Psychiatric Beds: Actual vs. Unfunded Promise
Each trajectory draws a replacement fraction from Normal(0.70, 0.10) — the share of closed state hospital beds replaced by community capacity.
Community centers ramp up on a sigmoid curve from 1965 to 1980, reaching full capacity by the late 1970s.
Per-year stochastic noise: Normal(0, 5 beds/100k).
The fan shows the 5th, 25th, 50th, 75th, and 95th percentiles across 500 runs.
Incarceration: The Cost of Unfunded Replacement
Fewer people per 100,000 would be incarcerated if community mental health infrastructure had been funded.
At a population of 340 million, that median gap represents — people who are in prison today who could have been in treatment.
Lionel Penrose (1939) observed that psychiatric hospital populations and prison populations move inversely. As one system empties, the other fills.
This model applies a 1:1 ratio: each bed/100k lost = 1/100k more incarcerated. Conservative estimates suggest the true ratio may be higher.
How the counterfactual was constructed
The question
What would American mental health infrastructure look like today if the Community Mental Health Centers Act of 1963 had been fully funded — if the community capacity promised as replacement for state hospitals had actually been built?
The model
For each of 500 trajectories, we sample a replacement fraction from Normal(0.70, 0.10), clipped to [0.30, 0.95]. This represents the share of closed state hospital beds that would have been replaced by community mental health center capacity.
Community capacity ramps up on a sigmoid curve beginning in 1965 (two years after the Act) and reaching full target capacity by 1980. This reflects realistic construction and staffing timelines for a national network of community centers.
Each year includes stochastic noise drawn from Normal(0, 5 beds/100k) to capture real-world variance in implementation, local politics, and funding fluctuations.
Incarceration coupling
The incarceration counterfactual applies Penrose's Law at a 1:1 ratio: each additional bed per 100,000 in the counterfactual reduces the incarceration rate by 1 per 100,000. This is consistent with research by Raphael & Stoll (2013) and Primeau et al. (2013) showing that psychiatric deinstitutionalization accounts for a significant fraction of mass incarceration.
What this is not
This is not a prediction. It is a structured exploration of a specific policy counterfactual. The model intentionally uses simple, transparent assumptions rather than complex causal inference. The point is not precision — it is to make visible the scale of what was lost when Congress promised community mental health infrastructure and never funded it.
Limitations
• Penrose's Law is an empirical regularity, not a causal law. The true relationship between beds and incarceration involves many mediating factors.
• The replacement fraction assumes a single national parameter; in reality, implementation would have varied dramatically by state.
• The model does not account for other drivers of mass incarceration (War on Drugs, mandatory minimums, policing changes).
• Community treatment capacity is not equivalent to institutional beds; the quality and nature of care would differ substantially.