Ordinal Models Research Articles

Competitiveness Determinants for the Egyptian Ports located on the Mediterranean: An Ordinal Logistics Regression Model

Competitiveness Determinants for the Egyptian Ports located on the Mediterranean: An Ordinal Logistics Regression Model

Assessing the Effectiveness of Workplace Wellness Programs in Promoting Healthy Behaviors and Reducing Absenteeism in Chinese Companies

Background: Health disparities in rural Chinese communities remain a significant concern. This study aims to determine the role of socioeconomic status in influencing these disparities. Methods: Data from the Chinese General Social Survey (CGSS) for the years 2009, 2014, and 2019 were analyzed. An ordinal complementary log-log model was employed to investigate health inequality and its determinants within China. Results: The analysis revealed that health status varies significantly with different social and individual factors. Key determinants of health disparities included gender, region, residential area, ethnicity, and income level. Notably, individuals with lower socioeconomic status demonstrated poorer health outcomes compared to those with higher socioeconomic status. Conclusion: This study provides evidence of the significant impact of socioeconomic factors on health disparities in rural China. The findings offer valuable insights for policymakers and healthcare professionals, potentially informing strategies to reduce health inequalities within the country.

G-space versus E-space: Are hornets (Hymenoptera: Vespidae) at equilibrium with honeybees in Asia?

This study delves into the concept of niche conservatism (NC) and its implications for how hornets (Hymenoptera: Vespidae) and honeybees respond to climate change. Our primary objectives are threefold: firstly, to assess whether distinct environmental niche spaces (E-space) exist between 12 hornets and honeybees like Apis cerana and Apis mellifera in Asia; Secondly, to explore the degree to which Asian hornets have attained geographic equilibrium alongside honeybee species. Lastly, is to investigate how the geographic niche overlap (G-space) between hornets and honeybees could potentially change under climate change scenarios. To accomplish these goals, we employed ordination and ecological niche modeling techniques to analyze 91 pairs of hornets and honeybees in both geographic (G-space) and environmental (E-space) contexts. Then, we projected the potential impacts of climate change on the future geographic overlap between hornets and honeybees, specifically under the SSP585 climate scenario for the year 2070. Our results demonstrated that the environmental niches (E-space) of hornets and honeybees can be treated as interchangeable, indicating they have similar environmental preferences despite being unrelated taxa. We found that Vespa velutina currently exhibits a moderate geographic niche overlap (G-space) of 0.63 with both honeybee species. Meanwhile, Vespa mandarinia demonstrates an overlap of 0.46 with Apis cerana and 0.63 with Apis mellifera. The overlap of Vespa velutina with Apis cerana might potentially decrease to 0.51 and 0.56 with Apis mellifera. For Vespa mandarinia, the overlap could reach 0.41 with Apis cerana and 0.6 with Apis mellifera under a climate change scenario. This study indicates that the limited spatial overlap between honeybees and hornets across certain areas in Asia is more likely influenced by geographical barriers rather than solely environmental unsuitability for hornets. In this study, we delve into the concept of niche conservatism (NC) and its implications for how hornets (Hymenoptera: Vespidae) and honeybees respond to climate change.

Preoperative prediction of gastric cancer T-staging based on ordinal regression models

Objectives. Study of ordinal regressions presented via the set of binary logistic regressions and their application in clinical practice for T-staging of gastric cancer.Methods. Methods of ordinal regression statistical models, model performance assessment, and survival analysis were used.Results. Basic ordinal regression models have been studied and applied to the clinical data of gastric cancer. Some clinical predictors have been added to the well-known prognostic criteria according to the TNM classification in the multifactor regression model, results seem appropriate for a personalized approach when planning the treatment volume for improving efficacy.Conclusion. The study showed that the analysis of ordinal models, along with multinomial ones, provides additional information that helps to understand the behavior of the latent variable in the complex cancer processes. The clinical part of the study facilitates a differentiated approach to preoperative planning of the treatment volume for patients with the same T-stage, based on modeling results.

The associations between religious/spiritual beliefs and behaviours and study participation in a prospective cohort study (ALSPAC) in Southwest England.

