The health effects of Swedish prisons

Does time in prison make people healthier, or less healthy? On the one hand, prisons are a challenging environment. They are stressful, and the risks of harm through violence are high. As we've discovered recently, they are also an excellent super-spreading environment for infectious diseases. And prisoners may suffer from reduced nutrition, or reduced access to health care. On the other hand, perhaps prisoners' nutrition and/or access to health care may be improved by being incarcerated. Many prisoners have untreated (or undiagnosed) mental health problems, or substance abuse problems, that can be more effectively treated in an institutional setting.

So, which is it? No doubt, it depends on the particular prison context, and the health of the prison population at the time they go into prison. Let's take a particular prison context: Sweden, which has an excellent reputation for rehabilitation (see here and here), and a relatively low prison population (74 per 100,000 population, compared with 155 in New Zealand, and 505 in the US). This recent article by Randi Hjalmarsson (University of Gothenburg) and Matthew Lindquist (Stockholm University), published in the American Economic Journal: Applied Economics (ungated earlier version here), looks at the impact of time in Swedish prisons on health outcomes.

Hjalmarsson and Lindquist make use of a neat natural experiment, being:

...Sweden’s 1993 and 1999 early release reforms, which held sentences constant but increased the share of time inmates were required to serve from 50 percent to 67 percent. Exposure to the two- thirds reform depended on the date of conviction and sentence length. Shorter sentences (4–12 months) were fully treated by the first reform and longer sentences (≥ 24 months) by the latter; intermediate sentences were partially treated by both.

Because time in prison changed, but prison sentences did not, Hjalmarsson and Lindquist look at how the increase in the number of days in prison affects health, while holding sentence length (and therefore, the severity of crime the prisoner is being sentenced for) constant. Their final sample consists of nearly 47,000 sentences of between 4 and 48 months, which commenced between 1992 and 2001. Because Swedes have an effective population register, Hjalmarsson and Lindquist are able to link prisoner records with hospital and mortality data. Looking at the impacts up to ten years after release from prison, they find a variety of impacts, including that:

...exposure to the two-thirds reform does not harm post-release health and actually improves it. Though the reduction in mortality risk is not quite significant when looking at the entire sample, these aggregate results mask important heterogeneity in two dimensions. First, significant reductions in the overall chance of death (especially in the first two years post release) are seen for positively selected subsamples, including those with no past prison exposure, property offenders, relatively young offenders, and those with some past employment. Second, significant effects are seen for the whole sample when zooming in on causes of death particularly relevant for this population. There is a large, significant, and immediate reduction in the chance of suicide; the chance of suicide is still reduced by 38 percent ten years after release. These suicide results are driven by individuals with previously identified mental health issues and by violent offenders.

Taken all together, these are positive results for the Swedish prison system. Why does it do so well? Hjalmarsson and Lindquist can't definitively tell, but note that:

First, health care in Swedish prisons is of high quality. Second, more time in prison is positively related to visits with medical professionals (doctors, nurses, and psychologists), medication, and starting and completing treatment programs. High-quality health care and treatment that increases with time served is consistent with our findings of the health-improving effects of the reform.

So, if we want to improve the health of prisoners, should we be keeping them in prison longer? That would probably be extending these results too far. Remember that context matters. As Hjalmarsson and Lindquist note, the health care available in the Swedish prisons is high quality, and prisoners access it readily. That is not the case in all prison settings. So, we shouldn't use these results to conclude that prisons improve health, but rather that Swedish prisons improve health, and that moving towards the Swedish model may have positive impacts. Of course, then we run into other problems, because Hjalmarsson and Lindquist also note that:

...Sweden spends more money per inmate than any other country...

If we want better health incomes for prisoners, this comes with an increase in cost. We'd need to weigh up those costs and benefits to make a sensible decision about what is best to do. As the saying goes, there is no such thing as a free lunch (as my ECONS101 students will learn when teaching starts next week! [*]).

[HT: Marginal Revolution, last year]

*****

[*] Not literally though. I'm not offering them free food. Instead, we will cover the concept of opportunity cost in the second lecture.

Local restrictions against unpopular retailers

When the Sale and Supply of Alcohol Act 2012 came into force, it gave local councils the ability to develop local alcohol policies (LAPs), to control the availability of alcohol in their city or district. One of the things that LAPs could do was to restrict alcohol licenses from being granted for premises that were in close proximity to 'sensitive sites' (like schools, churches, alcohol treatment providers, etc.). At the time, there was a lot of talk from some advocates about creating wide exclusion zones around these sensitive sites. I pointed out (to a number of people) that these exclusion zones wouldn't have to be very big in order to create a de facto ban on alcohol licenses entirely. Sanity prevailed, and those councils that have these sorts of restrictions in their LAPs have not made them excessively large.

