Leveling Socoeconomic Disparities: The Role of Service Availability in School Dropout Rates

Purpose: Adolescent school dropout remains a major concern in the USA, accounting for 5.3% of the entire high school students in 2022. This study aims to investigate the relationships between socioeconomic status (SES), the availability of mental health and substance use treatment facilities, and school dropout rates across thirteen states. Method: We used geospatial and machine learning techniques to integrate datasets and impute missing values. Regression analyses were used to examine the relationships among SES, service availability, and dropout rates. Results: Our findings suggest that computational techniques can help conduct multi-state analysis of educational data. The availability of services negatively moderates the relationship between SES and dropout. Conclusions: This study demonstrates the potential of geospatial and machine learning techniques for multi-level analysis. The findings provide implications for social work, public policy, and education, indicating that community-level resources may serve as protective factors in supporting youth educational continuity.

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Publication Details

Journal
Research on Social Work Practice
Published
2025-11-04
DOI
https://doi.org/10.1177/10497315251377009
Primary Topic
Early Childhood Education and Development
Type
article
Field-Weighted Citation Impact
0.00
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article

Leveling Socoeconomic Disparities: The Role of Service Availability in School Dropout Rates

Gie Myung Lee, Julia Hsin‐Ping Hsu, Jinyi Kim, Myeong Lee et al.
Research on Social Work Practice
Early Childhood Education and Development
article

Leveling Socoeconomic Disparities: The Role of Service Availability in School Dropout Rates

Gie Myung Lee, Julia Hsin‐Ping Hsu, Jinyi Kim, Myeong Lee, Gahyun Sohn
article en

Abstract

Purpose: Adolescent school dropout remains a major concern in the USA, accounting for 5.3% of the entire high school students in 2022. This study aims to investigate the relationships between socioeconomic status (SES), the availability of mental health and substance use treatment facilities, and school dropout rates across thirteen states. Method: We used geospatial and machine learning techniques to integrate datasets and impute missing values. Regression analyses were used to examine the relationships among SES, service availability, and dropout rates. Results: Our findings suggest that computational techniques can help conduct multi-state analysis of educational data. The availability of services negatively moderates the relationship between SES and dropout. Conclusions: This study demonstrates the potential of geospatial and machine learning techniques for multi-level analysis. The findings provide implications for social work, public policy, and education, indicating that community-level resources may serve as protective factors in supporting youth educational continuity.

Research on Social Work Practice
George Mason University (US), Baekseok University (KR)
Openalex Percentile: Top 65%
Early Childhood Education and Development
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