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This paper examines the determinants of happiness index ratings in European countries over 8 time points using unique data from the Eurostat, World Bank and World Happiness Reports.

To examine the determinants of happiness index ratings for EU-27 countries over the period 2012–2019, panel ordinary least square and quantile regression model are used to data obtained from all sample.

Evidence from European data on happiness index generate some important key outcomes; economic outcomes levels with both current taxes and inflation rate have a positively relationship on happiness index ratings (HIR), while total employment rate has a significant negativity on HIR. Additionally, in a quantile panel regression of 27 countries, the impact of financial inclusion on happiness index looks to change with a country's level of income. On the macroeconomic level, gross domestic product (GDP) improves the happiness index for the individual under certain conditions. Thus, GDP on 0.25th quantile levels positively and significantly impacts the HIR for leader countries.

Empirical evidence suggests that macro-economic variables and the labor market proxies of the countries play a key role in determining HIR as well.

The study extends the literature on developed countries and suggestions a particular perspective on the relationship between economic outcomes and happiness index. This study offers two main originalities: it simultaneously examines the “happiness-macroeconomic level” and “happiness-employment status dimension”, and it uses a quantile regression approach, including financial inclusion variation.

This study aims to investigate the relationship between oil prices and stock returns of renewable energy firms in India under different market conditions.

The authors use the panel quantile framework with Fama–French–Carhart’s (1997) four-factor asset pricing model. All renewable energy firms listed in the National Stock Exchange of India are considered in this study. Three oil prices, such as West Texas Intermediate spot price, Europe Brent oil price and Indian basket oil price, are used in the regression. The analysis is done for the whole sample and its subgroups.

In the whole sample, stock returns of renewable energy firms respond positively to oil price changes in extreme market conditions only. In the subgroups of the renewable energy firms, the relationship between stock returns and oil price is positive and more robust in higher quantiles across all subgroup firms.

The contribution of the study is explained as follows. First, this study helps to explore the relationship between oil and stock returns of the renewable energy sector under different market conditions in the Indian context. Second, existing studies explore the effect of oil prices on stock returns of the renewable energy sector at the industry level, and most of the studies are in developed countries. To the best of the authors’ knowledge, this is the first study in the context of India. Third, this is a firm-level study

The purpose of this research is to investigate the impact of the gender gap on the ecological footprint (EFP) corresponding to its different quantiles.

Quantile panel regression for 24 countries from the period 2006 to 2017 will be used, for the gender gap and other determinants of EFP.

Each factor affecting EFP differs in its impact depending on the level of EFP quantile it corresponds to. Gender gap was found to be increasing EFP for the higher quantiles and decreasing EFP for the lower quantiles.

Environmental institutions should be considering the role of gender equality as a factor affecting the environment. Socioeconomic factors sometimes hamper the role of the female gender in preserving the environment. There are variations on how EFP factors differ between individual countries and this opens areas for further studies.

This research contributes to the current research studies by testing the impact of the gender gap on EFP instead of CO₂ emission which is widely used in the literature. This topic is considered understudied and one of the few that uses the quantile panel regression to investigate this impact, none of which is used in gender and environment studies. Finally, the model used in the study uses a more comprehensive extension of the “Stochastic Impact by Regression on Pollution, Affluence and Technology” model compared to the existing empirical studies in this area.

Debt sustainability and the growing level of external debt in sub-Saharan African (SSA) continue to be significant research priorities. This study aims to examine the corruption-external debt nexus in SSA economies and whether different levels of corruption better explain this relationship.

The panel quantile regression approach was applied to account for the heterogeneous effect of the exogenous variables on external debts. The research covers 30 years of panel data from 30 selected SSA economies for the period spanning from 2000 to 2021.

The empirical findings of the regression analysis demonstrate the heterogeneous influences of the exogenous variables on external debt. While there was a positive impact of foreign direct investment (FDI) inflows on external debts, corruption established a negative relationship with external debt from the 10th to the 80th quantile. The findings showed a positive link between trade openness and external debt, while they also showed a negative relationship between gross fixed capital formation and external debt.

