Horse Energy

Key findings from rigorous research:

• Gangs function as alternative labor markets. Gang "foot soldiers" earn $3.30/hour—below minimum wage—yet when facing unemployment, that becomes the best available option.

• Employment interventions dramatically outperform enforcement. Summer jobs reduce violence by 43% with benefit-cost ratios of 4:1, compared to 0.7:1 for incarceration.

• The elasticity is substantial. A 1% increase in youth unemployment predicts a 2.5% increase in gang recruitment—3-4x larger than unemployment's effect on general crime.

• Six mechanisms operate simultaneously: opportunity cost reduction, income replacement, status provision, network effects, time allocation, and institutional deterioration.

• The policy implication is clear: We cannot arrest our way out of gang problems rooted in unemployment. We currently spend $80B annually on corrections, $1B on youth employment—yet quintupling employment spending would still cost less than incarceration while achieving far larger violence reductions.

With 5 interactive D3.js visualizations tracking recession impacts, city-level patterns, age effects, mechanisms, and policy cost-benefit comparisons. Includes 15 academic citations from Becker, Freeman, Levitt & Venkatesh, and recent RCT evidence.

Key findings from the empirical evidence:

• Task composition matters more than skill level. Non-routine tasks experience less wage pressure than routine tasks.

• Geography determines outcomes. Detroit's wages fell 6.3% as robot adoption accelerated, while San Francisco's grew 18.2% over the same period.

• Institutions matter enormously. German autoworkers' wages rose 18% with automation; American autoworkers' fell 2%. Same technology, different institutional context.

• Worker bargaining power is decisive. In unionized firms, automation raised wages 4.7%; in non-union firms, production workers saw just 0.8% gains.

With interactive D3.js visualizations showing robot density by country, task polarization trends, and within-firm wage effects. Includes 50+ academic citations and policy implications for the AI transition.

Current Reading: 75.3 / 100 (Extreme Risk)

This interactive tool shows how all four ingredients have evolved over time and reveals that today's risk profile is fundamentally different from past crises—driven by status threat and institutional collapse rather than unemployment.

1. Economic Crisis (~33% of effect): Financial crises trigger unemployment, debt, uncertainty → risk-seeking behavior. Meta-analysis of 36 studies confirms causal effect.

2. Status Threat (major driver): Perceived loss of national standing or group dominance. Stronger predictor than economics in 2016 US election.

3. Cultural Backlash (major driver): Demographic change, immigration → anxiety and scapegoating. Cultural concerns can drive economic discontent (reverse causation).

4. Institutional Weakness (accelerant): Young democracies, mainstream party failure → trust collapse.

The pattern: Weimar Germany, Greece 2009-2015, Hungary 2006-2014 all show these ingredients combining. When all four align during acute crises, extremist parties surge from 0.3-2.6% to 7-37% within years. Between 1920-1939, Europe's democracies fell from 24 to 11—a 54% collapse.

Investment goods (housing, healthcare, education) have become prohibitively expensive—73% less affordable for housing, 70% more expensive healthcare, 40% more burdensome education—while consumption goods (food) have become 15% MORE affordable.

You can afford to survive, but you cannot afford to thrive. Food is cheap. Building wealth is impossible. The things you need to eat are fine; the things you need to achieve economic mobility—buying a home, getting healthcare, earning a degree—are out of reach.

Even quality-adjusted (homes are 35% larger), housing is still 28% less affordable than historical norms. And you can't opt-out: zoning laws prevent smaller homes, you can't buy "1980s healthcare" at 1980s prices, and credentialism means you can't skip expensive degrees.

From 1947 to 2024, we've witnessed a dramatic reallocation of economic rewards. Labor compensation has fallen from 65% to 56.5% of national income. This represents approximately $1.8 trillion annually that has shifted from workers to capital owners, landlords, and corporate profits.

This was caused by a combination of technological displacement, globalization (particularly the China Shock), weakening labor institutions, and tax policies that increasingly favor capital over labor [...]

We've also discovered that current tax incentives actively encourage businesses to replace workers with machines, creating a self-reinforcing cycle of automation.