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Prompt:
What kinds of numbers do governments and policy often get wrong when they are legislated? Legislators aren't great at optimization problems because they are interested in signaling to the public rather than getting a number right. Worse they are likely to choose numbers likely to generate news as a result of how out of balance they are. What legislated numbers are way off of an ideal or a sense of reality?
Governments frequently embed specific numerical thresholds, rates, or quotas into legislation, but these figures are often chosen more for political appeal, public signaling, or generating headlines than for empirical optimization or alignment with reality. This can stem from a desire to appear tough on crime, protective of the environment, or economically prudent, even if the numbers lead to inefficiencies, injustices, or distortions. Below, I'll outline key categories and examples, drawing from historical and recent cases where such numbers have been criticized as arbitrary, extreme, or detached from evidence-based ideals. These often create "bright-line rules" that simplify enforcement but ignore nuance, resulting in over- or under-regulation.
Age limits are classic examples of arbitrary numbers in laws, often set to signal moral or protective stances rather than reflecting developmental science or cultural norms. They create hard cutoffs that don't account for individual maturity, leading to absurd outcomes.
Drinking Age (21 in the US): Established nationally in 1984 via the National Minimum Drinking Age Act, this was pushed as a safety measure but was largely a political response to drunk driving advocacy. Critics argue it's disconnected from reality—many countries set it at 18—and it hasn't optimally reduced underage drinking or related deaths. Instead, it fosters underground consumption and inconsistent enforcement (e.g., 18 for voting/military service but 21 for alcohol). This number was chosen for its round, memorable appeal to signal toughness on youth issues.
Statutory Rape and "Romeo and Juliet" Laws: Ages of consent (e.g., 16-18 in most US states) and close-in-age exemptions (often 2-4 years) are legislated numbers that vary wildly and lead to miscarriages of justice. For instance, a 19-year-old dating a 16-year-old with a fake ID can face mandatory "per se" offenses, resulting in lifelong sex offender status despite no intent or harm. These thresholds are set politically to signal child protection but ignore context, creating extreme penalties far from proportional justice.
Other Examples: Smoking age raised to 21 federally in 2019 (signaling anti-tobacco stance) or driving age at 16, both detached from brain development data showing maturity around 25.
Legislators often pick round or extreme numbers for penalties to generate news headlines about being "tough on crime," but these ignore criminological evidence on deterrence or rehabilitation, leading to overcrowded prisons and inequities.
Mandatory Minimum Sentences: US drug laws like the Anti-Drug Abuse Act of 1986 set arbitrary quantities (e.g., 5 grams of crack cocaine triggering a 5-year minimum vs. 500 grams of powder cocaine) that disproportionately affected minorities. These ratios were politically motivated to signal a war on drugs but were racially biased and far from optimal for reducing addiction or crime—later reformed but still criticized as arbitrary.
Blood Alcohol Content (BAC) for DUI (0.08 in most places): This threshold, adopted widely in the 1990s-2000s, was chosen as a compromise for enforceability and to signal road safety. However, impairment varies by individual (e.g., tolerance, weight), and studies show no sharp drop in accidents at exactly 0.08 vs. 0.05 or 0.10. It's often seen as too high for safety or too low for fairness, creating "per se" violations that punish non-impaired drivers.
Inflated Charging (e.g., Per-Item Counts): Prosecutors stack charges by treating each sub-item as separate offenses (e.g., one bank robbery charged as multiple counts per bill stolen) to inflate numbers and force pleas. This signals aggressive prosecution but distorts justice, turning minor crimes into life sentences.
Numbers here are manipulated or set arbitrarily to signal fiscal responsibility or growth, but they often distort markets or hide realities, as seen in government statistic fudging.
Economic Indicators Manipulation: Governments tweak numbers like inflation rates or deficits for political gain. Examples include Argentina's underreported inflation (officially ~10% in the 2010s while reality was 20-30%) to signal economic stability, or Greece's falsified deficits (hidden to meet EU's 3% GDP cap) that triggered the 2010s crisis. These aren't "legislated" per se but stem from laws requiring reporting, with thresholds like the EU's Maastricht criteria (60% debt-to-GDP) encouraging creative accounting far from transparent reality.
Minimum Wage Levels: Often set at round figures like $15/hour federally proposed in the US, chosen for populist appeal rather than local economic data. Critics argue it ignores regional costs (e.g., too high for rural areas, causing job losses; too low for cities), prioritizing signaling equity over optimization.
Tax Brackets and Rates: Arbitrary thresholds like the US's 37% top rate or $400,000 income cutoff for "wealthy" in policy debates signal fairness but don't align with economic models for growth or revenue maximization (e.g., Laffer curve suggests optimal around 20-30%).
These numbers signal environmental or safety commitments but can be extreme, stifling innovation or ignoring cost-benefit analysis.
Speed Limits (e.g., 55 mph National Limit in 1974): Imposed federally for fuel conservation during the oil crisis, it was arbitrary and ignored engineering data showing safer speeds around 65-70 mph on highways. Repealed in 1995, but similar local limits persist for revenue signaling rather than optimal safety.
Emission Standards: EU's CO2 caps (e.g., 95g/km for cars by 2021) or US CAFE standards (54.5 mpg by 2025, later adjusted) are set aggressively to signal climate action but often miss reality—leading to "Dieselgate" cheating or EV mandates ignoring grid capacity. Optimal levels per models are lower to balance economy and environment.
AI Risk Thresholds: Recent laws like Colorado's "high-risk" AI definitions (e.g., systems affecting decisions in health/employment) use vague numerical proxies, preempting innovation for signaling consumer protection. A proposed US moratorium on state AI laws could override these, showing federal signaling over local optimization.
These foundational numbers drift far from original ideals due to political inertia.
| Category | Example Number | Why Suboptimal/Arbitrary | Political Signaling | Real-World Impact |
|---|---|---|---|---|
| Age Limits | 21 (US Drinking) | Ignores science on maturity; inconsistent with other rights | Tough on youth issues | Underground drinking, enforcement waste |
| Sentencing | 5g Crack (5-Year Min.) | Racially biased ratio; no deterrence evidence | War on drugs | Mass incarceration |
| Economic | 3% GDP Deficit Cap (EU) | Encourages fudging stats | Fiscal responsibility | Crises like Greece's |
| Regulatory | 0.08 BAC | Varies by person; no sharp safety threshold | Road safety | Unfair convictions |
| Representation | 435 House Seats | Detached from population growth | Streamlined government | Vote dilution |
In summary, these numbers often emerge from compromises or populism rather than data-driven optimization, leading to policies that generate controversy (and news) but fail in practice. Tools like Benford's Law help detect manipulations in reported figures, highlighting how governments prioritize optics over accuracy. Reforms could involve sunset clauses or evidence-based reviews to align them closer to reality.
It seemed to give relatively libertarian answers. But there is an exception for implying 25 as a driving age.