asamoahkwadwosomuah-hash/youth-neet-analysis

Regional analysis of youth NEET (Not in Employment, Education, or Training) rates across Ghana's 16 regions using official Ghana Statistical Service data — Python, pandas, matplotlib. # Ghana Youth NEET Analysis: Regional Patterns in Youth Labour Market Exclusion **A national-development data analysis project — Python, pandas, matplotlib — using official Ghana Statistical Service data to identify where youth are most excluded from employment, education, and training.** ## Business/policy context Ghana has a young, fast-growing population (median age 21.1 years, ~37% under 15), and youth unemployment is one of the country's most persistent development challenges. **NEET (Not in Employment, Education, or Training)** is a broader and arguably more useful measure than the unemployment rate alone, because it also captures young people who have stopped looking for work or never entered the education/training system at all — not just those actively job-seeking. This project analyzes **official Ghana Statistical Service (GSS) data** to answer: *which regions of Ghana have the most severe youth NEET problem, is it an urban or rural issue, and is the situation improving?* **Dataset:** Table 5.10 from the GSS's 2022 AHIES Quarter 3 Labour Statistics Report — youth (15–35 years) NEET counts and rates for all 16 regions of Ghana, broken down by urban/rural locality, across the first three quarters of 2022. This is the most granular officially published regional NEET breakdown available. ## A note on data provenance The GSS publishes this data as formatted PDF tables rather than a downloadable CSV. The dataset in this repo (`data/ghana_youth_neet_by_region.csv`) was **manually transcribed** from Table 5.10 of the official PDF (page 41–42) — every figure can be verified against the source document linked above. As a data-quality check, I reconstructed the national NEET rate by summing all 16 regions' figures and compared it against the report's own stated national totals (26.1% / 24.2% / 23.7% for Q1/Q2/Q3) — the reconstruction matched to within a single person, confirming an accurate transcription. ## Business/research questions answered 1. Which regi …