Negative Externalities

It is not from the benevolence of the butcher, the brewer, or the baker that we expect our dinner, but from their regard to their own interest.

Adam Smith, The Wealth of Nations

Overjoyed, yet slightly appalled that Richard would even think of, let alone do, such a thing, the man gave him ten thousand dollars for the contract, and a second ten thousand dollars for the incredible suffering the mark had experienced.

"You did a good job," he said. Richard liked to please his customers; that was how his business had grown over the years.

 Philip Carlo, The Ice Man: Confessions of a Mafia Contract Killer

Economics, Sociology, Extrinsic and Intrinsic Motivation, and Variance, or, Advice for the Tribal Social Scientist

Someone whose name I've forgotten said that economics is all about how people act rationally and sociology is all about how they don't. That's catchy, but not very helful, because it makes you think about the exact meaning of the term "rational", and before you know it, you're writing a book. Let me propose instead (and of course I'm using the broad brush here) that economics is how people are driven by extrinsic motivation and sociology is about how they're driven by intrinsic motivation.

Of course, people are driven by both, so a good social scientist should consider both. But there's more to be said about the two. Extrinsic and intrinsic motivation are functional equivalents, and the less variance there is in one of the two, the more variance in your dependent variable the other is going to explain.

That's a little abstract, so here's an example. For the purposes of the example, please accept the simplification that the two types of motivation are completely independent of each other.

Consider a company unit in which variance in intrinsic work motivation is low, and the mean is also low - that is, everybody's a lazy bastard. Then variance in extrinsic motivation will explain a lot of variance in behaviour. That is, those who have a higher incentive, such as financial rewards, to work, will work harder, while those that have little incentive will work little (high variance, middling mean).

Now consider a company unit in which variance in intrinsic work motivation is low, and the mean is high. Here, everyone will work hard (low variance, high mean), and differences in incentives will have little effect.

Next, start by thinking about extrinsic motivation. If extrinsic motivation's variance is low with a low mean (everyone gets minimum wage, regardless), then how hard people work will be driven by their intrinsic motivation (high variance, middling mean).

Finally, if extrinsic motivation's variance is low with a high mean, everyone will try hard (low variance, high mean).

What does that leave us with? First, if variance in one independent variable is lower, then variance in the other dependent variable will explain more of the variance in the dependent variable. Admittedly, that's a mathematical necessity, and not a new insight. More specifically, it means that economists who want to stick it to the guys in the other building had better select topics where variance in people's intrinsic motivation is low, while sociologists who want to teach those arrogant dicks in the Adam Smith building a lesson should select reasearch areas in which variance in people's extrinsic motivation is low.

And if you own a company, you ought to put a lot of time into selecting people with high intrinsic work motivation, and also think hard about how to taylor rewards to employees' behaviour. You'll want to do both, because you'll be perfect at neither.

Genetics, Human Capital, and the Thomas Theorem

In this short presentation on "Genetics and Society" (video), Gregory Cochran points out an obvious incompatability between human capital theory and empirical findings in behaviour genetics: Human capital theory proceeds as though differences in human capital were solely the product of environmental influences, and especially decisions made by parents, but this is known to be false. Cochran goes on to say this has an impact on a point made in human capital theory concerning the quality-quantity tradeoff: The standard view is that you can have many kids and low investment per kid, leading to relatively low human capital in each kid, or you can have many kids, invest heavily in them, and expect them to exhibit high human capital. To the extent that human capital is influenced by genetics, and to the extent that it is not influenced by parents, this tradeoff does not exist. For example, IQ shows practically no response to differences in parental behaviour, hence your kids' expected IQ is independent from your investments, hence independent from the number of kids you have.

