Marginal Effect Research Articles

How ‘Inclusion’ Scales: From Marginal Effects to Systems Analysis in the Practice of Development

How ‘Inclusion’ Scales: From Marginal Effects to Systems Analysis in the Practice of Development

How ‘Inclusion’ Scales: From Marginal Effects to Systems Analysis in the Practice of Development

How ‘Inclusion’ Scales: From Marginal Effects to Systems Analysis in the Practice of Development

Assessing Marginal Effects of Non-Pharmaceutical Interventions on the Transmission of SARS-CoV-2 across Africa: A Hybrid Modeling Study.

Since 2019, a new strain of coronavirus has challenged global health systems. Due its fragile healthcare systems, Africa was predicted to be the most affected continent. However, past experiences of African countries with epidemics and other factors, including actions taken by governments, have contributed to reducing the spread of SARS-CoV-2. This study aims to assess the marginal impact of non-pharmaceutical interventions in fifteen African countries during the pre-vaccination period. To describe the transmission dynamics and control of SARS-CoV-2 spread, an extended time-dependent SEIR model was used. The transmission rate of each infectious stage was obtained using a logistic model with NPI intensity as a covariate. The results revealed that the effects of NPIs varied between countries. Overall, restrictive measures related to assembly had, in most countries, the largest reducing effects on the pre-symptomatic and mild transmission, while the transmission by severe individuals is influenced by privacy measures (more than 10%). Countries should develop efficient alternatives to assembly restrictions to preserve the economic sector. This involves, e.g. training in digital tools and strengthening digital infrastructures.

Analyzing the Suitability of Vehicle Telematics Data as a Surrogate Safety Measure for Short-Term Crashes

Traffic safety analysis relies heavily on the comprehensiveness of traffic crash data. However, crash occurrences are rare events. Researchers have sought alternative measures closely linked to crash occurrences, known as surrogate safety measures, to address this challenge. The advent of vehicle telematics has introduced a valuable source of data, consisting of time-stamped positional information of vehicles, facilitated through telecommunication networks. Wejo Data Services Inc. collects vehicle telematics data through collaboration with various automotive original equipment manufacturers (OEMs) to furnish connected vehicle data along with driver event variables, such as braking and acceleration. This study seeks to investigate the relationship between hard-braking events and the incidence of total crashes and peak period crashes across different facility types using analysis of variance and negative binomial models. The results show a strong correlation between hard braking and crash frequency. The ratio of hard-braking events to crashes exhibits notable variations across peak hours, off-peak periods, and nighttime. Further, the coefficient of hard braking is significantly higher on freeways compared with non-freeways. The model performance of different short period models improved better than the extent of improvement in total crash model after hard braking was introduced as an explanatory variable. The marginal effects of hard braking on crash frequency on freeways are consistently higher than those in non-freeway segments.

Investigation of the performance of a newly designed solar chimney for enhancing natural ventilation and mitigation of summer overheating

The significant challenge encountered in utilizing Trombe walls for winter heating is summer overheating, which limits the overall feasibility of the system. In response to this issue, the present study introduces a novel model of a Trombe wall-solar chimney system designed to enhance natural ventilation and address the problem of summer overheating. To assess the performance of the proposed model, a full-scale experimental test rig is constructed in Kirkuk, Iraq. This rig comprises a test room integrated with a Trombe wall-solar chimney system. Additionally, a numerical simulation based on the CFD approach along with a computer code is developed to investigate the behavior of the system. System analysis is conducted for the period August 14th–15th. The parameters studied include chimney height (ranging from 0.0m to 2.0m in 0.5m increments), chimney width (0.2m, 0.3m, 0.4m, and 0.5m), room inlet vent height (ranging from 0.2m to 0.4m with 0.05m increments), and chimney inlet vent height (ranging from 0.2m to 0.4m with 0.05m increments). The results highlight the crucial role of the massive storage wall in sustaining nighttime ventilation in the absence of solar energy by utilizing stored heat to warm the air and maintain the ventilation process. The study establishes that chimney height and width significantly influence airflow rate, while other factors have marginal effects. An empirical equation is formulated to predict the 24-h averaged air changes per hour (ACH), which is a critical measure of system performance in ventilation. This equation is developed using artificial neural network (ANN) techniques. Finally, the findings indicate that the suggested model effectively reduces summertime overheating while generating a satisfactory ventilation rate. This enhances the overall versatility and year-round usability of Trombe wall systems, making them more adaptive and efficient in regions with diverse climatic conditions.

