Impact of crop commercialization on multidimensional poverty in rural Ethiopia
Propensity score approach
Bibliographic Data
| ID | 22070992 |
|---|---|
| Authors | Anteneh Mulugeta Eyasu (Bahir Dar University, corresponding author), Temesgen Zewotir (0000-0003-1503-8055, University of KwaZulu-Natal), Zelalem G Dessie (0000-0001-9056-6822, Bahir Dar University) |
| Year | 2025 |
| Volume | 12 |
| Pages | 1412670-1412670 |
| Publication date | 2025-01-09 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2024.1412670 |
| PMID | 39850859 |
| OpenAlex | W4406228585 |
| Language | EN |
| Citations received | 1 |
| References cited | 70 |
Introduction: Reducing poverty through crop commercialization is one of the antipoverty efforts that helps promote health. This study explored the prevalence and the causal relationship between crop commercialization and rural Ethiopian households' multidimensional poverty using multilevel data. Methods: The study uses data from the most recent nationally representative Ethiopian socioeconomic survey 2018/19 to calculate the rural multidimensional poverty index using the Alkire and Foster technique. The data show 2,714 rural households nested in 59 administrative zones of Ethiopia. Based on several parameters (nutrition and health, education, living standards, rural livelihoods and resources, and risk), the investigation looks into the multidimensional poverty levels of Ethiopian rural households and how they differ across Ethiopian administrative zones. Results: The results indicate that 47.8% of the rural households of Ethiopians were multidimensionally poor in several dimensions; nutrition and health, education, living standards, rural livelihoods and resources, and risk. The living standard dimension is most deprivation-prone for the rural, multidimensional poor households. In addition, multidimensional poverty is more prevalent in Somali and Afar region rural areas. The best linear unbiased prediction estimates of multidimensional poverty vary substantially across Ethiopia's administrative zones. Specifically, the top poorest performing administrative zones concerning the likelihood of being multidimensional poor among rural households were Shebelle, Zone 2, Zone 3, Zone 4, and Konso special woreda. Conclusion: The results of the generalized linear mixed-effects model show that crop-commercialized households have reduced the odds of being multidimensionally poorer than those who did not. This study recommends policymakers focus on rural mumyltidimensional poverty reduction strategies
Agricultural economics · Business · Commercialization · Crop · Economic growth · Economics · Geography · Poverty · Propensity score matching · Statistics · Agricultural Innovations and Practices · Child Nutrition and Water Access · Food Security and Health in Diverse Populations · Mathematics · Medicine · Forestry · Marketing
Multidimensional Poverty Measurement and Analysis
Acute Multidimensional Poverty
Validating recommendations for coronary angiography following acute myocardial infarction in the elderly
Measuring Multidimensional Poverty
Full Matching in an Observational Study of Coaching for the SAT
Multilevel Modelling of Complex Survey Data
The coefficient of determination R 2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded
Reducing Bias in Observational Studies Using Subclassification on the Propensity Score
Variance Partitioning in Multilevel Logistic Models that Exhibit Overdispersion
Bayesian Inference for Causal Effects
Fitting Linear Mixed-Effects Models Using lme4
Determinants of rural multidimensional poverty of households in Southern Ethiopia
Revisions of the global multidimensional poverty index
A Multi-Country Analysis of Multidimensional Poverty in Contexts of Forced Displacement
Development as Freedom
Multilevel Analysis
Multidimensional poverty indices
Counting and multidimensional poverty measurement
The Use of Propensity Scores for Nonrandomized Designs With Clustered Data
An Evaluation of Weighting Methods Based on Propensity Scores to Reduce Selection Bias in Multilevel Observational Studies
An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
The Measurement of Multidimensional Poverty
Agricultural commercialization and nutrition revisited
The impact of agricultural commercialisation on household welfare in rural Vietnam
Agricultural commercialization and diversification
Welfare effects of vegetable commercialization
Agricultural commercialization, economic development, and nutrition
Measuring Acute Poverty in the Developing World
Agricultural commercialization and nutrition; evidence from smallholder coffee farmers
Commercialization of the small farm sector and multidimensional poverty
Vulnerability to Drought and Food Price Shocks
Consolidating and improving the assets indicator in the global Multidimensional Poverty Index
Gender Differences in Multidimensional Poverty in Brazil
Counterfactuals and causal inference
Matching Estimators of Causal Effects
The Estimation of Causal Effects From Observational Data
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2025 - 2025 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 1 |