Child Labor, Informality, and Poverty: Leveraging Logistic Regression, Indeterminate Likert Scales, and Similarity Measures for Insightful Analysis in Ecuador

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F24%3A10255657" target="_blank" >RIV/61989100:27510/24:10255657 - isvavai.cz</a>

Výsledek na webu

<a href="https://digitalrepository.unm.edu/nss_journal/vol66/iss1/9/" target="_blank" >https://digitalrepository.unm.edu/nss_journal/vol66/iss1/9/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.5281/zenodo.10937521" target="_blank" >10.5281/zenodo.10937521</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Child Labor, Informality, and Poverty: Leveraging Logistic Regression, Indeterminate Likert Scales, and Similarity Measures for Insightful Analysis in Ecuador

Popis výsledku v původním jazyce

The paper presents a comprehensive analysis of child labor in Ecuador, employing advanced statistical tools like logistic regression, neutrosophic Likert scales, and similarity measures to deepen the understanding of this social issue. The integration of these methodologies allows for a nuanced assessment of the various socio-economic factors contributing to child labor. By capturing the uncertainty in human responses, the research highlights the complex interplay between poverty, household income, education levels, and labor types on the incidence of child labor. Key findings suggest that rural location, the age of the child, and the informal nature of the head of the household&apos;s work are the most significant predictors of child labor. Notably, parental education appears to have a less direct influence. Despite various efforts, including government monetary transfers through programs like the BDH, child labor persists, indicating the need for more targeted interventions.The paper proposes future research to extend these models to a broader demographic and geographic data set, emphasizing the potential for these methods to be applied to a variety of social issues. The development of computational tools to automate neutrosophic analysis could greatly benefit large-scale studies, potentially aiding policymakers in designing more effective interventions for vulnerable populations. (C) (2024) All rights reserved.

Název v anglickém jazyce

Child Labor, Informality, and Poverty: Leveraging Logistic Regression, Indeterminate Likert Scales, and Similarity Measures for Insightful Analysis in Ecuador

Popis výsledku anglicky

The paper presents a comprehensive analysis of child labor in Ecuador, employing advanced statistical tools like logistic regression, neutrosophic Likert scales, and similarity measures to deepen the understanding of this social issue. The integration of these methodologies allows for a nuanced assessment of the various socio-economic factors contributing to child labor. By capturing the uncertainty in human responses, the research highlights the complex interplay between poverty, household income, education levels, and labor types on the incidence of child labor. Key findings suggest that rural location, the age of the child, and the informal nature of the head of the household&apos;s work are the most significant predictors of child labor. Notably, parental education appears to have a less direct influence. Despite various efforts, including government monetary transfers through programs like the BDH, child labor persists, indicating the need for more targeted interventions.The paper proposes future research to extend these models to a broader demographic and geographic data set, emphasizing the potential for these methods to be applied to a variety of social issues. The development of computational tools to automate neutrosophic analysis could greatly benefit large-scale studies, potentially aiding policymakers in designing more effective interventions for vulnerable populations. (C) (2024) All rights reserved.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10103 - Statistics and probability

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neutrosophic Sets and Systems

ISSN

2331-6055

e-ISSN

2331-608X

Svazek periodika

66

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

10

Strana od-do

136-145

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85195495788