Fast-forward to 2023, and the same arguments for exclusion zones are being made in relation to vape stores. For example, the Asthma and Respiratory Foundation was last year calling for a ban on retailers selling vaping products within one kilometre of a school. How feasible is that sort of control?

Steve at City Beautiful did the GIS work and reported it last year. Here's their map of all of the areas where a vape shop could set up, if you exclude all areas that are within one kilometre of a school, and exclude all areas that are not zoned commercial:

The grey areas are places where a vape shop could not be placed. The orange areas are the few places where a vape shop could be placed, in a commercial zone and more than one kilometre from a school. That's right. Almost nowhere could have a vape shop. As Steve notes in his post:

Yes, only those few tiny scraps of land shown in orange are where vape shops could go. A couple of remote industrial or business areas, sometimes with only a single site available. If there’s already a different shop there? Tough luck. The only significant area where a vape shop could actually go is right at the northern end of the city centre - and rather than being a realistic idea for every single vape user in central Auckland to come downtown every time they need to stock up, it just shows more than anything else, how desperately Auckland’s city centre lacks a school!

Urban planning is hard. Regulations that seem sensible can have unintended (as well as intended!) consequences. The problem here is that commercial zones tend to be right next to schools. If you think about your neighbourhood shops, I bet that there is a school next door to them, or just around the corner from them. Restricting unpopular retailers from being near to schools is essentially the same as restricting them from operating in most of the commercial zones in the town or city. And proponents of these regulations forget the smallest towns, of the kind that have a single school, and a single block of shops, usually on the same small stretch of main road.

There are better ways to control demerit goods than to effectively ban them. License the sellers and restrict the number of licences, tax the sale of the product, impose minimum prices, have stringent age restrictions, or do some combination of all of these things. A de facto ban, masquerading as an exclusion zone that only applies around schools, is not the way.

And besides, the location of stores selling these products becomes almost irrelevant when you consider online sales and deliveries. These products can be purchased anywhere, with very little in the way of controls. The last couple of weeks I've been conducting fieldwork looking at same-day delivery of alcohol (and that's why this blog has been pretty quiet of late). It's not quite the Wild West, but it's far from ideal at the moment. More on that in a future post.

[HT: Eric Crampton at Offsetting Behaviour]

Rainfall, military food shortages and the assassination of Roman emperors

Dictators maintain control of their country by maintaining control of the military. If they lose control of the military, they often lose control of their country, or worse. This 2018 article by Cornelius Christian (Brock University) and Liam Elbourne (St. Francis Xavier University), published in the journal Economics Letters (sorry, I don't see an ungated version online), looks at cases where Roman emperors lost control, and their lives (through assassination).

Christian and Elbourne first note some key facts drawn from past research:

(1) The Roman economy was largely agricultural, depending on rainfed agriculture (Harper, 2017).

(2) The bulk of the Roman army was stationed along the Western frontier, and relied heavily on local food sources (Roth, 1998; Elton, 1996)...

(3) Food transport, in Ancient Rome, was very slow (Terpstra, 2013).

Christian and Elbourne then infer that shocks to military food supply, arising from low rainfall in the frontier areas like Germania, would reduce support for the emperor, increasing the chances that the emperor is assassinated. They use data covering the period from 27 BCE to 476 CE, which covers the period from the beginning of the reign of Augustus to the fall of the Western Roman Empire. Over that period, the relationship between rainfall and the number of assassinations is captured in Figure 1 from the article:

If you squint really hard, you can probably see that there are more assassinations in times when rainfall is lower. This is most apparent in the middle of the study period, between 200 and 300 CE, when there was a century of relatively low rainfall and many assassinations. Turning to their quantitative results, Christian and Elbourne find that:

...a standard deviation decline in rainfall causes an 11.6% standard deviation increase in assassination probability... [and] a standard deviation drop in rainfall causes a 13.4% standard deviation increase in total assassinations.

Now, of course this paper doesn't definitively establish a causal relationship between rainfall and assassinations. However, it is interesting that when, instead of using rainfall from the year before, Christian and Elbourne use rainfall from the following year, the correlation with assassinations becomes statistically insignificant. They also find that rainfall is a statistically significant predictor of frontier mutinies, which is consistent with rainfall increasing disquiet in the military.

