But am I reading right that that one doesn't push through to a concrete demonstration of impacts on expected values of interventions?
Interesting question! Certainly it is the nonlinearities in the cost-effectiveness analysis that makes uncertainty matter to an expected value maximizer. If we thought that the cost-effectiveness of an intervention was best modeled as the sum of two uncertain variables (a simple example of a linear model), then the expected value of the intervention would be the sum of the expected values of the two variables. Their uncertainty would not matter.
The most serious effort I know of to incorporate uncertainty into the GiveWell cost-effectiveness analysis is this post by Sam Nolan, Hannah Rokebrand, and Tanae Rao. I was surprised at how little it changed the expected values---I think by a typical 10-15%, but I'm finding it a little hard to tell.
I think when the denominator is cost rather than impact of school construction on years of schooling, our uncertainty range is less likely to put much weight on the possibility that the true value is zero. Cost might even be modeled as log normal, so that it can never be zero. In this case, there would be little weight on ~infinite cost-effectiveness.
Good question! I think it brings out a couple of subtleties.
First, in putting forward the "universal" truth that education and earnings go hand-in-hand, I mean that this is a regularly found society-level statistical association. It does not mean that it holds true for every individual and it does not take a position on causality. So I don't mean to imply that if a particular child is made to go to school more this will "universally" cause the kid to earn more later.
In particular, while I think poorer regencies got more schools and that increased primary school completion, and while I believe that in general education and earnings go together, I'm much less sure that this particular exogenous perturbation in life paths had much impact. (One reason, which falls outside the study, you allude to: I don't know if they learned much.)
Of course, it is entirely plausible that getting more kids into school will cause them to earn more later. Which brings me to the second subtlety. It would be easy to read this post (and perhaps I need to edit it to make it harder) as judging whether there's any affect: does education raise wages. But I want it to be read as asking, given all we know about the world outside the Duflo study, should the Duflo study cause us to update our views much about the size of the impact of education on earnings?
I think the paper comes off as rather confident that it should persuade skeptics---see the block quote toward the end of my post. That is, if you were generally skeptical that education has much effect on earnings before reading the paper, it should change your mind, according to its author, because of the techniques it uses to isolate one causal link, and the precision of the resulting estimates. When I wrote "I wound up skeptical that the paper made its case," this is what I was referring to. If you generally think education increases earnings on average because of all you know about the world, or if you think the opposite, this paper probably shouldn't move you much.
I can do both--economics has a long tradition of accepting circulation of "working papers" or "preprints" without jeopardizing publication in journals. In fact, Esther suggested I hold off submitting a comment to the AER until she steps down as editor in a few weeks.
Actually, if clustering was not as common in 2001 as now, it was not rare. She clustered standard errors in the other two chapters in her thesis. My future colleague Mead Over coauthored a program in 1996 for clustering standard errors in instrumental variables regressions in Stata.
Hi Ozzie. I'm out of my depth here, but what I had in mind was the Uwezo program at one of my "this" links, which I believe was inspired by Pratham in India. I think these organizations originally gained fame for conducting their own surveys of how much (or little) children were actually learning, in an attempt to hold the education system accountable for results.
But that's surely just a small part of a large topic, how a citizenry holds a public bureaucracy more accountable. Specific solutions include "democracy"... You know, so just do that.
I should say that there is a strong and arguably opposing view, embodied by the evidence-based Teaching at the Right Level approach. The idea is to completely script what teachers do every day. It's very top-down.
Thanks for the feedback. I can see why that is confusing. You figured it out. I inserted a couple of sentences before the first table to clarify. And I changed "young-old pay gap" to "old-young pay gap" because I think the hyphen reads, at least subliminally, like a minus sign.
1. Is exactly the right question. My work is just one input to answering it. My coworkers are confronting it more directly, but I think nothing is public at this point. My gut is that the result is broadly representative and that expanding schooling supply alone is often pushing on a string. It is well documented that in many primary schools in poor countries, kids are learning pitifully little. Dig into the question of why, and it has do with lack of accountability of teachers and school systems for results, which in turn has to do with the distribution of power in society. That is not easily changed. But nor is it hopeless (this, this, this), so the problem is also potentially an opportunity.
2. Whoops! The table's header row got chopped during editing. I fixed it.
Hi Karthik. Without belaboring shades of emphasis, I basically agree with you. But you know, I've just spent thousands of words criticizing someone's work and I want to end positively, within reason.
I agree with much of this. A few responses.
As I see it, there are a couple of different reasons to fit hyperbolic growth models — or, rather, models of form (dY/dt)/Y = aY^b + c — to historical growth data.
I think the distinction between testing a theory and testing a mathematical model makes sense, but the two are intertwined. A theory will tend naturally to to imply a mathematical model, but perhaps less so the other way around. So I would say Kremer is testing both a theory and and model—not confined to just one side of that dichotomy. Whereas as far as I can see the sum-of-exponentials model is, while intuitive, not so theoretically grounded. Taken literally, it says the seeds of every economic revolution that has occurred and will occur were present 12,000 years ago (or in Hanson (2000), 2 million years ago), and it's just taking them a while to become measurable. I see no framework behind it that predicts how the system will evolve as a function of its current state rather than as a function of time. Ideally, the second would emerge from the first.
Note that what you call Kremer's "Two Heads" model predates him. It's in the endogenous growth theory of Romer (1986, 1990), which is an essential foundation for Kremer. And Romer is very much focused on the modern era, so it's not clear to me that "For the purposes of testing Kremer’s theory, the pre-industrial (or perhaps even pre-1500) data is nearly all that matters." Kuznets (1957) wrote about the contribution of "geniuses"—more people, more geniuses, faster progress. Julian Simon built on that idea in books and articles.
A lot of the reason I’m skeptical of Kremer’s model is that it doesn’t seem to fit very well with the accounts of economic historians and their descriptions of growth dynamics....it seems suspicious that the model leaves out all of the other salient differences that typically draw economic historians’ attention. Are changes in institutions, culture, modes of production, and energetic constraints really all secondary enough to be slipped into the error term?
Actually, I believe the standard understanding of "technology" in economics includes institutions, culture, etc.—whatever affects how much output a society wrings from a given amount of inputs. So all of those are by default in Kremer's symbol for technology, A. And a lot of those things plausibly could improve faster, in the narrow sense of increasing productivity, if there are more people, if more people also means more societies (accidentally) experimenting with different arrangements and then setting examples for others; or if such institutional innovations are prodded along by innovations in technology in the narrower sense, such as the printing press.
I dug waaay into this topic when investigating geomagnetic storms. I found it quite interesting and useful. See