Exploring the relationship between condition severity and health-related quality of life in people with haemophilia A across Europe: a multivariable analysis of data from the CHESS II study

BackgroundHaemophilia A (HA; Factor VIII deficiency) is a congenital X-linked bleeding disorder characterized by trauma-related or spontaneous bleeding events, most notably arising within the intraarticular space and resulting in chronic inflammation and degeneration of affected joints. Endogenous clotting factor activity relative to normal levels determines the severity of HA symptoms, as mild (> 5–40%), moderate (1–5%), or severe (< 1%). Within the current environment of rapid evolution in HA management, we seek to understand the interplay of condition severity and health-related quality of life (HRQoL) to characterise and differentiate unmet needs among people with HA (PwHA).MethodsA generalised linear regression model (GLM) was developed to explore the relationship between HA severity and EQ-5D-5 L index score from adult HA patients sampled in the “Cost of Haemophilia across Europe – a Socioeconomic Survey II” (CHESS II) cross-sectional, retrospective burden of illness study among adults with hereditary haemophilia A or B from eight European countries. HA patients of any severity with no active inhibitors during the 12 months prior to data capture and a completeEQ-5D-5 L response were included. A base GLM model was specified with covariates for demographic and clinical characteristics (age, body mass index, country, employment, HA severity, annual bleeding rate, problem joints, and chronic pain).ResultsOf 381 evaluable patients, 221 (58.0%) had severe HA, 96 (25.2%) had moderate HA, and 64 (16.8%) had mild HA. Among the covariates included in the GLM model and after controlling for haemophilia-related outcomes, a significant association was observed between mild HA and higher EQ-5D-5 L index score (average marginal effects, 0.084; p = 0.016) relative to severe HA. Patient country of residence and magnitude of HA-related chronic pain were also associated with significant differences in index scores, with the latter showing a negative relationship with HRQoL outcomes.ConclusionsCondition severity and chronic pain are significant predictors of HRQoL in PwHA. Durable bleeding protection and effective management of chronic pain have the potential to address unmet treatment needs in this population.

Impact of a regulatory target and external factors on the waste efficiency of Italian municipalities.

Due to increasing consumption and urbanisation, urban waste management and recycling are a primary concern in Italy. Italian waste collection underwent significant reform with the introduction of a sorted collection target of 65% of total collected waste in Legislative Decree No. 152/2006. In this article, we analyse the effect of this regulatory target on the efficiency of waste collection in 275 Italian municipalities in the years 2016-2019. We estimate the coefficients of the cost efficiencies of the sorted and unsorted waste without assuming functional forms for the efficient frontier or the distribution of efficiency. Our findings suggest that municipalities that met the 65% sorted waste target demonstrated higher efficiency as costs increased, whereas those that failed to meet the target demonstrated higher inefficiency as costs increased. Strong effects emerged for population and urban economic development on the success of waste collection, whereas only marginal effects were observed for population density and city size. To improve the situation of municipalities that are not meeting the 65% target, we propose several policy measures, including 'neighbourhood solidarity'.

The Effect of Net Interest Margin (NIM), Operating Expenses, Operating Income (BOPO), Loan to Deposit Ratio (LDR), on Return on Assets (ROA) at PT Bank Mayapada Internasional Tbk for the Period of 2014-2023

The Effect of Net Interest Margin (NIM), Operating Expenses, Operating Income (BOPO), Loan to Deposit Ratio (LDR), on Return on Assets (ROA) at PT Bank Mayapada Internasional Tbk for the Period of 2014-2023

Incident Psychotropic Medication Use Among US Commercially Insured Children and Adolescents from 2019 to 2022.

