Category Archives: Development

“International Trade and Institutional Change: Medieval Venice’s Response to Globalization,” D. Puga & D. Trefler

(Before discussing the paper today, I should forward a couple great remembrances of Stanley Reiter, who passed away this summer, by Michael Chwe (whose interests at the intersection of theory and history are close to my heart) and Rakesh Vohra. After leaving Stanford – Chwe mentions this was partly due to a nasty letter written by Reiter’s advisor Milton Friedman! – Reiter established an incredible theory group at Purdue which included Afriat, Vernon Smith and PhD students like Sonnenschein and Ledyard. He then moved to Northwestern where he helped build up the great group in MEDS which is too long to list, but which includes one Nobel winner already in Myerson and, by my reckoning, two more which are favorites to win the prize next Monday.

I wonder if we may be at the end of an era for topic-diverse theory departments. Business schools are all a bit worried about “Peak MBA”, and theorists are surely the first ones out the door when enrollment falls. Economic departments, journals and funders seem to have shifted, in the large, toward more empirical work, for better or worse. Our knowledge both of how economic and social interactions operate in their most platonic form, and our ability to interpret empirical results when considering novel or counterfactual policies, have greatly benefited by the theoretical developments following Samuelson and Hicks’ mathematization of primitives in the 1930s and 40s, and the development of modern game theory and mechanism design in the 1970s and 80s. Would that a new Cowles and a 21st century Reiter appear to help create a critical mass of theorists again!)

On to today’s paper, a really interesting theory-driven piece of economic history. Venice was one of the most important centers of Europe’s “commercial revolution” between the 10th and 15th century; anyone who read Marco Polo as a schoolkid knows of Venice’s prowess in long-distance trade. Among historians, Venice is also well-known for the inclusive political institutions that developed in the 12th century, and the rise of oligarchy following the “Serrata” at the end of the 13th century. The Serrata was followed by a gradual decrease in Venice’s power in long-distance trade and a shift toward manufacturing, including the Murano glass it is still famous for today. This is a fairly worrying history from our vantage point today: as the middle class grew wealthier, democratic forms of government and free markets did not follow. Indeed, quite the opposite: the oligarchs seized political power, and within a few decades of the serrata restricted access to the types of trade that previously drove wealth mobility. Explaining what happened here is both a challenge due to limited data, and of great importance given the public prominence of worries about the intersection of growing inequality and corruption of the levers of democracy.

Dan Trefler, an economic historian here at U. Toronto, and Diego Puga, an economist at CEMFI who has done some great work in economic geography, provide a great explanation of this history. Here’s the model. Venice begins with lots of low-wealth individuals, a small middle and upper class, and political power granted to anyone in the upper class. Parents in each dynasty can choose to follow a risky project – becoming a merchant in a long-distance trading mission a la Niccolo and Maffeo Polo – or work locally in a job with lower expected pay. Some of these low and middle class families will succeed on their trade mission and become middle and upper class in the next generation. Those with wealth can sponsor ships via the colleganza, a type of early joint-stock company with limited liability, and potentially join the upper class. Since long-distance trade is high variance, there is a lot of churn across classes. Those with political power also gather rents from their political office. As the number of wealthy rise in the 11th and 12th century, the returns to sponsoring ships falls due to competition across sponsors in the labor and export markets. At any point, the upper class can vote to restrict future entry into the political class by making political power hereditary. They need to include sufficiently many powerful people in this hereditary class or there will be a revolt. As the number of wealthy increase, eventually the wealthy find it worthwhile to restrict political power so they can keep political rents within their dynasty forever. Though political power is restricted, the economy is still free, and the number of wealthy without power continue to grow, lowering the return to wealth for those with political power due to competition in factor and product markets. At some point, the return is so low that it is worth risking revolt from the lower classes by restricting entry of non-nobles into lucrative industries. To prevent revolt, a portion of the middle classes are brought in to the hereditary political regime, such that the regime is powerful enough to halt a revolt. Under these new restrictions, lower classes stop engaging in long-distance trade and instead work in local industry. These outcomes can all be generated with a reasonable looking model of dynastic occupation choice.

What historical data would be consistent with this theoretical mechanism? We should expect lots of turnover in political power and wealth in the 10th through 13th centuries. We should find examples in the literature of families beginning as long-distance traders and rising to voyage sponsors and political agents. We should see a period of political autocracy develop, followed later by the expansion of hereditary political power and restrictions on lucrative industry entry to those with such power. Economic success based on being able to activate large amounts of capital from within the nobility class will make the importance of inter-family connections more important in the 14th and 15th centuries than before. Political power and participation in lucrative economic ventures will be limited to a smaller number of families after this political and economic closure than before. Those left out of the hereditary regime will shift to local agriculture and small-scale manufacturing.

