Martha Derthick and Policymaking for Social Security

derthickMartha Derthick died recently at the age of 81. I never met her, but I became intimately acquainted with her book Policymaking for Social Security. I stumbled when I was collecting raw material for a paper I wanted to write to illustrate the importance of what political scientists call “expressive” voting. The book turned out to be a gold mine. It has more page markers than any other book I own. (See the photo below.)

When the Social Security program was introduced in the 1930s, it covered only a portion of the labor force. In some recent posts (see e.g. here), Paul Krugman cites this fact to illustrate the harsh realities that policymakers face when they innovate.

The evidence in Derthick’s account drives this point home with the power of a pile driver. Policymaking is like battlefield medicine. The leaders who succeed sustain a clear-eyed commitment to triage.

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Site Refresh

It has taken much longer than I anticipated, but I’m finally ready to go live with a new version of my blog. It should be easier to read and explore, particularly on smaller devices. Getting something that works on screens of different size forces tradeoffs. If you are having trouble getting around on a small screen, try the Mobile option from the menu on the top.

I’ve tested this version on my Mac (with Chrome, Safari, and Firefox) plus a recent Android phone, an aging iPad, and an ancient iPhone. It is built on top of basic functionality offered by WordPress that has been tested more widely. But with software, you know something will go wrong. If you see anything amiss, let me know by email: blog AT

Where has all the excludability gone?

My previous post, which answered the question, “Why has growth has been speeding up?” made no use of the concept of excludability. So why did I make such a big deal about partial excludability in my 1990 paper?

At least since Marshall handed down his Principles of Economics (arguably since Adam Smith told the story of the pin factory), economists have fretted about how to reconcile the increasing returns associated with what Smith called increases in “the extent of the market” with the obvious fact that in real economies, lots of firms of all sizes compete with each other. One of most important things about growth theory that I learned from Chad Jones is that this question is separable from the question about why the growth rate has been speeding up.

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Speeding Up: Theory

In a previous post I described the evidence that pointed me toward the two big questions that guided me when I was building models of growth:

1. Why has the rate of growth been speeding up over time?
2. Why have so many poor countries failed to take advantage of the potential for rapid catch-up growth?

A sign of a good mathematical model is that once you understand it, you can state the answer it suggests very simply. These answers are the final output from the modeling exercise. The models are intermediate inputs.

Here I’ll summarize where things stand on the answer to the first question. In the next post, I’ll do the same for the second question.

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Speeding-up and Missed Opportunities: Evidence

The bar I set for a model is that it should yield answers we believe to questions that matter. For a model of growth, the two questions that matter most are:

1. Speeding-up: Why has the rate of growth at the technological frontier been increasing over time?

2. Missed Opportunities: Why have so many countries that start from far behind the frontier failed to achieve rapid catch-up growth?

This month, I’m writing a series of posts in response to a nudge from Joshua Gans noting that this is the 25th anniversary of the publication of my paper Endogenous Technological Change, JPE (1990). In this post, I’ll recapitulate the evidence that convinced me when I was writing the 1990 paper that these are the two big questions that growth theory should address. In a subsequent post, I’ll explain why the conceptual framework that I used in the 1990 paper, one that relies on partially excludable nonrival goods, is the bare minimum for answering them.

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Science Really Works: A Prize for A Careful Optimist

In The Great Escape, Angus Deaton concludes by saying that he is “cautiously optimistic” about the future. In his review of the book, David Leonhardt captured its real spirit: “Deaton’s central message is deeply positive, almost gloriously so.”

Deaton has made many contributions that make him such a great choice for today’s prize. (See here, here, and here.) I take special satisfaction from the validation it provides to Deaton’s optimism, which I would describe as careful, not cautious. It is an optimism that is grounded in careful attention to data and careful consideration of what measurements mean.
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Economic Growth


In an update on an old story, an investment banker asks the client to pay by placing one penny on the first square of a chessboard, two pennies on the second square, four on the third, doubling the number on each square that follows. If the banker had asked for this on only the white squares, the initial penny would double thirty-one times to \$21,474,836 on the last square. Using both the black and the white squares, the sum on the last square is \$92,233,720,368,547,758.

People are reasonably good at estimating how things add up, but for compounding, which involved repeated multiplication, we fail to appreciate how quickly things grow. As a result, we often lose sight of how important even small changes in the average rate of growth can be. For an investment banker, the choice between a payment that doubles with every square on the chessboard and one that doubles with every other square is more important than any other part of the contract. Who cares whether the payment is in pennies, pounds, or pesos? For a nation, the choices that determine whether income doubles in one generation or two dwarf all other economic policy concerns.
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Clear Writing Produces Clearer Thoughts

The oral tradition at the University of Chicago attributed the observation that “sloppy writing reflects sloppy thinking” to Milton Friedman. Of course, it echoes George Orwell’s claim that “the slovenliness of our language makes it easier for us to have foolish thoughts.”

Neither Friedman’s word “reflects” nor Orwell’s phrase “makes it easier” go far enough. The right verb is “produces.” Clear writing produces clearer thoughts. Sloppy writing produces sloppier thoughts. This is a natural consequence of the fact that anything stored in connections between neurons is part of a biochemical and electrical dynamic feedback loop. When we access one of these loops, we change it and connect it to other loops. To use an analogy from computer science, accessing neurons is never just a read. It is always a read and a rewrite.

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Human Capital and Knowledge

To me, one of the ways in which my 1990 paper, Endogenous Technological Change, was a step forward relative to the first round models of endogenous growth was the explicit distinction that it allowed between the stock of human capital H and the stock of knowledge A. To be sure, this was a very small step. In the model, they interacted the simplest possible way. Human capital H was an input that could be used to produce new knowledge A.

Stated in words, this sounds trivially obvious. But with mathematical theory, you have to think carefully to say even things that are trivial and obvious. Things such as what, precisely, do we mean by human capital?

I am a big fan of micro-foundations; provided, that is, that they are true. What has given a reliance on micro-foundations a bad name is letting people get away with using ones that are false and claiming that this has anything to do with science.

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Nonrival Goods After 25 Years

Joshua Gans has a generous post that notes the 25th anniversary of the publication of my 1990 JPE article. I could not agree more with his observation that “there is more to be done …” in understanding the economics of ideas.

His post helped me see how to respond to a conversation I had this summer. I’ll use the excuse of the anniversary to focus for the month on such basics as the meaning of the phrase nonrival good. Doing so will be a shift for this blog, which until now has been concerned primarily with economics as a science and incidentally with my day job, which focuses on the interaction between urbanization and development.
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