But that's a normative point. Empirically, how much people plan to invest in their kids and how many kids they hence choose to have, should be influenced not by the truth itself, but by what people believe to be true. Belief in genetic influences on people's characteristics decreased ca. 1920-1950 and is now low. While research results from the 1970s onwards have shown the popular view to be wrong, these results are not widely known and believed. Hence, decreases in the number of kids people have might still in part be explained by the above-mentioned aspect of human capital theory, if it is combined with the Thomas theorem: "If men define situations as real, they are real in their consequences."

Does Altruism Exist? Against the "Warm Glow" Argument

Models of human decision-making are just that: models, not the real thing. Another way to put this is to say that models are wrong. As the famous saying goes, all models are wrong, but some are useful. This leads to the question, useful for what? That is, just because a model is useful in one domain, doesn't mean it's useful in another domain.

Case in point: economists. Traditionally, they have been working with a model of rational, egoistic decision-makers. Pretty much everybody knows that model is wrong because people sometimes act altruistically, but the model is useful model for many purposes. The trouble starts when people forget that it's just a model that is supposed to be good for some purposes (and not others) and start to defend the model as though it were an entirely accurate description - a view that obviously does not conform to the empirical evidence.

Bryan Caplan is not one of those people. He points out that altruism is real. Anticipating counter-arguments, he adds:

Sure, true believers in ubiquitous selfishness can grasp at straws to protect their dogma.  Perhaps people donate blood for the free cookie, join the army because they might run for office one day, or give to charity in order to make business connections.  Or maybe millions of average joes are clueless enough to believe that the blood supply, the safety of the free world, and the availability of charity hinge on whatever they personally choose to do. 

Anything is possible, but that doesn't mean that anything is plausible.  [...] Genuine altruism is all around us.  Benevolence doesn't explain why bakers bake bread for paying customers, but it does explain why blood donors give blood to strangers for free.

Naturally, this prompts his readers to double down on the altruism-is-really-egoism stuff. For example, his reader Caliban Darklock writes:

I suggest that if there exists an incentive, the activity is not altruistic.

If I give a person $10 for drugs, and then I take the drugs and they give me an endorphin rush, that is not altruistic.

If I give a person $10 because it makes me feel good about myself, which gives me an endorphin rush, how is that altruistic?

I traded $10 for an endorphin rush either way. What is the rational distinction between them?

This is generally known as the "warm glow" argument: If you give money to charity, for example, you get a positive feeling ("warm glow") in return. A counterargument to this comes from Jon Elster (cited from memory): If you could raise your future utility by taking a pill that erases all your altruistic motivations, would you do it? Probably not, because you think it would induce you to do things that are morally wrong.

Antoher counterargument that I've just though up: If, for example, giving a person $ 10 makes you feel good about yourself, this already presupposes you're an alturist. If you weren't, it wouldn't make you feel good about yourself. That is, the argument presupposes what it tries to disprove. I guess philosophers have a name for this; I don't.

Florian Illies, Shamelessly Jumping on Angrist & Pischke's Train

Good books both.

Low Status and Economic Inequality: Two Points Often Overlooked

Lots of talk about economic inequality around U.S. blogs recently, on the occasion of the translation of Piketty's book. Here are two points that I think are often overlooked. Each of the points could hold if the other does not.

1. Let us say we know with certainty that low-status people suffer because most others are higher up the ladder. This suffering may come in the form of envy or of more distal outcomes such as poor health. This phenomenon, considered well-established by many, is often presented as an argument for reducing inequality. But does it follow that high-inequality societies are worse off, all other things equal? Of course not! Presumably, if low-status people suffer because they occupy a low rung, high-status people benefit because they occupy a high rung. The benefit experienced by high-status people might outweigh the suffering experienced by low-status people. Put differently, it is conceivable that, net of the influence of other factors, the avarage utility per person is as high or higher in a high-inequality society as it is in a low-inequality society. You might say that similarity of utility is desirable in and of itself, but then you'd be introducing an additional moral principle that not everybody might share. I'm saying "additional" because, as soon as you're arguing on the basis of people's suffering, you're already arguing on a utilitarian basis, whether or not you're aware of it.

