From 323f250f3204012e416624f6b3b9d6592f161f82 Mon Sep 17 00:00:00 2001
From: Nice! <61159338+nbajpai-code@users.noreply.github.com>
Date: Wed, 3 Dec 2025 16:30:39 -0500
Subject: [PATCH] Create more.md

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+# Team Table4: Challenge 4 - Women in Public Sector Jobs
+
+## Challenge Overview
+Assessing whether women's high public-sector employment reduces private-sector opportunities (crowding out) or expands them (crowding in).
+
+## What's Included
+- **Challenge Definition** ([project.md](DataDive25/Team_Projects/Table4/project.md:0:0-0:0)): Detailed problem statement with two competing hypotheses
+- **Data Acquisition** ([fetch_data.py](DataDive25/Team_Projects/Table4/fetch_data.py:0:0-0:0), [data_instructions.md](cci:7://file:///Users/nirajbajpai/.gemini/antigravity/scratch/DataDive25/Team_Projects/Table4/data_instructions.md:0:0-0:0)): Scripts and instructions for obtaining World Bank and ILO data
+- **Initial Analysis** ([analyze_data.py](DataDive25/Team_Projects/Table4/analyze_data.py:0:0-0:0), [analysis.md](cci:7://file:///Users/nirajbajpai/.gemini/antigravity/scratch/DataDive25/Team_Projects/Table4/analysis.md:0:0-0:0)): Analysis of public sector employment share across countries
+- **Visualization** (`results/public_sector_share_dist.png`): Distribution plot of public sector employment
+
+## Key Findings (Preliminary)
+- Top countries by public sector share: Maldives (47%), Russia (45%), Djibouti (43%)
+- Most countries have 10-30% public sector employment share
+
+## Next Steps
+- Obtain Female Labor Force Participation data
+- Correlate public sector share with private sector employment outcomes
+- Test crowding out vs crowding in hypotheses
+
+## Team
+Table 4