Gun policy: The making of a bad idea.

Here’s a story from a few days ago on one of Obama’s casserole of gun-policy related executive orders. He is apparently trying to stoke the market for “smart guns” that can only be fired by their registered user by directing federal agencies to “‘explore potential ways to further’ the use and development of smart gun technology as well as consult with other agencies that buy firearms to see if smart guns could be considered for acquisition and ‘consistent with operational needs.'”

Here’s why this is almost certainly a TERRIBLE idea (in a chain-of-events sort of list):

  1. Smart guns will only appeal to the most responsible and risk-averse gun users. So to the extent new smart guns are sold, they will be sold to the people least likely to shoot someone.
  2. Any smart gun sales not accompanied by some really attractive gun buyback program will increase the supply of used guns.
  3. Guns are extremely durable goods. A 40 year old gun will do just fine for most anyone who needs a gun in a pinch.
  4. Used guns will be bought and sold in secondary markets. Such markets are both more likely to be informal / unregulated and to be the source of most guns used in criminal acts. In fact, those seeking guns for mass shootings, gang shootings, etc. would almost certainly be seeking non-smart guns, so if this initiative creates even a small increase in non-smart-guns for sale, it is increasing the supply of guns most likely to be used to kill people.

When making gun policy, first DO NO HARM. Things are bad right now but it is pretty easy to make them worse and this is a prime example of how to do it.

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Donald Trump, omitted variable.

trump_skidmorephoto(photo by Gage Skidmore)

This article on a new study about the unusual demographics of Donald Trump’s supporters struck me as conceptually parallel to gaining access to a previously unobservable variable in a choice model. Many attempts to discern voter preferences are foiled by the lack of dimensions along which voters can express differential preferences, given the strong incentives for voters to cast a dichotomous vote for a Republican or a Democrat. In turn this limits the preferences a researcher may observe.

The determinants of this particular binary choice are so hard to study because of the large amount of preference bundling implicit in any given set of election results. Bayer and McMillan (2005) look at neighborhood sorting by race through this lens and use a structural approach to consider the bundling of inframarginal undesirable neighborhood characteristics (lower quality schools, higher crime, etc.) implicit in the decision of some blacks to live in black neighborhoods. Their key observation is that in many areas, there does not exist a large enough black population to offer meaningful choices across neighborhood characteristics conditional on having majority black residents. This leads to poor estimation of the magnitude of preferences less decisive than the marginal characteristic of race composition (leaving aside issues of actual de facto restrictions on inter-racial neighborhood sorting, which have been historically binding in many areas). In other words, when there aren’t enough different choices bundled together with your primary preference, all one can infer is information about your primary preference. This is often the case in our voting system. Interesting recent work by Kuziemko and Washington (2015) on this sort of problem in voting uses regularly collected Gallup survey data on racial attitudes as a new variable to shed light on the election results that led to the southern transition from the Democratic to the Republican party.

The Donald seems as if he may prove to be another dimension altogether in the election space this cycle. The Times article reports on an unusually large and more detailed survey of the (statistically weighted) opinions of Republican-leaning voters on Trump. Among the odd takeaways are that Trump’s “best voters are self-identified Republicans who nonetheless are registered as Democrats.” It goes on

…Mr. Trump has broad support, spanning all major demographic groups. He leads among Republican women and among people in well-educated and affluent areas. He even holds a nominal lead among Republican respondents that Civis estimated are Hispanic, based on their names and where they live.

Mr. Trump’s best state is West Virginia, followed by New York. Eight of Mr. Trump’s 10 best congressional districts are in New York, including several on Long Island. North Carolina, Alabama, Mississippi, Tennessee, Louisiana and South Carolina follow.

This candidate’s offered set of characteristics certainly seems to be fostering some very counter-intuitive groupings of voters. He may turn out to be just the sort of omitted variable that can cast a bit more light on the strange agglomerations of voters that end up deciding many US presidential elections. It appears that Trump’s breakout is based on his giving voice to some typically “left-unsaid” opinions about race and “otherness” but this is exactly what may make his candidacy so useful as a proxy for typically unobservable voter preferences.


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Reflections on my first year and a half as a grad student…


I’ve still got a long way to go to the degree, but as for coursework, I’m actually mostly finished. I have only two more prescribed courses, a couple of electives, and a pair of preliminary exams between me and the end of all the tests I will ever take and all the grades I will ever acquire. Not to downplay the astonishing amount of stuff I still don’t know or understand well enough, but I am finally genuinely starting to feel like I’m acquiring a reasonable grasp on the primary tools of economic analysis. With this break in the action, I thought it would be fun to reflect a little on the experience thus far. In particular, I would love to impart a few things that might be helpful to grad students who are in the shoes I was in a year and a half ago and also to share some resources and practices that I have found helpful as I’ve learned to survive and perhaps even thrive.