Indeed, we see all of these outcomes in Venetian history. Trefler and Puga use some nice techniques to get around limited data availability. Since we don’t have data on family incomes, they use the correlation in eigenvector centrality within family marriage networks as a measure of the stability of the upper classes. They code colleganza records – a non-trivial task involving searching thousands of scanned documents for particular Latin phrases – to investigate how often new families appear in these records, and how concentration in the funding of long-distance trade changes over time. They show that all of the families with high eigenvector centrality in the noble marriage market after political closure – a measure of economic importance, remember – were families that were in the top quartile of seat-share in the pre-closure Venetian legislature, and that those families which had lots of political power pre-closure but little commercial success thereafter tended to be unsuccessful in marrying into lucrative alliances.

There is a lot more historical detail in the paper, but as a matter of theory useful to the present day, the Venetian experience ought throw cold water on the idea that political inclusiveness and economic development always form a virtuous circle. Institutions are endogenous, and changes in the nature of inequality within a society following economic development alter the potential for political and economic crackdowns to survive popular revolt.

Final published version in QJE 2014 (RePEc IDEAS). A big thumbs up to Diego for having the single best research website I have come across in five years of discussing papers in this blog. Every paper has an abstract, well-organized replication data, and a link to a locally-hosted version of the final published paper. You may know his paper with Nathan Nunn on how rugged terrain in Africa is associated with good economic outcomes today because slave traders like the infamous Tippu Tip couldn’t easily exploit mountainous areas, but it’s also worth checking out his really clever theoretical disambiguation of why firms in cities are more productive, as well as his crazy yet canonical satellite-based investigation of the causes of sprawl. There is a really cool graphic on the growth of U.S. sprawl at that last link!

“The Rise and Fall of General Laws of Capitalism,” D. Acemoglu & J. Robinson (2014)

If there is one general economic law, it is that every economist worth their salt is obligated to put out twenty pages responding to Piketty’s Capital. An essay by Acemoglu and Robinson on this topic, though, is certainly worth reading. They present three particularly compelling arguments. First, in a series of appendices, they follow Debraj Ray, Krusell and Smith and others in trying to clarify exactly what Piketty is trying to say, theoretically. Second, they show that it is basically impossible to find any effect of the famed r-g on top inequality in statistical data. Third, they claim that institutional features are much more relevant to the impact of economic changes on societal outcomes, using South Africa and Sweden as examples. Let’s tackle these in turn.

First, the theory. It has been noted before that Piketty is, despite beginning his career as a very capable economist theorist (hired at MIT at age 22!), very disdainful of the prominence of theory. He, quite correctly, points out that we don’t even have any descriptive data on a huge number of topics of economic interest, inequality being principal among these. And indeed he is correct! But, shades of the Methodenstreit, he then goes on to ignore theory where it is most useful, in helping to understand, and extrapolate from, his wonderful data. It turns out that even in simple growth models, not only is it untrue that r>g necessarily holds, but the endogeneity of r and our standard estimates of the elasticity of substitution between labor and capital do not at all imply that capital-to-income ratios will continue to grow (see Matt Rognlie on this point). Further, Acemoglu and Robinson show that even relatively minor movement between classes is sufficient to keep the capital share from skyrocketing. Do not skip the appendices to A and R’s paper – these are what should have been included in the original Piketty book!

Second, the data. Acemoglu and Robinson point out, and it really is odd, that despite the claims of “fundamental laws of capitalism”, there is no formal statistical investigation of these laws in Piketty’s book. A and R look at data on growth rates, top inequality and the rate of return (either on government bonds, or on a computed economy-wide marginal return on capital), and find that, if anything, as r-g grows, top inequality shrinks. All of the data is post WW2, so there is no Great Depression or World War confounding things. How could this be?

The answer lies in the feedback between inequality and the economy. As inequality grows, political pressures change, the endogenous development and diffusion of technology changes, the relative use of capital and labor change, and so on. These effects, in the long run, dominate any “fundamental law” like r>g, even if such a law were theoretically supported. For instance, Sweden and South Africa have very similar patterns of top 1% inequality over the twentieth century: very high at the start, then falling in mid-century, and rising again recently. But the causes are totally different: in Sweden’s case, labor unrest led to a new political equilibrium with a high-growth welfare state. In South Africa’s case, the “poor white” supporters of Apartheid led to compressed wages at the top despite growing black-white inequality until 1994. So where are we left? The traditional explanations for inequality changes: technology and politics. And even without r>g, these issues are complex and interesting enough – what could be a more interesting economic problem for an American economist than diagnosing the stagnant incomes of Americans over the past 40 years?

August 2014 working paper (No IDEAS version yet). Incidentally, I have a little tracker on my web browser that lets me know when certain pages are updated. Having such a tracker follow Acemoglu’s working papers pages is, frankly, depressing – how does he write so many papers in such a short amount of time?

“Agricultural Productivity and Structural Change: Evidence from Brazil,” P. Bustos et al (2014)

It’s been a while – a month of exploration in the hinterlands of the former Soviet Union, a move up to Canada, and a visit down to the NBER Summer Institute really put a cramp on my posting schedule. That said, I have a ridiculously long backlog of posts to get up, so they will be coming rapidly over the next few weeks. I saw today’s paper presented a couple days ago at the Summer Institute. (An aside: it’s a bit strange that there isn’t really any media at SI – the paper selection process results in a much better set of presentations than at the AEA or the Econometric Society, which simply have too long of a lag from the application date to the conference, and too many half-baked papers.)

