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| dc.contributor.author | Anteneh, Mulugeta Eyasu | |
| dc.date.accessioned | 2026-10-05T13:34:32Z | |
| dc.date.available | 2026-10-05T13:34:32Z | |
| dc.date.issued | 2026-06 | |
| dc.identifier.uri | http://ir.bdu.edu.et/handle/123456789/17410 | |
| dc.description.abstract | Poverty remains one of the most critical development challenges globally, particularly in low income agrarian countries such as Ethiopia, where rural livelihoods depend heavily on agriculture and multidimensional deprivation remains widespread. Although previous studies have examined the welfare effects of agricultural commercialization, most have relied on income-based poverty measures, conventional regression approaches, or geographically limited data, with limited attention to multidimensional poverty, spatial heterogeneity, and causal inference. This dissertation addresses these gaps by examining the causal impact of crop commercialization on multidimensional poverty among rural households in Ethiopia using nationally representative data from the fourth round of the Ethiopian Socioeconomic Survey (ESS 2018/19). The study analyzed 2,714 rural households nested within 59 administrative zones, integrating household, cluster, and spatial information. Multidimensional poverty was measured using the Alkire–Foster framework based on five dimensions and sixteen indicators covering nutrition & health, education, living standards, rural livelihoods & resources, and risk. A range of advanced statistical methods was employed, including generalized linear mixed models, multilevel spatial models, Targeted Maximum Likelihood Estimation (TMLE), multilevel causal mediation analysis, generalized propensity score methods for multiple treatments, and causal forest machine learning algorithms. The findings show that 47.8% of rural households were multidimensionally poor, with living standards contributing the largest share of deprivation. Poverty was disproportionately concentrated among subsistence-oriented and female-headed households, with significant spatial clustering across administrative zones. Across multiple modelling approaches, crop commercialization was consistently associated with a statistically significant reduction in multidimensional poverty. TMLE combined with machine learning and ensemble methods produced the most efficient and least biased estimates. Commercial-oriented households had a 3.56 percentage point lower probability of multidimensional poverty than subsistence households, while semi commercial households experienced smaller gains. Mediation analysis indicated that household savings partially explained the poverty-reducing effect of commercialization. Heterogeneity analysis further revealed stronger benefits among households located closer to markets and roads, with larger landholdings and younger household heads. The results suggest that crop commercialization can serve as an effective pathway for reducing rural xi multidimensional poverty, but its benefits depend on complementary investments in rural infrastructure, financial inclusion, education, and market access. By integrating causal inference, spatial statistics, and machine learning, this dissertation provides robust and policy relevant evidence to support targeted and place-based rural development strategies in Ethiopia. | en_US |
| dc.language.iso | en_US | en_US |
| dc.subject | Statistics | en_US |
| dc.title | The Impact of Crop Commercialization on Multidimensional Poverty In Rural Ethiopia: Evidence from Advanced Mixed and Causal Models | en_US |
| dc.type | Dissartation | en_US |
