The Mentally Ill, Behind Bars

After more than 50 years of stability, federal and state prison populations skyrocketed from under 200,000 persons in 1970 to more than 1.3 million in 2002. That year, our imprisonment rate rose above 600 inmates per 100,000 adults. With the inclusion of an additional 700,000 inmates in jail, we now incarcerate more than two million people — resulting in the highest incarceration number and rate in the world, five times that of Britain and 12 times that of Japan.

What few people realize, though, is that in the 1940s and ’50s we institutionalized people at even higher rates — only it was in mental hospitals and asylums. Simply put, when the data on state and county mental hospitalization rates are combined with the data on prison rates for 1928 through 2000, the imprisonment revolution of the late 20th century barely reaches the level we experienced at mid-century.

I was aware of the high rate of incarceration in the US, but I wasn't aware that it had jumped so much from 1970 or that if you combined incarceration rates with mental hospitalization rates that we are lower than in the '30s, '40s and '50s.

How much of the increased rate of incarceration can be attributed to those who used to go to mental hospitals?

It should be clear why there is such a large proportion of mentally ill persons in our prisons: individuals who used to be tracked for mental health treatment are now getting a one-way ticket to jail. According to a study released by the Justice Department in September, 56 percent of jail inmates in state prisons and 64 percent of inmates across the country reported mental health problems within the past year.

Of course, there are important demographic differences between the two populations. In 1937, women represented 48 percent of residents in state mental hospitals. In contrast, new prison admissions have consistently been 95 percent male. Also, the mental health patients from the 1930s to the 1960s were older and whiter than prison inmates of the 1990s.

Looks like quite a bit, but the different demographics makes me think there are other things going on as well.

One of the most reliable studies estimates that the increased prison population over the 1990s accounted for about a third of the overall drop in crime that decade. However, prisons are not the only institutions that seem to have this effect. In a recent study, I demonstrated that the rate of institutionalization — including mental hospitals — was a far better predictor of serious violent crime from 1926 to 2000 than just prison populations.

If we can keep crime at the same rates but treat but send many people to mental hospitals or drug rehab rather than jail, I think that makes a lot of sense (and probably a lot of taxpayer money as well).

I am also intrigued by the Author's new book: Against Prediction: Profiling, Policing, and Punishing in an Actuarial Age. The book's description:

From random security checks at airports to the use of risk assessment in sentencing, actuarial methods are being used more than ever to determine whom law enforcement officials target and punish. And with the exception of racial profiling on our highways and streets, most people favor these methods because they believe they’re a more cost-effective way to fight crime.

In Against Prediction, Bernard E. Harcourt challenges this growing reliance on actuarial methods. These prediction tools, he demonstrates, may in fact increase the overall amount of crime in society, depending on the relative responsiveness of the profiled populations to heightened security. They may also aggravate the difficulties that minorities already have obtaining work, education, and a better quality of life—thus perpetuating the pattern of criminal behavior. Ultimately, Harcourt shows how the perceived success of actuarial methods has begun to distort our very conception of just punishment and to obscure alternate visions of social order. In place of the actuarial, he proposes instead a turn to randomization in punishment and policing. The presumption, Harcourt concludes, should be against prediction.

I am prone to go with what the actuaries say in a wonkish sort of way, but I am interested to see what his argument is.

via NY Times