Quantile Regression Research Articles

Determinants of poor health-related quality of life among outpatients with rheumatoid arthritis in Jordan.

The purpose of this study was to assess the health-related quality of life (HRQOL) and investigate the variables contributing to reduced HRQOL in patients with rheumatoid arthritis. The present cross-sectional study was conducted on patients diagnosed with rheumatoid arthritis at two teaching hospitals in Jordan using a convenience sampling technique. The participants were interviewed face-to-face during the scheduled appointment at the outpatient rheumatology clinic. The HRQOL was evaluated by the validated EuroQol-5 Dimension (EQ-5D) questionnaire, which included the EQ-5D utility index that evaluated HRQOL in terms of 5 domains, including mobility, self-care, usual activities, pain/discomfort, and anxiety/depression, and the EQ-5D visual analogue scale (EQ-5DVAS), which evaluated HRQOL on a vertical scale ranging from 0 (worst imaginable health) to 100 (best imaginable health). The validated short version of the 19-item Compliance Questionnaire for Rheumatology (CQR-5) was used to evaluate medication adherence. The Clinical Disease Activity Index (CDAI) was used to assess disease activity among the study participants. A stepwise quantile regression model (q = 0.5) was conducted to explore the factors associated with the EQ-5DUtility Index and EQ-5DVAS scores. In total, 261 patients with RA participated in the study. The median (interquartile range) of the EQ-5DUtility Index and EQ-VAS scores was 0.552 (0.006-0.726) and 0.506 (0.233-0.690), respectively. Regression analysis results demonstrated that medication non-adherence (regression coefficient (β) = -0.348, P<0.01), not performing regular physical activity (β = -0.209, P<0.01), and higher disease activity as measured by the CDAI score (β = -0.015, P<0.01) were significant predictors of a lower EQ-5DUtility Index score. In addition, medication non-adherence (β = -0.199, P<0.01), not performing regular physical activity (β = -0.117, P<0.01), increased body mass index (BMI) (β = -0.009, P<0.01), and higher CDAI score (β = -0.009, P<0.01) were significant predictors of low EQ-5DVAS score. Patients with RA experienced significantly impaired HRQOL. Medication non-adherence, not performing regular physical activity, increased body weight, and increased disease activity were identified as determinants of poor HRQOL among patients with RA in the present study. Treating physicians should encourage regular physical activity, maintaining a healthy body weight, and controlling disease activity to improve HRQOL in patients with RA.

Towards environmental disclosures: The role of intellectual capital

AbstractThe aim of our study is to investigate the impact of intellectual capital (IC) and its components such as human capital, structural capital, and capital employed on the environmental disclosures (ED) of a company. The IC has been measured using value‐added intellectual coefficient (VAIC) and alternate value‐added intellectual coefficient (A_VAIC). The study employs method of moments quantile regression (MM‐QR) approach to examine the heterogeneous effects of ED within firms by extracting relevant data from ProwessIQ and Bloomberg databases. The findings of the study indicate that the relationship between ED and IC is concave implying that IC initially has a significant positive impact on ED but beyond a point, it produces negative impact. Further, it is noticed that IC's impact increases as we move from lower (25th) to higher quantile (75th), indicating that low values of IC have low impact on ED, whereas high values of IC have greater impact on ED. This is the first study to examine the impact of IC on ED. It also adds to the scarce literature on the new statistical technique of method of moments quantile regression.

Deciphering the complex interplay: Heterogeneous, threshold, and mediation effects of trade openness on CO2 emissions in Africa.