However, the parts of the story included in this article don't quite feel like they add up to a coherent whole to me. The correlations are strong, but not necessarily entirely convincing. If the mechanism through which negative rainfall shocks increase emperor assassinations is through declining military support, why don't Christian and Elbourne use an instrumental variables approach, with rainfall as an instrument for frontier mutinies, and frontier mutinies as an endogenous variable explaining assassinations? Surely they tried this approach, so there being no mention of it in the paper is perhaps a signal to us that the approach doesn't work. In that case, we should be a little sceptical of the results.

Nevertheless, it is an interesting paper, and I'm sure there will be more work in this area. As attributed to both Napoleon Bonaparte and Frederick the Great, "an army marches on its stomach". When the stomach is not full, I guess the army stops marching, and eventually turns on its leaders.

Big data and artificial intelligence may not make pure command or socialist economies any more feasible

Economic systems can be considered to fall along a continuum. At one end is the pure market economy, where the government only provides the basic rule of law, enforcement of contracts and property rights, and some public goods. All property and the means of production are private owned. At the other end is the pure command economy, where the government is responsible for all key production and consumption decisions, and owns most property and the means of production, on behalf of citizens.

All modern economies fall somewhere in between these two extremes, and most developed countries are closer to the market economy end of the continuum than the command economy end of the continuum. That shouldn't be a surprise. Market economies have been incredibly successful over the past century and more, while socialist and command economies have mostly fallen away. [*] However, long before it was apparent that the command economy was a slow-motion train wreck, it was heavily critiqued by the Austrian school economist Ludwig von Mises, in his famous paper Economic Calculation in the Socialist Commonwealth (originally published in 1919, then translated to English in 1920). Von Mises essentially argued that money and money prices provided the information that was necessary for the market system to function efficiently. Because a socialist economy lacked money prices, a socialist planner could not properly coordinate the economy in as efficient a manner as would be achieved in the market economy. Von Mises concluded that "rational economic activity is impossible in a socialist commonwealth".

The difficulty of rational economic planning by a central planner was brought home later by economics Nobel Prize winner Leonid Kantorovich. In a story I first read in Tim Harford's book Adapt (which I reviewed here), Kantorovich applied linear programming methods to the production and allocation of Soviet steel for a single year (it is mentioned briefly in this biography of Kantorovich as well). Solving this problem using complex mathematics took several years. And that was for a single (albeit large) industry. For a single year.

However, computing power and computer science methods have progressed significantly since von Mises' and Kantorovich's time. The availability of big data, machine learning algorithms, and artificial intelligence might suggest that command economy planning will become more feasible. Not so, according to this new article by Karras Lambert (George Mason University) and Tate Fegley (Montreat College), published in the Journal of Economic Behavior and Organization (sorry, I don't see an ungated version online).

Lambert and Fegley go back to the original arguments of von Mises and outline how the basic problem of economic calculation is not resolved by big data, increased computation, or artificial intelligence. Specifically, they note that:

Can “big data”, absent the market pricing process, generate a commensurate cardinal unit with which to meaningfully compare costs and proceeds of productive actions? Does it provide a mechanism for translating ordinal preferences into commensurate cardinal numbers? Does it give a central planner the capability to engage in entrepreneurial price appraisal without genuine market prices for goods of all orders? The answers are evidently no on all counts. As a result, “big data” itself, while undoubtedly a useful tool for entrepreneurs operating within a market system, cannot replace the pricing process or render unnecessary private ownership and ex- change of the means of production.

The key point for me is that big data and algorithms, by construction, are based on past data. The most useful data for planning are data that come from the decisions of consumers and producers. If we moved from a market economy to a command economy, the extant data would become progressively outdated over time. Eventually, relevant data necessary to estimate big data models and algorithms would not exist at all.

It seems to me the argument about whether the pure socialist or command economy is feasible needs to be set aside for a while yet. Perhaps we come back to it when we get artificial general intelligence, or where we get so much computing power that we can simulate entire people, with all of their preferences, quirks, and sub-optimal decision-making capacities.

*****

[*] Some might argue that China is a counter-example. However, when you look at the characteristics of the Chinese economic system, it looks much more like a market economy than a command economy. Indeed, it is not so much a socialist or command economy, but socialism with Chinese characteristics.