Bustos and her coauthors ask, when can improvements in agricultural productivity help industrialization? An old literature assumed that any such improvement would help: the newly rich agricultural workers would demand more manufactured goods, and since manufactured and agricultural products are complements, rising agricultural productivity would shift workers into the factories. Kiminori Matsuyama wrote a model (JET 1992) showing the problem here: roughly, if in a small open economy productivity goes up in a good you have a Ricardian comparative advantage in, then you want to produce even more of that good. A green revolution which doubles agricultural productivity in, say, Mali, while keeping manufacturing productivity the same, will allow Mali to earn twice as much selling the agriculture overseas. Workers will then pour into the agricultural sector until the marginal product of labor is re-equated in both sectors.

Now, if you think that industrialization has a bunch of positive macrodevelopment spillovers (via endogenous growth, population control or whatever), then this is worrying. Indeed, it vaguely suggests that making villages more productive, an outright goal of a lot of RCT-style microdevelopment studies, may actually be counterproductive for the country as a whole! That said, there seems to be something strange going on empirically, because we do appear to see industrialization in countries after a Green Revolution. What could be going on? Let’s look back at the theory.

Implicitly, the increase in agricultural productivity in Matsuyama was “Hicks-neutral” – it increased the total productivity of the sector without affecting the relative marginal factor productivities. A lot of technological change, however, is factor-biased; to take two examples from Brazil, modern techniques that allow for double harvesting of corn each year increase the marginal productivity of land, whereas “Roundup Ready” GE soy that requires less tilling and weeding increases the marginal productivity of farmers. We saw above that Hicks-neutral technological change in agriculture increases labor in the farm sector: workers choosing where to work means that the world price of agriculture times the marginal product of labor in that sector must be equal to world price of manufacturing times the marginal product of labor in manufacturing. A Hicks-neutral improvement in agricultural productivity raises MPL in that sector no matter how much land or labor is currently being used, hence wage equality across sectors requires workers to leave the factor for the farm.

What of biased technological change? As before, the only thing we need to know is whether the technological change increases the marginal product of labor. Land-augmenting technical change, like double harvesting of corn, means a country can produce the same amount of output with the old amount of farm labor and less land. If one more worker shifts from the factory to the farm, she will be farming less marginal land than before the technological change, hence her marginal productivity of labor is higher than before the change, hence she will leave the factory. Land-augmenting technological change always increases the amount of agricultural labor. What about farm-labor-augmenting technological change like GM soy? If land and labor are not very complementary (imagine, in the limit, that they are perfect substitutes in production), then trivially the marginal product of labor increases following the technological change, and hence the number of farm workers goes up. The situation is quite different if land and farm labor are strong complements. Where previously we had 1 effective worker per unit of land, following the labor-augmenting technology change it is as if we have, say, 2 effective workers per unit of land. Strong complementarity implies that, at that point, adding even more labor to the farms is pointless: the marginal productivity of labor is decreasing in the technological level of farm labor. Therefore, labor-augmenting technology with a strongly complementary agriculture production function shifts labor off the farm and into manufacturing.

That’s just a small bit of theory, but it really clears things up. And even better, the authors find empirical support for this idea: following the introduction to Brazil of labor-augmenting GM soy and land-augmenting double harvesting of maize, agricultural productivity rose everywhere, the agricultural employment share rose in areas that were particularly suitable for modern maize production, and the manufacturing employment share rose in areas that were particularly suitable for modern soy production.

August 2013 working paper. I think of this paper as a nice complement to the theory and empirics in Acemoglu’s Directed Technical Change and Walker Hanlon’s Civil War cotton paper. Those papers ask how changes in factor prices endogenously affect the development of different types of technology, whereas Bustos and coauthors ask how the exogenous development of different types of technology affect the use of various factors. I read the former as most applicable to structural change questions in countries at the technological frontier, and the latter as appropriate for similar questions in developing countries.

“On the Origin of States: Stationary Bandits and Taxation in Eastern Congo,” R. S. de la Sierra (2013)

The job market is yet again in full swing. I won’t be able to catch as many talks this year as I would like to, but I still want to point out a handful of papers that I consider particularly elucidating. This article, by Columbia’s de la Sierra, absolutely fits that category.

The essential question is, why do states form? Would that all young economists interested in development put their effort toward such grand questions! The old Rousseauian idea you learned your first year of college, where individuals come together voluntarily for mutual benefit, seems contrary to lots of historical evidence. Instead, war appears to be a prime mover for state formation; armed groups establish a so-called “monopoly on violence” in an area for a variety of reasons, and proto-state institutions evolve. This basic idea is widespread in the literature, but it is still not clear which conditions within an area lead armed groups to settle rather than to pillage. Further, examining these ideas empirically seems quite problematic for two reasons, first because states themselves are the ones who collect data hence we rarely observe anything before states have formed, and second, because most of the planet has long since been under the rule of a state (with apologies to James Scott!)