Here’s how I would characterize my situation as an entering grad student:

  • I was woefully underprepared for graduate school by my undergraduate economics coursework. It was at a level appropriate for pretty lazy students minoring in econ and the level of intensity of the curriculum reflected that in a profound way. Not that I didn’t have some good classes that introduced me to ideas and motivated me to learn more, but I would characterize my undergrad experience as someone telling you in a general fashion about how wonderful swimming is while you stand some ways off from the ocean looking out over it then you start grad school and a bunch of hardcore swim instructors tell you to jump into very choppy water and start swimming. My undergrad econometrics course literally never veered from OLS under original Gauss-Markov assumptions and had no discussion of causality or endogeneity whatsoever. My intermediate micro was sans calculus, there was no discussion of duality, compensated demand, etc.
  • I took plenty of math but it was a struggle to do well and in my econ courses the math was never married to the concepts. As such, learning to look at problems through the lens of mathematical models was a difficult task. In particular, it was quite difficult to intuitively extract the meaning from the mathematics.
  • I also had difficulty understanding the real value of boiling complex ideas down to very simplified models in order to gain both insight and testable hypotheses for research. I imagine this stemmed at least in part from the way I backed into the field as a sort of leftish history buff with a lot of half-baked (one-quarter baked?) ideas about the value of various economic frameworks and  the work of various scholars. While a few of my prior dogmas have survived half a grad school education intact, many others have given way to a more nuanced understanding of work I was dismissive of and to a keen awareness of how much is lost in translation from a scholarly work to a popular media characterization of it.
  • I showed up to grad school with a very full-grown-adult point of view with respect to most of my cohort in terms of work history, world travel, knowledge of history and institutions amenable to economic intuition and especially in terms of self-motivation to learn. On the one hand, this made it a lot easier for me to interact with faculty, both in a I-need-to-know-this-for-coursework way and in an I-want-to-know-this-because-I’m-trying-to-become-an-economist way. Both of types of discussion are meaningful for a grad student, but I think the latter is especially so. The other, less positive effect, which I suppose is mostly an assumption on my part, is that I have been somewhat out of step with some in my cohort in terms of both a gap in life experience and also in terms of just the daily life of a grad student (I have a wife, kid, and home and have to split time between grad school and other responsibilities related to earning the outside income that, added to my grad student income, allows me to get by). Particularly after first year, where the type of bond that exists is as much like shipwreck victims clinging to a common piece of floating debris as it is like some deep scholarly bond, I had a difficult time getting otheres in my cohort to study together (at least those I think I could learn much from). This has, I think, been a common detriment within my cohort (a suspicion strengthened by comments from some professors over common weaknesses in our work) and my program more generally.

Yet I’m getting by. Maybe even better than getting by to hear a few people tell it. So for the interested, I’m going to share a bit of what I’ve learned about life as an econ grad thus far. Here, in no particular order, are various quality sources of learning, key pieces of advice, etc. that I’ve found meaningful/insightful/helpful:

  •  Nolan Miller’s microeconomics notes. These notes were indispensable for keeping from drowning in MWG during first semester grad micro. Essentially they are a translation of MWG into plain English (he mashes in some Varian too).
  • Macroeconomics notes by Dietrich Vollrath. I actually found these wonderful notes later than I needed to. These are chock full of basic explanations of canonical macro models and what they mean in very approachable prose and also using very reasonable uncluttered notation. But the BEST THING about them is a brief admonishment to new graduate students in the preface that seems to have been exercised from the link above (it’s in an older version). This little bit of wisdom is equal in value to anything in the notes proper (which are great). It goes like this:

    From your perspective, the goals of this class are to learn enough macroeconomics to pass the comprehensive exams, and to understand the material well enough that you can begin reading journal articles. I have several words of advice for you.

    1. This is your job. You are a poorly paid or unpaid intern in the economics profession, but you are a member of this profession now. Act professionally and take this seriously.
    2. This is not at all like your undergraduate classes. In those classes we were trying to get across a small number of very general concepts. In graduate school we are trying to get across a large number of very specific concepts. This requires you to study more evenly throughout the semester, as opposed to cramming everything in just prior to tests.
    3. Work with your classmates. You’ll all see different aspects of the problems you’ll be working on, and you’ll learn from their insights while they learn from yours. Also, it helps to have other people who like to make fun of the professor.
    4. Ask questions and interrupt class. If you aren’t getting what I’m saying, stop me. Sometimes all it takes is for me to explain things in a slightly different manner for things to click.
    5. Don’t compare yourself to your classmates. You all have vastly different backgrounds and preparations for this. I am perfectly happy to give all of you superior ratings on your comprehensive exams. There is no competition going on here.
    6. Do not ask “will this be on the test?” The answer is always yes. If your attitude is that you want to pass with as little effort as possible, then I’d suggest you find another line of work. If you really want to get a PhD, you should want to know everything.
    7. Do as many problems as possible. Do the homework problems I assign, and then do the extra problems you have access to. Do old midterm and final questions. Do old comprehensive questions. After you’ve done all these problems, do them again. They are the best way to understand this material and the best way to study for your comprehensive tests.

    I really wish I had a class with this guy. Some of these things I really needed to hear last year and some classmates of mine REALLY needed to hear these things as well.

