Quantiles Of Distribution Research Articles

Quantiles of the gain distribution of an early childhood intervention

Quantiles of the gain distribution of an early childhood intervention

The role of non‐base compensation in explaining the motherhood wage gap: Evidence from Italy

AbstractThis paper underlines the importance of accounting for non‐base compensation in explaining the motherhood wage gap. We consider two alternative measures of hourly wage using Italian EU‐SILC data from 2007 to 2019: the base‐wage and the full‐wage. The former refers to the contractual base wage, while the latter includes performance‐based bonuses, productivity bonuses, commissions, pay incentives, and other extra payments. We address the endogeneity issues of motherhood and examine the effect of motherhood status across various quantiles of the wage distribution for the two hourly wage measures. Empirical findings provide evidence of a motherhood base‐wage premium, which becomes nonsignificant when using the full‐wage measure, suggesting that non‐base compensation is a source of inequality for mothers. These findings are consistent across the wage distribution. Exploring potential heterogeneity across macro‐regions and periods, we find no notable regional disparities except minor distinctions for the Southern regions, alongside a decline in the base‐wage premium over time and the emergence of a full‐wage penalty in recent years. A comparative analysis with a sample of men reveals that fathers enjoy a premium with both wage measures. Nevertheless, fatherhood is also associated with reduced extra remunerations, yet to a lesser extent than motherhood.

Impact of oil price, economic globalization, and inflation on economic output: Evidence from Latin American oil-producing countries using the quantile-on-quantile approach

This research examines the impact of oil prices, economic globalization, and inflation on the economic output in oil-producing countries in Latin America. We employ advanced time series techniques to achieve precision in findings and reliability of policy inferences. We use cointegration techniques with and without structural breaks because oil prices are highly volatile, and the economies analyzed are unstable in the face of political and social changes. Additionally, we performed a sensitivity analysis using standard quantile and the newly developed quantile-on-quantile regressions to measure the impacts of the covariates on real per capita output. Finally, we use causality Fourier techniques to identify the direction of causal relationships between the series. We found that the oil price, economic globalization, inflation, and output have a long-term equilibrium relationship in the presence of structural breaks. Likewise, quantile models show that the impacts of inflation, economic globalization, and oil prices on economic output are extensively non-linear across the quantile distribution. We found a negative connection between inflation and economic output in the surveyed territories. We propose that those responsible for energy policy in the countries studied should rethink crude oil policies to maximize the benefits of oil exploitation under stagnant globalization and growing inflation.

Study on agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution

Agricultural water resource utilization efficiency in China is facing significant challenges due to the dual constraints of carbon emissions and water pollution. The inefficiency in water usage in agriculture not only impacts the sustainability of water resources but also contributes to environmental degradation through increased carbon emissions and water pollution. Agricultural water resource utilization efficiency under the constraint of carbon emission and water pollution has been a critical issue in China from 2005 to 2022. This study employs the Quantile Autoregressive Distributed Lag (QARDL) method to comprehensively assess and analyze the complex relationship that exists between agricultural water usage, carbon emissions, and water pollution. By analyzing distinct quantiles of the data distribution, the research investigates how different levels of water resource utilization efficiency relate to carbon emissions and water pollution under various conditions. The findings reveal nuanced insights into the dynamic interactions among these components within the agricultural sector. This research project focuses on the efficiency of water resource utilization in agriculture while considering the constraints of carbon emission and water pollution. Given the dynamic and time-dependent character of these components, the QARDL methodology makes it possible to get a detailed knowledge of how they interact within the framework of agriculture. The study aims to give significant insights and policy suggestions to improve agricultural practices while minimizing environmental concerns linked to carbon emissions and water pollution.

