🎯 EOGO Corruption Risk Analysis

Comprehensive corruption risk analysis including Political HHI, CRI scores, and Poverty correlation based on political dynasties and public procurement data

🏛️ Political HHI 🎯 CRI Analysis 📈 Poverty Correlation ⚠️ Limitations

🏛️ Political HHI Analysis

Political Herfindahl-Hirschman Index measures political dynasty concentration by province. Higher values indicate greater political concentration and potential corruption risk.

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📊 Political HHI Statistics
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Average HHI
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Highest HHI
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Total Provinces
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High Risk (>50)
Loading political dynasty data...

🎯 CRI Analysis

Corruption Risk Indicator combining multiple factors from the EOGO paper methodology.

📊 CRI Analysis Statistics
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Average CRI
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Highest CRI
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Total Provinces
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High Risk (>50)
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Computing comprehensive CRI scores...

📈 Poverty Correlation Analysis

Analysis of poverty incidence correlation with corruption risk factors as identified in the EOGO paper.

📊 Poverty Statistics
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Average Poverty
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Highest Poverty
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Total Provinces
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High Poverty (>20%)
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⚠️ Limitations of HHI and CRI Analysis

Understanding the weaknesses and constraints of our corruption risk indicators based on the EOGO paper methodology.

🚨 Critical Accuracy Issues
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HHI with Low Numbers
INACCURATE - Small sample sizes produce unreliable HHI scores
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CRI Approximation
ONLY APPROXIMATION - Missing 6 of 9 input variables

⚠️ Important: Our HHI calculations with low politician counts are statistically unreliable, and our CRI scores are approximations due to missing critical input variables from the EOGO paper methodology.

🏛️ Political HHI Limitations
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CRITICAL: Low Numbers = Inaccurate HHI: HHI calculations with fewer than 10-20 politicians are statistically unreliable. Small sample sizes produce misleading concentration scores that don't reflect actual political dynasty dominance.
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Missing Political Dynasty Data: We lack comprehensive elected officials data with surnames, positions, and terms needed to calculate Political HHI.
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No Historical Terms: Political HHI requires data across multiple election cycles (2004-2019) to measure dynasty persistence.
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Proxy Data Quality: Our current HHI calculations use contractor concentration as a proxy, which may not accurately reflect political dynasty concentration.
🎯 CRI Analysis Limitations
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CRI is ONLY an Approximation: Our CRI scores are rough approximations, not true corruption risk indicators. We can only calculate 3 of 9 factors from the EOGO paper, making our scores incomplete and potentially misleading.
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Missing Timeline Data: No publication dates, closing dates, or contract duration data needed for time-based red flags.
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No Cost Deviation Analysis: Missing initial estimated prices prevents calculation of contract cost inflation indicators.
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Simplified Methodology: Our CRI uses basic statistical aggregation instead of the sophisticated Item Response Theory (IRT) model from the original paper.
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Limited Validation: Without the full 9-factor CRI, we cannot validate our simplified approach against the academic methodology.
📊 Data Quality Issues
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PhilGEPS Data Gaps: Missing critical fields like entry dates, bidding timelines, and initial estimates limit comprehensive contract analysis.
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Contractor Data Inconsistencies: SEC verification status varies across data sources, affecting contractor concentration calculations.
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Geographic Mapping: Some contracts have incomplete or inconsistent province/region assignments.
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Temporal Coverage: Limited historical data prevents analysis of corruption risk trends over time.
🔬 Methodological Concerns
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Proxy Indicators: Using contractor concentration as a proxy for political dynasty concentration may not capture the same corruption mechanisms.
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Aggregation Methods: Our province-level aggregation may mask important municipal-level corruption patterns.
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Control Variables: Missing key socioeconomic controls (IRA dependency, poverty rates, ethnolinguistic fractionalization) limits causal inference.
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Endogeneity Issues: Without proper controls, observed correlations may reflect reverse causality or omitted variable bias.
💡 Recommendations for Improvement
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Enhanced Data Collection: Scrape complete PhilGEPS contract details including publication dates, closing dates, and initial estimates.
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Political Dynasty Database: Build comprehensive elected officials dataset with surnames, positions, and terms from COMELEC records.
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Socioeconomic Controls: Obtain poverty incidence, IRA allocations, and population data from PSA and DOF.
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IRT Implementation: Implement the full Item Response Theory model from the EOGO paper for proper CRI calculation.
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Academic Collaboration: Partner with Ateneo Policy Center to access their complete dataset and methodology.
📚 Detailed Documentation
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Complete EOGO Analysis: A detailed analysis of the EOGO methodology, data requirements, and implementation challenges is documented in analysis/EOGO.md
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Data Availability Assessment: The documentation includes a comprehensive assessment of what data we have vs. what's needed for full CRI calculation
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Methodology Details: Complete breakdown of the 9 CRI factors, Political HHI formula, and regression models from the original EOGO paper
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Implementation Roadmap: Detailed recommendations for collecting missing data and implementing the full EOGO methodology

⚠️ Important Disclaimer: This analysis represents a partial implementation of the EOGO corruption risk methodology. The limitations outlined above mean that our current HHI and CRI scores should be interpreted as preliminary indicators rather than definitive measures of corruption risk. Users should exercise caution when drawing conclusions from these incomplete analyses and consider the significant data gaps that affect the reliability of our findings.

📖 For complete details: See the comprehensive analysis in analysis/EOGO.md which documents the full methodology, data requirements, and implementation challenges.