  • Solving many comprehensive exam problems that have answers! This is the best way to study for comps, period. Solving problems without solutions is valuable too, but the problem with this is that you always think what you wrote down is right, that’s why you wrote it down. Two things are worth noting about studying for comps. One, there are canonical problems that crop up again and again and if you solve enough problems you will run across them. Two, many people directly reuse problems from others’ tests and once in a while you will simply get a lucky break and be presented with a problem you exactly solved during study (I did).
  • Gentzkow and Shapiro: Code and Data for the Social Sciences: A Practitioner’s Guide. Writing and distributing this is a great service to the profession. Save yourself from terrible coding and data storage/management habits and get your act together early.
  • LEARN LaTeX RIGHT AWAY! It is a modest upfront cost but the payoff lasts for a long time (maybe forever if you end up working as a research economist). Once you can work quickly in LaTeX, it really is not any slower than writing. Professors appreciate problem sets that can be read without special training in your crappy style of handwriting and on the margin, it may get you a grade bump to just have presentable work even if it is incorrect.
  • I was told this a number of times, but it is really true: Grades don’t matter (much) in grad school. If you get the PhD, no one is going to ask you about your GPA at an interview. Caveat: grades may matter as an internal signal early on in your program, especially if you are competing for funding, but for the most part you can be done sweating your GPA. Worry about really knowing the material. It is actually worth the worry. The greatest value I have gotten from many tests is learning what I am weak on and being able to go back and reconcile those weaknesses. If you get a shit test grade, learn what you need to learn and move on.
  • If you get imposter syndrome, keep your chin up and keep plugging away. It should happen numerous times. Finding out how much you don’t know just means you are really learning.
  • Read the myriad guides for surviving grad school that are out there. Almost every one of them has a few gems for any reader. Plus, it is just nice to know that others have been there too.
  • Go to your departmental seminars. This experience easily constitutes half of the usefulness of grad school. See how research designs are picked apart. See how people disagree about a lot of ideas your particular professor was quite comfortable with. Watch people have to defend their identification strategy on the fly. Also: meet with the speakers. Sometimes you will be the only person there. In this case, you can ask an economist about anything you’d like to know. Did you struggle in grad school? How did you arrive at your current research program? Can I run an idea by you? If you are lucky, you will get a very brilliant economist to spend 15-20 minutes discussing your research ideas with you.
  • If you are a grad TA and have the means, try to borrow and extend past work from TAs before you. At great schools, there is literally a line of scholarship within TA assignments. Fostering and contributing to such a tradition makes it easier for you as a TA and can greatly improve the quality of your graduate program. I was exposed to some section notes from a grad course and a very well-known school via a friend who apparently scoured them from the internet in some way that is well beyond my capability to understand and it was simply astonishing. The insights into MWG and basic modeling and problem solving would have saved me hundreds of hours of pain. This feature of graduate education is what, I think, separates good programs from great ones.
  • Finally, if you are in grad school anyway MAKE THE MOST OF IT AND FEEL GRATEFUL YOU HAVE THIS OPPORTUNITY. If you aren’t feeling it, it is a really major waste of time. Learn, learn, learn!

And a lot of other stuff I’m sure. But that’s enough procrastination for now. Back to work…

Posted in advice, epistomology, grad school, microeconomics, Uncategorized | Tagged , , , | 2 Comments

Is big business becoming a (relatively) leading progressive force in the U.S.?

As the Republican Party has consolidated power across the US legislature and the majority of state governments even while becoming increasingly isolated from mainstream political positions by its activist base, it has been surprising but encouraging to see how often business interests have been the effecting both unilateral change on key issues of concern to progressives and reigning in the worst plans of legislators and governors around the country.

Yes, they are doing these things because they are good for business. But feeling pressure to get right with your paying customers is actually a virtue of market capitalism. And in a world where some boycott initiative or corporate black-eye can go viral in a matter of days and there are a thousand hair-trigger trading algorithms ready to take a bite out of Firm X’s stock price at the first sign of public bad news in microseconds, consumers may actually be gaining a lot more traction to steer business practices. Even more surprising, regional and state business interests have weighed in during pitched legislative battles, mostly over culture war type issues, and have curtailed or reeled in some pretty odious legislation. Here consumers are steering business and business is steering government. It may be that Ralph Nader was onto something.

Here is a little roundup of news items that have fleshed out this little hypothesis of mine in recent months.

Businesses of major national scale moving to strategies based around positive PR / customer and employer-friendly practices:

Wal-Mart’s Minimum Wage Breakdown

Why Gap Is Raising Its Minimum Wage To $10

Wal-Mart Promises Organic Food for Everyone

McDonald’s May Sell More Organic Foods to Boost Sales

State and regional business interests (and municipal governments) applying pressure on state legislatures over economic issues

Indiana Law Denounced as Invitation to Discriminate Against Gays

Arkansas gov. sends back religious freedom law after Walmart pressure

Texas Sen. Doesn’t Want Clergy ‘Coerced’ Into Officiating Same-Sex Marriages (this headline doesn’t tell the story, click through)

Alabama’s governor moving to permanently remove Confederate flags (and associated flagpoles) from various prominent places after the Charleston tragedy, citing a focus on economic growth over divisive cultural battles:

Straddling Old and New, a South Where ‘a Flag Is Not Worth a Job’

Chase bank bumming out conservative Christians and right-wing political demagogues with their support of LGBT communities:

How Chase Bank and other Corporations Coerce and Bully Christians

Chase Bank Has Pattern of Partisanship on LGBT Issues

To be sure, a number of these stories has some kind of left rebuttal about why it is just a hollow ploy and so forth (though even the hard left seems to be able to get on board for firm-wide minimum wage increases), but I’m not trying to tell anyone these things are happening due to altruism, I’m saying exactly that they are happening because these businesses and their various aggregations have decided it is better business (meaning more or more sustainable levels of profit or growth) to pursue these policies. While some people are only happy being in the loyal opposition, I am learning to find a little joy and hope in this stuff. (Really, when the largest employer in America raises wages meaningfully above the national minimum wage, it is a big deal.) And to the extent that business can call the tune in legislatures these days (and they really can), such developments as those above may at least serve as an upper bound on the level of insanity that has been flowing out of Washington and various state legislatures in recent years.