A Nonlinear Quantile Regression for Accelerated Destructive Degradation Testing Data

Traditional regression approaches to accelerated destructive degradation test (ADDT) data have modeled the mean curve as representative. However, maximum likelihood estimates (MLEs) of the mean model are likely to be biased when the data are non-Gaussian or highly skewed. The median model can be an alternative for skewed degradation data. In this work, we introduce a nonlinear quantile regression (QR) approach for estimating quantile curves of ADDT data. We propose an iterative QR algorithm that uses the generalized expectation-maximization (GEM) framework to estimate the parameters of the nonlinear QR ADDT model, based on the asymmetric Laplace distribution to accommodate non-Gaussian and skewed errors. Using the asymptotic properties of the QR parameter estimates, we estimate variance-covariance matrix for the τ th QR parameters using order statistics and bootstrap methods. We propose a new prediction method of the quantile of the failure-time distribution in the normal use condition. Confidence intervals for the quantiles of the failure-time distribution are constructed using the parametric bootstrap method. The proposed model is illustrated using an industrial application and compared with the existing model. Various quantile curve estimates derived using the QR ADDT model provide a more flexible modeling framework than the traditional mean ADDT modeling approach.

Heterogeneity effect of prudential regulation on the stability of banks: evidence from WAEMU banks using quantile regression with fixed effects

Purpose This paper aims to analyze the heterogeneous effect of prudential regulation on the stability of banks in the West African Economic and Monetary Union (WAEMU). Design/methodology/approach The author uses in this study individual bank data from balance sheets, income statements of banks in the WAEMU space and annual reports of the banking commission formed into a three-year panel from the period 2017 to 2019. First, this study uses hierarchical clustering based on specific banking characteristics to determine whether the WAEMU region’s banking markets are heterogeneous or not. Second, this study uses quantile regression approach with fixed effects to explore how that prudential regulation affects the conditional distribution of WAEMU bank stability. Findings The analysis reveals heterogeneity resulting in two distinct groups. Using the quantile regression approach, this study demonstrates that prudential regulation has a significantly more substantial and positive effect on the upper quantiles than on the lower quantiles of the conditional distribution of WAEMU bank stability. Furthermore, the effect of banking regulation also varies among pan-African cross-border banks, national banks and foreign banks. Among these types of banks, pan-African cross-border banks remain the most stable by adopting prudential regulation. The results remain robust and vary across different WAEMU countries. Originality/value The contribution of this study to the literature is multifaceted. First, this study uses individual bank-level constituted in panel data from the WAEMU region to assess the effect of prudential regulation on the stability of the WAEMU’s banking sector. This approach allows for a more granular analysis as this study considers individual regional banks’ specific characteristics and behaviors. Second, this study considers the heterogeneous effect of regulation on the stability of banks within the WAEMU space. This means that this study acknowledges that not all banks are affected similarly by prudential regulations, and this research aims to identify and quantify these differences.

The tail risk of crude oil Price_Based on EPU and geopolitical risk perspective

This study investigates the conditional prediction of crude oil price growth rates as a function of geopolitical risk (GPR) and economic policy uncertainty (EPU), characterizing the extreme tail risk characteristics. Utilizing monthly data on crude oil futures price growth rates from 1997 Jan to 2022 Dec, we employ Quantile Regression and Growth at Risk models to capture the heterogeneous and asymmetric effects of GPR and EPU across different quantiles of the oil price distribution. The sample period covers major geopolitical events, economic recessions, and the emergence of new energy sources, providing a comprehensive view of the factors influencing crude oil market dynamics.The empirical findings reveal significant asymmetries in the forecast density function of crude oil price growth rates, with periodic right-skewed deviations during financial crises and epidemic situations, and prevalent left-skewed deviations during the remaining sample periods. The impact of EPU on crude oil prices is found to be more pronounced in the short to medium term, while the influence of GPR persists over a longer duration, exacerbating the upside risk of price growth. The study highlights the effectiveness of EPU and GPR in capturing the tail characteristics of crude oil price growth, offering valuable risk identification indicators for policymakers and market participants.Furthermore, the analysis uncovers the heterogeneous effects of EPU and GPR across different quantiles of the oil price distribution. The results indicate that the relationship between these uncertainty measures and crude oil prices varies depending on the market conditions and the specific quantile examined. This finding underscores the importance of considering the entire distribution of crude oil prices rather than focusing solely on the conditional mean.The study contributes to the existing literature by providing a comprehensive examination of the role of geopolitical risk and economic policy uncertainty in driving crude oil price dynamics, with a particular focus on tail risks. The findings have important implications for risk management strategies in the energy sector.By employing advanced econometric techniques and considering a wide range of geopolitical and economic factors, this study offers new insights into the complex and evolving nature of crude oil price dynamics. The results highlight the need for policymakers and market participants to consider the asymmetric and heterogeneous effects of uncertainty measures when assessing the risks and opportunities in the crude oil market.

