How is Government Like the Internet?

Reid Hoffman thinks we should view government as an internet platform upon which citizens build lives. He isn’t speaking literally, but he isn’t far off the mark either. Hoffman’s background is in philosophy, so it is not surprising that his professional work (he is the founder of LinkedIn) and his political opinions intersect. Taking the view that Hoffman suggests means thinking of government as a service and citizens as customers who have a choice over which services to use and which not to use. I also like that it inverts the usual view by placing government at the bottom, as a foundation, and citizens can rise as high as they want.

Another philosopher/sociologist, Kieran Healy, has an interesting analogy of internet and government. He says

The U.S. system of employer-sponsored healthcare provision is iTunes. It’s complicated and overburdened; it wasn’t originally designed to do most of the things it now does; in fact, at the outset its design wasn’t really thought through at all (there wasn’t time); many of those involved backed it as a distant second-best solution—better than nothing, but not nearly good enough. Over the years, new features were shoehorned into the basic structure. New problems and inconsistencies emerged and were partially patched. And, inevitably, groups who did pretty well out of the system emerged and entrenched themselves, too. In situations like this, some reforms are possible around the edges, but it’s clear to most people that real structural reform is needed.

In development terms, that means it is time for a refactoring or even a complete overhaul. This debugging of government could also incorporate what Eric Raymond calls Linus’s Law (“Given enough eyeballs, all bugs are shallow.”) by responding to the feedback of informed citizens.

How far can we take this metaphor? Is it too utopian?

Wednesday Nerd Fun: Oximetry with Ruby and R

These posts are getting pretty esoteric, which may be a sign that I should put the series on hold for a while. Feed back is welcome. In any event, here’s some midweek entertainment for the coders among you:

A popular and fast way to effectively get the heart rate is pulse oximetry. A pulse oximeter is a device placed on a thin part of a person’s body, often a fingertip or earlobe. Light of different wavelengths (usually red and infrared) is then passed through that part of the body to a photodetector. The oximeter works by measuring the amounts of red and infrared light absorbed by the hemoglobin and oxyhemoglobin in the blood to determine how oxygenated the blood is. Because this absorption happens in pulses as the heart pumps oxygenated blood throughout the body, the heart rate can also be determined.

We are not going to build an oximeter, but in this post we’ll use the same concepts used in oximetry to determine the heart rate. We will record a video as we pass light through our finger for a short duration of time. With each beat of the heart, more or less blood flows through our body, including our finger. The blood flowing through our finger will block different amounts of the light accordingly. If we calculate the light intensity of each frame of the video we captured, we can chart the amount of blood flowing through our finger at different points in time, therefore getting the heart rate.

Continue here.

Does State Spending on Mental Health Lower Suicide Rates?

That’s the title of a new paper (gated) in the Journal of Socio-Economics by Justin Ross, Pavel Yakovlev, and Fatima Carson. Here’s the abstract:

Using recently released data on public mental health expenditures by U.S. states from 1997 to 2005, this study is the first to examine the effect of state mental health spending on suicide rates. We find the effect of per capita public mental health expenditures on the suicide rate to be qualitatively small and lacking statistical significance. This finding holds across different estimation techniques, gender, and age groups. The estimates suggest that policies aimed at income growth, divorce prevention or support, and assistance to low income individuals could be more effective at suicide prevention than state mental health expenditures.

Their paper asks an interesting question, and apparently they are among the first to attempt an answer using empirical data. Suicide is one of the oldest topics of interest for social scientists, going back to Émile Durkheim‘s 1897 publication.

The main problem with the paper’s analysis is the use of observational data to make a causal claim.* As the authors themselves point out, state mental health spending is remarkably stable to the point that if a year of data were missing it could be interpolated by averaging the years before and after. There’s really no exogenous change observed in the sample period–no instance of a state dramatically increasing or reducing its spending is mentioned–so the comparisons are mostly between rather than within states. This setup fails to provide evidence for the authors’ claims such as, “a one percent increase in public mental health expenditures per capita would reduce the incidence of suicide among that group by 0.91 per 100,000 women in this age group [25-64].”