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How I learned to stop worrying and love the data

I have always been comforted by data. Just as I don’t think I will win the lottery, I also don’t think I will be involved in a plane crash. And now, as I have spent the past three years filling my head with mathematics and statistics, I also have become more prone to go ahead and run the numbers on how much I should worry about certain things.

Emily Oster, associate professor of economics over at U of C felt the same way as a pregnant future mother. She turned her obsession with getting to the bottom of all the dozens of “pregnancy rules” doled out (inconsistently) by OB/GYNs, doctors (and midwives) into a book (read the intro here). In it she finds that many of them are based on highly flawed or outdated studies, or that the rules that they foster are the strictest possible interpretation of less-than-clear study results. I wish we had had it available when our daughter was gestating. It would have allowed my wife to enjoy her day to day life as a pregnant woman a great deal more.

The book was critiqued in reviews like this one purporting to “debunk” it (by comparisons between Oster and anti-vaccination “activist” Jenny McCarthy. The author here comes down firmly on the side of listening to whatever often conflicting advice your doctor(s) give(s) out citing “Dr. Marsha McCormick, a professor of maternal and child health at Harvard Medical School and the Harvard School of Public Health, who can’t, for example, imagine a medical doctor evaluating economic data and dispensing policy recommendations or financial regulations for the housing industry.” This may be good advice for Dr. McCormick and other medical professionals to follow. A 1978 study in the New England Journal of Medicine found that 87% of a random sample of physicians, students and residents serving in hospitals of the Harvard Medical School could not answer the following question correctly:

If a test to detect a disease whose prevelance is 1/1000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease, assuming you know nothing about the person’s symptoms or signs?

If you are interested in how to answer such a question, which is a straightforward application of Bayes’ Theorem, go here and see how you do in getting there. The correct answer is that a positive result indicates a 1/51 (or approx 2%) chance of having the disease. Most answers from respondents in the study ranged from 95% likelihood of having the disease down to around 50%. Even this lower estimate overstates the risk posed by a positive test by 25 TIMES the actual risk! The authors’ conclusion was that “in this group of students and physicians, formal decision analysis was almost entirely unknown and even common-sense reasoning about the interpretation of laboratory data was uncommon.(Such errors have been replicated in numerous studies over many years up to the present day. Here is another more recent study of the same type without a paywall.)

While this sort of analysis is not super easy for a lay person, it really should be pretty manageable for a trained physician who makes diagnostic decisions based on the statistical data contained in medical studies. It is apparently not for many or even most physicians. But such analysis is very straightforward for any trained economist. Bayes’ theorem is covered in virtually any intermediate-level undergraduate economics course and again in any introductory or mid-level statistics or probability courses. By the time one completes an Econ PhD, simple applications of Bayes’ Theroem have been beaten into a students head for years and years. So it may well be that Emily Oster may be able to interpret the results of clinical studies in a manner that physicians should take note of based on the extent of her mathematical training alone. If we venture into the realm of potential problems with study design, discerning between correlation and causation, and other such methodological analysis of medical studies, then an economist has a clear advantage over most practicing MDs.

Another point that Oster makes in the intro above is that in economics the focus is on weighing trade-offs. Since most people don’t do things habitually that have no personal value to them at all, if someone prefers to do something it usually indicates that they gain some enjoyment or value (let’s follow tradition and call it “utility” henceforth) from doing so. We all seek utility from the things we do and we are constrained, by time, money, information and many other factors, from doing them exactly as we would prefer to. In pursuit of utility we all make trade offs with our health and our very lives every day. We eat too many sweets or we cross the street while texting or we smoke or we have a couple of drinks and figure it’s okay to drive home because the alternative is terribly inconvenient or we stay on the road when sleepy or we take our medicine in different doses than prescribed and so on. In every case, we are making a decision about risks and rewards. In most of these cases we don’t even have good data to guide us, just a gut feeling. We also worry too much about very, very low risk situations like the risk of an airplane accident or the risk of having our homes broken into.

Which is all a long way of introducing a trade-off I have been weighing in my own life. It is about the seemingly arcane decision of which way to face my 16 month old daughter’s car seat in our car. Flashback to pre-2002. The American Academy of Pediatrics (AAP) recommended at this time that children should face backwards in a car seat until they weighed 20+ lbs with good neck muscle strength, which tended to correlate with turning one year old. So the heuristic used by many was to say that you can face your child forward after they turn one year old. Then apropos of this 2007 paper “Car safety seats for children: rear facing for best protection” by Henary et al., the recommendations were revised to say that children should stay facing backwards in the back seat until they reach either age two or the limit of their rear facing car seat (which in infant to toddler seats is usually about 40 lbs).

If you search this sort of thing on the internet you are likely to come across a lay-person AAP press release such as this one. The meat and potatoes are as follows:

While the rate of deaths in motor vehicle crashes in children under age 16 has decreased substantially – dropping 45 percent between 1997 and 2009 – it is still the leading cause of death for children ages 4 and older. Counting children and teens up to age 21, there are more than 5,000 deaths each year. Fatalities are just the tip of the iceberg; for every fatality, roughly 18 children are hospitalized and more than 400 are injured seriously enough to require medical treatment.