2. Again, let us say people experience psychological costs because others do better than they do. But, clearly, there are positive externalities, too. In any society which uses taxation to pay for free or subsidized goods, poorer people benefit from having rich people around. That's because rich people pay disproportionate shares of the cost of amenities such as public libraries, clean drinking water, and a functioning criminal justice system. Low-income earners pay less than their share, even in flat tax regimes. Put differently, they get more than what they pay for. Would they really be better off if they switched to a regime in which they were less envious, but got Zimbabwe-level sewage and criminal justice systems? Probably not.

Exit, Voice, and Loyalty, and Spotify

O.k., I'll admit I've never read Hirshleifer's book, but I'm wondering. In the last post I describe how you can react to the Spotify relaunch, which makes the product considerably worse, by switching back to, and remaining on, an older version of the software, using a trick not approved by Spotify (subverting the auto-update function). In Hirshleifer's scheme, what is this? The post itself can, I guess, be interpreted as voice, but what I (and many others) do cannot. It is exit, sort of, because I exit the use of the current software, as envisioned by the company, but then, it's not, but rather loyalty, because I remain a paying customer. Should Hirshleifer have called his book Exit, Voice, Loyalty, and Creative Adjustment?

If You Make One Wrong Assumption, All Kinds of Shit Can Follow

From Steve Sailer's review of Gregory Clark's new book, The Son Also Rises (which I have not  read but might):

Economists [...] assumed that social mobility multiplies at the same rate with each new generation. If the correlation coefficient [...] of income between father and son is 0.4, then between grandfather and son it was imagined to be 0.4 squared or 0.16. Instead, it’s somewhat higher (0.26 in one study) due to regression toward the mean [...]. If a rich man has a son of only average income, his grandson is likely to earn somewhat above average.

This doesn't seem like such an advanced insight to come up with, so one might think that someone should have thought of it long ago and convinced all the others.

But then, it is not that surprising. Mainstream economics is a blank-slate science, as is the other discipline studying intergenerational mobility, sociology. To paint with a broad brush, the two differ in the timing of the influences they deem important. Standard economics sees everybody as basically the same, but subject to different opportunities and restrictions in a given situation. In contrast, sociologists typically think that people enter situations exhibiting vast differences, which result from social influences from birth onwards. Neither considers that large and important differences may already exist at birth (Hence, how could regression to the mean be important? What mean?).

This assumption has been known to be wrong for decades. Clinging to it causes all kinds of problems. Perhaps the main symptom of this in sociology is researchers' tendency to view a host of things as exogenous which are, in fact, endogenous. Such as, oh, socioeconomic status, the discipline's favourite variable. Once you realize there may be an endogenous component to status, you'll start doing lots of eyerolling when reading sociology journals. After a while, eyeache sets in.

Around the Blogs, Vol. 106

1. The importance of being able to enjoy the mundane (Ben Casnocha)

2. "Nonshared environment" might best be conceived of as noise, not environment, says Kevin Mitchell.

3. External validity alert: Are patients in medical trials selected for large treatment effects? (Andrew Gelman/Paul Alper)

4. Chris Bertram makes a surprisingly good case for the argument "Squeezing the rich is good: even when it raises no money".

5. "Is there no racial bias precisely because it seems like there is?" Ole Rogeberg takes us into the mind of the microeconomist.

6. The fun of being criticized for doing the right thing, namely keeping the normative and the positive apart (Bryan Caplan)

7. An argument against men attending childbirth (Dennis Mangan) (via)

8. 50 great book covers from 2013, collected by Dan Wagstaff (via)

9. The low-hanging fruit of immigration: Bryan Caplan offers another metaphor.

10. The argument that extremely high cognitive talent is specific. (Bruce Charlton)

11. The argument for being an asshole (Scott Adams)

12. What's it like to hear voices that aren't there? (Christian Jarrett/L. Holt and A. Tickle)

13. Which works did sociologists cite most in 2013? (Neal Caren)

The Best Blog Posts of 2013

It's about time, so here.