Despite having barely anything to do with the issue of CO2 emissions, Africa has been experiencing more severe climate change and its adverse effects than most other regions of the globe. However, the issue of CO2 emissions and its adverse effects has received relatively little attention in the African research arena. To this end, the present research assesses the effect of trade openness on the CO2 emissions utilizing panel data from 46 African countries spanning 2000 through 2022. To account for the possible heterogeneity and nonlinearity, the panel quantile regression and threshold methods were employed. Moreover, this study investigates the key mediating effects of the channel. The empirical findings show that greater trade openness is associated with significantly higher CO2 emission, additionally; it demonstrates that the influence is heterogeneous across different CO2 emission quantiles in African countries. Besides the result from the double threshold model reveals a complex, nonlinear relationship between trade openness and CO2 emissions in Africa. Moreover, the findings divulge that openness to trade indirectly reduces CO2 emissions through the substitution and technology channels whereas it indirectly increases carbon dioxide production via the economic track. Therefore, it is vital to promote the use of renewable energy, effectively leverage the knowledge spillover effects of trade to decrease energy intensity and formulate pertinent policies aimed at curbing carbon emissions and addressing the imminent threat of climate change in Africa. Besides, the nonlinear and heterogeneous effects of trade openness on CO2 emissions suggest that policies and interventions related to the impact of trade openness on CO2 emissions should consider the current level of carbon dioxide emissions.

Fiber Microstructure Quantile (FMQ) Regression: A Novel Statistical Approach for Analyzing White Matter Bundles from Periphery to Core.

The structural connections of the brain's white matter are critical for brain function. Diffusion MRI tractography enables the in-vivo reconstruction of white matter fiber bundles and the study of their relationship to covariates of interest, such as neurobehavioral or clinical factors. In this work, we introduce Fiber Microstructure Quantile (FMQ) Regression, a new statistical approach for studying the association between white matter fiber bundles and scalar factors (e.g., cognitive scores). Our approach analyzes tissue microstructure measures based on quantile-specific bundle regions . These regions are defined according to the quantiles of fractional anisotropy (FA) from the periphery to the core of a population fiber bundle, which pools all individuals' bundles. To investigate how fiber bundle tissue microstructure relates to covariates of interest, we employ the statistical technique of quantile regression. Unlike ordinary regression, which only models a conditional mean, quantile regression models the conditional quantiles of a response variable. This enables the proposed analysis, where a quantile regression is fitted for each quantile-specific bundle region. To demonstrate FMQ Regression, we perform an illustrative study in a large healthy young adult tractography dataset derived from the Human Connectome Project-Young Adult (HCP-YA), focusing on particular bundles expected to relate to particular aspects of cognition and motor function. Importantly, our analysis considers sex-specific effects in brain-behavior associations. In comparison with a traditional method, Automated Fiber Quantification (AFQ), which enables FA analysis in regions defined along the trajectory of a bundle, our results suggest that FMQ Regression is much more powerful for detecting brain-behavior associations. Importantly, FMQ Regression finds significant brain-behavior associations in multiple bundles, including findings unique to males or to females. In both males and females, language performance is significantly associated with FA in the left arcuate fasciculus, with stronger associations in the bundle's periphery. In males only, memory performance is significantly associated with FA in the left uncinate fasciculus, particularly in intermediate regions of the bundle. In females only, motor performance is significantly associated with FA in the left and right corticospinal tracts, with a slightly lower relationship at the bundle periphery and a slightly higher relationship toward the bundle core. No significant relationships are found between executive function and cingulum bundle FA. Our study demonstrates that FMQ Regression is a powerful statistical approach that can provide insight into associations from bundle periphery to bundle core. Our results also identify several brain-behavior relationships unique to males or to females, highlighting the importance of considering sex differences in future research.

Variational Bayesian EM Algorithm for Quantile Regression in Linear Mixed Effects Models

This paper extends the normal-beta prime (NBP) prior to Bayesian quantile regression in linear mixed effects models and conducts Bayesian variable selection for the fixed effects of the model. The choice of hyperparameters in the NBP prior is crucial, and we employed the Variational Bayesian Expectation–Maximization (VBEM) for model estimation and variable selection. The Gibbs sampling algorithm is a commonly used Bayesian method, and it can also be combined with the EM algorithm, denoted as GBEM. The results from our simulation and real data analysis demonstrate that both the VBEM and GBEM algorithms provide robust estimates for the hyperparameters in the NBP prior, reflecting the sparsity level of the true model. The VBEM and GBEM algorithms exhibit comparable accuracy and can effectively select important explanatory variables. The VBEM algorithm stands out in terms of computational efficiency, significantly reducing the time and resource consumption in the Bayesian analysis of high-dimensional, longitudinal data.