New research has found children are safer in rear-facing car seats. A 2007 study in the journal Injury Prevention showed that children under age 2 are 75 percent less likely to die or be severely injured in a crash if they are riding rear-facing. (My bold)

Now, the first paragraph above is kind of strange since it refers to children age 4 and up. This has nothing to do with children between the ages of one and two years. Also 5000 deaths per year in the US for children and teens up to age 21 sounds like the results of a very low probability event, and that is for everyone from birth to drinking age. Then from these unrelated statistics we suddenly jump to the report’s finding that children under 2 are 75% less likely to die or be severely injured if rear-facing. 75% sounds like a lot right? But 75% of what? The answer turns out to be 75% of children under the age of two who suffered severe injury or death in a car crash and who happened to be sampled in the National Automotive Sampling System Crashworthiness Database between the years of 1988 and 2003 (I will get to problems with this sample shortly). The habit of making claims like “children under 2 are 75% less likely to die or be severely injured in a crash…” Is sufficiently misleading to even doctors, let alone the general public, that it warranted a recent blog post on the website of the Journal of the American Medical Association highlighting the difference between relative risk ratios (what the authors of the car seat study tout as their finding) and absolute risk reduction (what I am about to explore below). Physicians are implored to understand the importance of this distinction, which would imply that many currently do not.

So I wondered what was the probability on average of my family getting into an accident that fit this set of criteria? One absolutely critical thing to understand is that your average risk of being in a crash is based on miles driven, if you don’t drive, you are not going to get in a car crash. If you drive very little it is much less likely you will be in a car crash and so on.

Using the State of Illinois crash statistics for 2012, here is a walk through a number of statistical steps to a very conservative approximation (meaning overstating the risk at each step*) of the probability of my child being severely injured or killed based on the direction I face her car seat, given my family’s driving profile.

(To minimize monotony henceforth, I will state here that all of these figures are average risk, based on annual statewide statistics, so that I can dispense with adding “on average” to every sentence I write. I would characterize my wife and I as pretty average drivers. We have each been involved in a separate, minor, non-injury, other-driver-was-at-fault, accident once in the last decade or so.)

1) Probabilty of being in a car crash: 1 in 2.6 million (1 / 2,600,000) per mile driven.

We drive with our daughter an average of about 200 miles per month (a generous overestimate). This works out to 2400 miles per year. So our most basic probability of being in car crash with our daughter is 1 in 1083 annually. But there’s also this:

2) Proportion of crashes between the hours of 8 AM and 8 PM: 72%

This time period contains nearly 100% of the time we ever drive with our child in the car and serves to multiply the denominator of the above probability by 1.385. Thus our average probability of being in a car crash with any injury is closer to 1 in 1505 annually. (Edited based on feedback about an error.While the distribution of miles driven by time is hard to find, the National Household Transit Survey gives time breakdowns that don’t exactly match these hours but suggest that perhaps 85% of driving is done between 8am and 8pm. This would give a multiplier of  (.72/.85) and moves the probability of a crash to about 1 in 1279 annually. (Figures below have been edited to reflect this change.)

I will plug this average probability of being in a any sort of crash with our daughter in the car into the data contained in the report cited by the AAP guidelines. This study looked at a sample of car crashes with children in the car drawn from the time period 1988-2003. This is an unorthodox (actually an invalid) method of creating a sample based on the way it is subsequently analyzed, but I will take the methodology as valid for the moment. I will split the probability into two cases, rear-facing car seat (RCFS) and front-facing car seat (FFCS). The authors of this study use the Maximum Abbreviated Injury Score (MAIS) system to rank injury and death. In this scale 0 is no injury, 6 is death, and 2 is “moderate injury”. The study uses greater than or equal to moderate injury as the baseline measure they call “moderate and severe injuries” (even though the study discusses the risk of severe injury or death as the finding of interest).

3) Proportion of children in crash sample with no injury or light injury: RCFS = 99.5%, FFCS = 98.9%.

This translates the 1 in 1505 figure for my family above into the following two ratios. The absolute probability of severe injury or death (as defined in the study) for my child in an RCFS is 1 in 255,800 annually. The same probability with a front facing car seat is 1 in 116,272 per year.

Now, that seems quite low to me, but if it doesn’t seem low enough, let’s stop assuming that the research design is valid (because it isn’t). Keep in mind two things as we dig in here. One, I am a pre-first year graduate student in economics as I write this and, two, that these criticisms are quite obvious and valid in spite of my “pre-economist” status. While most readers probably will or will not take my word for this instead of going through the trouble of researching the claims and links below, I am happy to correspond with any readers who may like to press me further on them.

The paper in question (Henary et al.) uses data collected from the National Automotive Sampling System Crashworthiness Data System (NASS-CDS) on children 2 years and under involved in crashes between the years 1988 and 2003. From this data they obtain an initial sample of 1870 crashes. From these they exclude unrestrained children and also explicitly improperly restrained children, which excludes 29% of the sample. They also exclude children with unknown restraint use/type, which excludes another 23% of the sample. After these exclusions, they end up with 870 crashes to analyze.