Longitudinal studies are key to understanding risk factors for health, well-being, and disease, yet associations may be biased if study invitation and participation are non-random. Religious/spiritual beliefs and behaviours (RSBB) are increasingly recognised as having potentially important relationships with health. However, it is unclear whether RSBB is associated with study participation. We examine whether RSBB is associated with participation in the longitudinal birth cohort ALSPAC (Avon Longitudinal Study of Parents and Children). Three RSBB factors were used: religious belief (belief in God/a divine power; yes/not sure/no), religious affiliation (Christian/none/other), and religious attendance (frequency of attendance at a place of worship). Participation was measured in three ways: i) total number of questionnaires/clinics completed (linear and ordinal models); ii) completion of the most recent questionnaire (logistic model); and iii) length of participation (survival model). Analyses were repeated for the ALSPAC mothers, their partners, and the study children, and were adjusted for relevant socio-demographic confounders. Religious attendance was positively associated with participation in all adjusted models in all three cohorts. For example, study mothers who attended a place of worship at least once a month on average completed two more questionnaires (out of a possible 50), had 50% greater odds of having completed the most recent questionnaire, and had 25% reduced risk of drop-out, relative to those who did not attend. In the adjusted analyses, religious belief and attendance were not associated with participation. However, the majority of unadjusted models showed associations between RSBB and participation. After adjusting for confounders, religious attendance - not religious belief or affiliation - was associated with participation in ALSPAC. These results indicate that use of RSBB variables (and religious attendance in particular) may result in selection bias and spurious associations; these potential biases should be explored and discussed in future studies using these data.

Using Alternative Definitions of Controls to Increase Statistical Power in GWAS.

Genome-wide association studies (GWAS) are often underpowered due to small effect sizes of common single nucleotide polymorphisms (SNPs) on phenotypes and extreme multiple testing thresholds. The most common approach for increasing statistical power is to increase sample size. We propose an alternative strategy of redefining case-control outcomes into ordinal case-subthreshold-asymptomatic variables. While maintaining the clinical case threshold, we subdivide controls into two groups: individuals who are symptomatic but do not meet the clinical criteria for diagnosis (subthreshold) and individuals who are effectively asymptomatic. We conducted a simulation study to examine the impact of effect size, minor allele frequency, population prevalence, and the prevalence of the subthreshold group on statistical power to detect genetic associations in three scenarios: a standard case-control, an ordinal, and a case-asymptomatic control analysis. Our results suggest the ordinal model consistently provides the greatest statistical power while the case-control model the least. Power in the case-asymptomatic control model reflects the case-control or ordinal model depending on the population prevalence and size of the subthreshold category. We then analyzed a major depression phenotype from the UK Biobank to corroborate our simulation results. Overall, the ordinal model improves statistical power in GWAS consistent with increasing the sample size by approximately 10%.

Bayesian transition models for ordinal longitudinal outcomes.

Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.

Numerical investigation of optical characterization of polycarbonate panels

Polycarbonate panels (PC panels) are state-of-the-art transparent insulating materials widely used in the construction industry due to their cavity structure, which provides exceptional thermal insulation and optimal optical performance. However, the inherent anisotropy of the three-dimensional cavity structure complicates radiative transfer and requires consideration of both azimuth and zenith angles in optical performance evaluation. This aspect has received limited attention in existing research. This study aims to accurately characterize the optical performance of PC panels through numerical simulations. A three-dimensional radiative transfer model based on the discrete ordinate radiation model is developed to solve the radiation transfer equation. The model's independency regarding mesh division, angular discretization, and accuracy is validated. The effects of incidence angle, geometric parameters, and optical properties of PC panels on optical performance are analyzed. The findings reveal a strong correlation between transmittance and absorption with variations in incident zenith and azimuth angles. The transmittance exhibits a consistent monotonic variation expressible as a rational bifunction. Notably, absorption peaks occur within specific solid angle ranges, with increased structural complexity resulting in heightened absorption and greater uncertainty. For conventional PC materials, maximum transmittance ranges from 46.9 % to 73 %, while maximum absorption ranges from 2.3 % to 13.5 %. Increasing absorption coefficients, refractive index, and surface scattering coefficients nonlinearly decrease transmittance while increasing absorption. Additionally, deviations in transmittance and absorption with azimuth angle amplify with an increase in non-horizontal structures. Sensitivity analysis indicates a significant influence of zenith angle on transmittance, and absorption coefficient predominantly affects absorption.