Innovation for a greener future: Disentangling the asymmetric impact of green finance on green technology in european union economies

Green finance has originated as a critical factor for green development, where green technology innovation is a primary approach. Yet, limited information is available regarding how green finance influences green technological creation. This work probes the asymmetric linkage between green finance and green technology innovation in the selected European Union countries (Germany, Sweden, France, Italy, Netherlands, Spain, Denmark, Norway, Belgium, and Finland). Prior works adopted panel data methods to get usual outcomes concerning the green finance-green technology nexus, nevertheless the reality is that various nations did not establish such connection separately. The present work, in contrast, utilizes a peculiar tool, 'Quantile-on-Quantile,' that facilitates research to probe temporal reliance in entire nations by giving global yet economy-specific intuitions on the variables’ correlation. Estimates exhibit that green finance enhances green technology innovation in almost all our chosen nations at definite quantiles of data distribution. Furthermore, the findings expose that the extent of asymmetry throughout various quantiles of our variables differs by the economy, emphasising the relevance of policymakers paying particular consideration while employing green innovation and green finance policies.

Flood frequency analysis using mean daily flows vs. instantaneous peak flows

Abstract. In many cases, flood frequency analysis (FFA) needs to be carried out on mean daily flows (MDF) instead of instantaneous peak flows (IPF), which can lead to underestimation of design flows. Typically, correction methods are applied to the MDF data to account for such underestimation. In this study, we first analyse the error distribution of MDF-derived flood quantiles over 648 catchments in Germany. The results show that using MDF instead of IPF data can lead to underestimation of the mean annual peak flow (MHQ) by up to 80 % and mainly depends on the catchment area but appears to be influenced by gauge elevation as well. This relationship is shown to differ for summer vs. winter floods. To correct such underestimation, different linear models based on predictors derived from MDF hydrograph and catchment characteristics are investigated. Apart from the catchment area, a key predictor in these models is the event-based ratio of flood peak to flood volume (p/V ratio) obtained by the MDF data. The p/V models applied to either MDF-derived events or statistics seem to outperform other reference correction methods. Moreover, they require a minimum data input, are easily applied, and are valid for the entire study area. The best results are achieved when the L moments of the MDF maximum annual series are corrected with the proposed model, which reduces the flood quantile errors by up to 60 %. The approach behaves particularly well in smaller catchments (<500 km2), where reference methods fall short. However, the limit of the proposed approach is reached for catchment sizes under 100 km2, where the hydrograph information from the daily series is no longer capable of approximating instantaneous flood dynamics and gauge elevations below 100 m, where the difference between MDF and IPF floods is very small.

