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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 chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

This paper studies the estimation of quantile regression panel duration models. We allow for the possibility of endogenous covariates and correlated individual effects in the quantile regression models. We propose a quantile regression approach for panel duration models under conditionally independent censoring. The procedure involves minimizing ℓ1 convex objective functions and is motivated by a martingale property associated with survival data in models with endogenous covariates. We carry out a series of Monte Carlo simulations to investigate the small sample performance of the proposed approach in comparison with other existing methods. An empirical application of the method to the analysis of the effect of unemployment insurance on unemployment duration illustrates the approach.

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.

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 general purpose of the paper is to examine the effect of financial globalization on income inequality. The specific purposes are: 1) To examine the effect of overall financial globalization on income inequality. 2) To analyze whether de facto and de jure financial globalization have differential effects on income inequality. 3) To scrutinize whether the effect of financial globalization on income inequality varies across countries of different income groups and quantiles of income inequality.

The authors employed panel quantile regression using 73 countries over 2000–2016 to examine the effect of financial globalization on income inequality. The authors employed fixed effect and panel quantile regressions and classified the countries into income groups to compare differential effects of financial globalization across different income groups. Further, the authors unbundled financial globalization into de facto and de jure financial globalizations to investigate whether their effects on income inequality vary.

Overall financial globalization raises income inequality more at lower quantiles of inequality. De jure financial globalization reduces income inequality in high-income countries. In high-income countries, de jure financial globalization has more favorable income distribution at lower quantiles of inequality. In contrast, de facto financial globalization raises inequality regardless of income classification of the countries.

To the best of the authors’ knowledge, the authors for the first time employed panel quantile regression to analyze whether financial globalization affects income inequality across different quantiles. In addition to de facto globalization, the authors used the newly developed de jure financial globalization index to examine its impact on income inequality. The de jure dimension is largely neglected in the literature. The authors provide empirical evidence on how the different dimensions of financial globalization, de facto and de jure, impact inequality in high-income, middle-income and low-income countries.

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.

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.

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

This paper aims to investigate empirically whether Islamic securities enhance economic growth in the Southeast Asian region based on the endogenous growth theory using the non-parametric analysis.

This paper applies panel quantile regression with Markov chain Monte Carlo optimization as an optimal non-parametric approach to investigate the effect of Islamic securities on economic growth starting from 2013Q4 to 2019Q4 in Southeast Asia. Total issued Islamic securities holdings are employed as a measure for Islamic securities, while the gross domestic product is employed as a proxy for economic growth. The sample includes all working Islamic financial foundations in the top progressive Islamic securities markets' countries of Southeast Asia (Malaysia, Indonesia and Brunei Darussalam).

The findings confirm that the increase of issuing Islamic securities in Islamic capital markets of Southeast Asia is increasing the levels of economic growth, reflecting the weighty role of the Islamic capital market development as an active contributor to economic growth.

This research would fill the literature gap by exploring Islamic securities–economic growth nexus in Southeast Asia using a robust non-parametric approach based on the endogenous growth theory for better estimation results. The findings of this review serve as a roadmap for financial analysts, policymakers and decision makers to stimulate the Islamic securities markets as another source of finance which can promote the economic growth.

This research is the first that investigates empirically the Islamic securities–economic growth nexus in Southeast Asia using a new empirical investigation built on the non-parametric analysis and outlined within the theoretical context of the endogenous growth model to gain robust evidence about this nexus.