AhmedEid02/who-would-we-miss-Somalia

Bayesian climate shock-to-action targeting analysis using SIHBS 2022 to identify high-risk unreached households and compare humanitarian targeting strategies. # Who Would We Miss? ## A Bayesian Climate Shock-to-Action Targeting Analysis Using SIHBS 2022 This repository contains a reproducible mini-project using Somalia Integrated Household Budget Survey (SIHBS) 2022 microdata to ask a decision-facing question: > If emergency support can reach only a limited share of households, which targeting rule misses the fewest high-risk unreached households? The analysis defines **high-risk unreached households** as households that: 1. experienced a climate/livelihood shock, 2. experienced moderate/severe food insecurity or erosive coping, and 3. reported no formal support from government, local NGO, or international organization. The model is a **Bayesian hierarchical logistic regression** with region-level random intercepts, fitted with variational Bayes. ## Headline findings - Analysis sample: **7,212 households** across **17 regions**. - Weighted climate/livelihood shock exposure: **71.9%**. - Weighted moderate/severe food insecurity proxy: **44.1%**. - Weighted formal support after shock: **3.8%**. - Weighted high-risk unreached outcome: **40.1%**. - At **20% support coverage**, Bayesian risk targeting reached **34.0%** of high-risk unreached households, compared with **27.8%** under climate/livelihood shock-only targeting and **19.5%** under poverty-only targeting. ## Repository contents ```text scripts/ Reproducible analysis scripts figures/ LinkedIn/GitHub-ready figures outputs/ Aggregated public-safe results data/metadata/ SIHBS module inventory data/processed/ Schema only; no household microdata included reports/ Mini-project report docs/ Methodology and LinkedIn post drafts ``` ## Data note Raw SIHBS microdata are **not included** in this public repository. To reproduce the analysis, place the official `.dta` files in `data/raw/` with the original filenames, then run: …