THE IMPACT OF DIGITAL TRANSFORMATION ON ENTREPRENEURIAL ACTIVITY. EMPIRICAL EVIDENCE FROM THE EUROPEAN UNION

The paper explores the impact of digital transformation (DT) on new business creation in the case of European Union member states over the period 2015–2020 by employing several econometric techniques such as Ordinary Least Squares (OLS) and quantile regression (QR). The results of OLS regression indicate a positive and significant link between DT and entrepreneurial activity. However, the quantile regression results highlight a parameter heterogeneity in the effect of DT on entrepreneurial activity. Furthermore, the magnitude of the impact is greater at the higher size quantiles of the new business density distribution. Overall, the empirical findings highlight the key role of DT in developing public policies aimed to spur entrepreneurial activity. Thus, this paper brings significant contributions to the extant literature on the macro determinants of firm creation.

Global evidence of multi-dimensional asymmetric effect of energy storage innovations on environmental quality: Delineating the role of natural resources, nuclear energy and oil consumption

The strive to lower reliance on fossil fuels and transition to clean energy sources necessitates innovations in energy storage. This study empirically investigates the effectiveness of energy storage innovations towards a greener environment on a global scale. To this end, we use quarterly frequency time series data on a global scale over the period 2000 to 2020 and the estimation techniques based on the nonparametric multivariate quantile on quantile regression (MQQR), multivariate quantile regression (MQR), and Kernel-based regularized least squares (KRLS). The empirical results reveal that energy storage innovation and nuclear energy are strongly related to a decline in carbon dioxide emissions (CO2e) but natural resources and oil consumption intensify the level of CO2e across quantiles. However, the effects of energy storage innovation, nuclear energy, natural resources, and oil consumption are heterogeneous leading to an asymmetric pattern across quantile distribution. The policy implication of this study is that, on a global scale, energy storage innovation and nuclear energy provide opportunities for attaining a greener environment and more environmentally sustainable future but natural resources and oil consumption impede policies toward a sustainable environment. Therefore, our study recommends, among other things, an aggressive boost of energy storage system and nuclear energy while decreasing reliance on fossil fuels.

Econometric Model Using Arbitrage Pricing Theory and Quantile Regression to Estimate the Risk Factors Driving Crude Oil Returns

This work presents a novel approach to determining the risk and return of crude oil stocks by employing Arbitrage Pricing Theory and Quantile Regression. Arbitrage Pricing Theory identifies the risk factors likely to impact crude oil returns. Subsequently, Quantile Regression estimates the relationship between the selected factors and the returns across different distribution quantiles. The West Texas Intermediate (WTI) crude oil price is used in this study as a benchmark for crude oil prices. WTI’s price fluctuations can significantly impact the performance of global crude oil stocks and, subsequently, the global economy. Various statistical measures are used in this study to determine the proposed model's stability. The results show that changes in WTI returns can have varying effects depending on market conditions and levels of volatility. This study emphasizes the influence of structural discontinuities on returns. These are likely generated by changes in the global economy and the unpredictable demand for crude oil during the pandemic. The inclusion of pandemic, geopolitical, and inflation-related explanatory variables adds uniqueness to the study as it considers current global events that can affect crude oil returns. Findings show that the key factors that pose significant risks to returns are industrial production, inflation, the global price of energy, the shape of the yield curve, and global economic policy uncertainty. This implies that while making investment decisions in WTI futures, investors should pay particular attention to these elements.

The dilemma of water, food, and greener energy nexus: A novel context of COP27 for G20 economies

AbstractIn the contemporary world, achieving sustainable food production has become an urgent task for the international community and policymakers due to the rapidly growing social challenges of mankind. Sustainable food production practices aid countries in adapting to the challenges posed by climate change, thereby ensuring a better and more sustainable future for all. This study examines the impact of land use, energy efficiency (ENE), water productivity (WP), renewable energy consumption (REC), and gross domestic product (GDP) on sustainable food production in G20 nations over the period of 1998–2020. We use quantile regression approaches to capture potential heterogeneity across various food value‐added distribution quantiles. The results show that arable land, WP, GDP, ENE, and REC are important factors affecting food value added in G20 nations. However, the nature of the relationship varies across different quantiles, suggesting heterogeneity in the relationships. The results show that ENE, renewable energies, and GDP are positively related to food production. However, arable land and WP are negatively related to food production. The findings can assist policymakers and stakeholders in making informed decisions to increase value added in the agricultural sector while promoting resilience and sustainability.

