Category: Medicine

A panel appointed by Mayor Rahm Emanuel to review taxpayer-subsidized health insurance for retired government workers suggested the city [Chicago] could drop coverage to help erase a financial shortfall…

Phasing out coverage for most retired city workers would leave the bulk of retirees dependent on the Affordable Care Act, also known as Obamacare.

Here is more, and for the pointer I thank The Wisdom of Garett Jones.

At Econ Talk, Marcia Angell discusses big Pharma with Russ Roberts. I think she gets a lot wrong. Here is one exchange on innovation.

Angell: The question of innovation–you said that some people feel, economists feel, [the FDA] slows up innovation: The drug companies do almost no innovation nowadays. Since the Bayh-Dole Act was enacted in 1980 they don’t have to do any innovation….

Roberts: But let’s just get a couple of facts on the table…[The] research and development budget of the pharmaceutical industry is, in 2009, was about $70 billion. That’s a very large sum of money. Are you suggesting that they don’t do anything–that that’s mostly or all marketing? That they are not trying to discover new applications of the basic research? It seems to me basic research is an important part. Putting that research into a form that can make us healthier seems to be a nontrivial thing. You think they are–what are they doing with that money?

Angell: If you look at the budgets of the major drug companies–just go to their annual reports, their Security and Exchange Commission (SEC) filings, you see that Research and Development (R&D) is really the smallest part of their budget. If you look at the big companies you can divide their budget into 4 big categories. One is R&D, one is marketing and administration; the other is profits, and the other is just the cost of making the pills and putting them in the bottles and distributing them. The smallest of those is R&D.

Notice that Angell first claims the pharmaceutical companies do almost no innovation then, when presented with a figure of $70 billion spent on R&D, she switches to an entirely different and irrelevant claim, namely that spending on marketing is even larger. Apple spends more on marketing than on R&D but this doesn’t make Apple any less innovative. Angell’s idea of splitting up company spending into a “budget” is also deeply confused. The budget metaphor suggests firms choose among R&D, marketing, profits and manufacturing costs just like a household chooses between fine dining or cable TV. In fact, if the marketing budget were cut, revenues would fall. Marketing drives sales and (expected) sales drives R&D. Angell is like the financial expert who recommends that a family save money by selling its car forgetting that without a car it makes it much harder to get to work.

Later Angell tries a third claim namely that pharma companies do no innovation because their R&D budget is mostly spent on clinical trials and, “it’s no secret how to do a clinical trial.” I find this line of reasoning bizarre. I define an innovation as the novel creation of value, in this case the novel creation of valuable knowledge. Is Angell claiming that clinical trials do not provide novel and valuable knowledge? (FYI, I have argued that the FDA is overly safety conscious and requires too many trials but Angell breezily and nastily dismisses this argument). In point of fact, most new chemical entities die in clinical trial because what we thought would work in theory doesn’t work in practice. Moreover, the information generated in the clinical trials feeds back into basic research. Angell’s understanding of innovation is cramped and limited, she thinks it begins and ends with basic science in a university lab. Edison was right, however, when he said that genius is one percent inspiration and ninety-nine percent perspiration–both parts are required and there is no one-way line of causation, perspiration can lead to inspiration as well as vice-versa. Read Derek Lowe on the reality of the drug discovery process.

Angell infuses normative claims to the industrial organization of the pharmaceutical industry. Over the past two decades there has been an increase in the number of small biotechnology companies, often funded by venture capital. Most of the small biotechs are failures, they never produce a new molecular entity (NME). But a large number of small, diverse, entrepreneurial firms can explore a big space and individual failure has been good for the small-firm industry which collectively has increased its discovery of NMEs. The small biotechs, however, are not well placed to deal with the FDA and run large clinical trials–the same is also true of university labs. So the industry as a whole is evolving towards a network model in which the smaller firms explore a wide space of targets and those that hit gold partner with one of the larger firms to pursue development. Angell focuses in on one part of the system, the larger firms and denounces them for not being innovative. Innovation, however, should be ascribed not to any single node but to the network, to the system as a whole.

Angell makes some good points about publication bias in clinical trials and the sometimes too-close-for-comfort connections between the FDA, pharmaceutical firms, and researchers. But in making these points she misses the truly important picture. Namely that new pharmaceuticals have driven increases in life expectancy but pharmaceutical productivity is declining as the costs of discovering and bringing a new drug to market are rising rapidly (on average ~1.8 billion per each NME to reach market). In my view, the network model pursued on a global scale and a more flexible and responsive FDA, both of which Angell castigates, are among the best prospects for an increase in pharmaceutical productivity and thus for increases in future life expectancy. Nevertheless, whatever the solutions are, we need to focus on the big problem of productivity if we are to translate scientific breakthroughs into improvements in human welfare.

Here is the job market paper of Grace Weishi Gu, from Cornell:

The Cost of Benefits and Employment Dynamics in Recent U.S. Recoveries

Abstract: I document (1) the slow rebound in U.S. aggregate employment following recent recessions, despite recoveries in output, as well as (2) a rising trend in per worker benefit costs, the cyclicality of those costs, and a positive correlation of the cyclical benefit costs with employment growth cycles. I show how these two phenomena are related. Then I develop a DSGE model that includes firms’ dynamic benefit costs, financial conditions (i.e., borrowing capacity), and the tradeoff between extensive and intensive labor margins to explore the effects of these features on post-1990 employment dynamics. I find that the benefit costs’ rising trend, cyclicality, and interactions with financial conditions have contributed significantly to the observed slow employment growth following the three most recent recessions in the U.S. This paper offers two main improvements over the standard model, which lacks such arrangements: (1) delivering 2-to-7-quarter delays relative to NBER business cycle troughs for the employment recoveries from the 1990, 2001, and 2007 recessions while generating no delay for the pre-1990 period, which is in line with the data; and (2) explaining 40-90 percent of employment volatility and harmonizing with that of output and per worker hours.

I would like to see more testing of this against alternative explanations, but still this is provocative and important work.