De la Sierra brings some economics to this problem. What is the difference between pillaging and sustained state-like forms? The pillager can only extract assets on its way through, while the proto-state can establish “taxes”. What taxes will it establish? If the goal is long-run revenue maximization, Ramsey long ago told us that it is optimal to tax elements that are inelastic. If labor can flee, but the output of the mine can not, then you ought tax the output of the mine highly and set a low poll tax. If labor supply is inelastic but output can be hidden from the taxman, then use a high poll tax. Thus, when will bandits form a state instead of just pillaging? When there is a factor which can be dynamically taxed at such a rate that the discounted tax revenue exceeds what can be pillaged today. Note that the ability to, say, restrict movement along roads, or to expand output through state-owned capital, changes relevant tax elasticities, so at a more fundamental level, capacity by rebels along these margins are also important (and I imagine that extending de la Sierra’s paper will involve the evolutionary development of these types of capacities).

This is really an important idea. It is not that there is a tradeoff between producing and pillaging. Instead, there is a three way tradeoff between producing in your home village, joining an armed group to pillage, and joining an armed group that taxes like a state! The armed group that taxes will, as a result of its desire to increase tax revenue, perhaps introduce institutions that increase production in the area under its control. And to the extent that institutions persist, short-run changes that cause potential bandits to form taxing relationships may actually lead to long-run increases in productivity in a region.

De la Sierra goes a step beyond theory, investigating these ideas empirically in the Congo. Eastern Congo during and after the Second Congo War was characterized by a number of rebel groups that occasionally just pillaged, but occasionally formed stable tax relationships with villages that could last for years. That is, the rebels occasionally implemented something looking like states. The theory above suggests that exogenous changes in the ability to extract tax revenue (over a discounted horizon) will shift the rebels from pillagers to proto-states. And, incredibly, there were a number of interesting exogenous changes that had exactly that effect.

The prices of coltan and gold both suffered price shocks during the war. Coltan is heavy, hard to hide, and must be shipped by plane in the absence of roads. Gold is light, easy to hide, and can simply be carried from the mine on jungle footpaths. When the price of coltan rises, the maximal tax revenue of a state increases since taxable coltan production is relatively inelastic. This is particularly true near airstrips, where the coltan can actually be sold. When the price of gold increases, the maximal tax revenue does not change much, since gold is easy to hide, and hence the optimal tax is on labor rather than on output. An exogenous rise in coltan prices should encourage proto-state formation in areas with coltan, then, while an exogenous rise is gold prices should have little impact on the pillage vs. state tradeoff. Likewise, a government initiative to root out rebels (be they stationary or pillaging) decreases the expected number of years a proto-state can extract rents, hence makes pillaging relatively more lucrative.

How to confirm these ideas, though, when there was no data collected on income, taxes, labor supply, or proto-state existence? Here is the crazy bit – 11 locals were hired in Eastern Congo to travel to a large number of villages, spend a week there querying families and village elders about their experiences during the war, the existence of mines, etc. The “state formation” in these parts of Congo is only a few years in the past, so it is at least conceivable that memories, suitably combined, might actually be reliable. And indeed, the data do seem to match aggregate trends known to monitors of the war. What of the model predictions? They all seem to hold, and quite strongly: the ability to extract more tax revenue is important for proto-state formation, and areas where proto-states existed do appear to have retained higher productive capacity years later perhaps as a result of the proto-institutions those states developed. Fascinating. Even better, because there is a proposed mechanism rather than an identified treatment effect, we can have some confidence that this course is, to some extent, externally valid!

December 2013 working paper (No IDEAS page). You may wonder what a study like this costs (particularly if you are, like me, a theorist using little more than chalk and a chalkboard); I have no idea, but de la Sierra’s CV lists something like a half million dollars of grants, an incredible total for a graduate student. On a personal level, I spent a bit of time in Burundi a number of years ago, including visiting a jungle camp where rebels from the Second Congo War were still hiding. It was pretty amazing how organized even these small groups were in the areas they controlled; there was nothing anarchic about it.

“Does Ethnicity Pay?,” Y. Huang, L. Jin & Y. Qian (2010)

Ethnic networks in trade and foreign investment are widespread. Avner Greif, in his medieval trade papers, has pointed out the role of ethnic trade groups in facilitating group punishment of deviations from implicitly contracted behavior in cases where contracts cannot be legally enforced. Ethnic investors may also have an advantage when investing in their home country, due to better knowledge of local profit opportunities.

