More on Piketty -- Oh God No, Please, No...

Piketty, Piketty, Piketty! How did the Piketty phenomenon happen? Surely Piketty must be one of the all-time great economists. Maybe even as great as Marx.

Yes, parts of the emerging backlash against Piketty's Capital resonate with me. Guido Menzio nails its spirit in a recent post, announcing to the Facebook universe that he'll "send you $10 and a nice Hallmark card with kitties if you refrain from talking/writing about Piketty's book for the next six months." (The irony of my now writing this Piketty post has not escaped me.)

As I see it, the problem is that Piketty's book is popularly viewed as a landmark contribution to economic theory, which it most definitely is not. In another Facebook post, leading economic theorist David Levine gets it right:

People keep referring to economists who have favorable views of Piketty's book. Leaving aside Krugman, I would be interested in knowing the name of any economist who asserts that Piketty's reasoning ... is other than gibberish.

    So the backlash is focused on dubious "reasoning" touted as penetrating by a book-buying
    public that unfortunately can't tell scientific wheat from chaff. I'm there.

But what of Piketty's data and conclusion? I admire Piketty's data -- more on that below.  I also agree with his conclusion, which I interpret broadly to be that the poor in developed countries have apparently become relatively much more poor since 1980, and that we should care, and that we should try to understand why. 

In my view, Piketty's book truly shines on the data side. If much of its "reasoning" is little more than neo-Marxist drivel, much of its underlying measurement is nevertheless marvelous (assuming of course that it's trustworthy). Its tables and figures -- there's no need to look at anything else -- provide a rich and jaw-dropping image, like a new high-resolution photo of a previously-unseen galaxy. I'm grateful to Piketty for sending it our way, for heightening awareness, and for raising important questions. Now we just need those questions answered.

Absent No More

Hello my friends. I'm back. It's been a crazy couple of weeks, with end-of-year travel, crew regattas, graduations, etc.

A highlight was lecturing at European University Institute (EUI) in Florence. I tortured a pan-European audience of forty or so Ph.D.'s, mostly from central bank research departments, with nine two-hour seminars on almost every paper I've ever written. (The syllabus is here, and related information is here.) But seriously, nothing is so exhilarating as a talented and advanced group completely interested in one's work. Above I show some of the participants with me, on the porch of our villa looking outward, and at right I show part of the the storybook Tuscan scene on which they're gazing (just to make you insanely jealous).

Anyway, great things are happening in econometrics these days at EUI / Florence. Full-time EUI Economics faculty include Fabio Canova and Peter Hansen, frequent EUI visitors include Christian Brownlees (Universitat Pompeu Fabra, Barcelona) and Max Marcellino (Bocconi University, Milan), and just down the road is Giampiero Gallo (University of Florence). Wow!

Student Advice III: Succeeding in Academia

Lasse Pedersen's advice is wonderful. Study it. Of course there's something or another for everyone to quibble with. My pet quibble is that it's rather long. Lasse correctly suggests roughly twenty pages for a ninety minute talk, so presumably this slide deck is for a talk approaching three hours. But who cares? Study it, carefully. (And thanks to Glenn Rudebusch for calling it to my attention.)

Predictive Modeling, Causal Inference, and Imbens-Rubin (Among Others)

When most people (including me) say predictive modeling, they mean non-causal predictive modeling, i.e., addressing questions of "What will likely happen if the gears keep grinding in the usual way"? Examples are ubiquitous and tremendously important in economics, finance, business, etc., and that's just my little neck of the woods.

So-called causal modeling is of course also predictive (so more accurate terms would be non-causal predictive modeling and causal predictive modeling), but the questions are very different: "What will likely happen if a certain treatment (or intervention, or policy -- call it what you want) is applied"? Important examples again abound.

Credible non-causal predictive modeling is much easier to obtain than credible causal predictive modeling. (See my earlier related post.) That's why I usually stay non-causal, even if causal holds the potential for deeper science. I'd rather tackle simpler problems that I can actually solve, in my lifetime.

The existence of competing ferocious causal predictive modeling tribes, even just within econometrics, testifies to the unresolved difficulties of causal analysis. As I see it, the key issue in causal econometrics is what one might call instrument-generating mechanisms.

One tribe at one end of the spectrum, call it the "Deep Structural Modelers," relies almost completely on an economic theory to generate instruments. But will fashionable theory ten years hence resemble fashionable theory today, and generate the same instruments?

Another tribe at the other end of the spectrum, call it the "Natural Experimenters," relies little on theory, but rather on natural experiments, among other things, to generate instruments. But are the instruments so-generated truly exogenous and strong? And what if there's no relevant natural experiment available?

A variety of other instrument-generating mechanisms lie interior, but they're equally fragile.

Of course the above sermon may simply be naive drivel from a non-causal modeler lost in causal territory. We'll see. In any event I need more education (who doesn't?), and I have some causal reading plans for the summer:

Re-read Pearl, and read Heckman's critique.

Read White on settable systems and testing conditional independence.

Read Angrist-Pischke. (No, I haven't read it. It's been sitting on the shelf next to me for years, but the osmosis thing just doesn't seem to work.)

Read Wolpin, and Rust's review.

Read Dawid 2007 and Dawid 2014.

Last and hardly least, get and read Imbens-Rubin (not yet available but likely a blockbuster).

Gary Becker, 1930-2014

Very sad to report that Gary Becker has passed away.  See news.uchicago.edu/article/2014/05/04/gary-s-becker-nobel-winning-scholar-economics-and-sociology-1930-2014?utm_source=newsmodule .