As usual, brackets are appended to each link to indicate whether the post is Long, Medium lenght or Short; High-Brow, Mid-Brow or Low-Brow, and Funny or Not.

For other years' lists, use the tag.

15. Offsetting Behaviour: "Social Costs and HPV", by Eric Crampton

14. Discover: "Why Race as a Biological Construct Matters", by Razib Khan (L; HB; N)

13. The Power of Goals: "Home Sweet Home", by Mark Taylor (L; MB; N)

12. Crooked Timber: "New Tools for Reproducible Research", by Kieran Healy (S; MB; F)

11. German Joys: "The Metamorphosis (US Summer Movie) Elevator Pitch", by Andrew Hammel (S; MB; F)

10. Code and Culture: "You Broke Peer Review. Yes, I Mean You", by Gabriel Rossman (L; MB; N)

9. EconLog: "The Homage Statism Pays to Liberty", by Bryan Caplan (M; MB; N)

8. Scatterplot: "Annals of Self-Refuting Tweets", by Jeremy Freese (S; MB; F)

7. Overcoming Bias: "Future Story Status", by Robin Hanson (M; HB; N)

6. Gulf Coast Blog: "Defamiliarization, Again for the First Time", by Will Wilkinson (L; MB; N)

5. Armed and Dangerous: "Preventing Visceral Racism", by Eric S. Raymond (L; MB; N)

4. Askblog: "It Is Sometimes Appropriate . . .", by Arnold Kling (M; HB; N)

3. EconLog: "Make Your Own Bubble in 10 Easy Steps", by Bryan Caplan (M; LB; N)

2. Armed and Dangerous: "Natural Rights and Wrongs?", by Eric S. Raymond (M; HB; N)

1. Falkenblog: "Great Minds Confabulate Like Small Minds", by Eric Falkenstein (L; HB; N)

Thanks and congrats to all above.

Around the Blogs, Vol. 105

1. The home advantage and the move to a new stadium (Mark Taylor)

2. Innovation: The view that government serves as a "useful irritant" (Seth Roberts)

3. Useless (German) news (Andrew Hammel)

4. Fun with Google ads (John Holbo) (By the way, I couldn't replicate that result. Generally speaking, it seems we get much less Google ads here in Germany than they get in the U.S. Or does Google know I rarely buy stuff on the net?)

Pebbles, Vol. 45

1. Disagreement about the effects of violent video games on aggression (Michael Venables)

2. Vielleicht wäre die angemessene Antwort: "Überweisen Sie mir doch 500 Euro, ohne dass ich etwas für Sie tue." (Harald Martenstein)

3. How science is not like faith (Jerry Coyne)

4. Apparently, the case against "too much choice" is not solid (contains link to meta-analysis) (Matt Ridley).

5. "Since the end of 2007, police in the UK have run a secret network of fully-furnished fake apartments and townhouses, solely for the purpose of capturing local burglary suspects" (Geoff Manaugh).

6. Short research article: Evidence against the hypothesis that red sports clothing causes winning (Thomas V. Pollet and Leonard S. Peperkoorn). If I read that correctly, though, assignment is not random.

7. 15 types of movie posters (Houke de Kwant). Arguably, this is a rational business strategy. If you have a way of signaling "This is an action movie with lots of explosions", that's what you should do. After all, posters are marketing devices first.

8. Health advertising: The Corman strategy; the "cute hypocrite" strategy.

9. Another good illustration of how hard it is to do social science research; nominal question: is stop and frisk effective? (Benjamin Wallace-Wells)

10. "10 highly embarrassing photos of visitors freaking out in an Ontario haunted house" (National Post)

11. Syntax is hard: A tublr dedicated to data visualizations gone beautifully wrong (via)

12. Correlates of polygamy in Africa (James Fenske) (via)

13. "12 Unusually Placed Sports Venues"

14. "We suggest that a psychoanalytic approach based on conflict/compromise model put forward by Freud, then articulated by Brenner, and unconscious group functioning models integrated with Keynes’s psychologically minded views can provide a framework towards explaining the recent housing bubble" (Şule Özler). You read the paper so I don't have to.

