Understanding household dynamics differently: A multilevel analysis of individual, household, and community influences on household size for sustainable development in Northern Ghana

• Multilevel modeling reveals community-level factors dominate household size determinants. • Individual characteristics (age, gender, education) are less influential than community norms. • Significant variability in household size across communities underscores cultural impacts. • Findings advocate for community-centric policies and tailored interventions in rural development. • Study aligns with SDGs 1, 2, 3, 4, and 11, promoting sustainable development through multilevel insights. This study examines household size determinants in Northern Ghana using multilevel modeling across individual, household, and community levels. Analysis of 240 households across 20 communities in three northern regions reveals that community-level factors overshadow individual characteristics in determining household size. The average household size of 12.91 members (SD = 8.67) shows substantial community-level variance (12.700), accounting for 42.3% of the total variance. Individual-level predictors (age, gender, education) fail to achieve statistical significance ( p > 0.05) when community context is properly accounted for. Communities exhibit marked differences in household sizes, ranging from 7.75 to 19.4 members. Food security analysis indicates that very large households (>15 members) report better outcomes, with only 30.5% experiencing food insufficiency compared to 73.7% of small households (≤5 members). These findings challenge conventional approaches that prioritize individual behavior change, instead highlighting the necessity for community-centric policies that recognize household structures as adaptive responses to local conditions. This study emphasizes the importance of tailored, community-specific interventions for effective rural development by aligning with Sustainable Development Goals (SDGs) including No Poverty, Zero Hunger, Good Health and Well-being, Quality Education, and Sustainable Communities. This multilevel perspective offers a more nuanced framework for understanding household dynamics in rural settings, with significant implications for designing sustainable livelihood interventions in Northern Ghana and similar contexts.

Authors

Institutions

Publication Details

Journal
World Development Sustainability
Published
2025-09-02
DOI
https://doi.org/10.1016/j.wds.2025.100250
Primary Topic
Gender, Labor, and Family Dynamics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Understanding household dynamics differently: A multilevel analysis of individual, household, and community influences on household size for sustainable development in Northern Ghana

Seidu Abdulai Jamatutu, Sidique Gawusu, Solahudeen Tando Moomin, Mohammed Abdul‐Fatawu et al.
World Development Sustainability
Gender, Labor, and Family Dynamics
article

Understanding household dynamics differently: A multilevel analysis of individual, household, and community influences on household size for sustainable development in Northern Ghana

Seidu Abdulai Jamatutu, Sidique Gawusu, Solahudeen Tando Moomin, Mohammed Abdul‐Fatawu, Mutala Gawusu Sidik, Ahmed Dong Shuaib
article en

Abstract

• Multilevel modeling reveals community-level factors dominate household size determinants. • Individual characteristics (age, gender, education) are less influential than community norms. • Significant variability in household size across communities underscores cultural impacts. • Findings advocate for community-centric policies and tailored interventions in rural development. • Study aligns with SDGs 1, 2, 3, 4, and 11, promoting sustainable development through multilevel insights. This study examines household size determinants in Northern Ghana using multilevel modeling across individual, household, and community levels. Analysis of 240 households across 20 communities in three northern regions reveals that community-level factors overshadow individual characteristics in determining household size. The average household size of 12.91 members (SD = 8.67) shows substantial community-level variance (12.700), accounting for 42.3% of the total variance. Individual-level predictors (age, gender, education) fail to achieve statistical significance ( p > 0.05) when community context is properly accounted for. Communities exhibit marked differences in household sizes, ranging from 7.75 to 19.4 members. Food security analysis indicates that very large households (>15 members) report better outcomes, with only 30.5% experiencing food insufficiency compared to 73.7% of small households (≤5 members). These findings challenge conventional approaches that prioritize individual behavior change, instead highlighting the necessity for community-centric policies that recognize household structures as adaptive responses to local conditions. This study emphasizes the importance of tailored, community-specific interventions for effective rural development by aligning with Sustainable Development Goals (SDGs) including No Poverty, Zero Hunger, Good Health and Well-being, Quality Education, and Sustainable Communities. This multilevel perspective offers a more nuanced framework for understanding household dynamics in rural settings, with significant implications for designing sustainable livelihood interventions in Northern Ghana and similar contexts.

World Development SustainabilityVol. 7
Center for Global Development (US), Nanjing University of Science and Technology (CN), University for Development Studies (GH)
Openalex Percentile: Top 71%
Gender, Labor, and Family Dynamics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.