Characterising information gains and losses when collecting multiple epidemic model outputs

BackgroundCollaborative comparisons and combinations of epidemic models are used as policy-relevant evidence during epidemic outbreaks. In the process of collecting multiple model projections, such collaborations may gain or lose relevant information. Typically, modellers contribute a probabilistic summary at each time-step. We compared this to directly collecting simulated trajectories. We aimed to explore information on key epidemic quantities; ensemble uncertainty; and performance against data, investigating potential to continuously gain information from a single cross-sectional collection of model results. MethodsWe compared projections from the European COVID-19 Scenario Modelling Hub. Five teams modelled incidence in Belgium, the Netherlands, and Spain. We compared July 2022 projections by incidence, peaks, and cumulative totals. We created a probabilistic ensemble drawn from all trajectories, and compared to ensembles from a median across each model’s quantiles, or a linear opinion pool. We measured the predictive accuracy of individual trajectories against observations, using this in a weighted ensemble. We repeated this sequentially against increasing weeks of observed data. We evaluated these ensembles to reflect performance with varying observed data. ResultsBy collecting modelled trajectories, we showed policy-relevant epidemic characteristics. Trajectories contained a right-skewed distribution well represented by an ensemble of trajectories or a linear opinion pool, but not models’ quantile intervals. Ensembles weighted by performance typically retained the range of plausible incidence over time, and in some cases narrowed this by excluding some epidemic shapes. ConclusionsWe observed several information gains from collecting modelled trajectories rather than quantile distributions, including potential for continuously updated information from a single model collection. The value of information gains and losses may vary with each collaborative effort’s aims, depending on the needs of projection users. Understanding the differing information potential of methods to collect model projections can support the accuracy, sustainability, and communication of collaborative infectious disease modelling efforts.

Agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation: A comprehensive quantile analysis

This paper investigates the asymmetric inter-linkages and causality between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in four main agricultural countries i.e., China, the United States, Japan and Canada, by employing monthly data from 1990 to 2019. Accordingly, Quantile-on-Quantile (QQ) regression and two causality-in-quantile approaches are applied to conduct a comprehensive quantile analysis of the asymmetric relationship for all quantiles of the above distribution. Our results exhibit a positive association between agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation in China, the United States and Canada. Besides, the strength of the positive association depends largely on the development level of agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation. However, except for the positive impact between fertilizer consumption and agricultural greenhouse gas emissions, I find a negative nexus between agricultural greenhouse gas emissions and technological innovation in Japan. Compared with the other three sample countries, Japan has done the best in agricultural greenhouse gas emission mitigation. The results also demonstrate that agricultural greenhouse gas emissions, fertilizer consumption, and technological innovation follow a bidirectional quantile-causality relation in all sample countries. Overall, I find that fertilizer consumption does increase agricultural greenhouse gas emissions, and that technological innovation has not played a full role in mitigating greenhouse gas emissions in most countries. Finally, our findings have significant implications for formulating reasonable emission reduction measures in the agricultural sector.

Exploring the determinants of methane emissions from a worldwide perspective using panel data and machine learning analyses

This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO2 emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change.

Public–private wage differentials in Tunisia: Consistency and decomposition

AbstractIn this paper, we estimate and decompose the public–private wage differentials for urban areas, using the 2012 Tunisia urban youth survey. Oaxaca decomposition results suggest that, on average, public sector workers earn more than their private counterparts. Additionally, the results indicate that a substantial part of the conditional gap in urban areas can be attributed to observed characteristics. Human capital, particularly education, are the main reason behind the observed log‐wage advantages. Using unconditional quantile decomposition, our findings reveal that, for urban areas, the discrimination effect becomes more pronounced at the upper quantiles of the wage distribution. Separate analyses by gender and educational levels show that male workers across both sectors receive higher compensation than their female counterparts, with a more pronounced gender gap in private sector. Less educated workers are compensated much more in the public sector than in the private sector, while the wage differential for skilled workers decreases rapidly through the distribution.