Huang, Jin and Qian investigate the ethnic advantage using an amazing database of the universe of Chinese industrial firms. The database tags firms formed using FDI (perhaps as a joint venture) from Hong Kong, Macao and Taiwan; in the latter two cases, nearly 100 percent of Chinese FDI is from ethnic Chinese. Amazingly, firms funded with FDI from these regions performs worse, as measured by ROI, ROA or margins, than Chinese firms funded with FDI from other countries. In the first years after the firms are founded, there is only a small difference between Chinese-funded firms and others, but over time, the disadvantage grows; it is not just that ethnic Chinese investors invest in companies with low profitability at the beginning, but that they actually get worse over time. Restricting the sample just to Taiwanese electronics firms’ FDI compared to Korean electronics firms’ FDI, the Koreans make more profitable investments, both at the beginning and as measured by relative performance over time.

What’s going on here? It’s not just that ethnic Chinese are making low profit investments in their ancestral hometown; omitting Fujian and Guangdong, ancestral source of most HK, Macao and Taiwan Chinese, does not change the results in any qualitative way. Instead, it appears that ethnic Chinese-funded firms do substantially less work building up intangible assets and human capital in the firms they invest in. Stratifying the firms, if Chinese-funded firms would have grown their human capital (as proxied by employee wage) or intangible assets (as measured in accounting data) at the same rate as non Chinese-funded firms, there would have been no difference in ROI over time.

This leads to a bigger question, of course. Why would ethnic investors fail to build up intangible capital? Certainly there are anecdotal stories along these lines, particularly when it comes to wealthy minority investors; think Lebanese in West Africa, Fujianese in Indonesia, or Jewish firms in 19th century Europe. I don’t have a model that can explain such behavior, however. Any thoughts?

2010 NBER working paper (IDEAS version)

“The African Growth Miracle,” A. Young (2013)

Alwyn Young, well known for his empirical work on growth, has finally published his African Growth paper in the new issue of the JPE. Africa is quite interesting right now. Though it is still seen by much of the public as a bit of a basket case, the continent seems to be by-and-large booming. At least to the “eye test”, it has been doing so for some time now, to some extent in the 1990s but much more so in the 2000s. I remember visiting Kigali, Rwanda for the first time in 2008; this is a spotless, law-abiding city with glass skyscrapers downtown housing multinational companies. Not what you may have expected!

What is interesting, however, is that economic statistics have until very recently still shown African states growing much slower than other developing countries. A lot of economic data from the developing world is of poor quality, but Young notes that for many countries, it is literally non-existent: those annual income per capita tables you see in UN data and elsewhere involve pretty heroic imputation. Can we do better? Young looks at an irregular set of surveys from 1990 to 2006, covering dozens of poor countries, called the Demographic and Health Survey. This survey covers age, family size, education level and some consumption (“do you have a bicycle?”, “do you have a non-dirt floor?”). What you see immediately is that, across many items, the growth rate in consumption in African states surveyed more or less matches the growth rate in non-African developing countries, despite official statistics suggesting the non-African states have seen private consumption growing at a much faster clip.

Can growth in real consumption be backed out of such statistics? The DHS is nice in that it, in some countries and years, includes wages. The basic idea is the following: consumption of normal goods rises with income, and income rises with education, so consumption of normal goods should rise with education. I can estimate very noisy Engel curves linking consumption to education, and using the parts of the sample where wage data exists, a Mincerian regression with a whole bunch of controls gives us some estimate of the link between a year of education and income: on average, it is on the order of 11 percent. We now have a method to go from consumption changes to implied mean education levels to real consumption changes. Of course, this estimate is very noisy. Young uses a properly specified maximum likelihood function with random effects to show how outliers or noisy series should be weighted when averaging estimates of real income changes using each individual product; indeed, a simple average of the estimated real consumption growth from each individual product gives a wildly optimistic growth rate, so such econometric techniques are quite necessary.

What, then, does this heavy lifting give us? Real consumption in countries in the African sample grew 3.4% per household per annum in 1990-2006, versus 3.8% in developing countries outside Africa. This is contra 1% in African and 2% in non-African countries, using the same sample of countries, in other prominent international data sources. Now, many of these countries are not terribly far from subsistence, so it is impossible for most African states to have been growing at this level throughout the 70s and 80s as well, but at least for the 90s, consumption microdata suggests a far rosier past two decades on the continent than many people imagine. Clever.

Final working paper (IDEAS version). I am somehow drawing a blank on the name of the recent book covering the poor quality of developing world macro data – perhaps a commenter can add this for me.

“Railroads of the Raj: Estimating the Impact of Transportation Infrastructure,” D. Donaldson (2013)

Somehow I’ve never written about Dave Donaldson’s incredible Indian railroad paper before; as it has a fair claim on being the best job market paper in the past few years, it’s time to rectify that. I believe Donaldson spent eight years as LSE working on his PhD, largely made up of this paper. And that time led to a well-received result: in addition to conferences, a note on the title page mentions that the paper has been presented at Berkeley, BU, Brown, Chicago, Harvard, the IMF, LSE, MIT, the Minneapolis Fed, Northwestern, Nottingham, NYU, Oxford, Penn, Penn State, the Philly Fed, Princeton, Stanford, Toronto, Toulouse, UCL, UCLA, Warwick, the World Bank and Yale! So we can safely say, this is careful and well-vetted work.