High-Intensity Aphasia Intervention Is Minimally Fatiguing in Chronic Aphasia: An Analysis of Participant Self-Ratings From a Large Randomized Controlled Trial.

High-intensity therapy is recommended in current treatment guidelines for chronic poststroke aphasia. Yet, little is known about fatigue levels induced by treatment, which could interfere with rehabilitation outcomes. We analyzed fatigue experienced by people with chronic aphasia (>6 months) during high-dose interventions at 2 intensities. A retrospective observational analysis was conducted on self-rated fatigue levels of people with chronic aphasia (N=173) collected during a previously published large randomized controlled trial of 2 treatments: constraint-induced aphasia therapy plus and multi-modality aphasia therapy. Interventions were administered at a higher intensity (30 hours over 2 weeks) or lower intensity (30 hours over 5 weeks). Participants rated their fatigue on an 11-point scale before and after each day of therapy. Data were analyzed using Bayesian ordinal multilevel models. Specifically, we considered changes in self-rated participant fatigue across a therapy day and over the intervention period. Data from 144 participants was analyzed. Participants were English speakers from Australia or New Zealand (mean age, 62 [range, 18-88] years) with 102 men and 42 women. Most had mild (n=115) or moderate (n=52) poststroke aphasia. Median ratings of the level of fatigue by people with aphasia were low (1 on a 0-10-point scale) at the beginning of the day. Ratings increased slightly (+1.0) each day after intervention, with marginally lower increases in the lower intensity schedule. There was no evidence of accumulating fatigue over the 2- or 5-week interventions. Findings suggest that intensive intervention was not associated with large increases in fatigue for people with chronic aphasia enrolled in the COMPARE trial (Constraint-Induced or Multimodality Personalised Aphasia Rehabilitation). Fatigue did not change across the course of the intervention. This study provides evidence that intensive treatment was minimally fatiguing for stroke survivors with chronic aphasia, suggesting that fatigue is not a barrier to high-intensity treatment.

Cumulative ordinal quasi-symmetry model and its separation for square contingency tables with ordered categories

Summary This study deals with square contingency tables, which are two-way contingency tables in which the row and column variables consist of the same classification. When the categorical variables are grouped into ordered categories from quantitative variables by cut points, a score that approximates the distance between the midpoints of the quantitative scale categories may be used to reflect the characteristics of the data. As an asymmetry model based on scores, this study proposes the cumulative ordinal quasi-symmetry model and ordinal marginal homogeneity model based on cumulative probabilities. The asymmetric parameter in the proposed models would be useful for making inferences such as that a row variable is stochastically less than a column variable or vice versa. This study also gives a separation of the cumulative ordinal quasi-symmetry model, that is, the cumulative ordinal quasi-symmetry model holds if and only if both the cumulative quasi-symmetry and ordinal marginal homogeneity models hold. The usefulness of the proposed models and the proposed separation is demonstrated through real data analysis.

Spatiotemporal modeling of household's food insecurity levels in Ethiopia

The state of moderate and severe food insecurity in Ethiopia has not been significantly reduced for a long time due to cultural, natural, and manmade shocks, which cover most part of the country with considerable magnitude and have adverse effects on the health and economy. This temporal evolution of the wider geographic distribution of the food insecurity levels has not been investigated for the feeding culture and shocks effect in Ethiopia, though previous studies have indicated significant geographic distribution and related factors. In addition, the longtime zone-specific comprehensive drivers were not assessed. Therefore, this study focused on investigating the feeding culture-adjusted food insecurity levels (FCSL) and their evolutional sustainability across zones by identifying the factors causing each level of household's food insecurity tailored to a specific zone.In this study, Ethiopian socio-economic longitudinal data from years 2012, 2014, and 2016 with a sample size of 3835 households were analyzed. An ordinal spatiotemporal model with different interactions under empirical Bayes estimation was adopted, and the type III interaction with Markov random field was selected to reveal the evolution of FCSL sustainability across zones and find-out the causing factors of households to each level of food insecurity tailored to zone-level by analyzing the effects of diverse factors and shocks. The result reveals that a greater portion (52.07 %) of households’ population is moderately and chronically food insecure. Basically, households living in neighboring zones have significantly similar food insecurity levels, and slight improvements were observed over time. This transition over time within neighboring zones, revealed that chronic food insecurity in neighboring zones has transited to moderate food insecurity, particularly in most of the northern and southwestern areas of Ethiopia. However, the interaction term showed that the change in food insecurity levels for households living in neighboring zones is similar at one time point, but the evolution is not sustainable. Therefore, working on major zone-specific factors, such as enhancing zone-level urbanization through improving market and road linkage to rural areas, education, employment, non-agricultural businesses, and promoting integrated farming by conserving soil with consideration of additional confounding factors, will bring change and can sustainably eradicate moderate and chronic food insecurity in zone households through controlling dependency ratios, shocks, and small land size farming. This study brings resilient, manageable, and zone-specific mitigation for households living in food-insecure and vulnerable zones.

