Monday, November 23, 2015

Going to Oxford

I'm overjoyed to announce that I have been selected as a 2016 Rhodes Scholar. I'm deeply grateful to the selection committee and am excited for this wonderful opportunity to study at Oxford. More details to come.

Monday, October 26, 2015

The Swiss Shock: A Case Study

In January 2015, the Swiss central bank removed its floor on the exchange rate between the Swiss franc and the euro, allowing its currency to appreciate without limit. The immediate effect was a 20-percent increase in the value of the Swiss franc relative to the euro -- one of the largest revaluations of a developed-world currency in recent history.

The move sent tremors through the financial markets, which had been using the Swiss franc as a funding currency for carry trades and Swiss banks as a haven from the chaos of the Eurozone. Swiss exporters and the tourism industry screamed that the central bank's move would render the country uncompetitive.

I've been fascinated by this move -- whose proximate cause, I have suggested, was the resumption of capital inflows after a two-year pause, and the Swiss central bank's latent unwillingness to sterilize further inflows -- and so I've been waiting to do some post-mortem work.

What are the effects of changes in exchange rates on the macroeconomy? Switzerland provides a beautiful, clean case study. The revaluation was unanticipated before it happened and huge when it did.

To do my analysis, I'll use the synthetic control method that has been pioneered by Alberto Abadie at Harvard. You can read about that method here, but the basic intuition is that you can construct a comparison for the treated unit (in our case, Switzerland) by taking the weighted average of untreated units, where the weights are optimized so that the "synthetic Switzerland" matches actual Switzerland before the treatment as closely as possible.

The two macro variables I want to look at are stock prices and consumer prices. What I find are that the revaluation has reduced consumer prices by 1.5 percentage points but had no significant real effect on Swiss stocks.

I construct synthetic Switzerlands for consumer prices and stock returns separately. For consumer prices, the algorithm says that synthetic Switzerland is a mix of nine European countries, but is mostly a mix of Slovakia, Sweden, Netherlands, and Denmark. I use monthly data from 2004 to 2014 to do the matching. I found it interesting that it picked smaller countries, many of which have their own currencies.

What we find is that, in both actual and synthetic Switzerland, prices were flat prior to the exchange-rate shock. Such is Europe in 2014. Then, starting in January 2015, we see actual Swiss prices begin to diverge from consumer prices in synthetic Switzerland. As of September 2015, Swiss prices are now 1.5 percentage points lower than they would have been absent the revaluation.

I use data from the iShares MSCI indexes for stock returns, and I find that synthetic Switzerland is mostly a mix of Netherlands, Belgium, Sweden, and the United Kingdom. (Worth noting: On a totally different dataset, the algorithm picks roughly the same states.) Turns out we can predict daily stock returns in Switzerland quite well, as this scatterplot of actual versus synthetic Switzerland shows.

But I'm not finding any significant effect on Swiss stocks. Here are the cumulative returns for actual versus synthetic Switzerland from January 2014 to present, and any effect should appear starting in January 2015.

Perhaps this makes American investors less concerned about the effect of the appreciating U.S. dollar on their portfolios. The implication of these findings is that any nominal decline in stock prices is offset by currency appreciation.

I'll try to look next Swiss unemployment, their trade balance, and other real macroeconomic variables.

Monday, September 28, 2015

Boom and Bust and Biotech

Biotechnology and pharmaceuticals stock prices have declined about 20 percent in the last week, wiping out hundreds of billions of dollars in market capitalization. That drop is in the wake of popular outrage at the headline-grabbing Martin Shkreli, whose firm acquired the antimalarial drug Daraprim and had planned to raise its price 50-fold, as well as rumblings of a substantive public-policy response to pharmaceutical prices from the Hillary Clinton campaign.

It seems to me this market reaction raises two possibilities.

First, is this decline just the "inevitable" correction (in the sense of Blanchard and Watson's rational bubbles) for five years of strong performance from biotech stocks?

Blanchard and Watson proposed an idea of bubbles in which an asset price rises faster than other asset prices most of the time but has a small chance of falling catastrophically. As crazy as that sounds, it works fine in expected-value terms, relative to other investments, because the two cancel each other out.

There is some evidence for this proposition in the stylized fact that it's been the riskiest, best-performing biotech/pharma stocks which have corrected most sharply. For example, just compare the new-school Valeant, Celgene, Regeneron, and Biogen with the old-school Pfizer, Merck, Novartis, and Eli Lilly.

Alternatively, let's suppose that some of the decline reflects actual changes in the expected future profits of biotech. Shouldn't it be disturbing that a rough draft of a policy proposal to restrict drug prices caused biotech to implode? What does that say about the social value of these biotech innovations?

Not good things, I think. To the extent that price regulations hit firms differentially, they will hit firms most dependent on the high-price business model that regulators find objectionable. The market has just told us that new-school biotech is built around this model.

Intuitively, a good product does not depend on which way the regulatory winds blow. A lot of the new-school biotech firms just proved they absolutely do. That should concern anyone who is hopeful about the future of health.

Saturday, September 26, 2015

Up. But Not Up, Up, and Away.

For the last year, I've been putting together simple short-term forecasts for inflation using only oil prices. Since oil has been driving so much of the movement lately, the forecasts have been quite accurate. This January, before most of the decline in inflation, I said there was a 50-50 chance of deflation -- and, sure enough, PCE inflation hit just 0.2 percent in the summer.

What oil giveth, however, the year-over-year calculation taketh away.

