Monthly Archives: March 2015

Michael Mandel’s Question About Health Care Innovation

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At this event, he asked why we do not see any of the signs of an innovation boom in health care that we saw with personal computers and the Internet. No spectacular new companies. No surge in demand for life sciences knowhow comparable to the surge in demand for computer programming skills. As he wrote last year, he believes that the FDA’s requirement that new treatments be more efficacious than old ones has the effect of stifling disruptive innovation, in which new products first gain traction on the basis of lower price rather than better quality.

Others at the panel pointed out that it may not be the FDA that dictates the innovation pattern. It may be the fact that third party payments dominate American health care. Patients who are not paying for their own health care are not going to provide a market for radically cheaper treatments. And insurance companies are not going to want to pay for radically better treatments that cost a lot. So the only innovations that survive are incremental ones.

However, I want to go back to the original question of why we do not see an innovation boom. My thoughts:

1. My guess is that we have not yet reached a point where all the pieces are in place to produce an innovation boom. Remember that it took several decades to go from the invention of the transistor to the appearance of the personal computer.

2. We do not have an institutional breeding ground for biotech innovation. No equivalent of Bell Labs, or Xerox PARC or the Homebrew Computer Club.

3. Someone in the audience asked a provocative question about whether some other country provides a role model, which country provides a role model for health care innovation? If you thought that the only roadblocks were American customs and regulations, health care innovation would take place in other countries.

Null Hypothesis Watch

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“Scott Alexander” writes,

When they caught up with these kids at age 25, the intervention group was found to have an odds ratio of around 0.6 to 0.7 of having developed various psychiatric disorders the study was testing for, including antisocial personality disorder, ADHD, depression, or anxiety. They had odds ratios around 0.7 of developing drug and alcohol abuse problems by various measures. They reported less risky sexual behavior, less domestic abuse, and fewer violent crimes. All of this was significant at the p < 0.05 level, and some of it was significant at much higher levels like p = 0.001 or below. Subgroup analysis found the data were very similar when you restricted the analysis to various subgroups like boys, girls, whites, blacks, highest-risk, lowest-risk, and by study site (it was a multi-site study)

This was a randomized, controlled study of a group of many interventions. “Scott” goes on to point out a number of caveats. The group of interventions was expensive. A lot of other indicators, including employment rates, did not improve. We do not know whether the results came from one or two of the interventions, or from the combination of all of them.

Still, it looks as though something managed to defeat the null hypothesis. As a controlled trial, it gets over the hurdle of confusing correlation with causality. As a study of long-term outcomes, it gets over the hurdle of fade-out. The results are numerically significant, not just statistically significant. The only remaining hurdle is replicability. My guess is, given the complexity of all those interventions, that the replicability hurdle will be a challenge.

Paul Krugman Sentences I Might Have Written

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I certainly agree with this:

the professional economists who either play important roles in making policy or appear to have influence on the discussion got their Ph.Ds from MIT in the second half of the 1970s. An incomplete list, with dates of degree:

Ben Bernanke 1979
Olivier Blanchard 1977
Mario Draghi 1976
Paul Krugman 1977
Maurice Obstfeld 1979
Kenneth Rogoff 1980

Larry Summers was at Harvard during the same period, but he was an MIT undergrad and very much part of that intellectual circle. Also, just about everyone on the list studied with Stan Fischer, who remains very much in the middle of policy-making.

Note that we are talking about macroeconomic policy. But some important microeconomic policy makers came out of that period as well. Carl Shapiro comes quickly to mind.

Of course, Krugman has other sentences that I could not have written.

Analytically, empirically, the MIT style has had an astonishing triumph.

As you know, I think that macroeconomic data can be twisted to “prove” any theory. You can look at reasonable, credible blog posts by Scott Sumner or Tyler Cowen pointing out many discrepancies between recent macroeconomic performance and the Krugman-style Keynesian analysis. Empirical macroeconomics seems to me to boil down to a pure exercise in confirmation bias.

As you also know, I have a less exalted view of MIT’s approach to economics and of Stan Fischer’s role as the Genghis Khan of macro. See, my recent post on academic hiring networks, my memoirs of a would-be macroeconomist, or my recent essay on camping-trip economics. Read that essay next to Krugman’s post.

Marginal vs. Average Debt to Equity in Housing

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Alejandro Justiniano and others write,

if the relaxation of collateral constraints had been widespread, it should have resulted in a surge of mortgage debt relative to the value of real estate.

In the data, however, household debt and real estate values rose in tandem, leaving their ratio roughly unchanged over the first half of the 2000s, as shown in Figure 3. In fact, this ratio only spiked when home prices tumbled starting in 2006.

Pointer from Mark Thoma.

Suppose that back when lenders asked for 20 percent down, three families bought houses for $100,000 each and put $20,000 down each. Total mortgage debt is $240,000 and total home values are $300,000. The ratio of household to real estate debt is 80 percent.

Next, lenders allow someone to buy a house with no money down. As a result, home prices rise to $130,000. Adding $130,000 debt to the debt of the other three households (ignoring any equity they may have built up through paying down mortgage principal), we have total mortgage debt of $370,000. But total home values are $520,000, so that the average ratio of debt to equity has actually fallen, to just over 70 percent.

As long as home prices are rising, the last thing you should expect is for the average debt to equity ratio to rise. The fact that it did not fall is an indication of how powerful the boom in credit was. Only if you use a silly representative-agent model, in which there is no difference between average and marginal borrowers, would you predict something different. I have not read the paper, but I suspect that is what the authors did.