<p>Rosenbaum bounds sensitivity analysis.</p>

416 smallholder farmers in Ethiopia's Siltie Zone were surveyed to investigate Climate-Smart Agriculture adoption. The study, authored by Jemil Yasin Shifa, uses GSEM, 2SLS, and PSM methods to address endogeneity between CSA adoption and a constructed Multidimensional Poverty Index. Results indicate CSA adoption significantly reduces multidimensional poverty, highlighting its potential for targeted policy interventions.

Use Cases

• Conduct sensitivity analysis for causal inference based on the Rosenbaum bounds method mentioned in the title.
• Model the relationship between agricultural practice adoption and poverty metrics based on the described CSA and MPI variables.
• Replicate econometric analyses (GSEM, 2SLS, PSM) based on the cross-sectional survey data from 416 farmers.
• Assess policy interventions for poverty reduction based on the link between specific CSA practices and multidimensional deprivation.

Strengths

• Dataset is openly licensed under CC-BY-4.0, permitting broad reuse.
• Analysis is based on primary survey data from 416 smallholder farmers.
• Employs multiple analytical approaches (GSEM, 2SLS, PSM) to address potential endogeneity.

Limitations

• Dataset is very small at 9.5 KB, indicating limited scope or variable count.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment for certain modeling tasks.

Provenance

Source

Jemil Yasin Shifa via figshare.

Collection Method

Cross-sectional data collected via multi-stage sampling from smallholder farmers.

Time Range

null

Freshness

Last updated 2026-05-04 17:36:11; freshness should be verified.

Geography

Siltie Zone, Central Ethiopia.