Pandemic insights: analysing public transport with logit models

The impact of the Covid-19 pandemic on public transportation usage in Bursa, Turkey, was examined in this work. The usage of major transit systems (buses, trams and light rail) before and during the pandemic were assessed. Changes in public transportation behaviour were captured through an online survey of 767 participants from key activity centres. Using two ordinal logit models, the factors influencing preferences were identified. It was found that age, gender and home-to-work distance were influential before the pandemic, while educational status and distance became crucial during the pandemic. The research further explored the tendency to walk, cycle or use an e-scooter if safe, separated non-motorised transportation routes had been provided, considering various scenarios of home–workplace distance. The results of this study will help decision makers make sustainable transportation plans by taking into account catastrophic periods such as the Covid-19 pandemic.

Is prolonged luteal phase a problem in lactating Holstein cows?

In this study, the main objective was to assess if long luteal phases could have other causes than pregnancy losses. We enrolled Holstein dairy cows ≥50 d in milk (DIM) from a commercial herd in Brazil from October 2016 to August 2017. All cows received an estradiol-based synchronization protocol, and, on the day of insemination (d 0), were randomly assigned either an artificial insemination (AI) or a placebo insemination (PBO) in a 3:1 ratio. An ultrasound was used to assess the presence of a CL on d17, 24, and 31, which, combined to the information from patches for the detection of estrus, was used to determine the length of the luteal phase following AI or PBO. Pregnancy was assessed by ultrasound on d 31 and cows that were pregnant were excluded from the analyses. The length of the estrous cycles was categorized as short (<17 d), normal (17-23 d), long (24-30 d), and very long (≥31 d). We compared the proportion of cows in each category between the AI and PBO groups using a cumulative ordinal mixed model. We define prolonged luteal phase as estrous cycles ≥24 d and tested its association with potential risk factors (parity, season, DIM, uterine size and position score, milk production, body condition score, and the presence of a corpus luteum (CL) at enrollment to the synchronization protocol) using mixed logistic regression models. Results are presented as odds ratio (OR) and 95% credible intervals (BCI). Data from 876 inseminations (AI: n = 616, PBO: n = 260) was collected. Overall, 12% of estrous cycles were short, 31% were normal, 19% were long, and 38% were very long. There was no difference in the odds of being in longer estrous cycle categories for the AI compared with the PBO group (OR = 0.92, 95% BCI = 0.76-1.10). Season and presence of a CL at enrollment were associated with prolonged luteal phase. In the AI group, there was a possible effect of early pregnancy losses on the lifespan of the CL, but not the PBO group, which led us to conclude that long and very long estrous cycles were not all caused by the embryonic loss. In fact, the high prevalence of cows with an extended CL lifespan in the present study suggests this could be an under- or miss-reported characteristic of high-producing lactating Holstein cows. This finding may have important repercussions in the understanding of the CL function physiology of lactating Holstein cows.

Forecasting the direction of the Fed's monetary policy decisions using random forest

AbstractThe federal funds target rate is commonly considered to be an important indicator of the state of the US economy and is of keen interest to individual investors, financial firms, and other economic agents. In this paper, we focus on the discrete changes in the federal funds target rate during the period from January 1994 to June 2022 and apply the ordinal forest model, a random forest‐based prediction method for ordinal response variable. We examine the model's performance with 45 predictor variables which include macroeconomic and financial variables as well as forward‐looking survey measures. For an accurate and honest measure of the model performance, we employ single‐period‐ahead out‐of‐sample forecasting accuracy instead of evaluating the in‐sample fit. Our empirical results show the ordinal forest method significantly outperforms a benchmark that uses the most recent data among previous studies on federal funds target rate. We find that TB spread is the most informative from a forecasting perspective along with GDP, initial jobless claims, and survey measures.