The association between dietary flavonoid intake and frailty risk among middle-aged and elderly US adults: A nationally representative study

Given the global ageing population, exploring potential dietary prevention strategies to reduce the public health burden of frailty is critically important. Dietary flavonoids offer multiple health benefits; however, there is a scarcity of research evidence regarding their association with the risk of frailty in middle-aged and older adults. This study aimed to assess the association between dietary flavonoid intake and frailty risk among middle-aged and older U.S. adults and to analyse the potential roles of different flavonoid subclasses. This study involved 9063 participants aged ≥40 years. Frailty was evaluated using a frailty index, and flavonoid intake was estimated through two 24-h dietary recalls. Analyses were carried out via multivariable logistic regression, restricted cubic spline models, weighted quantile sum regression, and quantile g-computation models. Additionally, subgroup analyses and interaction tests were conducted for age and gender. The results indicated that higher intakes of total flavonoids and subclasses (anthocyanidins, flavan-3-ols, flavanones, flavones, flavonols) were significantly associated with a lower prevalence of frailty, but isoflavone intake was not associated with frailty. A nonlinear, L-shaped dose-response relationship was observed between total flavonoids and frailty risk. Flavonols and flavanones were identified as the main contributors to the overall effect. Age influenced the association between dietary flavonoids and frailty, with middle-aged adults benefiting more than older adults. This study provides new evidence supporting an inverse association between dietary flavonoid intake and frailty risk, especially for foods rich in flavonols and flavanones.

Retraction Note: Does foreign direct investments impair the ecological footprint? New evidence from the panel quantile regression.

Retraction Note: Does foreign direct investments impair the ecological footprint? New evidence from the panel quantile regression.

Days at Home After Traumatic Brain Injury: Moving Beyond Mortality to Evaluate Patient-Centered Outcomes Using Population Health Data.

Administrative data are invaluable for assessing outcomes at the population level. However, there are few validated patient-centered outcome measures that capture morbidity following traumatic brain injury (TBI) using these data. We sought to characterize and validate days at home (DAH) as a measure to quantify population-level outcomes after moderate to severe TBI. We additionally assessed the earliest feasible outcome assessment period for patients with TBI using this outcome measure. This multicenter retrospective cohort study used linked health administrative data sources to identify adults with moderate to severe TBI presenting to trauma centers in Ontario, Canada, between 2009 and 2021. DAH at 180 days (DAH180 days) reflects the total number of days spent alive and at home excluding the days spent institutionalized across care settings. Construct validity was determined using hierarchical quantile regression to assess the associations between clinical and injury covariates with DAH180 days. Predictive validity was assessed using Spearman rank correlation. We estimated minimally important difference (MID) in DAH180 days to aid with outcome measure interpretability. There were 6,340 patients who met inclusion criteria. Median DAH180 days was 70 days (interquartile range 0-144). Mortality occurred in 2,162 (34.1%) patients within 90 days following injury. Patients in the lower DAH180 days group were more commonly older (absolute standardized difference [ASD] = 0.68) with higher preinjury health resource utilization (ASD = 0.36) and greater injury severity (ASD = 0.81). Increased baseline health resource utilization (-10.1 days, 95% CI -17.4 to -2.8, p = 0.0041), older age (-4.6 days, 95% CI -5.7 to -3.4, p < 0.001), higher cranial injury severity (-84.6 days, 95% CI -98.3 to -71.0, p < 0.001), and major extracranial injuries (-14.2 days, 95% CI -19.5 to -8.93, p < 0.001) were significantly associated with fewer DAH180 days. DAH180 days was positively correlated with DAH at up to 3 years (r = 0.91, 95% CI 0.90-0.92) and negatively correlated with direct health care expenditure (rs = -0.89, 95% CI -0.88 to -0.90). The average MID estimated from anchor-based and distribution-based methods was 18 days. We validate DAH180 days as a potentially useful outcome measure with construct, predictive, and face validity in a population with moderate to severe TBI. Given the intensity of acute care requirements for patients with TBI, our work highlights DAH180 days as a feasible and sufficiently responsive outcome measure.

Peer effects of health behaviors and the moderating role of Internet use among middle-aged and older adults: a nationally representative cross-sectional survey in China.