Objective: To compare the proportion of children and adolescents with incident psychotropic medication use from 2019 through 2022. Methods: This cross-sectional study used the IQVIA PharMetrics® Plus for Academics health plan claims database. Our study sample consisted of children and adolescents ages 6-18 who had at least one psychotropic medication in March 2019-February 2022. We examined psychotropic medication use in three distinct study periods: pre-pandemic (March 2019 to February 2020), pandemic-year-1 (March 2020-February 2021), and pandemic-year-2 (March 2021-February 2022). Incident use was defined as no evidence of psychotropic medication in the 12 months preceding the child and adolescent's first psychotropic dispensing in each study period. We estimated incident psychotropic use in the three study periods. Average marginal effects tested for significant differences in psychotropic initiation, overall and stratified by age and sex. Results: In our sample of 42,346 children and adolescents who were dispensed any psychotropic medication during the study period, incident psychotropic users were 27.8% in pre-pandemic, 26.0% in pandemic-year-1, and 27.8% in pandemic-year-2. Incident use of antidepressants was 51.4% in pandemic-year-1 and 54.6% in pandemic-year-2. The probability of incident psychotropic use was 2.4% lower in pandemic-year-1 than in the pre-pandemic year (p < 0.001). The proportion of 6-11-year-olds and females initiating a psychotropic was higher in pandemic-year-2 than pre-pandemic. Conclusion: Incident psychotropic use was most notable in younger and female children 2 years after the pandemic onset.

Tipping point analysis for the between-arm correlation in an arm-based evidence synthesis

Systematic reviews and meta-analyses are essential tools in contemporary evidence-based medicine, synthesizing evidence from various sources to better inform clinical decision-making. However, the conclusions from different meta-analyses on the same topic can be discrepant, which has raised concerns about their reliability. One reason is that the result of a meta-analysis is sensitive to factors such as study inclusion/exclusion criteria and model assumptions. The arm-based meta-analysis model is growing in importance due to its advantage of including single-arm studies and historical controls with estimation efficiency and its flexibility in drawing conclusions with both marginal and conditional effect measures. Despite its benefits, the inference may heavily depend on the heterogeneity parameters that reflect design and model assumptions. This article aims to evaluate the robustness of meta-analyses using the arm-based model within a Bayesian framework. Specifically, we develop a tipping point analysis of the between-arm correlation parameter to assess the robustness of meta-analysis results. Additionally, we introduce some visualization tools to intuitively display its impact on meta-analysis results. We demonstrate the application of these tools in three real-world meta-analyses, one of which includes single-arm studies.

Are gaps in rates of retention on buprenorphine for treatment of opioid use disorder closing among veterans across different races and ethnicities? A retrospective cohort study

IntroductionThe U.S. Veterans Health Administration has undertaken several initiatives to improve veterans' access to and retention on buprenorphine because it prevents overdose and reduces drug-related morbidity. We aimed to determine whether improvements in retention duration over time was equitable across veterans of different races and ethnicities. MethodsThis retrospective cohort study was conducted among veterans who initiated buprenorphine from federal fiscal years (FY) 2006 to 2020 after diagnosis of opioid use disorder. Using an accelerated failure time model, we estimated the association between time to buprenorphine discontinuation and FY of initiation, race and ethnicity, and other control covariates. We followed veterans from buprenorphine initiation until they discontinued or had a censoring event. We then estimated the predicted median days retained on buprenorphine, the average marginal effect of initiating in a later FY, the same measure by race and ethnicity, the incremental effect of the various racial and ethnic identities in contrast to non-Hispanic White, and the total change in the size of the gap over the 15 years of the study between veterans with a minoritized racial or ethnic identity compared to non-Hispanic White veterans. ResultsMost of the 31,797 veterans in the sample were non-Hispanic White (74.5 %), from urban areas (83.5 %), male (92.0 %), and had significant comorbidities, most frequently anxiety disorders (51.0 %) and depression (63.0 %). Overall, 49.8 % of veterans were retained at least 180 days. The average marginal effect of FY was 7.0 days [95%CI:5.3, 8.8] but was significantly smaller among veterans identifying as Black or African American [3.2 days, 95%CI:2.4, 4.1] or Asian [3.6 days, 95%CI:1.6, 5.7] compared to veterans who identify as non-Hispanic White [7.9 days, 95%CI:5.9, 9.9]. Additional measures of change were significant for veterans identifying as Hispanic White or with two or more races. ConclusionAlthough buprenorphine retention in OUD treatment improved for all veterans over the 15-year study period, veterans from most minoritized racial and ethnic groups fell further behind as gains in duration on therapy accrued primarily to non-Hispanic White veterans. Targeted interventions addressing specific challenges experienced by veterans with minoritized identities are needed to close gaps in retention on buprenorphine.