Since inflation is usually examined on a year-over-year basis, there are often "base effects" -- if prices fall or rise sharply in one month, that shock is carried over for the next 11 months. And then, the next month, the effect vanishes as the shocked month drops off the base, sending inflation just as sharply in the opposite direction.

Here we go, then. Since much of the decline in oil prices happened in the summer and fall of 2014, inflation is likely to rise sharply in winter 2015. That's what you can see in the forecast above. My baseline forecast is that core and headline will be at or around 2 percent by January 2016.

The model is not really a forecasting model, because almost all its power comes from simple propagation of the oil price into inflation and then the capture of the base effect, so I would not take seriously any longer-term forecasts it produces. It's not designed, for example, to consider the effects of unemployment or wage growth on inflation, which are arguably more important to inflation in the medium run. It's just meant to give you some visibility six months or so ahead. There it succeeds.

So inflation will go up -- but not up, up, and away. The Fed's inflation target is a two-percent annual increase in prices, as measured by the PCE price index. It looks as though, once the oil shock dissipates, the inflation will be on target.

My only concern is what happens when everything snaps into place so quickly. In six months, with inflation at 2 percent and unemployment in the high 4 percent range, that might produce a substantial amount of pressure for the Fed to tighten quickly, even when the Fed should be responding to the signal in the inflation process, not oil-driven noise.

Update (10/13/15): I added +/- 1 standard error bars to the forecast generated by Monte Carlo simulation. I think a reasonable confidence that inflation will return to target is, at this point, warranted.

Friday, September 25, 2015

What's the Case for Big Banks?

Ever since the global liberalization and deregulation of financial services began in the 1980s, banks have argued that it would be better if they were bigger.

Allow consolidation, bank executives have said, and customers can have what they want from their bank: a full suite of financial services from a bank with global reach and a deep knowledge of financial markets.

To be that kind of bank, however, takes scale. A smaller bank could take deposits and lend so people could buy cars and homes and so businesses could make payroll. But it would never be able to promise an individual a competitive interest rate or to help a business pay a supplier in Japan.

All that seems fair enough. There's a problem with the argument, though: It doesn't seem to be true in the data. If large banks had a competitive advantage, we would be able to see it in their return on assets. If return on assets doesn't increase with bank size, then it's hard to see why free markets would favor scale in banking.

To the banks' point, it does seem to be true that community banks -- those with assets under $1 billion -- operate at a severe disadvantage. The data suggest that, holding leverage constant, they could increase their value by roughly 20 to 30 percent from scale efficiencies. (This probably explains that the consolidation has been concentrated in these banks.)

Yet there's no evidence that returns to scale are increasing beyond that point. And community banks only control 14 percent of bank assets. So that's a free-market case against community banking, rather than one for the rise of the very largest banks.

This was something that researchers noticed in the early 1990s, back when bank consolidation was just beginning. It remains true. So, tell me, what's the free-market argument for big banks? In a world of "too-big-to-fail," the benefits that are supposed to offset the cost of implicit government subsidies are elusive.

Thursday, September 17, 2015

More Thoughts on Productivity

Well, that could have gone better.

Josh Bivens and Larry Mishel have written a response to my blog post on labor productivity and compensation. Since we've had a short discussion over email today, I figured it would be worthwhile to outline a short reply.

Let's stipulate that the analysis could have been better. For example, it would have been useful for me to have better-defined, non-overlapping industry categories or data on the value of intermediate inputs. Above all, the ability of industry data at all to give insight into individual labor productivity is limited -- and they allude to the compositional issue in their blog post -- but, without some unit of analysis above the individual, measuring labor productivity is almost impossible. To the extent that we want to make any comparison at all between productivity and compensation, we need to accept certain trade-offs. This is one of them.

And the mistake I made in preparing the data, of course, is on me.

Yet I think Bivens and Mishel don't recognize that there is a good reason to look at nominal definitions of productivity and compensation. Notably, they misrepresent the analysis with an analogy to Zimbabwe's hyperinflation, saying that inflation invalidates my results. This is wrong.

My results are driven by relative changes in compensation and in productivity. This means that, had I deflated all my data by any measure of prices -- CPI, PCE, etc. -- it would not change my results.

What Bivens and Mishel do in their blog post, I would say, examines an different relationship than than I do, because they adjust productivity for industry-specific price indexes. So we reach two distinct conclusions that are both correct, which is easily missed in their write-up:

  • I show that there is a robust relationship between changes in the economic value of output produced per hour and changes in the hourly compensation of employees.
  • They show that there is no relationship between changes in the volume of output produced per hour and changes in the hourly compensation of employees.

The key difference, of course, is that I examined the value of output, and they examined the volume. Both results are meaningful. Together, they imply that, to a considerable extent, the economy adjusts to industries' different rates of productivity growth by changing the relative prices of output. You might think of how consumer technology is both much cheaper and produced more efficiently, than say, haircuts.

There is an underlying normative issue here as to whether workers should be compensated for the gains in the economic value of their output or in increases in the volume of it. To say that one analysis is "right" or "wrong" implicitly takes a position on the normative issue. I don't have any special insight on it.

Addendum: Brad DeLong and Mike Konczal have asked me to say how my interpretation has changed, and rightly so. Originally, I found a relationship between growth in labor productivity and in labor compensation strong enough to explain most (80%) of the variance across industries from 1987 to 2013. In my revised results, this drops to a third. If before I would have said that compensation growth is very well explained by productivity growth, now I think a reasonable view of my results is to say that it is a substantial contributor, but not by any means the full story.