Quantile ratio regression

We introduce quantile ratio regression. Our proposed model assumes that the ratio of two arbitrary quantiles of a continuous response distribution is a function of a linear predictor. Thanks to basic quantile properties, estimation can be carried out on the scale of either the response or the link function. The advantage of using the latter becomes tangible when implementing fast optimizers for linear regression in the presence of large datasets. We show the theoretical properties of the estimator and derive an efficient method to obtain standard errors. The good performance and merit of our methods are illustrated by means of a simulation study and a real data analysis; where we investigate income inequality in the European Union (EU) using data from a sample of about two million households. We find a significant association between inequality, as measured by quantile ratios, and certain macroeconomic indicators; and we identify countries with outlying income inequality relative to the rest of the EU. An R implementation of the proposed methods is freely available.

Institutional quality, FDI inflows, human capital development and poverty: a case of Indonesia

PurposeThe objective of this paper is to investigate the impact of institutional quality, foreign direct investment (FDI) inflows and human capital development on Indonesia’s poverty rate.Design/methodology/approachThe quantile regression on data ranging from 1984 to 2019 was used to capture the relationship between the impact of the independent variables (FDI inflows, institutional quality and human capital development) on Indonesia’s poverty rate at different quantiles of the conditional distribution.FindingsThe empirical results reveal that low-quantile institutional quality is detrimental to poverty eradication, whereas FDI inflows and human capital development are significant at higher quantiles of distribution. This implies that higher-value FDI and advanced human capital development are critical to lifting Indonesians out of poverty.Practical implicationsPolicymakers should prioritise strategies that advance human capital development, create an enticing investment climate that attracts high-value investments and improve institutional quality levels.Originality/valueThis study contributes to the existing literature because, compared to previous studies that focussed on estimating the conditional mean of the explanatory variable on the poverty rate. It rather provides a more comprehensive understanding of the quantiles of interest of FDI inflows and institutional quality on the Indonesian poverty rate, allowing for more targeted policies.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2023-0733

Smoking effects on labor income: new evidence for Brazil

PurposeThis study aims to investigate the effect of smoking on the income of workers in the Brazilian labor market.Design/methodology/approachUsing data from the 2019 National Health Survey (PNS), we initially address the sample selection bias concerning labor market participation by using the Heckman (1979) method. Subsequently, the decomposition of income between smokers and nonsmokers is analyzed, both on average and across the earnings distribution by employing the procedure of Firpo, Fortin, and Lemieux (2009) - FFL decomposition. Ñopo (2008) technique is also used to obtain more robust estimates.FindingsOverall, the findings indicate an income penalty for smokers in the Brazilian labor market across both the average and all quantiles of the income distribution. Notably, the most significant differentials and income penalties against smokers are observed in the lower quantiles of the distribution. Conversely, in the higher quantiles, there is a tendency toward a smaller magnitude of this gap, with limited evidence of an income penalty associated with this habit.Research limitations/implicationsThis study presents an important limitation, which refers to a restriction of the PNS (2019), which does not provide information about some subjective factors that also tend to influence the levels of labor income, such as the level of effort and specific ability of each worker, whether smokers or not, something that could also, in some way, be related to some latent individual predisposition that would influence the choice of smoking.Originality/valueThe relevance of the present study is clear in identifying the heterogeneity of the income gap in favor of nonsmokers, as in the lower quantiles there was a greater magnitude of differentials against smokers and a greater incidence of unexplained penalties in the income of these workers, while in the higher quantiles, there was low magnitude of the differentials and little evidence that there is a penalty in earnings since the worker is a smoker.