Average treatment effects on binary outcomes with stochastic covariates.

When evaluating the effect of psychological treatments on a dichotomous outcome variable in a randomized controlled trial (RCT), covariate adjustment using logistic regression models is often applied. In the presence of covariates, average marginal effects (AMEs) are often preferred over odds ratios, as AMEs yield a clearer substantive and causal interpretation. However, standard error computation of AMEs neglects sampling-based uncertainty (i.e., covariate values are assumed to be fixed over repeated sampling), which leads to underestimation of AME standard errors in other generalized linear models (e.g., Poisson regression). In this paper, we present and compare approaches allowing for stochastic (i.e., randomly sampled) covariates in models for binary outcomes. In a simulation study, we investigated the quality of the AME and stochastic-covariate approaches focusing on statistical inference in finite samples. Our results indicate that the fixed-covariate approach provides reliable results only if there is no heterogeneity in interindividual treatment effects (i.e., presence of treatment-covariate interactions), while the stochastic-covariate approaches are preferable in all other simulated conditions. We provide an illustrative example from clinical psychology investigating the effect of a cognitive bias modification training on post-traumatic stress disorder while accounting for patients' anxiety using an RCT.

Why leave items in the shopping cart? The impact of consumer filtering behavior

Online product information provides crucial cues for consumer shopping behavior; however, the impact of consumer-side information manipulation on non-purchase behavior (e.g., shopping cart abandonment) remains unclear. Filtering, a common manipulation strategy, was initially considered synonymous with searching, both of which contribute to consumer purchase behavior. However, recent research suggests significant differences, particularly in terms of cost and product matching. This study explores how such differences affect information manipulation and subsequently shape shopping cart abandonment, drawing on information foraging theory. Based on the premise that a forager's interaction with the environment is mediated by cognition, we find that consumer filtering behavior influences perceptions of serendipity (i.e., discovering unexpected items) and similarity (i.e., encountering similar items), thereby influencing shopping cart abandonment. Mediation analyses reveal that perceived serendipity and similarity are equally potent mediators. Marginal effects analyses demonstrate that a one standard deviation increase in consumer perceptions of serendipity (similarity) in filtering results increases the likelihood of shopping cart abandonment by 32.2% (22.1%). Theoretical contributions and practical insights for information behavior researchers and digital marketers are also provided.

Laboratory evaluation of performances and thermo-oxidative aging characteristics of high-viscosity modified asphalt

High construction temperatures and complex application conditions make the high-viscosity modified asphalt (HVMA) susceptible to aging. This work presents a comprehensive investigation of the performances and thermo-oxidative aging characteristics of HVMA. Fistly, an instant high-viscosity modifier (HVM) was synthesized and then optimized by entropy weight method. The modification mechanism and high- and low-temperature performances of HVMAs were investigated by the Fourier transform infrared spectroscopy (FTIR), temperature sweep, and low temperature creep tests. Besides, thermo-oxidative aging characteristics were evaluated by the frequency sweep, multiple stress creep recovery (MSCR) and fluorescence microscopy (FM) tests. Results shows that the recommended formulation of instant HVMA (HVMA-I) was 12 % instant HVM and 0.2 % stabilizer. Grafting and cross-linking reactions involved in HVMA-I, thus ensuring the excellent high- and low-temperature performances. The thermo-oxidative aging of HVMA consisted of the modifier degradation and the binder oxidation. Short-term aging produced a marginal effect as a fluctuating change direction for complex modulus. Long-term aging had pronounced effects on the performances of HVMA. At the macroscopic level, the complex modulus was increased and the nonrecoverable creep compliance was decreased; at the microscopic level, the fluorescence area was reduced and phase network was degraded.

