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.

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.

Sunday, September 13, 2015

Raising the Retirement Age

New advancements in linking have made it possible, and indeed easy, to determine the year of birth, and almost the month of birth, for individuals surveyed in the Current Population Survey.

In an important effort, three researchers at the Minnesota Population Center have made it substantially easier to track individuals over time through the Current Population Survey. While economists have known this was possible for the better part of twenty years, it's been both imperfect and time-consuming. This new linking tool makes linking much better and dramatically easier.

It could spark a wave of new research in the social sciences. In fact, I've realized it's easy to determine almost exactly the month and year of birth for about half of individuals who participate in the Current Population Survey.

Survey participants show up in two sets of four consecutive months, so there are six opportunities for their ages to change going from one month to another. If you're, say, 21 in August but 22 in September, then you must have had your birthday between August and September. (This is where the technicality of "almost" the month of birth comes in, since the U.S. government does surveys in the middle of the month.)

So, what can we use this for? Here's one application: What is the effect of changes in the full retirement age for Social Security on the labor-force participation of the elderly?

Starting in 2000, that age rose from 65 to 66 in increments of two months. It will start rising again 2017. Given this two-month pattern, it has been challenging to get sharp estimates of the effect of the age increases. (The best work I know of on this topic comes from Giovanni Mastrobuoni.)

Yet this effort gets much easier when you know birth year and month more precisely. That's because you can look at the change in labor-force participation in the months right around the retirement age. What we should see, as the retirement age has increased, is a "moving bump" in the labor force participation rate -- the jump should be at 65 years precisely for some, 65 years and two months for the next year, and so on.

And it turns out we do see that: There's a 7-percent drop in labor force participation right at the month of the full retirement age that has shifted out with the statutory increases. Since the majority of Americans have already retired by their mid-60s, this means that the full retirement age knocks out a substantial share (about a quarter) of the remaining elderly workforce.

Raising the retirement age, then, would keep some older workers working. Whether we should do that is, of course, an entirely different question -- one for voters, not economists, to answer.

*      *     *

My dataset (15 MB) is available here.

Wednesday, September 9, 2015

China's Fish Story

China Travel Fish Stall
Even Li Keqiang, the Chinese prime minister, has said he doesn't trust his country's official GDP figures. To keep tabs on China's growth, Keqiang famously watches three numbers that are harder to fake: electricity consumption, railroad cargo traffic, and bank lending.

His skepticism echoed many similar comments from small industry of experts who try to estimate Chinese GDP growth from other data points. But why does China have a data-quality problem at all?

It's not a Chinese conspiracy to keep its growth secret. If that were the case, Keqiang would surely be among those in the know. Yet he is as in the dark about China's growth as you or I am. Instead, the story is one of weak political institutions.

To explain the fish story of China's GDP data, in fact, it helps to turn to an actual fish story -- the story about how, for most of the 1990s, China likely reported fake data on fishing in its territorial waters to the Food and Agriculture Organization of the United Nations.

Reg Watson and Daniel Pauly, who have spent their lives studying global fisheries, found back in 2001 that something strange was going on in Chinese waters: They had become vastly more productive over the last decade and were now far more productive than a statistical model, one that fit the rest of the world's fisheries well, said they should have been.

Where were all those extra fish coming from? China, Watson and Pauly concluded, was just making them up. They were overstating their annual catch by some 5 million tons, which was half of the official figures.

Usually fishing data is underreported, as fishermen conceal some of their catch and governments lowball with the numbers to meet quotas. Why, then, the overstatement? Watson and Pauly:
We believe that explanation lies in China's socialist economy, in which the state entities that monitor the economy are also given the task of increasing its output. Until recently, Chinese officials, at all levels, have tended to be promoted on the basis of production increases from their areas or production units. This practice, which originated with the founding of the People's Republic of China in 1949, became more widespread since the onset of agricultural reforms that freed the agricultural sector from state directives in the late 1970s.
This, to me, sounds much like what could be going on in Chinese GDP data today. Like Chinese officials promoted on fishy data about, well, fish, others are being judged on the basis of economic statistics: GDP growth, unemployment, and industrial output.

And we see the same pattern in Chinese GDP data as we did in the old fish data -- growth that looks both too strong and too smooth. It should be an important precedent for Christopher Balding and other skeptics who argue China might be overstating its GDP by more than 10 percent.

If China wants reliable economic data, it must stop judging its officials on their regions' economic track records and establish real independence for its statistical agencies. Letting officials control their own information and then judging them on it is a recipe for fraud and ignorance. And China's current crisis should teach its leaders that the costs of ignorance are far too high.