This study investigates peer effects on the health behaviors of middle-aged and older adult people in China and examines the moderating role of Internet use. A total of 16,188 respondents were selected from the China Health and Retirement Longitudinal Study (CHARLS) 2020 data set. Ordinary Least Squares and Quantile Regression were used to validate the peer effect of health behaviors on middle-aged and older adults, and a moderating effects model was used to test the moderating roles of Internet use and Internet proficiency. Finally, the peer effect was analyzed for heterogeneity according to Internet use preferences. The peer effect had a positive influence on the individual health behaviors of middle-aged and older adult people in China (β = 0.5341, p < 0.001). Middle-aged and older adult people were more sensitive to the average health behavior level of the community when they lived in rural communities and/or had a low level of education. Internet use positively moderated the health behavior peer effect in the older adult population (β = 0.0094, p < 0.1), and Internet proficiency negatively moderated the peer effect of the health behaviors of the middle-aged population (β = 0.1589, p < 0.1). According to the magnitude of the influence of peer effect, the Internet preference type can be ranked from social and entertainment (β = 0.6250, p < 0.01), to cultural and entertainment (β = 0.5835, p < 0.01), to comprehensive (β = 0.4622, p < 0.01). There is a peer effect on the health behaviors of middle-aged and old-aged individuals, in which Internet use plays a moderating role. The construction of healthy communities should be promoted, giving full play to the community's active role in health promotion. Attention should be paid to the health-enabling function of the Internet, encouraging middle-aged and older adult people to use the Internet actively, moderately, and diversely, and making full use of the advantages of short videos for online health education.

The Daydream Spectrum: The Role of Emotional Dysregulation, Internalized Stigma and Self-Esteem in Maladaptive Daydreaming Among Adults With ADHD, ASD, and Double Diagnosis.

Maladaptive daydreaming (MD) is characterized by vivid, excessive fantasy activity that becomes prioritized over other activities and may result in avoidant coping strategies. Little is known about the relationship between MD and internalized stigma in the neurodivergent sample. The current study aimed to examine emotional dysregulation, escapism and self-perception (self-esteem and internalized stigma) as potential determinants of MD in three groups: adults on the autism spectrum, with ADHD, and both diagnoses ("AuDHD"), including their neurodivergent symptoms (autistic traits, empathizing, ADHD symptoms). A sample of 293 persons (139 with ADHD, 74 on the autism spectrum, and 80 with both diagnoses) completed self-report scales concerning ADHD symptoms, autism spectrum symptoms, empathizing, self-esteem, daydreaming as escapism, emotional dysregulation, maladaptive daydreaming, and internalized stigma. A correlation analysis was implemented. Additionally, a group comparison and quantile regression for the 25th, 50th, and 75th percentile of maladaptive daydreaming in the three groups were executed. The results showed that internalized stigma, emotional dysregulation, escapism, and self-esteem have significant associations with MD in the neurodiverse sample. The ADHD group achieved the lowest scores in emotional dysregulation, discrimination experience, and social withdrawal. The groups did not vary in MD rates, stereotype endorsement, stigma resistance, nonacceptance of emotional responses, and impulse control difficulties. The AuDHD group achieved higher results than the ASD group only in the ADHD symptoms. Quantile regression revealed differences in the predictors of maladaptive daydreaming in the three groups. ADHD symptoms and self-suppression escapism were significant predictors for the higher levels of MD in persons with singular autism or ADHD diagnosis. The results showed no differences in MD rates, although there were significant variations in predictive features among the three samples. The role of internalized stigma and self-esteem was highlighted in the results as significant associates of MD prevalence. These outcomes expand the current knowledge in the context of maladaptive daydreaming, internalized stigma, self-esteem and emotional dysregulation in a neurodiverse sample and allow for proposing clinical applications and further research directions.