Around the Blogs, Vol. 102

1. The experiment Milgram chose not to publish (Tom Bartlett/Gina Perry) (via)

2. Disagreement on the bellicosity of stateless societies (Larry Arnhart)

3. Velvet glove, iron fist, or: How government force works. (Bryan Caplan)

4. The "forced rider problem" and disutility from "public goods" (Eric Crampton). Useful concept!

5. The view that TV shapes people's thinking by not mentioning aspects of topics (Steve Sailer/NOTA)

6. Why people dislike photos of themselves: Mirrors meet the mere exposure effect (Robert T. Gonzales). But don't miss the link in the last paragraph.

7. 26 great words from the OED (Carolyn Kellogg/Ammon Shea)

8. Differences in preferences for competitiveness as an explanation of the male-female wage gap (John List and Uri Gneezy)

9. "This must be the strangest publicity still ever taken." (Sherbrooke)

10. Three by Tim Harford: Why pro-free-market arguments don't work well for finance; banks are relying less on leverage; how to use e-mail efficiently at work.

11. Paging Quetelet: Why song lenghts are not normally distributed (Gabriel Rossman) (via)

12. People respond to incentives, giving to the poor edition (Eric Crampton/Jillian Keenan)

13. Feelings of extreme bliss produced by targeted brain stimulation (Christian Jarrett/Fabienne Picard, Didier Scavarda, and Fabrice Bartolomei). Gimme, gimme, gimme!

14. Why wages don't fall during recessions (Bryan Caplan/Truman Bewley)

15. How to make voters better informed (Bryan Caplan)

16. Another bonkers graphic presented by Kaiser Fung.

17. The impact on wages of: height; smoking; testosterone (Economic Logician/Petri Böckerman and Jari Vainiomäki/Julie Hotchkiss and Melinda Pitts/Anne Gielen, Jessica Holmes and Caitlin Myers).

Pebbles, Vol. 44

1. When absence of evidence is evidence of absence (xkcd)

2. An attempt to answer the question, "What were Stanley Kubrick's favourite films?" (Nick Wrigley)

3. One more time: Changing football coaches seems to make no sense (on average, within the observed range) (Hannah Barnes)

4. Where does the phrase "I, for one, welcome our new insect overlords" come from?

5. Researching the effect of stop-and-frisk is really hard. (Benjamin Wallace-Wells)

6. Andrew Gelman on Axelrod's interpretive overreach concerning trench warfare and the multiple comparisons problem and expressing uncertainty.

7. Who guards the guardians? Cameras could. (Randall Stross)

8. "Slot machine sounds can manipulate players, researchers say" (Meerie Kim)

The Methodology of Positive Economics - Reversed

Leigh Caldwell, a behavioural economist, writes about microfoundations in economic models. Microfoundations means that you don't just talk about aggregate-level variables, but model the decision-making of economic agents (typically persons) in your theory, and develop the aggregate-level predictions on that basis. Caldwell correctly points out that homo oeconomicus isn't very realistic and that, consequently, microfounded theories based on the idea of homo oeconomicus are wrong.

He goes on to outline two typical responses to the critique that humans aren't the superrational decision-makers many economic models portray them as. One, actual people are pretty close to homo oeconomicus; two, let's forget about microfoundations. Caldwell suggests that instead economic models be built on more realistic microfoundations.

Between the two of us, Caldwell's the only economist, but I'll still try to make the case that the above is all besides the point. Naturally, it all leads back to Friedman (1953). The article - "The Methodology of Positive Economics" - is all about microfoundations. Friedman's point is simple: He doesn't care whether the microfoundations are correct, as long as they give the right macropredictions, and they often do. 