Two are Better Than One but Three is Best: Fast-Tracking the Attainment of the Sustainable Development Goals (SDGs) Among In-School Adolescents in Nigeria

Abstract Background With 60% of Africa’s population under the age of 25 years, investing in youth will accelerate progress towards achieving the Sustainable Development Goals (SDGs). The United Nations concept of ‘Development Accelerators’ is operationalized by examining factors associated with multiple SDGs among adolescents in Nigeria. Methodology Through data collected on the health of 1800 adolescents in Southwest Nigeria (Ibadan), ten SDG targets were identified. Accelerator protective factors that could lead to the attainment of two or more SDG targets were identified. Associations were assessed using multivariable logistic regression adjusting for sociodemographic covariates and multiple testing. Cumulative effects were tested by marginal effects modelling. Results Participants’ mean age was 15.02 years (SD = 2.27). Family (parenting support), environmental (no survival work and food security) and school (safe schools) related factors emerged as development accelerators. For seven of the identified SDG-aligned targets, a combination of two or more accelerator factors showed cumulative positive associations, suggesting accelerator synergies with a combination of three accelerators showing the greatest increase. For instance, perceiving the school environment as safe, being food secure and having optimal mental health was associated with an 67% chance of not using psychoactive substances. While with safe school alone it was 39% (29%-51%), with food security alone it was 42% (31%-54%) and with optimal mental health alone it was 54% (42%-66%). Conclusion Several development accelerators related to family, environmental and school factors can have additive effects on the attainment of the SDGs amongst adolescents in Nigeria. This has practical and policy utility in the fastest growing economy in Africa.

Followers beat content: social media and the managers in initial coin offerings (ICOs)

AbstractOur research investigates the role of social media communication in amplifying high-quality information and its impact on the success of ICOs in achieving their soft cap. We analyzed data from 3,644 ICOs and the demographics of 1,987 CEOs, CFOs, and CTOs to compare their quality attributes against their number of social media followers. Our findings reveal that the most significant factors for reaching the soft cap are the number of followers and team size, while the competencies (education and skills) of the management team have a very marginal effect, even when enhanced through social media. This indicates that widespread social media signals can positively influence investor behavior without necessarily reducing information asymmetries regarding the quality of the team. We propose that this effect arises from the combination of minimal investment amounts and stimulated herding behavior among investors.

Beyond the gender binary: a survey of gender marginalization and social boundaries in Australian jazz and improvisation

Jazz and improvisation have typically been associated with ideals of freedom and liberty; however, in practice these genres are known to be constrained by entrenched patterns of male domination and gender discrimination. Despite a large number of qualitative accounts evidencing persistent sexism and gender exclusion in the field, there exists a lack of empirical data to assess the scale of this phenomenon and substantiate smaller-scale research on gender inequality. In this paper, we employ boundary theory to report on a quantitative investigation of gender marginalization in jazz and improvisation in the Australian context, positioning gender as a symbolic boundary resulting in the social exclusion and marginalization of gender diverse individuals and women. An anonymous survey (n=124) was run over a period of five months, to explore the beliefs, attitudes, and experiences concerning gender, of people participating in Australian jazz and improvisation. A means comparison found that gender was a statistically significant indicator (p ≤0.05) on almost all measures, with gender diverse respondents significantly more likely to report the effects of marginalization than their (cisgender) counterparts. Additionally, the results indicated contrasting forms of musical engagement and marginalization across gender groups, with women perceiving exclusion to a lesser extent than gender diverse practitioners, and differing in their opinions regarding work opportunities. Lastly, a widespread but historically unspoken awareness of sexual harassment in the Australian jazz and improvisation industry was reported by all genders. This paper concludes with three recommendations for future research, policy and practice: 1. Specific targeted strategies are needed to address the manifold and complex forms of marginalization experienced by gender diverse people; 2. Heightened institutional visibility for marginalized groups is needed to change gendered narratives and highlight awareness of inequities; and 3. Enhanced safety measures are critically needed to address sexual harassment throughout the industry.