It is implied that corruption “sands the wheels” of external debts in the selected SSA countries. Therefore, the amount of external debt that flows into SSA is inversely correlated with corruption activity.

To the best of the authors’ knowledge, this study is one of the first to use panel quantile regression to analyze how corruption affects debt dynamics across different levels of debt, allowing for a more nuanced understanding of how corruption affects debt dynamics. Based on the findings of this study, SSA countries should create enabling environments to attract FDI inflows and to continue to drive domestic revenue mobilization and capital so as to be less dependent on external debts.

This study aims to introduce a tree-structured linear and quantile regression framework to the analysis and modeling of equity returns, within the context of asset pricing.

The approach is based on the idea of a binary tree, where every terminal node parameterizes a local regression model for a specific partition of the data. A Bayesian stochastic method is developed including model selection and estimation of the tree structure parameters. The framework is applied on numerous U.S. asset pricing models, using alternative mimicking factor portfolios, frequency of data, market indices, and equity portfolios.

The findings reveal strong evidence that asset returns exhibit asymmetric effects and non- linear patterns to different common factors, but, more importantly, that there are multiple thresholds that create several partitions in the common factor space.

To the best of the authors' knowledge, this paper is the first to explore and apply a tree-structured and quantile regression framework in an asset pricing context.

The purpose of this paper is to fill the momentous gap by explicitly investigating the asymmetric effects of monetary policy (MP) on non-bank financial intermediation (NBFI) assets.

The authors utilized panel data from 29 countries for the period of 2012–2020 and used the quantile regression estimation. In addition to simultaneous quantile regression (SQR), the authors also employ quantile regression with clustered data (Parente and Silva, 2016) and the generalized quantile regression (GQR) method (Powell, 2020).

The empirical results show a significant heterogeneous impact of MP. While there is a positive relationship between MP and NBFI assets (“waterbed effect”) at lower quantiles of NBFI assets, at middle and higher quantiles, MP has a negative impact on NBFI assets (“search for yield” effect). The authors further find that negative impact strengthens as the quantile levels of NBFI assets rise from mid to high. Findings also reveal that “procyclicality” (except higher quantile) and “institutional demand” hypotheses hold. However, regarding “regulatory arbitrage,” mixed results are observed indicating the impact of Basel III requirements.

Previous empirical studies have concentrated on either the Dynamic Stochastic General Equilibrium (DSGE) framework or conditional mean regression approaches and delivered mixed findings of the MP effects on NBFI. The current paper takes a step toward dealing with this issue by deploying quantile regression methodology, which shows the impact of MP on NBFI at different conditional distributions (quantiles) of NBFI assets instead of just NBFI's conditional mean distribution.

The purpose of this paper is to investigate sustainable green economy in sub-Saharan African (SSA) countries over the period 1990–2019 using a quantile regression approach, considering the nexus between urbanization, economic growth, renewable energy, trade and carbon dioxide (CO₂) emissions.

The study used a dynamic panel quantile regression to investigate the conditional distribution of CO₂ emissions along the turn-points of urbanization, economic growth, renewable energy, trade and the regressors via quadratic modeling specifications.

The main findings are established as follows. There is strong evidence of the Kuznets curve in the nexus between urbanization, economic growth, renewable energy, trade and CO₂ emissions, respectively. Second, urbanization thresholds that should not be exceeded for sustainability to reduce CO₂ emissions are 0.21%, and 2.70% for the 20th and 75th quantiles of the CO₂ emissions distribution. Third, growth thresholds of 3.64%, 3.84%, 4.01%, 4.36% and 5.87% across the quantiles of the CO₂ emissions distribution. Fourth, energy thresholds of 3.64%, 3.61%, 3.70%, 4.02% and 4.34% across the quantiles of the CO₂ emissions distribution. Fifth, trade thresholds of 3.37% and 4.47% for the 20th and median quantiles of the CO₂ emissions distribution, respectively.