There are already problems at this point, so let’s dig in. In doing statistical research and analysis, there are two primary types of data sets. The first is cross-sectional data. This is a set of data taken from one point in time. Examples might include data from the 2000 census or retail sales for Black Friday 2013. Such data sets look at some (often large) number of people, sales, car crashes (or what have you) at one particular point in time. These data sets can be used to analyze differences in outcomes between observations given the conditions that prevailed at the time of the sample.

The second primary type of data set is time-series data. This is data looking at the same set of subjects at different points in time. Examples of such data sets are changes in the value of the stocks in the Dow Jones Industrial index between 1990 and 2000 or changes in US automotive fatalities between 1988 and 2003.

Hopefully that last example gives a clue as to a major, major problem with the data set used in the Henary paper. This sample has been created by aggregating discrete events across many years and analyzing them as if they all happened at one point in time (in other words, they are collecting time-series data into a cross-sectional data set…this is a no no). It took the authors 15 years to collect together 870 crashes they considered useful to look at. (They then apply a statistical weighting based on a single year’s accident trends to claim the sample represents a nationally representative population sample.) By discarding time variables from their observations they are implying that cars, car seats, aggregate fatalities and any other thing you might care to wonder about did not change between 1988 and 2003 (and in fact they do not address this issue even in passing in the report). Let’s unpack the implications of doing this in a few parts.

First aggregate fatalities. Between 1988 and 2003 total fatalities declined from 3.44 per million miles driven to 1.48 per million miles. That represents a decline of more than 2/3rds over the time period that has been engineered to be a single period. The authors came up with a total of 42 infant car seat deaths over these 15 years (an average of around 3 per year) but we have no information about what years these deaths took place. Probability suggests that more of them came from the late 80s/early 90s than from the early 2000s. Look at the car seat samples below and think about whether this information would matter.

Now cars. It was the advent of the passenger seat airbag that resulted in the recommendation of putting car seats only in the rear of a car. During the period the sample was drawn from, the average age of the US auto fleet hovered around 10 years old so, among cars in this sample, airbags went from being non-existent to being an option for the driver’s side, to being an option for the passenger’s side, to being standard for both front seats in all cars, to including standard rear seat airbags. Would that seem to matter? Many of the front-facing deaths in this sample may have occurred in the front seat of the car as the standard protocols of car seat placement were only just being formulated during the earlier years of this sampled period. Airbags may have played a role in a number of these deaths as well.

And what of child restraint systems (CRS)? Virtually all the norms of car seat installation, operation and placement took place during this time as well, meaning that in many cases there were no established recommendations as to how and where to install quite primitive car seats. To give a sense of the dramatic differences in car seat safety knowledge around the sampled period, here are a few excerpts from this timeline of the history of child restraint systems.

1987: Survey of CRS use in 19 cities across the country shows 80% usage (correct and incorrect) of restraints for children under age 5.

1989: Evidence of CRS misuse grows; car seat inspection clinics in Virginia and California find high levels (87-93%) of errors.

1991: Almost all states have passed safety belt use laws. Many do not cover rear seat occupants and can be enforced only if the driver is stopped for another violation.

1993: CDC issues a the first public health warning on interaction between air bags and rear- facing child restraints (MMWR, Vol 42/No 4, April 16, 1993)

1995: July: First death of infant from being struck by passenger air bag while riding in the front seat in a rear-facing restraint. (First infant known to have been injured by air bag, November, 1994.)

1999: September: The tether part of the universal child restraints anchorage standard (LATCH) began with the requirement that all forward-facing CRs must pass a reduced head excursion test, for which almost all employ a tether strap.

2000: September: Phase-in of the tether anchor requirement of the universal child restraints anchorage standard (LATCH) continues, with 100% of all model-year 2001 passenger vehicles (including SUVs, pickup trucks, or vans) being required to have tether anchors.

So in the early part of the sampled period, as many as 90% of the approximately 80% (a high estimate it appears) who used CRS at all were probably doing so incorrectly. Then for years, many people placed children in the front seat with armed airbags. Also the entire modern system of anchoring for child seats was conceived of during this period and was only full implemented in the last year or two of the sample period.

Finally as to the seats themselves, the period 1988-2003 encompasses much of the development of car seats from rickety metal frameworks that would look more at home next to the kitchen table to the modern formed plastic with shock absorbent foam models that we are familiar with today. Here are some examples of car seats in use during the sample period (sourced from here).

Techart – 1988

Fisher Price T-Shield – 1990

Renolux GT2000 – 1993
Evenflo Medallion – 2000

These car seats are all lumped together as if they were on the road at the same time. It is pretty apparent that both in general and in particular with respect to side impact crashes (a metric the report places quite a bit of emphasis on) a lot changed over the 15 year period sampled and also in the ensuing decade since. Do you think the first and last car seats have the same protective properties? It seems highly unlikely that they do even leaving aside the profound differences in tethering systems that prevailed between the decade and a half that the sample is taken across.

So, in short, this report provides no significant evidence about the efficacy of forward or rear facing child seat placement in terms of modern automobiles and child car seats. It MAY BE that there are some significant issues with respect to car seat direction, but the report that underpins the AAP recommendation that people are following is devoid of value as a piece of research.

And then finally, there is the issue of utility. Since my daughter has been facing forward in our car she is a much happier camper nearly all the time that we drive. We have little or no crying and just about any parent can tell you that a crying (or screaming) baby, particularly when you cannot see them at all makes for some very distracted driving. Most of the time now she just babbles to herself about the view. Better and safer for us both.