Exploring the Supporting Role of Finance in the Development of Clean Energy in China Based on the Panel Vector Autoregressive Model

The efficient development and widespread utilization of clean energy hold global significance, particularly for developing countries like China, which has committed to carbon peak and neutrality targets. In this context, the financial sector plays a crucial role in supporting the renewable energy industry, ensuring a reliable energy supply for economic growth. To statistically assess the impact of financial characteristics—such as financial efficiency, financial size, and green finance—this paper employs a panel vector autoregressive (PVAR) model with province-level data from China spanning the period 1991 to 2018. The key findings demonstrate that (1) financial factors significantly contribute to the development of clean energy in China, and among these factors, financial scale has a greater impact than financial efficiency and green finance; (2) there are distinct regional variations in how financial development affects the clean energy sector, and the role of financial scale is particularly pronounced in the central and western regions of China while the impact of financial efficiency on the clean energy industry is not significant across all regions; and (3) other drivers—including industrial structure, financial expenditure, and technological advancements—also spur the growth of the clean energy industry. However, due to diminishing marginal effects, the forces driving its growth may gradually diminish. Therefore, the article proposes critical policy suggestions for promoting clean energy development in China. These policies should consider the regional context and address both financial and non-financial aspects. Understanding the interplay between finance, regional dynamics, and clean energy development is crucial for achieving sustainable and resilient energy systems in China.

Soil Marginal Effect and LSTM Model in Chinese Solar Greenhouse.

The food crisis has increased demand for agricultural resources due to various factors such as extreme weather, energy crises, and conflicts. A solar greenhouse enables counter-seasonal winter cultivation due to its thermal insulation, thus alleviating the food crisis. The root temperature is of critical importance, although the mechanism of soil thermal environment change remains uncertain. This paper presents a comprehensive study of the soil thermal environment of a solar greenhouse in Jinzhong City, Shanxi Province, employing a variety of analytical techniques, including theoretical, experimental, and numerical simulation, and deep learning modelling. The results of this study demonstrate the following: During the overwintering period, the thermal environment of the solar greenhouse floor was divided into a low-temperature zone, a constant-temperature zone, and a high-temperature zone; the distance between the low-temperature boundary and the southern foot was 2.6 m. The lowest temperature in the low-temperature zone was 11.06 °C and the highest was 19.05 °C. The floor in the low-temperature zone had to be heated; the lowest value of the constant-temperature zone was 18.29 °C, without heating. The minimum distance between the area of high temperature and the southern foot of the solar greenhouse was 8 m and the lowest temperature reading was 19.29 °C. The indoor soil temperature tended to stabilise at a depth of 45 cm, and the lowest temperature reading at a horizontal distance of 1400 mm from the south foot was 19.5 °C. The Fluent and LSTM models fitted well and the models can be used to help control soil temperature during overwintering in extreme climates. The research can provide theoretical and data support for the crop areas and the heating of pipelines in the solar greenhouse.

The Marginal Effect and LSTM Prediction Model under the Chinese Solar Greenhouse Film

The solar greenhouse is a significant agricultural facility in China. It enables the cultivation of crops during periods that do not coincide with the natural growing season, thus alleviating the pressure on the supply of fruits and vegetables during the winter months. The primary rationale behind the necessity of greenhouse cultivation lies in the fact that the temperature conditions conducive to optimal crop growth can be precisely replicated within this controlled environment. However, it is important to acknowledge that a distinct low-temperature area persists under the film during the overwintering period, with the precise delineation of its boundaries and distribution patterns remaining uncertain. In order to investigate the characteristics of the temperature distribution within the marginal region under the solar greenhouse film, experimental studies, CFD simulations, and LSTM prediction models were employed. The results of these studies indicate that, during the overwintering period, a low-temperature region was observed with approximately equal temperatures near the film membrane. The maximum horizontal distance from the south-side bottom corner was 6130 mm, while the minimum height from the ground was 600 mm. The lowest temperature in the low-temperature region was 4 °C, and the maximum observed temperature difference within the same period in different months was 1 °C. Additionally, a region of elevated temperatures was observed under the film. The lowest temperature in this region was 36.7 °C, and the highest temperature point was within the optimal range for crop growth. The CFD numerical simulation results were consistent with the actual observations, and the LSTM prediction model demonstrated high reliability. The findings of this study offer a theoretical foundation for the distribution of high and low temperatures in solar greenhouses. Furthermore, the developed prediction model provides the necessary buffer time for control, thus enhancing the efficiency of greenhouse cultivation.