A social network analysis approach to assess COVID19-related disruption to substance use treatment and informal social interactions among people who use drugs in Scotland.

To assess the extent of Coronavirus-related disruption to health and social care treatment and social interactions among people with lived or living experience of substance use in Scotland, and explore potential reasons for variations in disruption. Cross sectional mixed methods interview, incorporating a social network 'egonet interview' approach asking about whether participants had interactions with a range of substance use, health, social care or third sector organisations, or informal social interactions. Five Alcohol and Drug Partnership Areas in Scotland. 57 (42% women) participants were involved in the study, on average 42 years old. Five-point Likert scale reporting whether interactions with a range of services and people had gotten much better, better, no different (or no change), worse, or much worse since COVID19 and lockdown. Ratings were nested within participants (Individuals provided multiple ratings) and some ratings were also nested within treatment service (services received multiple ratings). The nested structure was accounted for using cross classified ordinal logistic multilevel models. While the overall average suggested only a slight negative change in interactions (mean rating 2.93), there were substantial variations according to type of interaction, and between individuals. Reported change was more often negative for mental health services (Adjusted OR = 0.93 95% CI 0.17,0.90), and positive for pharmacies (3.03 95% CI 1.36, 5.93). The models found between-participant variation of around 10%, and negligible between-service variation of around 1% in ratings. Ratings didn't vary by individual age or gender but there was variation between areas. Substance use treatment service adaptations due to COVID19 lockdown led to both positive and negative service user experiences. Social network methods provide an effective way to describe complex system-wide interaction patterns, and to measure variations at the individual, service, and area level.

Temporary employment and financial distress in times of crisis

PurposeThis study investigates the impact of temporary employment on various forms of financial distress for firms during the COVID-19 pandemic.Design/methodology/approachThe authors apply a logit model to evaluate the differences in the probabilities of experiencing financial distress for firms with or without temporary reemployment and for firms with different intensities of temporary workers. As an additional test, an ordinal logistic model is applied to reflect different degrees of financial distress.FindingsOur main results indicate that firms with temporary employment are more likely to experience financial distress than firms without temporary employment, regardless of the severity of financial distress. Among firms with temporary employment, our analysis suggests that a firm’s likelihood of experiencing financial distress depends on its relative share (quantile) of temporary workers.Practical implicationsOur findings provide valuable insights for evaluating the impact of temporary employment on firms’ vulnerability during the COVID-19 crisis and suggest strategies for firms to enhance resilience to similar future crises.Originality/valueOur study is the first one that explores the relationship between temporary employment and financial distress. Firms around the world have been pursuing flexible labor to improve resilience and firm performance. The pandemic may further ramify this trend, creating a future “new normal” regarding employment relationships, job segmentation and gender equality in the job market. This article adds a new dimension to the evaluation of the new normal, which may help firms evaluate the consequences of temporary employment, especially in times of crisis.

Sex Differences in Wellness Scores in Collegiate Athletes

Subjective wellness scores reflect athlete responses to training, fatigue, personal experiences, and recovery, and evidence suggests these scores may differ between athletes based on sex. This study aimed to evaluate the differences in daily wellness scores between sexes in collegiate soccer, swimming, and basketball athletes. Athletes (n = 294, 63% female) completed daily wellness surveys for energy, health status, mood, muscle readiness, sleep quality, sleep duration, stress, diet, and overall wellness. Variables were evaluated on a 5-point Likert scale, except sleep duration, quantified in hours. Repeated measures, mixed linear models, repeated measures, and ordinal logistic models were used to assess sex differences in wellness outcomes. In basketball, females have 0.30 times the odds (95% CI 0.19, 0.48) of males of a worse health status score. For soccer, females have 2.45 times the odds (95% CI 1.32, 4.44) of males with a worse stress wellness score. In swimming, females, on average, slept 0.40 more hours of sleep per night (95% CI 0.02, 0.79) as compared to males. These data suggest that differences in wellness subcomponents are nuanced by sport. This information can be used to provide targeted programming for athletes to improve wellness and reduce stress, which may subsequently enhance mental health, academic performance, and sports performance.