Marketing inputs and outcome heterogeneity: Using a quantile regression framework in the entertainment industry

AbstractThe success of entertainment products such as movies or books varies tremendously, and managers strive to increase the odds by deciding on the right marketing input. Aiming to improve managerial decision making, we suggest and test a quantile regression framework to detect outcome heterogeneity effects of marketing inputs in the entertainment industry. By analyzing the spread of the .9 and the .1 conditioned quantile to the .5 (median) conditioned quantile, we study how much an increase (decrease) of an input factor (star power and quality) changes the spread of the expected outcome (revenues and sales). The spread serves as an indicator for the heterogeneity effect of the input factor regarding the outcome. In two empirical studies, we show how marketing instruments increase (or decrease) outcome heterogeneity by estimating quantile regressions and provide generalizable findings regarding the outcome heterogeneity effects of star power (increases outcome heterogeneity) and quality evaluations (reduces outcome heterogeneity) in the entertainment industry.

Correlation of dietary flavonoid intake with chronic bronchitis, emphysema, and asthma in U.S. adults: A large, national, cross-sectional study.

To explore the relationship between dietary flavonoids and bronchitis, emphysema and asthma. A total of 11743 United States adults were extracted from the National Health and Nutrition Examination Survey (NHANES) in 2007-2008, 2009-2010 and 2017-2018. Of these, 47.7% were male and 52.3% female. Dietary flavonoid intake assessed using FDNNS and 24-hour dietary recall data. Inclusion of demographics (gender, age, education, family income), behavioral factors (BMI, smoking, drinking status, diet), chronic disease information (diabetes, hypertension) as covariates to eliminate confounding. Stepwise logistic regression was used to analyze the association between total dietary flavonoid intake and the risk of chronic respiratory disease. Weighted quantile sum regression (WQS) was used to analyze the association between 29 dietary flavonoids and the risk of chronic respiratory disease. Restricted cubic spline was used to analyze the dose-response relationship between dietary flavonoid intake and risk of chronic respiratory disease. Stepwise logistic regression results showed that higher flavonoid intake in men was associated with a lower risk of CB and asthma (OR of CB: 0.55(0.31-0.97); OR of asthma: 0.72(0.52-0.99)), and WQS results showed a mixed health effect for total flavonoids and chronic respiratory tract in response to the 29 flavonoid fractions (OR of asthma: 0.97(0.94-0.99); OR of emphysema: 0.95(0.90-0.99)). Glycitein had the highest health contribution of 26.2% for emphysema; Eriodictyol had the highest health contribution of 32.13% for asthma, respectively. The RCS showed a dose-response relationship between flavonoids and respiratory tract health. The maximum dose for ingesting flavonoids to gain respiratory health benefits is 1500 mg/d. Higher dietary flavonoid intake was associated with lower chronic respiratory risk in adult U.S. men. Also 29 dietary flavonoid components have an overall health effect on respiratory health. Glycitein and Eriodictyol may have potential health effects on the respiratory system. 1500 mg/day may be the Tolerable Upper Intake Level of dietary flavonoids for respiratory health in U.S. adults.

Quantile and expectile copula-based hidden Markov regression models for the analysis of the cryptocurrency market

The role of cryptocurrencies within the financial systems has been expanding rapidly in recent years among investors and institutions. It is therefore crucial to investigate this phenomenon and develop statistical methods able to capture their interrelationships, the links with other global systems and, at the same time, the serial heterogeneity. Here we introduce hidden Markov regression models for jointly estimating quantiles and expectiles of cryptocurrency returns using regime-switching copulas. The proposed approach allows us to focus on extreme returns and describe their temporal evolution by introducing time-dependent coefficients, evolving according to a latent Markov chain. Moreover to model their time-varying dependence structure, we consider elliptical copula functions defined by state-specific parameters. Maximum likelihood estimates are obtained via an expectation-maximization algorithm. The empirical analysis investigates the relationship between daily returns of five cryptocurrencies and major world market indices.

