The Incidental Economist

This is my annual plug for a terrific fellowship program that changed the direction of my career.

Four years into my very technical, very mathy graduate training, I spent the summer of 1998 in DC learning a few things about science and technology policy, the workings of Congress, and politics. I was a fellow at the National Academy of Sciences (NAS), Office of Congressional and Government Affairs. I spent most of the summer soaking in congressional hearings of all types but with a focus on Y2K issues. At that time some thought it’d be a big deal. Turned out, not so much.

It was a gratifying experience, and it propelled me into a career in policy-relevant research. I made a transition from science and technology to health, but the lessons learned about government during my summer internship at NAS remain relevant and significant.

For those interested in learning more about the fellowship program, below is an official blurb. For readers with affiliations with or connections to academic institutions, please share this fellowship opportunity with your institution’s graduate-level programs in science- or technology-relevant disciplines.

The Christine Mirzayan Science & Technology Policy Graduate Fellowship Program of the National Academies—consisting of the National Academy of Sciences, National Academy of Engineering, Institute of Medicine, and National Research Council—is designed to engage its Fellows in the analytical process that informs U.S. science and technology policy. Fellows develop basic skills essential to working or participating in science policy at the federal, state, or local levels.

This 12 week educational and hands-on training takes place in Washington, DC.  To learn more about the program, visit: www.national-academies.org/policyfellows.

After our e-mail exchange about the doc fix, Arnold Relman was kind enough to send me his book about his vision for a U.S. health system makeover, A Second Opinion. It’s a book worth reading, full of insight and well-supported arguments.  I liked it, agree with much of it, but not with every nuance of every idea in it.

Relman argues that corporate interests have overtaken health care provision and insurance. Dominated by a drive for income maximization and return on investment rather than high quality patient care, provider and insurer organizations are failing us, he says. The high costs for mediocre outcomes (relative to other OECD countries) that we observe are a natural consequence.

Relman’s proposed solution has two parts. One is a move to a global budget based single-payer system overseen by a National Medical Care Agency (similar to the SEC, the FTC, and the Fed–accountable to Congress but with independent regulatory authority). That cuts the insurers out of the picture. The other part is to pay provider organizations on a capitated basis–a fixed payment for each individual, risk-adjusted but not directly tied to specific services rendered. He suggests that salaried physicians organized as non-profit multi-specialty medical groups that receive such capitated payments would eliminate the perverse incentives of fee-for-service arrangements that are pervasive today. That cuts many current medical businesses out of the picture (they’d have to reorganize and tell investors to take a hike).

There are obvious political obstacles with Relman’s ideas, which he acknowledges. However, he also believes they will be overcome relatively soon. The book was published in 2007, and throughout most of  of it he writes that major reform in the direction he suggests might occur within ten or more years but in at least one place he writes that it might occur in five to ten years. My view is that if we ever eliminate insurers and for-profit medical businesses it will be many decades from now. I don’t expect major changes unless and until the path established by the Affordable Care Act (ACA) is proven ineffective. That will take a minimum of 20 years (recall the Cadillac tax doesn’t begin to bite until 2018). I also think “never” is a good estimate. We’re talking insurers and medical providers here! They’ve got a wee bit of clout.

Normally when someone argues strongly for a politically moribund idea I dismiss it quickly. Why contemplate the impossible? But Relman’s ideas are worth more consideration. He’s on to something (a few things, actually), and they can be separated from the politically unlikely package and contemplated independently.

He makes a very good argument, citing many studies, that the profit (or income) motive in health care is a problem. It’s not the drive for profit alone that is an issue. (If it were, we’d be trying to remove the profit incentive from every industry. Bad idea!) The problem with health care is that when the profit motive is combined with fee-for-service and third-party payment, information asymmetry that promotes provider induced demand, and life-and-death decisions it is far too easy–even individually rational if socially inefficient–to spend too much for too little.

The question is, what can be done? Different people propose different solutions that focus on different facets of the problem. Advocates of consumer directed health plans (CDHPs) argue that it’s principally the degree third-party payment that should be adjusted. If consumers directly pay more for their care they’ll make better decisions about what care to buy and how much it is worth. There is something to this notion. It’s not obviously wrong. It’s not obviously right either. There are potential problems, to which Relman devotes a chapter. Problems aside, the CDHP concept is being tested in the commercial market, and more will be learned about the degree to which it works over time.

Rather than targeting third-part payment, Relman’s focus is on the fee-for-service nature of payment. His proposal is to compensate providers with capitated payments. It’s a good idea, but isn’t obviously right or wrong either. I can see some merits and some problems. Fortunately, it has some similarity to the accountable care organization (ACO) concept that will be tested in Medicare and Medicaid. Thus, we’ll learn more about what such a payment system can do as well. CDHPs or ACOs, what are the strengths and weaknesses of each? We can speculate, but we won’t know for sure until we try them. So, it’s an empirical question and in time we’ll know a lot more.

But neither CDHPs nor ACOs are tied to single payer. They can exist apart or along side it. So, progress can be made without a single payer system. (In an earlier draft of this post I wrote that single payer is dead. Aaron Carroll is not so sure and points to recent events in Vermont. I still cannot imagine insurers and for-profit medical providers being legislated out of existence nationally. But I’ll hold off declaring singe payer dead in this post out of respect for Aaron.)

My final thought about A Second Opinion is that Relman is too hard on health economists, suggesting we’re partially responsible for the market-based system he blames as the source of our health care problems. If only health economists had so much power! I’m not sure what body of literature suggests health economists are strong and persuasive advocates for “corporate health care.” Some may be. Some may not be. About most policy issues, I’d say we’re mixed (on these very points see the Victor Fuchs article that Relman himself cited in his book). But, more than that, I’d say we largely keep our mouths shut about how things should be and focus on how things are and how they might turn out under proposals for change. As a community, we’ve diagnosed the problems Relman points to and analyzed suggested solutions to them. I consider that a service worthy of praise. (I’m biased though.)

In conclusion, Relman is right to put the focus on how physicians and hospitals are paid. I think most health economists would agree with him.  But physicians and the hospital industry won’t. That’s the problem. Relman’s book doesn’t solve it, nor could it. I’m not sure anything can.

Economics is not physics, and that bothers some people. Such physics envy seems odd to me. It isn’t the fault of practitioners that economics models don’t correspond to reality with the same fidelity as physics models. A system of humans and human institutions is just fundamentally harder to model. It’s not time invariant. People are not particles. In some respects physics is easier.

In Fundamentals of Physics, freely available as part of the Open Yale Courses program, Ramamurti Shankar masterfully reveals just how easy and fun it can be. My prior knowledge of Shankar is through his excellent quantum mechanics book, which I read as a Cornell undergraduate majoring in Applied and Engineering Physics. So, I’ve taken a lot of physics courses and enjoyed nearly all of them, but few as much as Shankar’s. What makes it so good is that he develops all concepts presented from first principles yet finds astonishingly short and simple–but correct–paths to results.

For instance, he patiently led his students toward the correct interpretation of Newton’s F=ma and each variable in the equation. How does one measure acceleration? What about force? What does each side of the equation mean? Why is it not tautological? How would you find the mass of an object? (The answer is not, “Put it on a scale.”) When one really ponders these questions seriously, as Shankar insists, one finds they’re surprisingly deep. Yet the answers are simple, once you understand what you’re after. Then you’ve learned something!

Shankar is also very funny, both in class and on his website where he lists the following among his accomplishments:

Discovered a small parameter that justifies most calculations performed in physics: 1/ego, where ego is the author’s ego.

Identified a new dangerously irrelevant variable: Sarah Palin.

Beyond just explaining physics well, Shankar brings the subject to life by relating some of its history. For instance in the seventh lecture, on Kepler’s Laws Shankar says,

By the way, Newton took a long time to publish this Law of Gravitation. Does anybody know why he was holding back for a long time? …

[O]riginally Newton had a law of gravitation between two point objects, namely point-like. The distance between them is unambiguously the distance between the points, and he got this law. But in the end, he wants to apply it to the Earth and the apple; they are close enough for you. You cannot pretend the Earth looks like a point from where I am. It looks like a big, fat thing. You cannot say it’s point-like. So, what you really should do is divide the Earth into tiny pieces, each one of which is point-like, find the force on each from each chunk of the Earth, using this law, and add it up. And if you’re lucky, it will look as if all the pull is coming from one point at the center, carrying the entire mass of the Earth. So, what branch of mathematics do you have to use to get that result? …

[H]e had to then invent integral calculus also. So, if he felt that no one around him was doing any work, it was probably justified because they just dumped the whole thing on this kid and said, why don’t you do [integral calculus too]? That’s why it took him a long time to verify, using integrals, that the sphere behaves like a point particle at the center. … [T]hat’s what held back a publication.

As with this bit of history, most of the material of the course was familiar to me, though Shankar’s novel presentation style made it fresh. Some of the details of Special Relativity were new to me, however. Somehow, in my education, I missed (or completely fail to recall) the packaging of variables (time and space; energy and momentum) into four-vectors and how doing so facilitates manipulation and solution of problems. Seeing (hearing, really, as I “took” the course aurally by iPod) that topic presented in this fashion cleared away much of the confusing clutter of Special Relativity and let me focus on some of its wonders.

For example, I found myself marveling at the photon. Why does it get to be so special? It gets to travel at the same speed for all observers. It gets momentum and energy without mass. It just doesn’t seem to belong in the same model as other particles. In fact, it’s the wave nature of light that shows up in the four-momentum for photons. In the class, the form of the photon’s four-momentum is just asserted. I could almost feel Shankar restraining himself to explain that further when he covered waves.

Likely he’s saving it for the next class, in which he covers electricity and magnetism and quantum mechanics. But that class is not available via Open Yale Courses right now, sadly. The class I listened to covers classical and relativistic mechanics, including brief introductions to waves, fluid mechanics, and thermodynamics. If/when Yale posts Shankar’s next class I’ll listen eagerly.

(See also my review of Yale’s Astro 160.)

I used to blog more on personal finance. That’s because it used to occupy more of my interest. Once I got my finances in order and made big decisions about how to save for retirement and kids’ college expenses, how much life insurance to buy, and so forth, there was less to think and write about. Being a Boglehead-style, buy-and-hold, index fund type investor, I don’t need to pay much attention to my portfolio or the markets. An hour or two a few times a year is more than enough and hardly fodder for blogging.

I lost interest in personal finance books too, having read many, including a shelf of textbooks included in Certified Financial Planning curricula. If one is interested in the Boglehead approach, there is really no need to read more than a few books (see my comments on the Bogleheads’ Guide books).

But I heard that Perter Bernstein’s books were different. Less about investing guidance and more about the history of finance, markets, and the theoretical and academic underpinnings of them, they promised a different view of the subject. I’ve just completed his Capital Ideas: The Improbable Origins of Modern Wall Street, and it lived up to that promise. A review at MarketThoughts.com sums it up well.

The book traces the development of Modern Finance Theory, from the first recorded attempt at developing a mathematical theory to explain stock prices (Louis Bachelier’s dissertation entitled “Theory of Speculation” in 1900), to the work of the Cowles Foundation in the 1930s, and, of course, Harry Markowitz’s famous 1952 short paper titled “Portfolio Selection” – all the way to more modern theorists and practitioners, such as Paul Samuelson, Bill Sharpe, Fischer Black, Myron Scholes, Robert Merton … and Franco Modigliani and Merton Miller. Mr. Bernstein weaves his discussions together in one, neat timeline, interspersed with many fascinating stories along the way.

What fascinates me may not fascinate another (and vice versa). But trust me, if you’ve got any interest in finance theory and the history of markets, you’ll be intrigued by something in Bernstein’s telling of the story. One example of something I learned: I had not been aware of the role of portfolio insurance in the October 1987 stock market crash (nor the development of the ideas that contributed to portfolio insurance itself). Actually, there’s some controversy over the extent of that role, which Bernstein explains.

I also found interesting that Markowitz published a 1956 paper in National Research Logistics Quarterly titled “The Optimization of a Quadratic Function Subject to Linear Constraints.” Such an optimization problem is at the heart of the approach I took in my Master’s Thesis and a published paper in which I developed a computationally efficient technique for locating an anomalous region in an image formed from tomographic measurements. (I’d love to read that paper by Markowitz, but I can’t find it online.)

Bernstein has written several other books on the history of finance, some of which I purchased in a boxed set. They’re on my stack. Once I read them, I’ll post my thoughts.

Josh Angrist and Jörn-Steffen Pischke sent me a copy of their modestly titled book Mostly Harmless Econometrics: An Empiricist’s Companion (let’s call it MHE for short). If the title sounds slightly familiar then you’re probably a Douglass Adams fan–he wrote a Mostly Harmless book too–and you’d be correct in assuming that MHE is not your ordinary econometrics text.

Angrist and Pischke claim their style has a “certain lack of gravitas.” With an emphasis on the practical and intuitive use of the most common, widely applicable, and relatively simple econometric models they provide a far less intimidating tour than most texts of techniques for the evaluation of social experiments, whether artificially or naturally randomized. Nevertheless, this book has math, more than I cared to study closely on first read, particularly in later chapters covering more advanced material.

Yet, the writing style is far less stodgy than typical academic texts. The fun begins in Chapter 1 (Questions about Questions), in which Angrist and Pischke write,

Research questions that cannot be answered by any experiment are FUQs: fundamentally unidentified questions. What exactly does a FUQ look like? …

Suppose we are interested in whether children do better in school by virtue of having started school [at age 7 instead of 6]. … To be concrete, let’s look at test scores in first grade.

The problem with this question … is that the group that started school at age 7 is older. And older kids tend to do better on tests, a pure maturation effect. … The problem here is that for students, start age equals current age minus time in school. … [T]he effect of start age on elementary school test scores is impossible to interpret even in a randomized trial, and therefore, in a word, FUQed.

Putting aside the FUQed, Angrist and Pischke explain the essentials of causal analysis for observational studies, beginning with a gentle introduction to the selection problem and regression in Chapter 2 (The Experimental Ideal). One can gain tremendous insight with little heavy lifting by reading that brief, 12 page chapter alone.

The real guts of the subject are presented in Chapter 3 (Making Regression Make Sense) and Chapter 4 (Instrumental Variables in Action). Slightly more advanced material is found in the final four chapters, covering fixed effects, differences-in-differences, regression discontinuity, quantile regression, and standard error estimation. I skimmed those final chapters only closely enough to know what’s there, for future reference. My main interest was in improving my understanding of IV basics, for which close reading beyond Chapter 4 is not necessary.

MHE is not only an econometrics reference and tutorial, it’s also a guide to a subset of the observational study literature that applies sound technique. Every method is motivated and illuminated by reference to or examples from published work. That’s particularly valuable to the publishing practitioner who needs to demonstrate adherence to proven methodology by reference to prior studies.

Thus, MHE is better than “mostly harmless,” and I recommend it highly, particularly to those who evaluate social programs, clinical trials, or otherwise wish to estimate causal effects from experimental or observational data. Yet I can think of a few, small ways MHE could be enhanced. My least important suggestion is an index of stylized facts. There are a handful of main points that the practitioner should carry around in his head, knowing he can look up the details when necessary. These might include, for example, that propensity scores only control for observable differences between treatment and control groups (pp. 86-87);  the fact that the instrument is independent of potential outcomes is a different idea than an exclusion restriction (p. 155; this, by the way, is a mind-bender and took me some time to appreciate); don’t include an outcome as one of the regressors (pp. 64-68); that non-linear models are very rarely necessary and very often lead to trouble (p. 190); among others.

One problem with nonlinear models is that they generate biased results with two-stage prediction substitution, a fact Angrist and Pischke discuss in Chapter 4. It deserves to be mentioned, but they didn’t, that one can obtain unbiased estimates of causal effects with nonlinear models using two-stage residual inclusion (2SRI), which is surprisingly simple and easy to implement (Terza, Basu and Rathouz, 2008). This is only important in the small subset of circumstance in which linear models won’t do. One such circumstance arises in my work in which models are put to use for policy simulations. In that case, linear approximations that don’t reproduce crucial nonlinear features of a distribution can be a problem, if only in presentation (which is important).

I’ll conclude by noting a large issue lurking in the background to which Angrist and Pischke only allude. That’s theory (by which I mean anything outside the data). What’s it for? Can one really conclude causality from data alone? The answer is “no,” but the reason is subtle. The topic almost arises twice, once in a discussion of how to decide whether a control variable is or is not an outcome variable. When one can’t use time to determine what can be the cause of what then “clear reasoning about causal channels requires explicit assumptions about what happened first, or the assertion that none of the control variables are themselves caused by the regressor of interest.” (p. 68) That’s theory folks.

Later, on page 156 the authors write, “There is nothing in IV formulas to explain why [treatment] affects [outcomes]; for that, you need a theory.” OK then! Theory has a role. In fact, its role is larger than implied by these quotes. I assert that one can’t begin to understand if or when selection on observables or unobservables (or endogeneity in general) might occur without theory. Put it another way, the model one chooses to estimate and the manner in which one does so comes in part from theory, a point stressed by Andrew Gelman in his review of MHE (a review worth reading, by the way).

In many cases, that theory is our own intuition, not some formal mathematical model. We know something about the world, about what can affect what, that we bring to the data. Without those extra-data notions, we wouldn’t even know what to study or how, let alone how to interpret what we find. I think this is something applied economists should appreciate. The data can reveal the size of causal effects, but only after we have decided what can cause what. Without such ideas, finding potentially valid instruments would be next to impossible. If you don’t believe me, next time you approach your analysis, ask a colleague to rename all your variables v1, v2, v3, etc. (and not provide you with a crosswalk to their actual names). Good luck!

Later: See also the Mostly Harmless blog.

References

Terza JV, Basu A, Rathouz PJ. Two-stage Residual Inclusion Estimation: Addressing Endogeneity in Health Econometric Modeling.  J Health Economics 2008: 27: 531-43.

The entirety of my negligible relationship with Brad DeLong is blog-based. We cite a tiny fraction of each other’s stuff just infrequently enough that almost nobody notices. That’s it. Given the volume and quality of posts he produces one would be forgiven (though not by him, perhaps) in thinking blogging is all he does. In contrast, given the lack of volume and quality of posts I produce one would be forgiven (though not by me, perhaps) in suggesting I do other things.

Well I do (whew!). So does DeLong (OMG!). And one of his other things is teaching Berkeley’s Econ 113, American Economic History. To deepen our relationship and learn a few things, I downloaded (and listened to) the Fall 2008 version of the course audio via iTunes University. It, along with lecture notes, is online as well.

Nearly all courses begin like this post, with a throwaway opening. Full of administrative tasks and an overly general course summary, the first lecture of a class is usually dreadful. Not so DeLong’s. You know a course promises great entertainment when in the first lecture the professor: (a) Motivates his students to do the course work with an argument that relates the income distribution of state taxation to the state’s tuition subsidy and the effects of technology and education on income disparity; (b) Reaches back to post-war higher education policy to explain why the course texbook doesn’t cover recent history as thoroughly as pre-war history; And (c) leans on the invention of the printing press in an explanation of class size.

True to its first lecture, the course is entertaining and brimming with detailed historical accounting of all (well, not all) things American. I’m a detail guy, but not a DeLong-style detail guy. I don’t tend to reach back 500 years to explain, well, anything. DeLong does so in describing what he ate for breakfast (or so it seems). For this reason I had a hard time keeping up, and I am certain I could not have handled the class as an undergraduate. In fact, this was probably the most difficult course I’ve “taken” by podcast (see reviews of others). Yes, it is harder than game theory.

Nevertheless, I did manage to learn a few things, or re-learn them. The major themes came though even without explicit emphasis from DeLong: (1) The pre-civil war American economy was dominated by geography. It’s a story of waterways, mountain ranges, fertile land, and natural resources. These days we’re so intellectually removed from nature, the fact that our nation was built on it is easily overlooked. (2) The technological innovations of the industrial revolution dominated the post-civil war economy. (3) The history of the 20th century is largely a story about the Great Depression and World Wars and recovering from them. (That doesn’t seem so hard, but I left out a few details.)

And now we languish in the shadow of the worst recession since the Depression, one that included a frightening financial crisis that hit its lowest low during the class through which I listened. This was the Fall of 2008, and the turmoil in financial markets and the 2008 campaign for president were notable sub-themes of the course. DeLong switched gears fluidly and frequently jumped out of chronology to discuss these topics.

In lecture 10, for example, he abandoned the class material entirely and opted for a teach-in on the financial crisis, which was unfortunately cut due to faulty audio, or so I guess. (Aside: I am very sorry to say that the course suffered terribly from technical problems–poor audio, missing lectures and segments thereof, and many moments of professorial grief caught on “tape” (electrons?) as the overhead or laptop wouldn’t function.) But in lecture 10 the audio held strong long enough to capture an amusing story of DeLong’s failed attempt to debate Kevin Hassett, co-author of Dow 36,000. A week or so prior, DeLong had been asked to take the Obama side in a debate on the 2008 presidential candidates’ economic agenda. Hassett would have been his opponent, but he refused to leave his hotel room on the grounds that DeLong had rudely critiqued his book. (DeLong’s opinion of Hassett is still less than favorable.)

So, Econ 113 is plenty of fun, but not so easy to hear through all the popping and hissing (and nonexistent) audio. I’m sorry to say that Yale produces far better distance learning products, at least technically though not necessarily in terms of content. No doubt there’s another economic point here to be made about technology and education, something to do with the fact that size of endowments really matter. Humorously enough, DeLong himself notes in the course that Berkeley had asked him to consider providing more material for distance learning. He rightly points out that until the institution can manage to keep fresh batteries in the microphone it is hardly worth his time and effort. But I’m glad he tried anyway.

Google Wave is a relatively new Google product that claims to be a “real-time communication and collaboration. A wave can be both a conversation and a document where people can discuss and work together using richly formatted text, photos, videos, maps, and more.” One obtains a Google Wave account by invitation only. I received my invitation late in 2009 and fiddled around with Wave a little. (If you want an invitation, send me your e-mail address.)

It didn’t take me very long to decide how to use Google Wave. The answer is: not at all. I haven’t found a single thing that makes Google Wave a good alternative to other ways of communicating and sharing. That’s not to say that I don’t believe it is possible it is better. It’s just not there yet.

Part of the trouble is that it is not intuitive. I watched as a half-dozen other Wave users edited a blog post of mine for possible use on the Bogleheads Wiki (*). It was pretty hard for me to follow the editing process because it wasn’t clear to me what the master document was. All the edits and communication appear in one long window, very little of which is visible at any one time. I think it’d be far better if the document could be viewed in a full-screen mode with some sort of mark-up or linking convention that indicated who did what to which part and when. (I see that some of my requests may be possible, but as I said it isn’t intuitive.)

I recognize that my impression of Google Wave is based on very little use and on a beta version of the product. I also recognize that Google has produce an 80-minute video on Wave’s features. But I’m not going to watch a long video to learn how to use a communication/document sharing product. It should be intuitive. If Google has any hopes of making a splash with Wave they’d better make it so, and fast. If they don’t, my prediction is that this will not be the only bad review.

Perhaps I’ll be wooed by subsequent versions of Wave. I’ll try to keep an open mind. I think the burden of proof that Google Wave has value is on Google. Build something obviously good and users will come.

(*) About 2.5 months ago I asked these Wave users and others for feedback on their experience. I did so in Wave itself. None responded. I take that as an indication that either (a) they’re not using Wave and didn’t see my query or (b) they don’t have a strong opinion of Wave (positive or negative). Either way, it does not seem Wave is a wild success with this crowd.

The Finance Buff (TFB) has just published a truly wonderful little book called Explore TIPS: A Practical Guide to Investing in Treasury Inflation-Protected Securities. Nearly everyone should include TIPS in their portfolio. If you’re familiar with TIPS but don’t own any or if you’ve never heard of them, this book is for you.

I read a draft copy of TFB’s book and found it to be an excellent guide to TIPS, covering all important elements efficiently and clearly.

Two papers in the current issue of Health Economics look interesting to me. I may not have time to read them but others might wish to. They’re listed below with links and abstracts.

The first addresses the question of whether the fact that individuals switch health plans results in lower use of preventative services. Since provision of preventative services is a current investment for a future return, high turnover offers an opportunity for an insurer to benefit from the investments of others and to dodge the consequences of its own under-investment.

The second paper below documents the variation in value of a quality adjusted life year (QALY) across countries. Since figures are not reported in the same currency they are hard to compare. But the authors also estimated the discount rate of QALY value across countries. The QALY discount rate in Japan is almost twice that in the U.S., for example.

Many preventive healthcare procedures are widely recognized as cost-effective but have relatively low utilization rates in the US. Because preventive care is a present-period investment with a future-period expected financial return, enrollee turnover among private insurers lowers the expected return of this investment. In this paper, I present a simple theoretical model to illustrate the suboptimal provision of preventive healthcare that results from insurers ‘free riding’ off of the provision from others. I also provide an empirical test of this hypothesis using data from the Community Tracking Study’s Household Survey. I use lagged market-level measures of employment-induced insurer turnover to identify variation in insurers’ expectations and test for the effect of turnover on several different measures of medical utilization. As expected, I find that turnover has a significantly negative effect on the utilization of preventive services and has no effect on the utilization of acute services used as a control.

Takeru Shiroiwa, et al., International survey on willingness-to-pay (WTP) for one additional QALY gained: what is the threshold of cost effectiveness?

Although the threshold of cost effectiveness of medical interventions is thought to be £20 000-£30 000 in the UK, and $50 000-$100 000 in the US, it is well known that these values are unjustified, due to lack of explicit scientific evidence. We measured willingness-to-pay (WTP) for one additional quality-adjusted life-year gained to determine the threshold of the incremental cost-effectiveness ratio. Our study used the Internet to compare WTP for the additional year of survival in a perfect status of health in Japan, the Republic of Korea (ROK), Taiwan, Australia, the UK, and the US. The research utilized a double-bound dichotomous choice, and analysis by the nonparametric Turnbull method. WTP values were JPY 5 million (Japan), KWN 68 million (ROK), NT$ 2.1 million (Taiwan), £23 000 (UK), AU$ 64 000 (Australia), and US$ 62 000 (US). The discount rates of outcome were estimated at 6.8% (Japan), 3.7% (ROK), 1.6% (Taiwan), 2.8% (UK), 1.9% (Australia), and 3.2% (US). Based on the current study, we suggest new classification of cost-effectiveness plane and methodology for decision making.

Another dump of new NBER papers just came through. There are several I’d like to read and possibly summarize for this blog. As a service, I’m including the abstracts for those below. My lament is that I may not get to them all. There is too much to read and not enough time. And this is just a small slice of the new literature from all sources.

Hence, this offer: if anyone out there reads any of these and wants to send a guest post summary I’ll consider posting. I can’t promise publication in advance or else that blows any chance at quality control. FYI, I’ve printed the last one listed, on empirical industrial organization, because it is most relevant to my work. If I summarize any of these for this blog that one will be first.

Economics of estate taxation: a brief review of theory and evidence use, Wojciech Kopczuk, NBER Working Paper No. 15741. This paper provides a non-technical overview of the economic arguments related to the desirability of transfer taxation and a summary of empirical evidence surrounding these issues. Understanding optimal transfer taxation throughout the distribution requires understanding the nature of a bequest motive, a topic on which there is little consensus. However, I argue that progress still can be made on the question of desirability and optimal level of estate taxation at the top of the distribution, because interpersonal externalities implied by the presence of bequest motive are irrelevant from the welfare point of view when the focus is on the wealthy. I also examine the role of negative externalities from wealth concentration in providing justification for considering this type of taxation.

Students Choosing Colleges: Understanding the Matriculation Decision at a Highly Selective Private Institution, Peter Nurnberg, Morton Schapiro, David Zimmerman,  NBER Working Paper No. 15772. The college choice process can be reduced to three questions: 1) Where does a student apply? 2) Which schools accept the students? 3) Which offer of admission does the student accept? This paper addresses question three. Specifically, we offer an econometric analysis of the matriculation decisions made by students accepted to Williams College, one of the nation’s most highly selective colleges and universities. We use data for the Williams classes of 2008 through 2012 to estimate a yield model. We find that—conditional on the student applying to and being accepted by Williams—applicant quality as measured by standardized tests, high school GPA and the like, the net price a particular student faces (the sticker price minus institutional financial aid), the applicant’s race and geographic origin, plus the student’s artistic, athletic and academic interests, are strong predictors of whether or not the student will matriculate.

Foreclosures, Enforcement, and Collections under the Federal Mortgage Modification Guidelines, Casey B. Mulligan,  NBER Working Paper No. 15777.  Federal mortgage modification initiatives, targeting millions of borrowers, are intended to prevent foreclosures of underwater home mortgages. Those initiatives discourage principal reductions in favor of interest reductions, despite the possibility that the former would be a more durable foreclosure prevention tool. The programs also impose marginal income tax rates substantially in excess of 100 percent. Using the framework of optimal income taxation, this paper shows how alternative means-tested modification rules would simultaneously improve collections, efficiency, the number of foreclosures, and their total cost. As a result, lenders have an incentive to foreclose on borrowers deemed modification eligible by the federal programs.

“Unfunded Liabilities” and Uncertain Fiscal Financing, Troy Davig, Eric M. Leeper, Todd B. Walker,  NBER Working Paper No. 15782.  We develop a rational expectations framework to study the consequences of alternative means to resolve the “unfunded liabilities” problem—unsustainable exponential growth in federal Social Security, Medicare, and Medicaid spending with no plan to finance it. Resolution requires specifying a probability distribution for how and when monetary and fiscal policies will change as the economy evolves through the 21st century. Beliefs based on that distribution determine the existence of and the nature of equilibrium. We consider policies that in expectation combine reaching a fiscal limit, some distorting taxation, modest inflation, and some reneging on the government’s promised transfers. In the equilibrium, inflation-targeting monetary policy cannot successfully anchor expected inflation. Expectational effects are always present, but need not have large impacts on inflation and interest rates in the short and medium runs.

Empirical Industrial Organization: A Progress Report, Liran Einav, Jonathan D. Levin,  NBER Working Paper No. 15786. The field of Industrial Organization has made dramatic advances over the last few decades in developing empirical methods for analyzing imperfect competition and the organization of markets. We describe the motivation for these developments and some of the successes. We also discuss the relative emphasis that applied work in the field has placed on economic theory relative to statistical research design, and the possibility that a focus on methodological innovation has crowded out applications. We offer some suggestions about how the field may progress in coming years.