Marriage, cohabitation, and institutional context: Household specialization among same‐sex and different‐sex couples

AbstractObjectiveThis study examines how marriage‐cohabitation gaps in household specialization (labor supply and earnings) vary across institutional contexts for same‐sex couples (SSCs) and different‐sex couples (DSCs) in Canada.BackgroundPrior research suggests that marriage‐cohabitation gaps are smaller in contexts where cohabitation is more prevalent, but it has overlooked how legal protections (at the contextual level) and gender composition (at the couple level) moderate this association. As a result, little is known about whether differences in household specialization stem from heightened gendered expectations attached to marriage or stronger legal protections for married couples. This study posits that marriage‐cohabitation gaps will be larger in contexts where legal protections for cohabitors are less marriage‐like.MethodsUsing the 2006 and 2016 Canadian Census and the 2011 National Household Survey, I estimate ordinal and fractional logit models to examine marriage‐cohabitation gaps in specialization among all couples (N = 2,788,055) and couples with young children (N = 826,305).ResultsAmong DSCs, marriage‐cohabitation gaps were larger in Québec than in English Canada vis‐à‐vis earnings but not labor supply. Patterns among SSCs were more heterogeneous: gaps in labor supply were larger in English Canada for female couples but larger in Québec for male couples. Gaps in earnings were generally larger in Québec, with few exceptions. However, DSCs consistently specialized more than SSCs.ConclusionWhile existing research suggests marriage‐cohabitation gaps in household specialization are largely explained by the prevalence of cohabitation, my results indicate that legal protections (at the contextual level) and gender composition (at the couple level) play a more decisive role.

Modeling of ordinal longitudinal accelerometer data

Improvements in accelerometer technology has led to new types of data on which more powerful predictive models can be built to assess physical activity. This paper implements an ordinal random forest model with recursive forecasting to take into account the ordinal, longitudinal nature of responses. The data comes from 28 adults performing activities of daily living in two visits, while wearing accelerometers on the ankle, hip, right and left wrist. The first visit provided training data and the second testing data so that an indepen- dent sample, cross-validation approach could be used. For this type of data, prior responses are not available at the testing stage or in practice. However, recursive forecasts can be made with prior predictions in place of lagging responses on models which were built to use lagging responses as explanatory variables. Models are fit to account for multiple time series, with different time series for each participant in the study. We found that ordinal random forest, when the time series is taken into account, produces better accuracy rates and better linearly weighted kappa values than both ordinary ordinal forest and random forest. On the testing set, the lowest error rates were produced by the ankle (28.0%), followed by the left wrist (28.7%), hip (28.9%) and then the right wrist (30.2%) using the best performing decision model for a four-activity level response. In addition, linearly weighted kappa values indicated substantial agreement. The approach of this work can be adapted to other types of longitudinal ordinal models for improvements in modeling techniques. Journal of Statistical Research 2022, Vol. 57, No. 1-2, pp.1-19

Determinants of Financial Inclusion in Kenya: A Demand-Side Perspective

This study sought to analyze the underlying financial inclusion determinants in Kenya. The study applies ordinal logit regression to examine the effect of the residential area, gender, education level, marital status, and employment type on financial inclusion. Financial inclusion is measured by developing a financial inclusion index for ten binary financial services variables. From the index, three financial inclusion levels are designed. These include low financial inclusion with scores of zero to three, medium with scores of four to six, and high level with scores of seven to ten. The estimates of the ordinal model are statistically significant for all factors considered except gender. Area of residence, age, education type, income, and marital status positively affect the log odds of financial inclusion, while employment is negatively linked. Education, employment, and marital status have interaction effects on financial inclusion. This study recommends that the Kenyan government formulate and strengthen policies to tackle challenges such as gender disparity, rural bank infrastructure development, fostering an environment conducive for entrepreneurship to address unemployment and income disparities, advocating for secondary school completion, and addressing social issues impacting family stability, including separation or the absence of marriage.