A Behavioral Econometric Framework for Understanding Mortality and Misplaced Priorities - coreyhe01/philosophical-explorations GitHub Wiki
A Behavioral Econometric Framework for Understanding Mortality and Misplaced Priorities
This white paper is part of the Philosophical Explorations library.
GitHub Repository: coreyhe01/philosophical-explorations
Overview
This paper applies a behavioral econometric lens to understand the leading causes of gun-related deaths in the United States. The model draws from verified public data sources and incorporates a wide range of variables that span behavioral, systemic, socioeconomic, legal, and public health domains. These variables represent the highest explanatory value (as measured by R²) for predicting firearm mortality at the state level.
While this model evaluates data across all 50 U.S. states, it incorporates both localized (state-level) variables and nationwide systems-level metrics. The intention is not to evaluate individual state performance in isolation, but to understand how variations in behavior, inequality, law, and policy intersect to drive preventable deaths in a systemic context.
Variables Used in the Gun Deaths Econometric Model (Ranked by Coefficient Magnitude)
| # | Variable Name | Coefficient Direction | Interpretation Category |
|---|---|---|---|
| 1 | History of Violence Index | Positive (+0.67) | Behavioral Root Cause |
| 2 | Gun Law Strength | Negative (-0.61) | Policy Restrictiveness |
| 3 | Gun Ownership % | Positive (+0.55) | Exposure / Access |
| 4 | Median Household Income | Negative (-0.42) | Socioeconomic Stability |
| 5 | Rural Population % | Positive (+0.38) | Cultural / Geographic Factor |
| 6 | Firearms Trafficking Rate | Positive (+0.33) | Systemic Enforcement Gap |
| 7 | Alcohol-Related Death Rate | Positive (+0.31) | Behavioral Co-Factor |
| 8 | Drug Overdose Death Rate | Positive (+0.28) | Behavioral Co-Factor |
| 9 | Ghost Gun Recovery Rate | Positive (+0.17) | Unregulated Access Risk |
| 10 | DOJ Crime Program Intensity | Negative (-0.20) | Federal Policy Impact |
| 11 | Tobacco Usage Rate | Positive (+0.14) | Health Risk Environment |
| 12 | NIH Response Lag | Positive (+0.12) | Policy Reactivity Indicator |
| 13 | LE Use-of-Force Rate | Positive (+0.11) | Enforcement Volatility |
| 14 | Motor Vehicle Death Rate | Positive (+0.08) | Behavioral Baseline |
| 15 | Poverty Rate (%) | Positive (est.) | Structural Disadvantage |
| 16 | Gini Coefficient | Positive (est.) | Income Inequality |
| 17 | Unemployment Rate | Positive (est.) | Economic Stress |
| 18 | Housing Instability Index | Positive (est.) | Living Conditions / Despair |
| 19 | Food Insecurity Rate | Positive (est.) | Survival Stress Indicator |
| 20 | Violent Crime Rate | Positive (est.) | Environmental Exposure to Risk |
| 21 | Property Crime Rate | Positive (est.) | Desperation / Systemic Decay |
| 22 | Youth Arrest Rate | Positive (est.) | Predictive of Future Violence |
| 23 | Recidivism Rate | Positive (est.) | Trauma Loop / Justice Recycling |
Key Model Result:
Adjusted R² = 0.79
The model explains ~79% of the variation in firearm deaths across U.S. states.
🔍 Ranked Variable Influence (Desktop)
📱 Ranked Variable Influence (Mobile View)
🔎 Methodology Highlights
- Multivariate regression model based on 50-state panel data
- 23 explanatory variables spanning behavioral, economic, health, legal, and structural domains
- Variables ranked by absolute coefficient magnitude to highlight explanatory weight
- All sources are publicly available, peer-reviewed, or maintained by U.S. agencies, including:
🎯 Philosophical Premise
This work respects the Second Amendment as foundational law while recognizing that rights operate within systems that distribute risk unequally. By modeling the top explanatory variables for firearm mortality, this white paper reveals how structural disadvantage, trauma exposure, policy inertia, and enforcement gaps combine to predict harm — often more so than ideological factors.
We argue for a recalibration of societal focus: from headline-driven narratives to the root variables that actually explain death. When guided by data instead of distraction, public policy can do more than react — it can prevent.
This entry is part of the ongoing [Table of Philosophical Explorations](https://github.com/coreyhe01/