What Friedman doesn't tell you is this: Economic models are consistent with a lot of predictions, and hence a lot of microfoundations.

Let's take the economic theory of crime. If you ask economists, it starts with Becker (1968).* Among other things, the theory predicts that if the likelihood of being punished for a crime goes up, the volume of crime goes down, all other things equal. Ehrlich (1973) translated that into a microfounded model in which agents make decisions in part based on their rational calculations about the likelihood of being punished if they commit a crime. It's basically the same thing (frankly, I fail to see the point). Both models predict: Punishment up, crime down.

But it doesn't say by how much. And it's not just the economic theory of crime. All that mathematical modeling in economics is highly misleading: It looks exact, but all they'll really tell you is the direction of an effect. The rest is left to empirical estimation. 

You might think that's a big problem for economics, and compared to an ideal world of exact predictions, it is. But, in fairness, it's not as though the other social sciences deliver anything else. As far as I can see, all social science theory is about signs.**

Back to the example: If you can show that a rise in the likelihood of punishment leads to a reduction in crime, that's consistent with superrational decision-makers. It is also consistent with some decision-makers being superrational and all the other people not responding at all to the change in the likelihood of punishment. Etc. If you think it through you end up with something like the following: The finding is consistent with some of the people being sorta rational some of the time, and their effects outweighing the effects that are due to people behaving contrary to what the theory says. 

For Friedman and me, that's fine. Leave psychology to the psychologists.

But there is one very important consequence of all this: If your macro-level finding is consistent with a theory built on a microfoundation that assumes rational agents, this does not show many people act rationally in any substantial sense. This is important because economists like to argue otherwise, and soon you arrive at the stance that all drugs should be legal because drug addicts are rational. I'm open to the idea that all drugs should be legal, but a finding that increases in financial or nonfinancial drug prices decrease demand does not provide a strong argument in favour of legalization. If you want to argue individual decision making, bring individual-level data.

Note. All cites from memory. And not even a list of references!

____________

*Of course, the ideas formalized in Becker's theory had been around for centuries, even in writing. E.g., Cesare Beccaria.

**There's also quite a bit of "theory" in the social sciences that's not really theory in the sense of a system of falsifiable hypotheses. Sociology is big in this department.

Around the Blogs, Vol. 101: Long Wait, Long List

Because I've been collecting for so long, it's so many links. Because it's so many links, I'm posting it early.

1. If the effect in question was found in a particularly small sample, should that strengthen or weaken your belief in the effect? (Eric Falkenstein) From the same author: A critique of Stevenson and Wolfers' happiness research.

2. Thoughtful, personal essay by Eric S. Raymond about the emotion and cognition of racism.

3. A body-mind theory of lefties and righties (Agnostic)

4. "Annals of Self-Refuting Tweets" (Jeremy Freese presents the American Sociological Association make an ass of itself)

5. Wie intensiv werden die Deutschen eigentlich von der eigenen Regierung ausgespäht? Man weiß es nicht. (Niko Härting) (via)

6. "A conservative estimate is that we’re spending a million dollars per year per terrorist, maybe more – that’s not even counting Iraq and Afghanistan." (Gregory Cochran)

7. The case against (eating lunch) outside (Matthew Yglesias) (via)

8. Matthew Desseem reviews Rififi.

9. Person fixed effects and psychological testing.

10. The theory that Marcia Lucas contributed more to Star Wars' quality than is usually acknowledged. (Fabio Rojas)

11. A discussion of reviewing and reviewers (with a focus on sociology) (olderwoman and commenters)

12. Is US violent crime actually down? Looking at non-police data. (Steve Sailer)

13. "William Boyd’s Taxonomy of the Short Story" (Will Wilkinson)

