (29 states and 7 UTs). The empirical evidence is based on the panel data method and the findings support Dunning's OLI theory. As the data for some indicators for the institutional environment is not available at the state level, hence we used component analysis to arrive at the single component for the institutional factor. The study takes into account corruption, legal system, industrial disputes, man-days lost, labour availability, political risk, protection of IPR and agglomeration as potential macroeconomic and institutional determinants.

Results show that FDI inflows into Indian states is driven mainly by institutional environment. From our analysis, the author infers that the institutional variables such as legal system, IPR, corruption, political instability play an important role in determining the distribution of FDI inflows at the state level in India. Together with that GFCF and agglomeration are also important determinants of state-wise FDI inflows.

The major limitation of the study is that it doesn't include moderated impact of economic and institutional determinants of FDI inflows in Indian states, which can be an avenue for future research. Future research can also carried out taking district-level data to further examine the determinants at district level in India.

The contribution of the present paper is three-fold, first, the author constructs a measure of different institutional variables, after normalization of data for the period 2000–2019, and the author choose the highest explaining factor with the highest variance explained then we constructed the indices for select variable, which further has been used in the panel data analysis technique. The author has found that macroeconomic variables, as well as institutional variables, are significant to attract FDI at the state level in India. The paper shows that corruption, political risk, IPR and legal system are the major institutional determinants of FDI inflows in India at the state level. States with higher domestic investment attract more FDI inflows, moreover, agglomeration is a very important determinant as the investors are more confident in investing at the same location, the reason behind this may be that the investors want to avoid the registration procedure for new land, administrative formalities or they feel more secure at the same place and keen to invest at the same place again.

This article aims to answer two research questions that remain controversial in the accounting and corporate governance literature: (1) how corporate disclosure is related to board monitoring and (2) how this link is affected by the institutional environment and firm-level governance.

The study is based on S&P data on corporate disclosure by Russian companies collected over 2002–2010 and supplemented by information from the SKRIN database. The dataset covers 125 non-financial companies, with 559 observations in total. We use three indicators of board monitoring: the percentage of non-executive directors, a dummy for two-tier boards, and a dummy for an audit committee. The firm’s governance is proxied by a dummy for single class stock, while the institutional environment is proxied by a dummy for ADRs/GDRs. We apply conventional methods of panel data analysis with several robustness checks, including the random- and fixed-effects models, 2SLS that addresses the potential endogeneity of board composition, alternative definitions of the dependent variable, and an extended list of controls.

We find a positive (complementary) relationship between the amount of disclosure and the proxies for board monitoring employed. This complementary relationship turns out to be the strongest among companies that have better internal governance but face a weaker institutional environment. There is little evidence of such complementarity under strong institutions.

The findings may be of interest to investors and policymakers. As to the former, the results warn of firms that provide limited disclosure in the presence of strong corporate governance arrangements, such as independent boards, as these factors are not substitutes for each other. As to the latter, the results support comprehensive policies aimed at simultaneous improvements in both board governance and corporate disclosure in weak institutional settings.

This paper uses a unique setting and rich, partly proprietary data to extend the existing literature on the relationship between corporate disclosure and board monitoring, with an emphasis on the moderating role of the institutional environment and firm-level governance. It is also one of the very few studies of corporate disclosure in Russia, an important emerging economy of the early 2000s.