Donaldson’s study considers the importance of infrastructure to development; it is, in many ways, the opposite of the “small changes”, RCT-based development literature that was particularly en vogue in the 2000s. Intuitively, we all think infrastructure is important, both for improving total factor productivity and for improving market access. The World Bank, for instance, spends 20 percent of its funds on infrastructure, more than “education, health, and social services combined.” But how important is infrastructure spending anyway? That’s a pretty hard question to define, let alone answer.

So let’s go back to one of the great infrastructure projects in human history: the Indian railroad during the British Raj. The British built over 67,000 km of rail in a country with few navigable rivers. They also, luckily for the economist, were typically British in the enormous number of price, weather, and rail shipment statistics they collected. Problematically for the economist, these statistics tended to be hand-written in weathered documents hidden away in the back rooms of India’s bureaucratic state. Donaldson nonetheless collected almost 1.5 million individual pieces of data from these weathered tomes. Now, you might think, let’s just regress new rail access on average incomes, use some IV to make sure that rail lines weren’t endogenous, and be done with it. Not so fast! First, there’s no district-level income per capita data for India in the 1800s! And second, we can use some theory to really tease out why infrastructure matters.

Let’s use four steps. First, try to estimate how much rail access lowered trade costs per kilometer; if a good is made in only one region, then theory suggests that the trade cost between regions is just the price difference of that commodity across regions. Even if we had shipping receipts, this wouldn’t be sufficient; bandits, and spoilage, and all the rest of Samuelson’s famous “iceberg” raise trade costs as well. Second, check whether lowered trade costs actually increased trade volume, and at what elasticity, using rainfall as a proxy for local productivity shocks. Third, note that even though we don’t have income, theory tells us that for agricultural workers, percentage changes in total production per unit of land deflated by a local price index is equivalent to percentage changes in real income per unit of land. Therefore, we can check in a reduced form way whether new rail access increases real incomes, though we can’t say why. Fourth, in Donaldson’s theoretical model (an extension, more or less, of Eaton and Kortum’s Ricardian model), trade costs and differences in region sizes and productivity shocks in all regions all interact to affect local incomes, but they all act through a sufficient statistic: the share of consumption that consists of local products. That is, if we do our regression testing for the impact of rail access on real income changes, but control for changes in the share of consumption from within the district, we should see no effect from rail access.

Now, these stages are tough. Donaldson constructs a network of rail, road and river routes using 19th century sources linked on GIS, and traces out the least-cost paths from any one district to another. He then non-linearly estimates the relative cost per kilometer of rail, sea, river and road transport using the prices of eight types of salt, each of which were sold across British India but only produced in a single location. He then finds that lowered trade costs do appear to raise trade volumes with quite high elasticity. The reduced form regression suggests that access to the Indian railway increased local incomes by an average of 16 percent (Indian real incomes per capita increased only 22 percent during the entire period 1870 to 1930, so 16 percent locally is substantial). Using the “trade share” sufficient statistic described above, Donaldson shows that almost all of that increase was due to lowered trade costs rather than internal migration or other effects. Wonderful.

This paper is a great exercise in the value of theory for empiricists. Theory is meant to be used, not tested. Here, fairly high-level trade theory – literally the cutting edge – was deployed to coax an answer to a super important question even though atheoretical data could have provided us nothing (remember, there isn’t even any data on income per capita to use!). The same theory also allowed to explain the effect, rather than just state it, a feat far more interesting to those who care about external validity. Two more exercises would be nice, though; first, and Donaldson notes this in the conclusion, trade can also improve welfare by lowering volatility of income, particularly in agricultural areas. Is this so in the Indian data? Second, rail, like lots of infrastructure, is a network – what did the time trend in income effects look like?

September 2012 Working Paper (IDEAS version). No surprise, Donaldson’s website mentions this is forthcoming in the AER. (There is a bit of a mystery – Donaldson was on the market with this paper over four years ago. If we need four years to get even a paper of this quality through the review process, something has surely gone wrong with the review process in our field.)

“Pollution for Promotion,” R. Jia (2012)

Ruixue Jia is on the job market from IIES in Stockholm, and she has the good fortune to have a job market topic which is very much au courant. In China, government promotions often depend both on the inherent quality of the politician and on how connected you are to current leaders; indeed, a separate paper by Jia finds that promotion probability in China depends only on the interaction of economic growth and personal connections rather than either factor by itself. Assume that a mayor can choose how much costly effort to exert. The mayor chooses how much dirty and clean technology – complements in production – to use, with the total amount of technology available an increasing function of the mayor’s effort. The mayor may personally dislike dirty technology. For any given bundle of technology, the observed economic output is higher the higher the mayor’s inherent quality (which he does not know). The central government, when deciding on promotions, only observes economic output.