14. How not to get published. (Andrew Gelman/Brian Nosek, Jeffrey Spies, and Matt Motyl)

15. Getting the priorities straight (Foseti) (on this blog)

16. Male feminists: Demand and supply. (Nick Borman)

17. Real life cases of amnesia that are stranger than fiction. (Christian Jarrett)

18. Season of birth is endogenous (Eric Crampton/Kasey S. Buckles and Daniel M. Hungerman)

19. A model of how the internet works (Marco Arment) (via)

Around the Blogs, Vol. 100: My Mama, She Don't Told Me, My Papa Don't Told Me, Too

1. Four types of story elements, and why one may be particularly popular in rich societies (Robin Hanson/Orson Scott Card)

2. Trying to infer psychological events from neural activations (Neuroskeptic) (via)

3. The limits of stereotype accuracy (Bryan Caplan/Clark McCauley)

4. The correct way to argue with Milton Friedman (Daniel Davies) (see also)

5. Now that's what I call selection effects! (Andrew Gelman/Sharon Begley and Robin Respaut)

Estimating the Effect of Helmet Laws on Cycling-related Injuries: You Can't Do It Like That

In some places there are laws that require people to wear helmets when cycling. One may wonder what effects these regulations have on injuries. That's a question a paper (open access) by Jessica Dennis, Tim Ramsay, Alexis F. Turgeon and Ryan Zarychanski is trying to answer. They use data from the Canadian provinces, some of which introduced helmet legislation for minors only, while in other provinces the laws apply to people of all ages, and yet others introduced no such legislation. Have a look at the basic data:

Red lines are for adults, blue lines for minors. The dotted lines indicate when the legislation was introduced. You'll note that the provinces differ in when they introduced the laws. There are no clear breaks in the trends when the laws are introduced. On the other hand:

The rate of hospital admissions for cycling related head injuries in Canada among young people decreased from 17.0 to 4.9 per 100 000 person years between 1994 and 2008 (fig 1⇓). In provinces that implemented helmet legislation, the rate decreased steeply between 1994 and 2003, the time over which legislation was implemented, from 15.9 to 7.3 per 100 000 person years, corresponding to a 54.0% (95% confidence interval 48.2% to 59.8%) reduction. In provinces and territories that did not implement helmet legislation, the rate of admissions for cycling related head injuries also decreased between 1994 and 2003, but to a lesser degree. The reduction in provinces without legislation was 33.2% (23.3% to 43.0%), corresponding to a decrease from 19.1 to 12.9 per 100 000 person years. Among adults, the rate of admissions for cycling related head injuries was low in all provinces and across all study years. Between 1994 and 2003, the rate of head injuries in adults in provinces with helmet legislation decreased by 26.2% (16.0% to 36.3%), a reduction from 3.0 to 2.2 per 100 000 person years, compared with a negligible increase in rates in provinces and territories with no legislation, from 2.7 to 2.8 per 100 000 person years.

That's the authors' preliminary, narrative analysis. They point out that other cycling-related injuries also decreased. The authors then make some data analysis decisions which I would describe as suboptimal. First, they run an interrupted time series regression for each province separately, adjusting for trends. Second, they do not differentiate between provinces in which the laws apply only to minors and those where they apply to all, on the basis that some other study found spillover effects of legislation aimed at young people on helmet use in adults. Third, they take as their dependent variable hospital admissions for cycling-related head injuries as a ratio of hospital admissions for all cycling-related injuries.

The authors estimate no significant effects and conclude that "the incremental contribution of provincial helmet legislation to reduce the number of hospital admissions for head injuries is uncertain to some extent, but seems to have been minimal."

But you cannot conclude that from their analysis. First, recall that the provinces introduced their laws in different years. Dennis et al. throw that variation away and hence cannot control for time effects. Just pool the data and run a regression controlling for both province and year fixed effects! I guess that's almost all you need for identification, but one might consider controlling for differences in weather, which surely must have some effect on cycling.

Second, why not differentiate between laws applicable to all cyclists and minors only, respectively? Just use two different dummies. If the minors-only laws have effects on adults, that's information you want to explicate.

