Empirical Institutions Reading List

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The next installment of reading lists for my grad growth and development class this fall has to do with one explanation for the fundamental source of cross-country differences: institutions. “institutions” is a very nebulous concept, and so most of the recent, empirically sound, research on institutions focuses not on “institutions” in general but on some specific type of institution. Almost without fail, this leads to studies of former colonies. This is because they tend to have a very useful quality: a specific institution may have been thrust upon them, or changed arbitrarily.

Empirically, we want to find the causal effect of some specific institution on development (output per worker, wage levels, education levels, what have you). In general, what we call institutions are hopelessly endogenous, so we’re looking for natural experiments where an institution was dropped from the sky, so to speak, on top of some country. If so, we can possibly isolate the independent effect of that institution on development. Colonies make for a good test bed, as one can make a (perhaps) plausible argument that the colonizer exogenously altered, added, or subtracted some specific institution to the colony.

I try to avoid any papers that look at simple cross-country regressions of income per capita on some index of institutions, although I do talk through the Acemoglu, Johnson, and Robinson papers because of their prevalence across the literature. This is because of a point I raised before, which is that one can arbitrarily make an “institutions index” significant in a regression if you simply wiggle around the index values the right way. [I could code the U.S. a 1, Turkey a 0.5, and Zimbabwe a 0, or code them as 10,9, and 0, respectively. I preserve the ordering, but completely change the possible implications in a regression].

The reading list, which is posted under the “Papers” page on this site, thus focuses on recent within-country work related to specific institutions. One exception is the African work by Stelios Michalopoulos and Elias Papaioannou, which covers most of the continent, and tends to look at institutions as a generic concept. The interesting comparison here is the differing results: one paper finds that pre-colonial institutions do have persistent effects on development of ethnic groups, while the other finds that national institutions are not relevant. This just highlights that “institutions” taken as a whole are not necessarily a good predictor of development. Rather, one can find examples of specific institutions that matter.

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Cochrane on Growth and Macro

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John Cochrane recently ran a little review of his experience at NBER (h/t to Noah Smith). It’s got a really interesting observation on growth versus macro.

A last thought. Economic Fluctuations merged with Growth in the mid 1990s. At the time there was a great confluence of method as well as interest. Growth theorists were studying growth with Bellman equations, dynamic general equilibrium models of innovation and transmission of ideas, thinking about where productivity shocks came from. Macroeconomists were using Bellman equations, and studying dynamic general equilibrium models with stochastic technology, along with various frictions and other propagation mechanisms.

That confluence has now diverged. I enjoyed spending an hour or two thinking about how religion has blocked or adapted to ideas over the centuries, and Paul’s view on social norms or neuroeconomics. But I don’t really have any expertise to contribute to that debate. Questions like whether young CEOs head more innovative companies, or whether, like deans, what matters is the age of the faculty are a little closer to home, since I spend a lot of time consuming corporate finance. But the average sticky-price macro type does not. Likewise, when Daron Acemoglu, who seems to know everything about everything, has to preface his comments on macro papers with repeated disclaimers of lack of expertise, it’s clear that the two fields really have gone their separate ways. Perhaps it’s time to merge fluctuations with finance, where we seem to be talking about the same issues and using the same methods, and growth to merge with institutions and political or social economics.

This is similar in flavor to John Seater’s comment that I wrote about here. Has growth economics become different enough from mainstream macro that we should separate them from one another?

I’d argue yes. Growth is about development now – meaning that it’s motivating question is “Why are some countries rich and some poor?”. (See my earlier post on this topic here). The exploration of answers to this question are much more about big static differences in institutions, cultures, technologies, and the like, and less about transition paths and dynamics.

On what growth would look like if it did separate (literally at NBER and intellectually as a field) from macro, Cochrane gave us perhaps a pointer:

I’m not sure in the end though whether Paul[Romer] was approving or bemoaning the shift back towards literature in economic analysis. Certainly his vision for the future of growth theory, centered on values, social norms, biology, and so forth, does not lend itself easily to quantification.