The Impact of Green Trade Openness on Air Quality

Environmental degradation is among the most pressing issues the world faces today. Air pollution is one of the many forms of environmental degradation and can drastically impact human health and ecosystem functioning. Research shows that rapid intervention strategies are required to achieve the environmental targets set out by international agreements. One strategy that has been widely accepted to combat this issue of environmental degradation is the introduction of green products. The adoption of green products can be increased through trade. However, knowledge of the impact of trading these products is limited. This study examines the relationship between the trading of green products and air quality in a case study of 33 Organization for Economic Cooperation and Development (OECD) member countries from 2003-2016. We employ several panel strategies, such as the Westerlund (2008) Durbin-H method for cointegration and the Method of Moments Quantile Regression (MM-QR). The trading of green products was proxied by the newly developed ‘Green Openness Index.’ Nitrous oxide (NOx) and sulfur oxide (SOx) emissions served as proxies for air pollution. The relationship between the variables was explored in the context of the Environmental Kuznets Curve (EKC). The cointegration analysis reveals that there is a long-run relationship among the series. Long-run estimations prove that the EKC hypothesis is valid for the analyzed series. Additionally, the empirical findings reveal that trading environmentally friendly products increases air quality while energy consumption decreases. We provide several policy suggestions based on the study’s outcomes, such as supporting the trade liberalization of green goods, reducing tariff and non-tariff barriers for environmentally friendly products, and promoting the adoption of environmentally friendly goods by providing subsidies and other tax incentives to consumers.

Worldwide Recessions and Herding Behaviour: A Comparative Analysis of Three Countries

The general suggestion that behavioral science plays a part in creating abnormalities within the financial sector has been studied and proposed many times in the past. This study aims to prove the existence of behavioral sciences, specifically herding behavior, in three countries with different market conditions: Indonesia (Emerging), Vietnam (Frontier), and the United States (Developed). We developed our methodology using quantile regression to study the existence of herding behavior, and our findings were as follows: (1) As expected, the US didn’t have any indication of a statistically significant herding presence; they do, however, indicate an insignificant presence of herding behavior in the post-covid period under bearish conditions (2) Vietnam does not indicate significant herding tendencies, (3) Surprisingly, Indonesia did not exhibit statistically significant herding presence, but both Indonesia and Vietnam exhibited the slight presence of herding behavior but still relatively insignificant.

Trends in the quality of maternal and neonatal care in Sweden and Norway as compared to 12 WHO European countries: A cross-sectional survey investigating maternal perspectives during the COVID-19 pandemic.

Maternal-neonatal healthcare services were severely disrupted during the COVID-19 pandemic in even high-income countries within the World Health Organization (WHO) European Region. The objective of this study was to compare trends in the quality of maternal and neonatal care (QMNC) in Sweden and Norway to 12 other countries from the WHO European Region during the COVID-19 pandemic, and to identify domains for improvement. This cross-sectional study included women giving birth in Europe from March 1, 2020 to December 31, 2022. Women answered an online, anonymous questionnaire which included 40 WHO Standard-based Quality Measures collectively scored as the total QMNC index (0-400) and separately in four subdomains (0-100): provision of care, experience of care, availability of human and physical resources, and reorganizational changes due to COVID-19. To assess reported QMNC changes over time, we used adjusted quantile regression models. gov Identifier: NCT04847336. Of the 45151 women included in the study, 13 117 (29.1%) were from Sweden and Norway and 32034 (70.9%) from the 12 WHO European countries. The total QMNC index for Sweden and Norway (median: 325, IQR: 285-355) was higher than the 12 WHO European countries (median: 315, IQR: 265-350, p < 0.001) as were trends in QMNC index over time (Sweden and Norway median: 310-345; 12 WHO European countries median: 305-340). Sweden and Norway also had higher scores in three-of-four QMNC subdomains, with the 12 WHO European countries scoring higher only for reorganizational changes due to COVID-19. In adjusted quantile models of the total QMNC index, Sweden and Norway had higher scores, with largest differences in the lower quantiles (p < 0.001 in all percentiles). Across Europe, there are significant gaps in the quality of maternal-neonatal healthcare services. Although women giving birth in Sweden and Norway reported higher QMNC scores in all subdomains except for "reorganizational changes due to COVID-19," there is room for improvement and shared learning across Europe. Policymakers should prioritize long-term investments in maternal and neonatal healthcare, ensuring that facilities are adequately equipped during public health crises and that all women have access to high-quality, evidence-based, equitable, and respectful care.