Third, and most importantly, you really, really do not want to adjust for all cycling-related injuries. The authors state that they do this in order to adjust for changes in cycling. But this makes no sense, and doubly so. (i) You automatically adjust-out any differences that the laws might make by reducing cycling. I believe there are studies suggesting such an effect, but I have not seen them. It would certainly make sense: Forcing people to wear a helmet makes cycling less attractive to some. (ii) There is a large literature on the topic of the consumption of risk (Peltzman effect). The idea is that when safety measures are put into place, people are going to consume some of that risk by adjusting their behaviour. For example, cyclists might cycle faster. So some of the effect of the law should be on cycling-related injuries not to the head.

In other words, this is an ideal design to find no effects even if there are some. I'm not saying that's deliberate - maybe it is more appropriate to say that this reflects disciplinary differences. For a medical researcher, it's probably natural to ask how much a helmet helps once there is an accident, which is roughly what the adjust-for-all-injuries strategy does. But if you measure that, you're not measuring the full effect of the law, which is the authors' stated aim. The concept of consumption of risk is standard knowledge in economics, and also known in other social sciences. And any undergraduate who has taken in, say, Wooldridge's Introductory Econometrics, should be able to suggest the design I outlined above, especially given the yummy data structure. Maybe that's just not obvious if your training was in medicine.

In this case, and as a noneconomist, I'll say it's the (hypothetical) economists who get it right. Oh, and I don't think you should use significance tests with this data.

Around the Blogs, Vol. 98

1. Race as a biological and social construct (Razib Khan)

2. The case for taxing oral sex (Eric Crampton)

3. "My hypothesis is that progressives, conservatives, and libertarians view politics along three different axes." (Arnold Kling)

4. Don't try to become good at something (Ben Casnocha)

5. If you really set your mind to it, you can find unfair inequalities everywhere. Scatterplot's mike3550 shows how to.

6. "Want to Know What Someone Really Thinks?" (Gretchen Rubin) The case seems way overstated to me, but you may want to add that tool to your box.

7. Wikipedia's most controversial topics (Samuel Arbesman/Taha Yasseri, Anselm Spoerri, Mark Graham, János Kertész) (via). Who would have guessed that the most controversial topic in German Wikipedia is Croatia?

That Rise in U.S. Crime [Edited]

The FBI released preliminary data on crimes known to the police in 2012. The New York Times will let you know only about a portion of the data. Their author Timothy Williams doesn't tell you that property crimes are down by .8%, but presents a story about how violent crime has increased by 1.5%. Then he find an academic who's willing to go into story time:

Joseph Pollini, another John Jay College professor, said that one possibility was that there were fewer police officers on patrol in some metropolitan areas that have cut spending sharply in recent years because of the recession.

“You’re dealing with depleted police resources,” he said of budget cuts that have caused a reduction in the size of nearly every urban police department.

That's a foolish statement to make even if the rise in overall crime were 1.5%, which it is not. That's because 1.5% is very little. It doesn't call for a big explanation. That's not to say that the rise in violent crime is uncaused, but rather, that you'll have a hard time explaining such a small rise. And, to reiterate, property crime is down (calling into question Pollini's police story). The tables I've found won't give you numbers for total index crimes, but given that property crimes known to the police are much more common than violent crimes (e.g., a ratio of about 8:1 in 2011), this means that the overall number of crimes known to the police is down, contrary to the impression you could get from reading the New York Times.

Of course, one might wonder how valid these numbers are in the first place. O'Brien (1996; gated link) concludes that changes in violent crime were measured with high accuracy between 1973 and 1992, and if the convergence between victimization and police data in more recent years (e.g., here, pp. 391-393) is anything to go by, one may think that the accuracy of police data has gone up rather than down. Taken together with other research, this literature leads me to believe that changes reported by the FBI are probably close to the true change rate for overall violent crime, robbery (+.6), burglary (-3.6), and motor vehicle theft (+1.3). Taken together, this still ain't much of a trend.