Is this a feature or a bug? Perhaps the big question of “Why are some countries rich and some poor?” is not answerable in any solid empirical way. Perhaps the highest achievement here is “literature” in the sense of some overarching theory that one uses to examine history. Think of Pomeranz’s The Great Divergence or Robert Allen’s The British Industrial Revolution in Global Perspective as examples. While both books certainly appeal to economic intuition and occasionally something approaching formal theory, neither considers anything like a Bellman equation.

The counter would be that we can do better than just “literature” in growth by writing down model (perhaps static models, but no matter) that allow us to quantify the forces that people like Pomeranz and Allen propose as relevant. That is, write down an explicit model, and calibrate or simulate it to assess whether a proposed explanation has a plausibly large quantitative effect on output per worker. The issue here is, as Cochrane says, it’s essentially impossible to quantify religion or values. What is the parameter you stick in your quantitative model that captures the effect of a belief in the afterlife on your willingness to work today? If you cannot possibly hope to measure that parameter, then you cannot quantify it’s effect on output per worker.

So if we’ve entered the world where we think that values (or culture or religion) are fundamental to development, then we may be left with “literature” as the only valid form of research output. My guess is that growth economists will resist this kind of transition, mainly because we’ve invested a lot in knowing fancy dynamic models and calibration techniques, and we don’t want those skills to become worthless.

Why Don’t Growth Economists Study Growth Anymore?

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John Seater (NC State) left a really interesting comment on one of my recaps of the NBER Growth session papers.

It appears from the summaries in this blog that none of the other five papers was a growth paper. Now, literally anything in economics can have an effect on growth, so one could say that all five papers had implications for growth. However, it sounds as if none of the five papers summarized here addressed those implications. I am curious about why static analysis dominated a meeting ostensibly dedicated to studying economic growth. My impression from the programs for the Growth meeting in recent years is that most of the papers presented there are not about growth. What is going on? Has the Growth meeting ceased to be a growth meeting?

The short answer, John, is yes. The NBER Growth meeting really has ceased to be about growth, per se. I guess the broader question lurking around is whether this is a good or a bad thing. Let me see if I can take a shot at answering it from both directions.

The positive (or neutral) response is that growth papers aren’t about dynamics any more because the dynamics are determined by changes in steady states. People study the comparative statics of steady states in their models. Transition between those steady states – the dynamics – then just depend on the rate of accumulation of capital stocks (human and physical). Those rates don’t seem to be very different, so the transition rate isn’t the interesting aspect to study. The static difference in steady states is what determines the growth rates.

In terms of trend growth rates (how fast the economy grows in steady state), people probably implicitly have in their heads that those trend rates are similar across countries. Why? Because you look at the long-run paths of output per worker in most countries and they seem parallel, growing at the same rate in steady state. So that seems relatively less important in explaining cross-country differences.

The negative (or skeptical) response is that we’re missing something crucial by ignoring variation in growth rates. We’re assuming that the transitional growth rate is the same no matter what causes the static shift in steady states. Maybe that isn’t right. More importantly, maybe the trend growth rate isn’t identical across countries. While a lot of relatively well-off countries grow at very similar rates in steady state, poor countries don’t. Several of them grow very slowly, so slowly that they are falling behind rich countries.

Differential growth rates mean that we cannot just look at static differences across countries. Those differences are growing over time, so our static stories cannot be enough to explain them. We need explicit theories of why poor countries grow slowly, not just why the are poor to begin with.

Furthermore, even if countries do grow at the same rate in steady state, we’re still really interested in what that rate is. Growth at 2% per year doubles income every 35 years. Growth at 1% doubles it every 70. That’s a big difference in living standards over time. So studying growth rates is important in and of itself, outside of the question of cross-country comparisons.

I’ll freely admit that as a field, growth generally has strayed away from studying “growth”, in the traditional sense. But I don’t have a huge problem with where we are on this – I find the “what makes rich countries rich” question to be somewhat more compelling than the “is growth 1 or 2 percent per year” question. But it’s worth remembering that the latter question on growth rates has huge ramifications for absolute living standards over long periods of time – never underestimate compound growth.

Hsieh and Moretti on Allocations across Cities

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Last post on the NBER growth session. Chang-Tai Hsieh (Chicago) and Enrico Moretti (Berkeley) presented a paper on wage dispersion across cities in the U.S. Wage dispersion (New Yorkers earn more than people in Cleveland) either represents compensation for living costs (housing in New York is more expensive than in Cleveland), a real difference in productivity (New Yorkers are more productive than Clevelanders), or some combination of the two.