Adaptive Bi-Level Variable Selection for Quantile Regression Models with a Diverging Number of Covariates

The paper develops an innovatively adaptive bi-level variable selection methodology for quantile regression models with a diverging number of covariates. Traditional variable selection techniques in quantile regression, such as the lasso and group lasso techniques, offer solutions predominantly for either individual variable selection or group-level selection, but not for both simultaneously. To address this limitation, we introduce an adaptive group bridge approach for quantile regression, to simultaneously select variables at both the group and within-group variable levels. The proposed method offers several notable advantages. Firstly, it adeptly handles the heterogeneous and/or skewed data inherent to quantile regression. Secondly, it is capable of handling quantile regression models where the number of parameters grows with the sample size. Thirdly, via employing an ingeniously designed penalty function, our method surpasses traditional group bridge estimation techniques in identifying important within-group variables with high precision. Fourthly, it exhibits the oracle group selection property, implying that the relevant variables at both the group and within-group levels can be identified with a probability converging to one. Several numerical studies corroborated our theoretical results.

The Impact of Financial Inclusion on Household Incomes in China: An Empirical Study

Financial inclusion is considered one of the important determinants for improving the incomes of households. This research analysis is to estimate the impact of financial inclusion on household incomes in China. The data was obtained from China Household Financial Survey (CHFS) for the year 2017. This paper measures financial inclusion through its four dimensions, namely formal account, formal saving, formal credit, and formal insurance, based on the definition of financial inclusion by the World Bank, 2017. Three different econometric techniques, namely Ordinary Least Squares, Quantile Regressions, and Instrumental variable methods, are used to empirically test the influence of inclusive finance on income in China. The instrumental-variable method was used to cope with the problem of endogeneity. The results of this research showed a significant relationship between households’ incomes and formal accounts, formal savings, formal credit, and formal insurance in China. This study found that the main driver of household income is financial inclusion, as these two are positively correlated. The main determinants of financial inclusion are significant variables for households’ incomes in China. Thus, this study concluded that financial inclusion helps to reduce income inequality. Hence, this research work recommended that policymakers in China should devise such policies to further improve the status of financial inclusion, ensuring a reduction in income inequalities.

How neighbourhood effects vary by achievement level

Abstract After long-standing calls for research into ‘for whom’ neighbourhood matters, the literature has recently gained traction, focussing on background characteristics such as gender, race, and socioeconomic background. However, there is still scarce and only indirect evidence on whether neighbourhood effects vary by children’s academic ability. In this paper, we combine the Neighbourhood Choice Model with quantile regressions to investigate whether neighbourhood effects vary by children’s academic proneness. Using Norwegian register data, we demonstrate heterogeneity in the effects of neighbourhood deprivation as a function of students’ proneness to academic achievement. Students with the very lowest and highest academic potential are the least affected by neighbourhood disadvantage, while those with below average—but not at the lowest end—are most strongly affected. Moreover, while girls are less affected than boys, and children from less educated and affluent families are more affected by neighbourhood disadvantage than their more privileged peers, the effects of these observed moderators are considerably smaller than the heterogeneity observed as a function of academic proneness. Overall, our use of quantile regressions opens up new venues for understanding ‘for whom’ neighbourhoods matter, with individual-level academic vulnerability and strengths being an influential source of neighbourhood effects variation.

Role of industrial growth on economic development: A comparative analysis of developed and developing countries

Industrialization has played a pivotal role in shaping economic transformation, influencing growth patterns globally across different stages of development. This study investigates the role of industrialization on economic development by controlling labor force, capital formation, school enerollment, and institutional quality. The panel data of 62 developing and 96 developed countries are collected from World Bank from 1990 to 2023. The empirical results are estimated by using the Panel Quantile Regression (PQR), and its robustness is checked through the Method of Moverment-Quantile Regression (MM-QR) approach. The empirical results found the U-shaped relationship between industrialization and economic development in both samples. The labor force significantly increases economic development in developed countries while declines in developing countries. Gross capital formation and preprimary education also significantly enhance economic development. The institutional quality surges economic development in developed countries and declines in developing countries. The MM-QR approach also shows similar results, proving the PQR approach's robustness. This study recommends that all countries should focus on industrial diversification, improving infrastructure and energy access, strengthening institutions, building a skilled workforce, and ensuring environmental sustainability.