So, I concede that was perhaps a long blog post but, to paraphrase Chomsky, it takes time to contradict the conventional wisdom. The moral of this story I suppose is twofold. First, don’t be afraid to question putatively authoritative sources. Sometimes they are just plain wrong. We used to douse whole neighborhoods in DDT every summer to combat mosquitos, we used to give hard narcotics to babies with colic. These were not accidents, they were policies or recommended practices. Second, question any rules or policies that consider only risks and not benefits (or vice versa!). Very rarely planes crash and almost everyone always dies but no one is telling you that the safest policy is to not fly. Even more germane to this topic, if you want to be as safe as possible with your baby with respect to driving, simply abstain from any driving that is not absolutely essential to the health and well-being of your baby. That is the safest way, hands down.

Mostly, just think of life as it really is – full of trade-offs that we have to make every day – and make your decisions accordingly.

Thanks for reading!

* With the sole exception that we do most of our driving in Cook County, which has a disproportionate share of all Illinois accidents. Accidents per mile were unavailable to me on the county level so using statewide data may slightly understate our risk vis a vis driving in Cook County. But we also drive about half of our miles on the highway between Illinois and Wisconsin and, since highway driving is a good deal safer on average than urban driving, this abnormality is likely a wash.

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Governance, aesthetics and architecture

I have been meaning to write up a few thoughts that came out of a beer-fueled conversation with a friend some weeks ago. We both lived in Austin, TX for some time during the 1990s and now have both lived in Chicago for all of the 21st century thus far. The discussion was about the architectural transition that has taken place over the last decade plus in Austin.

Our conversation began with my friend noting how depressing the bad architecture explosion was in Austin. Now, it needs noting that Austin has seen a population explosion that I might guess is one of the biggest in the nation over the last decade or so. In 1990 Austin's population was around 500,000. Today it is around 850,000. That is population growth of 70% over the period, or almost 6% per year on average. This is more in the ballpark of the growth of urban areas in China than of urban growth in the US. So obviously some things had to be built. Over the same 13 years the average price of housing has (by my own informal observations) increased by about fivefold. So, according to basic principals of supply and demand, tall buildings are a good use of space as land increases in price and population grows. Until I began writing this, had it in my mind that there was some fairly onerous building code height restrictions in Austin in the past that had someone to do with the UT tower, perhaps based on a story I heard about some big fight over the Dobie dormitory a few blocks away. However, that story may have been apocryphal based on a web search and I can't find anything about maximum height restrictions more generally either (Austin has a somewhat controversial McMansion ordinance from 2008 but this concerns maximum non-permeable cover on residential lots and such things). Readers, please correct me if I am wrong on this. As a side note, for a taste of the problems these sorts of restrictions can pose to affordable housing in particular, consider the cost of housing in Washington DC over the last decade and also read about the cautionary tale of Boulder, CO, which has stringent restrictions on both height and sprawl.

So, let's just say that some tall buildings are gonna get built. Tall buildings have the perhaps unfortunate feature that their pulchritude, or the lack thereof, travels much further than that of low buildings. An ugly skyscraper is ugly from really far away. A one-story building is only ugly from right in front of it. And it is fair to say that Austin's skyline is not terribly distinguished. Perhaps the most emblematic building is the Frost Bank tower (in the middle in the picture below), which looks like the improbable headquarters of some superhero's corporate alter ego in a Marvel Comics movie.

While I may disagree about the extent of the badness my friend was bemoaning overall, I will concede that, from the vantage point of Chicago (picture credit)…

It is a reasonable critique. But it got me to thinking about some of the main differences between Chicago and Austin at the level of why what gets built gets built. And it seemed to me that Austin's rather hodge-podge-y skyline is reflective of the fairly anonymous and pluralistic sort of governance that takes place there, while the skyline of Chicago, bristling as it does with world-class works of architecture, rather reflects the autocratic and undemocratic form of governance here.

Perhaps most to the point, since 1955 Austin has had 15 mayors and the longest serving among them (I think three total) served maximum terms of six years. On the other hand, Chicago has had 5 mayors in this 58 year period and the two Richard Daleys account for 43 years (or nearly three quarters) of it. While Chicago technically has a “weak mayor” form of governance (though this is something I have just read, I don't really understand the mechanics of this assertion), we have de facto mayoral control over virtually all things beyond the ward level, and even there, the mayor can make things happen by hook or by crook, particularly the Daleys. In fact, the Balkanized Ward system is a key to assuring the mayor an easy route to policy dominance. Just divide and conquer. It is even easier with TIFs now.

Austin on the other hand has a new mayor virtually every term and there is no political dynasty of any sort, period. The city also has a city council elected on an at-large basis, so council members have to satisfy every voter in the city to some degree. While I am rusty on current Austin politics, the city manager is perhaps the most powerful policy maker in some ways as they typically have a tenure of several years, at least in recent decades. Overall, there are puh-lenty of people who will animatedly criticize city government. Austin, but the crazy thing is that some of them, such as long-time journalist-city-council-gadfly-turned-city-council-member Daryl Slusher, end up serving in it, while many others actually accomplish meaningful reforms through their agitation before the council.