Fig. 1: Ross, Yakovlev, and Carson (2012)

Given the large between-state differences, a cleaner design might have looked at suicide risk for individuals who moved from one state to another. Of course, this introduces the problem that individuals who commit suicide never move to another state afterward. Furthermore, this individual-level data would like be difficult to collect. However, even a small survey of individuals would be a nice complement to this paper’s focus on aggregate statistics. (The authors are careful to point out that their paper does not assess the effectiveness of mental health treatment on suicide outcomes.)

On the positive side, it is nice to see a null finding published in a journal. Findings that are “qualitatively small lacking statistical significance” are not often seen in print even when they are justified. I only wonder in this case whether the findings will hold up.

Some other posts that I have written on mental health issues can be found here and here.


*Yes, the same could be said of much social science. That doesn’t make it OK, nor does it mean that NSF Political Science should be defunded.

Stated vs Revealed Preferences: Health Care Edition

In social science, much research centers on the idea of preferences. Preferences are a key element of decision theory, which suggests that individuals have some order of preference over a set of outcomes. More generally, we can think of such varied topics as voter choice and grocery shopping as preferences. There are two ways to measure preferences.

The first is stated preferences. Often, individuals even have preferences over things that they cannot control or we cannot measure directly. Say, for example, that we are trying to ascertain how strongly people prefer environmentally friendly products to less eco-friendly products. If we just want to know their stated preferences, we can simply ask them. (Although, if we want to run a scientific survey, it gets less simple the more accurate we want to be.)

On the other hand, say we want their revealed preferences, so we observe their actual behavior. Behavior with regard to eco-friendly products centers on purchases, so we look at whether individuals tend to purchase eco-friendly products more than the alternatives. (It turns out they don’t. h/t to Adam Ozimek) Economists generally prefer to work with revealed preferences, and as a political scientist so do I. The definitions of “hard” or “soft” data might be fuzzy, but when I talk about hard data this is the kind of thing I mean: actual, observable behavior. Sometimes survey research is your only alternative, but I have a hard time thinking of an instance in which it would be ideal. [edit: it’s important to note–which I neglected to do earlier–that revealed preferences often get screwed up outside of the context of a free market; in the US even sugar isn’t a free-market commodity thanks to high tariffs; this potentially undermines what I say later about health care but I think the main point still holds]

Now to apply this to health care. Karl Smith had a post over the weekend citing several studies that indicate that consumers don’t actually care how effective certain hospitals/treatments are when making health care decisions. This is based on actual patient behavior (revealed preferences). Of course if you ask anyone, they would say that they want the most effective treatment possible.

This is the difference between stated and revealed preferences–everyone says they want effective treatment, but when it comes to making health care decisions, as Karl argues, often they simply feel the need to do something. On one level this is completely fine: if you’re spending your own money and it’s just to make you feel better in an emotional sense, go for it. Whether that means purchasing lots of movie tickets, lollipops, health care, whatever. But when you start spending other people’s money (government health care) it seems reasonable that the public (“other people”) take an interest in the efficacy of various procedures or treatments. I’m not talking about death committees or anything like that, but if the government is funding your health care then they have a right to say no to certain treatments. This is the same principle that leads me to believe that the government should have a right to tell people in NYC that they can’t buy cola with food stamps. It doesn’t mean you can’t buy cola (or health care), it just means that you have to use other (ahem, your own) money to do it. If it’s too expensive, find a cheaper alternative. That might mean more competitiveness in the market for health care. It might mean people going to other countries for treatment (not ideal but certainly possible). And it might even mean that more people become aware of homeopathic alternatives.

On a lighter note, here’s a clip from Scrubs that always comes to mind when I think about the effectiveness vs cost of certain health care procedures: