Monday, August 31, 2015

How Are Economists Connected?

The National Bureau of Economic Research, an organization of top economists that serves as a sort of clearinghouse for new research papers, counts nearly 1,400 members. Their interests vary widely, but upon joining the NBER, they sign up for research programs that represent their favored topics.

The NBER has 20 such programs, and economists usually sign up for one or two, although some sign up for more. (Twelve economists are signed up for five programs. Andrei Shleifer is the only one signed up for six.) As a result, we have over 700 connections between topics.

With so many members signing up for different combinations of programs, the NBER's member interest list gives a picture into the field. Not only can it tell us what fields are popular and unpopular, but also, it shows us what combinations are comparatively more or less common -- a window, perhaps, into the connections economists draw within their own field.

So I scraped the NBER's member list and got started. (As always, my data set is available here.)

The first metric I looked at was the correlation of registrations, as you can see in the matrix below. (Click it to enlarge.) You should interpret a significantly positive cell as "economists often put these topics together," a cell with a value near zero as "there are no strong connections between these two topics," and a significantly negative cell as "economists tend not to put these two topics together."

Some things immediately popped out at me. NBER members that were interested in monetary economics, for instance, also tend to be interested in economic fluctuations and growth. Those that are interested in the economics of education also tend to do work on labor economics. Both of those connections make a great deal of sense!

The areas where economists seem to pick and choose are also fascinating. Labor economists seem to dislike asset pricing. Those interested by economic fluctuations and growth stay away from education. And so on.

But this thinking is a little fuzzy. A research interest in one field may be associated with another mostly because of a third field. Or there could actually be extensive connections between the two topics. How can we discern one from the other?

The math behind this is a bit too difficult to explain in a blog post -- it comes from graphical models in machine learning -- but we can produce something called a "precision matrix" which effectively allows us to isolate the "true" connections among pairs of topics.

And what we see is again pretty cool. Some topics seem to invite a lot of cross-over. Doing work in economic history or health economics or asset pricing, for instance, makes you quite a bit more likely to have a second field.

Friday, August 28, 2015

What Ails the American Startup?

For all the hoopla about Silicon Valley, the data are clear: These are rough times to be a young business in America. In the early 1980s, about 12 percent of all firms were less than a year old. In 2012, however, only 8 percent were.

This raises a good question: What's going on? Why are new firms struggling to gain a foothold? Data from the Business Dynamics Statistics of the US Census offer an interesting answer: The problem isn't with the startups. It's with the economy in which they are starting up.

To reach that conclusion, though, we first need to learn a little bit about entrepreneurship in America. You've probably heard the factoid that 9 out 10 restaurants fail in their first year -- it's false, but never mind -- and actually, only about a quarter of all new firms go bust in their first year. Five years later, 45 percent of firms have survived. It's a pattern, technically called a "survival function," that has repeated itself since at least 1977, when the Census began collecting this data, as the next graph shows.

Let's take that survival function for granted, then, and focus on two specific phenomena. The first is a year-level effect: something that hits all firms in a given year the same amount, no matter when they were founded. The second is a cohort-level effect: something that hits firms founded in a given year the same amount and sticks permanently with that cohort of firms. (Economists: Scroll to the end of the post for the modeling details.)

You might think of the first as a cyclical or structural shock to the economy and the second as whether it was just a big or small "class" of new firms that year. Using the Census data, we can track the number of firms in each cohort for their first five years of existence, allowing us to disentangle the cohort and year effects. We can answer the question: Are the startups getting worse? Or is survival getting harder?

I find that about half of the decline in new firms from 1977 to 2012 can be ascribed to the year-level effect, and that there has been no average change in the cohort-level effect over the same period. The startups aren't that much worse, essentially, but the economy is much harsher towards them. With the same cohort strength but the prior economy, we would have about 200,000 more startups per year -- and about 700,000 more firms less than five years old. Since the US has about 5 million firms, that's a substantial change.

We can compare the actual decline to a counterfactual without the year-level effects:

Here are few more graphs to make sense of this. The first shows the cohort-level effect, and you should notice the lack of a down trend, but also the strong cyclicality, which shows the "smothered in the cradle" effect of recessions on new firm formation. High cohort effects can be thought of as years in which lots of startups launched successfully, whereas low cohort effects are bad years, with few successful launches.

The second shows the year-level effect, and you should notice the persistent down trend, indicating that, for any given firm, survival is becoming harder.

I've also taken the change in the year-level effect, so that we can see more clearly when survival has become harder. What we see, clearly, are two bloodbaths -- the 1980 and 2008 recessions -- and then a slow decline between them, without any obvious cyclicality.

There's a big takeaway here: The decline in new firms seems to be driven by changes that are making new firm survival more difficult in general, not just a decline in the cohort size itself.

*   *   *

Technical explanation

Let nft be the log number of firms founded in year f and alive in year t. I specify the model:

nft = bf + bt + bt-f + eft,

where all the b terms are OLS coefficients and e is an error term. Then bf can be thought of as a cohort-level effect, bt as a year-level effect, and bt-f as a survival function. Note that this isn't actually a survival model but rather more of a quick-and-dirty test with panel-data techniques, and if bt increases year-over-year, the model doesn't make any sense. (Fortunately, this isn't a problem for our data set.)

My cleaned dataset is available here.

Tuesday, June 2, 2015

Who Is On the RUC?

For the last year, I have been working to reconstruct the membership of the RUC, which is probably the most important policy entity in healthcare you've never heard of. The short of it is that RUC is a private organization with a critical public function: it advises the Centers for Medicare and Medicaid Services on how to set the relative prices for physician reimbursement within Medicare.

For example, it's the RUC's job to decide that, say, one treatment of a heart attack is equivalent in value to two treatments of pneumonia. It has come under extensive criticism -- see here, here, and here -- for basically being an unaccountable shadow government that acts in the interest of the American Medical Association and specialist doctors, rather than the medical community as a whole, patients, or the taxpayer. To be clear, I am repeating, not endorsing, that phrasing of the critique of RUC.

Initially, it was my intention, working with Judd Cramer, a friend and grad student at Princeton interested in labor economics, to try to link changes in the composition of the RUC to changes in Medicare's relative prices, known in health-policy circles as RVUs. But we never finished the project, mostly because I was overwhelmed with work this year -- I took a more-than-full load of classes and also wrote this research paper as independent work on the side.

Then the plan was to publish the list in an article with extensive commentary and discussion. In particular, I was very interested in potential conflicts of interest among RUC members, as prior work by Roy Poses has shown this to be a real problem. Yet, to do that, I really needed a complete and fully accurate membership list. That, as I have learned over the last few months, is basically impossible. RUC has been overseen by the AMA since 1991. It now has 32 seats, though it has expanded over the years. This means there are 736 person-years to account for. I could get all but 23 of them.

Over the last year, however, various health-policy researchers have found out that I have been working on this project -- and so I have an increasingly long list of people whom I've been telling to wait.

Yet I've decided that it's in the public interest for me just to publish the list already. (It's the document at the top of this post.) I do so with two honest caveats. First, it's incomplete. I'm missing a handful of years for certain seats, as my efforts to track down some person-years failed. Second, there are probably some inaccuracies. I do not think it is ridden with errors, but I would frankly be surprised if I got everything right. That's just the nature of trying to research a body that has made an extraordinary effort to remain cloaked in secrecy. (The type of error that I think is most likely is that I got some of the years wrong. I think all the names are correct; I am pretty sure anyone I claim was on RUC was in fact on RUC, for approximately the period I say they were. My guess is that I will be off by a year, say, for 10 percent of the people.)

Here is how I put this list together: dozens of hours of archival research. First, I managed to track down old AMA Board of Trustees reports. Those sometimes contained RUC appointments. Second, the medical-specialty newspapers and journals often mention who is currently serving on the RUC on the specialty's behalf. Third, the résumés and websites of ex-RUC doctors often list their full years of service; sometimes you can also find these in articles for the medical-specialty publications when they retire. Fourth, the AMA recently began publishing the current membership as part of an (admirable, but highly incomplete) effort towards transparency. Fifth, I relied on other efforts that Roy Poses and Brian Klepper, among others, have made, to identify RUC members.

I will also try to release some of the related research that I have done on RUC in the coming days. It was past time for me, however, to share this document. Thank you to the many who helped or cheered along this project.

SNAP and Food Security

"SNAP and Food Security: Evidence from Terminations" is the title of my first-ever working paper, which I wrote for my junior-year independent work at Princeton. What I do in the paper is try to measure very carefully the effect of participating in SNAP on households' food security, and the basic idea of how I do that is pretty simple:
[C]onsider two similar groups of households. The first group receives SNAP benefits in both November and December of a given year. The second group receives SNAP benefits in November but not in December. The difference in December food security between the two groups provides an intuitive estimate of the effect of SNAP benefits on food security in December.
 With that kind of comparison in mind, here's what I find:
SNAP participation increases the probability of food security by 10 percentage points (22 percent), with gains concentrated in reducing the probability of extreme food insecurity by 8 percentage points (36 percent), an effect that is broadly comparable to that of a change in household income from $10,000 to $20,000.
Naturally, there's a whole lot more in the paper itself.

Monday, May 25, 2015

Is Growth Understated?

Martin Feldstein has a nice op-ed in The Wall Street Journal arguing that the Bureau of Economic Analysis is understating GDP growth because of difficulties in adjusting for quality improvements and new products. It goes well with recent technical reports from Goldman Sachs and the Fed's Board of Governors. And read Paul Krugman for skepticism on whether technological progress is a big deal.

Here is a closely-related claim: Free access to Internet utilities like Google and Facebook means that market-based consumption growth understates growth in total consumption and therefore GDP growth understates gains in social welfare.

The way we should be thinking about this claim is "household production." My ability to use Google and Facebook doesn't require additional spending, just additional time. Spending time on the Internet rather than buying the newspaper, therefore, is functionally similar to making a sandwich at home from cold cuts in the fridge rather than buying a ready-made one at the deli.

Consider, then, the idea that these free Internet utilities are becoming more important, more powerful, more valuable, or whatever. That's identical to an improvement in my sandwich-making skills. And we would think that, as I become a better sandwich-maker, I will substitute market goods for home production by reducing my consumption of deli sandwiches. In particular, I'll cut back until the next deli sandwich is worth as much to me as the next homemade one.

The implication is that, if the marginal value of time on the Internet is actually rising due to Google, Facebook, and similar utilities, we should be seeing substitution away from the relevant alternative uses of time.

Do we? Yes, from Business Insider:

Consumers are substituting digital media, much of it free, for media sources they pay for, like TV and print. Maybe, then, we should also take the omission of free goods seriously, too, when we consider the divergence of GDP from a fuller, hypothetical measure of social welfare.

Thursday, May 14, 2015

Macro Mysteries and Non-Mysteries

There has been an interesting, if rather theoretical, debate between Roger Farmer, Brad DeLongPaul Krugman, and John Cochrane. The gist of it is simple enough: Is the current standard toolkit of macroeconomic models enough to explain the 2008 recession and limp recovery?

So that all blog-readers are on the same page, Keynesian macroeconomics has rallied around a certain framework since the 1980s. You start with a very classical model of the economy -- an economy that is always at potential, always has the right prices, and always has efficient allocations of resources -- and add some frictions, usually sticky prices or some sort of borrowing constraint. The result is a model where business cycles happen (and can be very severe) but where, eventually, the economy returns to potential. Krugman largely defends this theoretical tradition or, more precisely, a more primitive version of it.

This is not what Roger Farmer wants. Instead, Farmer wants economists to be thinking about models in which "potential" is not well defined -- that is, where it is very much possible for the economy to find equilibrium at many different levels of production. In short, Farmer wants ideas like multiple equilibria, nonlinearity, and self-fulfilling expectations back on the theoretical agenda. And, on the empirical side, Farmer has been trying to show empirically that we see this phenomena in key economic variables like unemployment and output.

In moderating the debate, DeLong faults Krugman's defense of the standard toolkit and argues that Farmer deserves some credit. The standard toolkit, DeLong contends, doesn't get the size of the recession right:
When I look at the size of the housing bubble that triggered the Lesser Depression from which we are still suffering, it looks at least an order of magnitude too small to be a key cause... To put it bluntly: Paul is wrong because the magnitude of the financial accelerator in this episode cries out for a model of multiple--or a continuous set of--equilibria. And so Roger seems to me to be more-or-less on the right track.
I do not think DeLong is correct when he says that the magnitudes come out wrong. My sense has been that the standard toolkit -- with the financial accelerator and sticky prices -- actually does get it right. It follows that, at the moment, we do not have compelling evidence that the stuff Farmer wants to put into macroeconomic models is needed.

Matteo Iacoviello, for instance, showed back in 2005 that textbook financial-accelerator models match what we see in the data. There's no mystery to be solved about why declines in home prices have such severe, protracted effects on economic growth. More recently, Atif Mian and Amir Sufi have put forward a lot of evidence that the hit to household balance sheets during the 2008 recession explains the decline in employment. On my part, I am doing some work to extend this line of inquiry to Spain's housing bubble, with some initial results showing that the boom and bust in mortgage lending, driven by wholesale finance, fully explains the boom and bust in housing prices.

Simon Gilchrist and Egon Zakrajšek have shown something similar is true in corporate bonds -- a financial market that, when hit with an adverse shock, propagates the shock into corporate investment and employment. Daniel Leigh and an army of economists at the International Monetary Fund have shown that, across the set of developed economies, the drop and sluggish recovery in business investment also lines up with the predictions of the textbook model.

Another approach is to put these financial frictions into a more developed model of the economy's structure, as in some recent work by Marco Del Negro, Marc Giannoni, and Frank Schorfheide. When you hit that model economy with the the kind of shocks that preceded the 2008 recession, the downturn that pops out of the model looks quite a lot like the 2008 recession.

I am not trying to say here that the 2008 recession raises no interesting questions. It does. But I think that a review of the empirical research would suggest that "why was the downturn so severe?" and "why has the recovery been so weak?" are not among them. When DeLong and Farmer say that our theoretical framework is insufficient to explain the evidence, I do not know what evidence they have in mind.

Farmer does some informal statistical work to try to show that real output drifts rather than returns to a trend. The problem with this argument is that, when you separate out permanent and transient shocks -- something Farmer doesn't do -- the transient ones look like shocks to demand, the permanent ones to supply. (Cochrane's post has a lot more to say on these statistical issues.)

Farmer might find some stronger evidence for his view that "potential" is a nebulous concept in some fascinating new work by Larry Ball, which compares the revision of estimates of potential output to the actual downturn in output. Where the downturn was worse, Ball shows, the loss of potential has been worse. However, there's some (very different) evidence from the bombings of Japan and Vietnam showing that long-run economic potential is almost indestructible.

Trying to find solid footing on this issue will be a challenge. It's terribly difficult, from the standpoint of research, to show that short-run fluctuations transmit into long-run catastrophes. "Permanent" is hard to distinguish from "long-lasting."

My feeling then, is that the heat in this debate is pretty misplaced. We have a mountain of evidence showing that financial shocks can generate long-lasting, deep recessions -- and yet, we are only at the beginning when it comes to understanding whether recessions do permanent damage, let alone how much. Why don't we start there?