What Chang and Enrico find is that the increase in wage dispersion across cities in the U.S. over the last thirty-ish years is due almost entirely to rising house prices in six cities: NY, DC, Boston, San Fran, San Jose, and Seattle. Wages have gone up rapidly in those cities, but that is basically just compensating their citizens for the higher costs of living.

Now, given the costs of living, the allocation of population across cities in the U.S. is efficient. That is, there is no reason for someone from Cleveland to move to New York on the margin. Their increased wage in New York only compenstates them for the higher housing cost, and so there is no change in their real wage.

However, if we do not take the costs of living as given, then the allocation of population is not efficient – there is a (surprise again!) misallocation. If there were not housing restrictions in NY and San Fran, and housing prices were not so ridiculously high, lots of people would move there because these are high-productivity cities. So one can back out the implied cost of housing restrictions across the whole U.S., and Chang and Enrico find that aggregate output is lower by about 10-14% because of them. That is, by preventing new housing in San Fran, restrictions drive up housing prices, which keep Clevelanders from moving, when in fact Clevelanders would be more productivite in San Fran.

The best part of the paper is the implied change in city sizes if you did remove restrictions. Chang and Enrico calculate that New York’s population would rise by 890%(!!) without restrictions on housing.

As an aside, Ed Glaeser (Harvard) gave the discussion of this paper. It was my first exposure to Ed, and all I have to say is that he should do the last discussion of the day at every conference, everywhere. Just when you are checking your phone for messages and thinking about beer, he steps in and gives a great energetic talk that keeps your attention. And the bow tie. The man can actually pull off a bow tie.

Herrendorf and Schoellman on Labor Allocations

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The next post on the NBER growth session. Berthold Herrendorf (ASU) and Todd Schoellman (ASU) looked at the (surprise!) misallocation of labor between agriculture and non-agriculture. They look at the wage gap between ag and non-ag in a panel of 39 countries. The question is how much of the gap is due to human capital differences between ag and non-ag workers.

Across their sample, the average wage premium for non-ag is 1.79. That is, non-ag workers earn about 79% more than ag workers per hour. If you control for human capital using a standard Mincerian return to years of schooling of 10%, then this average premium falls to 1.36. The wage \textit{per unit of human capital} in non-ag is 36% higher than in agriculture. The raw wage premium is 1.79 because non-ag workers have higher average education levels.

What Todd and Berthold do to advance on this is to consider the possibility that the returns to education are different between sectors. They provide evidence that this is in fact the case. For each year of schooling, agricultural workers get a smaller bump in wage than do non-ag workers. Thus non-ag workers have even higher implied human capital than ag workers. They have more years of schooling, and those years of schooling provide them with more human capital. If you make this adjustment, then the average wage premium for non is 0.92, or non-ag workers earn about 8% \textit{less} per unit of human capital than in ag. Essentially, Todd and Berthold can account for the entire observed wage gap.

This is intriguing because it suggests that the labor markets in these countries are getting things roughly right. This doesn’t mean ag workers earn the same as non-ag workers, they don’t. But this is because ag workers provide less human capital to the market than non-ag workers, not because ag workers are underpaid for their human capital. I’ll do some self-promotion in that their work complements my own finding that wage gaps between sectors in developing countries are not a big source of aggregate productivity losses.

One conclusion from their work is that movements of workers between sectors are not by themselves a source of growth. With the marginal return to HC being the same across sectors, there is no boost to productivity coming just from moving workers around. If we do observe shifts of labor from ag. to non-ag then that represents shifts due to differential productivity growth in the sectors or to non-homothetic preferences.

Restuccia and Santaeulalia-Llopis on Land Misallocation

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Next entry in the NBER growth session recap. Diego Restuccia (Toronto) and Raul Santaeulalia-Llopis (Wash. U.) presented their paper on land misallocation and productivity. Essentially, what Diego and Raul are trying to do is apply the firm-level study of Hsieh and Klenow (2009) methodology to farms (you might be starting to sense a theme to the day by now). Specifically, they have detailed data on farm plots from Malawi, and use this to see how much the marginal product of land varies across farming households.