Since mayors with good connections have a higher probability of being promoted for any level of output in their city, the marginal return to effort and the marginal return to dirty technology are increasing in the connectedness of the mayor. For any given distaste for pollution among the mayor, a more connected mayor will mechanically want to substitute clean for dirty technology since higher output is more valuable to him for career concerns while the marginal cost of distaste for pollution has not changed. Further, by a Le Chatelier argument, higher marginal returns to output increase the optimal effort choice, which allows a higher budget to purchase technology, dirty tech included. To the extent that the government cares about limiting the (unobserved) use of dirty tech, this is “almost” the standard multitasking concern: the folly of rewarding A and hoping for B. Although in this case, empirically there is no evidence that the central government cares about promoting local politicians who are good for the environment!

How much do local leaders increase pollution (and simultaneously speed up economic growth!) in exchange for a shot at a better job? The theory above gives us some help. We see that the same politician will substitute in dirty technology if, in some year, his old friends get on the committee that assigns promotions (the Politburo Standing Committee, or PSC, in China’s case). This allows us to see the effect of the Chinese incentive system on pollution even if we know nothing about the quality of each individual politician or whether highly-connected politicians get plum jobs in low pollution regions, since every effect we find is at the within-politician level. Using a diff-in-diff, Jia finds that in the year after a politician’s old friend makes the PSC, sulfur dioxide goes up 25%, a measure of river pollution goes up by a similar amount, industrial GDP rises by 15%, and non-industrial GDP does not change. So it appears that China’s governance institution does incentivize governors, although whether those incentives are good or bad for welfare depends on how you trade off pollution and growth in your utility function.

Good stuff. A quick aside, since what I like about Jia’s work is that she makes an attempt to more than simply find a clever strategy for getting internal validity. Many other recent job market stars – Dave Donaldson and Melissa Dell, for instance – have been equally good when it comes to caring about more than just nice identification. But such care is rare indeed! It has been three decades since we, supposedly, “took the ‘con’ out of Econometrics”. And yet an unbearable number of papers are still floating around which quite nicely identify a relationship of interest in a particular dataset, then go on to give only the vaguest and most unsatisfying remarks concerning external validity. That’s a much worse con than bad identification! Identification, by definition, can only hold ceteris paribus. Even perfect identification of some marginal effect tells me absolutely nothing about the magnitude of that effect when I go to a different time, or a different country, or a more general scenario. The only way – the only way! – to generalize an internally valid result, and the only way to explain why that result is the way it is, is to use theory. A good paper puts the theoretical explanation and the specific empirical case examined in context with other empirical papers on the same general topic, rather than stopping after the identification is cleanly done. And a good empirical paper needs to explain, and needs to generalize, because we care about unemployment (not unemployment in border counties of New Jersey in the 1990s) and we care about the effect of military training on labor supply (not the effect of the Vietnam War on labor supply in the few years following), etc. If we really want the credibility revolution in empirical economics to continue, let’s spend less seminar and referee time worrying only about internal validity, and more time shutting down the BS that is often passed off as “explanation”.

November 2012 working paper. Jia also has an interesting paper about the legacy of China’s treaty ports, as well as a nice paper (a la Nunn and Qian) on the importance of the potato in world history (really! I may be a biased Dorchester-born Mick, but still, the potato has been fabulously important).

“The Human Capital Stock: A Generalized Approach,” B. Jones (2012)

(A quick note: the great qualitative economist Albert O. Hirschman died earlier today. “Exit, Voice and Loyalty” is, of course, his most famous work, and probably deserves more consideration in the modern IO literature. If a product changes or deteriorates, our usual models have consumers “exiting”, or refusing to buy the product anymore. However, in some kinds of long-term relationships, I can instead voice my displeasure at bad outcomes. For instance, if the house has a bad night at a restaurant I’ve never been to, I simply never return. If the house has a bad night at one of my regular spots, I chalk it up to bad luck, tell the waiter the food was subpar, and return to give them another shot. Hirschman is known more for his influence on sociology and political science than on core economics, but if you are like me, the ideas in EVL look suspiciously game theoretic: I can imperfectly monitor a firm (since I only buy one of the millions of their products), they can make costly investments in loyalty (responding to a bad set of products by, say, refunding all customers), etc. That’s all perfectly standard work for a theorist. So, clever readers, has anyone seen a modern theoretic take on EVL? Let me know in the comments.)

Back to the main article in today’s post, Ben Jones’ Human Capital Stock paper. Measuring human capital is difficult. We think of human capital as an input in a production function. A general production function is Y=f(K,H,A) where A is a technology scalar, K is a physical capital aggregator, and H (a function of H(1),H(2), etc., marking different types of human capital) is a human capital aggregator. Every factor is paid its marginal product if firms are cost minimizers. Let H(i)=h(i)L(i) be the quantity of some class of labor (like college educated workers) weighted by the flow of services h(i) provided by that class. We can measure L, but not h. The marginal product of L(i), the wage received by laborers of type i, is df/dH*dH/dH(i)*h(i). That is, wage depends both on the amount of human capital in workers of type i, as well as contribution of H(i) to the human capital aggregator.