So my argument was that in a city like Austin, even assuming that some majority of the mayor and city council think a proposed building is ugly or garish, if some builder does everything according to city ordinances and follows all the rules, they can build a butt-ugly building because essentially Austin is a well-functioning government. On the other hand, in the Daleys' Chicago if the mayor especially didn't like a proposed building, it is safe to say that it would not get built. I may lack documented examples of this, but to extrapolate from the contrapositive, consider the building of the campus of the University of Illinois at Chicago (my alma mater) under the guiding hand of Richard J, the elder.

Undergraduate Center to a full-fledged four-year institution. After a long and controversial site decision process, in 1961, Mayor Daley offered the Harrison and Halsted Streets site for the new campus…In a report on August 28, 2008, by newsman Derrick Blakely, CBS TV reported that in 1963, the decision to build the University of Illinois decimated Taylor Street's little Italy. Florence Scala, Chicago’s legendary Taylor Street activist and long time Hull House cohort, blamed the board of directors of Hull House for betraying the thriving, vibrant, tight knit neighborhood. They encouraged Daley to go ahead and destroy the neighborhood. Her challenge as to why the Hull House neighborhood and not the vacated and easily accessible Dearborn Station resulted in the bombing of her home. In addition on November 10, 2003, WTTW Irv Kupcinet related a story about Mayor Richard J. Daley asking him what he thought was his most crowning achievement. Daley answered “Putting the school in the Italian neighborhood.” Meaning the old Taylor Street neighborhood being condemned to make way for the Chicago Circle Campus. Today, the University's main academic library is named for Daley.

The man who mowed down an entire neighborhood to build a highly controversial, brutalist, university campus could be expected to have no big problems making a zoning problem arise or a downpour of bureaucratic hold ups appear for a disfavored project. And hiring world-class architects to design news-making buildings was probably a good way to assure that your building was smiled upon by the ruler.

Millennium Park is a case study for Richard M Daley's ample “juice”. The park, featuring a Frank Gehry designed centerpiece concert performance space, was completed four years behind schedule (or 996 years early as the saying often goes in Chicago) and for over three times the original budget, ending up at $475 million from a $150 million starting price tag. As the cost overruns mounted, the mayor also came up with an astonishing amount of suddenly willing private donors to keep the city's portion of the overruns to only $120 million. Try that Lee Leffingwell!

It may be that the price of good governance is potentially mediocre architecture. Though Chicago is still getting some pretty noteworthy buildings like the Aqua Tower from time to time, the days of Mies and friends are most likely over. So carry on Austin. For a city dealing with profound runaway growth, I think the relatively earnest work of the short-lived and anonymous politicians of Austin is going reasonably well.

(Full disclosure: Your author has a history of having a fairly congenial view of Austin, though he would argue that it is an eminently supportable position under cross-examination.)


Posted in the built environment | Tagged , , , , , | 2 Comments

What to do with all the time?

I am now done being an undergraduate. I'm still short a piece of paper as of this writing but it appears that I hold a BA in economics and a minor in mathematics. I racked up a total of four Bs in three years of schooling, which was a bit of a disappointment, but I will get over it once I am safely enrolled in grad school.

I think that my last semester gave me a pretty good taste of what grad studies will be like. Perhaps two of my four classes were about as hard as first-year grad work (at least my analysis class was, based on comparisons by my classmate who was in this class and grad micro at the same time) and I still worked about 20-24 hrs./wk over most of the semester at my job and RA work. I also completed 2 grad school applications during finals time. Plus the psychic cost of pure math classes versus any course putatively related to economics in some concrete-ish way was, for me, very high. I am THRILLED to be done with pure math classes (meaning no disrespect, pure math, you just aren't my cup of tea). Thus ends three years of nonstop schooling. Spring, summer, fall, spring, summer, fall, spring, summer, fall.

One thing that was a good sign for the future was that I absolutely loved working on my honors thesis. It was a study examining the link between the principal labor market in Chicago high schools under school reform, where principals are hired and fired by parent-majority local school councils, and student outcomes. This was a pretty good proxy for working on a dissertation and also doing research as a future economist and I loved it and was totally obsessed with the project. Digging through data and doing background research and thinking about the nuts and bolts of applied research was just a really rewarding and thrilling experience. It turned out rather well too (with considerable help from a fourth year grad student friend and my project supervisor).

So I now wait for grad school decisions to be made. I believe I am a safe bet to get into my alma mater, which would be a really decent outcome and is allowing me to rest considerably easier than I otherwise would. Additional admissions would just be gravy. Still, it will be a big relief to know what the next four to five years will hold, as the range of possibilities is pretty varied, particularly a couple of long shots that would involve relocation.

But for now I wait and sort of relax. There is something funny about becoming so expert in keeping all the balls of work, school and family in the air at one time. Now that one ball is taken away my rhythm is a bit awkward, even though it is easier overall. I have been enjoying spending extra time with my daughter and wife (though I like to think I kept the family balance pretty reasonable during my studies). But I cannot rest for too long. I have already begun to dig into some self-study. I got a taste of how subpar my econometrics coursework was while doing my honors thesis so I am now trying to do the work that a quality econometrics course should have put me through. I am also endeavoring to wade into the mathematical material I will be seeing this fall and am also planning to sit in on a grad health economics course. Take my spare time, please!

At any rate, I also hope to return to this blog a bit more regularly and also to take it back to a less solipsistic place. There is much to discuss and I'm going to try to do so with some regularity. Thanks for continuing to read…


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