They then ask the counter-factual question of: how much would agricultural output rise if we re-allocated land across farmers to equalize the marginal product of land? Essentially, if I could make sure that high-productivity farmers were able to receive more land, and low-productivity farmers less, how much could we raise aggregate output? Their finding is that output would go up by a factor of 3! This is way off the charts compared to anything that people have done with firms (where output might rise by a factor 1.5).

Big numbers like that lead to questions. The obvious concern is that they are incorrectly measuring the marginal product of land. Working in their favor is that they have clear measures of actual real output (bushels of maize). This means they do not have to make any assumptions about prices to try and deflate revenues, as we often need to do with firms.

You might also be worried that they are attributing land-quality differnces to households. That is, household A looks really productivity in the data because they have good land, not because they are really good at farming. Diego and Raul have really fine-grained measures of land characteristics. Conceptually they can control for land quality. But how do you construct a single-valued index of land quality from 11 different characteristics (slope, elevation, acidity, etc…). There is conceivably some kind of “true” agronomic function that transforms these into a single measure of quality, but it is almost certainly highly non-linear and involves all sorts of weird covariances between the measures. So if you want to be skeptical about their results, I think this is the primary worry.

Let’s take their results as true for the moment, though. One explanation for the vast degree of misallocation goes back to subsistence constraints, as I discussed in a different context before. If I need to ensure that all farming households can achieve a minimum output (and markets are not developed enough for people to borrow/lend over time if they can’t), then you’re bound to have big misallocations. You’d in fact have to give bad farmers even more land than good farmers. A very low absolute level of productivity is thus a determinant of misallocation.

On the other hand, if all the land is really highly productive, then small plots are sufficient to ensure subsistence. This means that we can give all the bad farmers small plots, and let the good farmers operate the surplus land. So a high level of absolute productivity will ensure a better allocation. Inherent land productivity thus has two separate influences on outcomes: a direct one through increasing output, and an indirect one in ensuring better allocations of land.

David, Hopenhayn, and Venkateswaran on Misallocation

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Next installment of the NBER growth session recap. The second paper was by Joel David (USC), Hugo Hopenhayn (USC), and Venky Venkateswaran (NYU Stern). Their jumping off point is the apparent mis-allocation of factors of production across firms. The standard of comparison here is Hsieh and Klenow (2009), who find that mis-allocation of factors is lowering output by something like 50% in China and India.

So why are factors mis-allocated? David, Hopenhayn, and Venkateswaran propose that this is partly due to informational issues. That is, firms themselves do not know ex-ante (when they are deciding on how much capital or labor to hire) exactly what their productivity will be ex-post. Hence they make mistakes, and part of what we observe in the ex-post data are these mistakes. So rather than explicit taxes, subsidies, or other frictions, poor information about future productivity drives mis-allocation.

To get some quantitative feel for how important this is, they focus on listed companies. These have the advantage of an extra source of information on future prospects, the stock price. There is a neat little information extraction problem they show solves nicely that allows them to use the observed productivity of firms and the stock prices to back out the degree of uncertainty firms have ex-ante. With this, they suggest that in the U.S. roughly 40% of variation in productivity firms is a surprise to firms. In India, about 80% of variation in productivity is a surprise. Because of the poorer information, Indian firms make bigger mistakes on average, and so there is more ex-post mis-allocation.

It’s a clever explanation for mis-allocation, and is one of those stories that in some sense has to be true to some extent. There is no way firms have perfect information on future productivity (or demand, which is essentially the same thing in these models). The question is how big of an effect it is, and they suggest it’s pretty sizable.

One question that came up in my head afterwards was whether the degree of uncertainty is related to the level of returns. That is, Indian firms have a lot of uncertainty (risk) in their productivity draws, apparently. Is that high risk associated with higher rewards? If it is, then we can’t really say that this is mis-allocation, per se. Firms are making optimal decisions ex-ante, and there happens to be a willingness to tolerate risk in the economy. If, on the other hand, high risk is associated with low rewards, then there really is a mis-allocation in the sense that they are making uninformed decisions.