The empirical shreds of evidence offer policy implications in such that building sustainable development and environment requires maintaining the critical mass, not beyond those insightful thresholds to achieving sustainable development and environmentally friendly SSA countries.

Sustainable cities and communities in an era of economic recovery path COVID-19 mitigate greenhouse gas. The policy relevance is of particular concern to the sustainable development goals.

The study is novel considering the extant literature by providing policymakers with avoidable thresholds for policy formulations and implementations in the nexus between urbanization, economic growth, renewable energy and trade openness.

This study aims to analyse the relationship between health human capital and economic growth for a maximum sample of 92 countries over the period 1980-2010 taking into account countries’ heterogeneity by assessing how health variables affect different countries according to their position on the conditional growth distribution.

The paper estimates a growth regression applying the methodology proposed by Canay (2011) for regression by quantiles (Koenker, 1978, 2004, 2012a, 2012b) in a panel framework. Quantile regression analysis allows us to identify the growth determinants that present a non-linear relationship with growth and determine the policy implications specifically for underperforming versus over achieving countries in terms of output growth.

The authors’ findings indicate that better health is positively and robustly related to growth at all quantiles, but the quantitative importance of the respective coefficients differs across quantiles, in some cases, with the sign of the relationship greater for countries that recorded lower growth rates. These results apply to both positive (life expectancy) and negative (infant mortality rate, undernourishment) health status indicators.

Given the predominantly public nature of health funding, cuts in health expenditure should be carefully balanced even in times of public finances sustainability problems, particularly when growth slowdowns, as a decrease in the stock of health human capital could be particularly harmful for growth in under achievers. Additionally, the most effective interventions seem to be those affecting early childhood development that should receive from policymakers the necessary attention and resources.

This study contributes to the existing literature by answering the question of whether the growth effects of health human capital can differ in sign and/or magnitude depending on a country’s growth performance. The findings may help policymakers to design the most adequate growth promoting policies according to the behaviour of output growth.

The study investigates the impact of bank market competition and concentration on the bank default-risk using the data for 36 Indian scheduled commercial banks from 1999 to 2017.

The study adopts the dynamic panel generalised method of moments (GMM) and panel quantile models to obtain the results.

Bank market competition and concentration foster financial fragility in terms of high default-risk. This implies that concentration does not mean a lack of competition in the Indian banking market. The findings from the quantile model reveal that the stated relationships become weaker under the tails of the conditional distribution of the risk measure.

The authors recommend that non-structural measures (Lerner index and H-statistic) should be preferred over the concentration measures (HHI and CR3) to characterise bank market competition in India. Based on the evidence of persistence in the bank risk variable, from the methodological perspective, dynamic panel data models are better choices for bank-level analyses compared to the conventional panel data models.

To improve the health of banks, price competition should be reduced among them. This objective should be achieved by creating new avenues to increase the banks' non-interest income parallelly with the consolidation of the market.

First, it tries to answer whether concentration implies a lack of competition for a banking system like India. Second, the quantile regression technique enables us to understand the varying nature of the impact of market competition on bank risk at different locations on the latter's conditional distribution. Earlier studies have not looked at these aspects in the Indian context.

This paper aims to investigate how information and communication technology (ICT) services and digital finance affect the development of international tourism.

The two-way fixed effect panel regression model, spatial econometric model, panel threshold regression model and panel quantile regression model are used. Data on tourism, economic and social development in 198 Chinese cities from 2011 to 2020 are analyzed.

This study finds that digital economy including ICT services and digital finance has significantly promoted the development of international tourism industry, while there is a negative spatial spillover effect. The promotion effect of international tourism increases significantly after digital innovation reaches the threshold value. International tourism is benefiting more from digital economy with the development of international tourism industry.

The development quality of international tourism industry has not been analyzed due to data limitations, and the mechanism has not been tested.

This study creatively reveals the development of international tourism industry in the digital economy era from ICT services and digital finance perspectives. This study also shows the spatial, nonlinear and asymmetric relationship between digital economy and international tourism.