Consider the ratio of wages w(i)/w(j)=[dH/dH(i)*h(i)]/[dH/dH(j)*h(j)]. Again, we need to the know how each type of human capital affects the aggregator to be able to go from wage differences to human capital differences. If the production function is constant returns to scale, then the human capital aggregator can be rewritten as h(1)*H(L(1),[w(2)*dH/dH(1)]/[w(1)*dH/dH(2)]…). If wages w and labor allocations L were observed, we could infer the amount of human capital if we knew h(1) and we knew the ratios of marginal contributions of each type of human capital to the aggregator. Traditional human capital accounting assumes that h(1), the human capital of unskilled workers, is identical across countries, and that the aggregator equals the sum of h(i)L(i). Implicitly, this says each skill-adjusted unit of labor is perfectly substitutable in the production function: a worker with wage twice the unskilled wage, by the above assumptions, has twice the human capital of the unskilled worker. If you replaced her with two unskilled workers, the total productive capacity of the economy would be unchanged.

You may not like those assumptions. Jones notes that, since rich countries have many fewer unskilled workers, and since marginal product is a partial equilibrium concept, the marginal productivity of unskilled workers is likely higher in rich countries than in poor ones. Also, unskilled worker productivity has complementarities with the amount of skilled labor; a janitor keeping a high-tech hospital clean has higher marginal product than an unskilled laborer in the third world (if you know Kremer’s O-Ring paper, this will be no surprise). These two effects mean that traditional assumptions in human capital accounting will bias downward the relative amount of human capital in the wealthy world. It turns out that, under a quite general function form for the production function, we only need to add the elasticity of unskilled-skilled labor substitution to our existing wage and labor allocation data to estimate the amount of human capital with the generalized human capital function; critically, we don’t need to know anything about how different types of skilled labor combine.

How does this matter empirically? There seems to be a puzzle in growth accounting. Highly educated countries almost always correlate with high incomes. Yet traditional growth accounting finds only 30% or so of across-country income difference can be explained by differences in human capital. However, empirical estimates of the elasticity of substitution of unskilled and skilled labor are generally something like 1.4 – there are complementarities. Jones calculates for a number of country pairs what elasticity would be necessary to explain 100% of the difference in incomes with human capital alone. The difference between Israel (the 85th percentile of the income distribution) and Kenya (the 15th percentile) is totally explained if the elasticity of substitution between skilled and unskilled labor is 1.54. Similar numbers prevail for other countries.

So if human capital is in fact quite important, why explains the differences in labor allocation? Why are there so many more skilled workers in the US than in Congo? Two things are important to note. First, in general equilibrium, workers choose how much education to receive. That is, if anyone in the US is not going to college, the difference in wages between skilled and unskilled labor cannot be too large. For the differences in wages to not grow too large, there must be a supply response: the amount of unskilled laborers shrinks, causing each unskilled worker’s marginal product to rise. Israel has a ratio of skilled to unskilled labor 2300% higher than Kenya, but the skilled worker wage premium is only 20% higher in Israel than in Kenya. If the elasticity of substitution is 1.6, service flows from skilled workers in Israel are almost 100 times higher than in Kenya, despite an almost identical skilled-unskilled wage premium. That is, we will see high societal returns to human capital in the share of skilled workers rather than in the wage premium.

Second, why don’t poor countries have such high share of human capital? Adam Smith long ago wrote that the division of labor is limited by the size of the market. At high levels of human capital, specialization has huge returns. Jones gives the example of a thoracic surgeon: willingness to pay for such a surgeon to perform heart surgery is far higher than willingness to pay a dermatologist or an economics professor, despite similar levels of education. Specialization, therefore, increases the societal return to human capital, and such specialization may be limited by small markets, coordination costs, low levels of existing advanced knowledge, or limited local access to such knowledge. A back of the envelope calculation suggests that a 4.3-fold difference in the amount of specialization can explain the differences in labor allocation between Israel and Kenya, and that this difference is even lower if rich countries have better ability to transmit education than poor countries.

This is all to say that, in some ways, the focus on TFP growth may be misleading. Growth in technology, for developing countries, is very similar to growth in human capital, at least intuitively. If the Solow residual is, in fact, relatively unimportant once human capital is measured correctly, then the problem of growth in poor countries is much simpler: do we deepen our physical capital, or improve our human capital? This paper suggests that human capital improvements are most important, and that useful improvements in human capital may be partially driven by coordinating increased specialization of workers. Interesting.

2011 working paper, which appears to be the newest version; IDEAS page.

“Trafficking Networks and the Mexican Drug War,” M. Dell (2011)

Job market talks for 2012 have concluded at many schools, and therefore this is my last post on a job candidate paper. This is also the only paper I didn’t have a change to see presented live, and for good reason: Melissa Dell is clearly this year’s superstar, and I think it’s safe to assume she can have any job she wants, and at a salary she names. I have previously discussed another paper of hers – the Mining Mita paper – which would also have been a mindblowing job market paper; essentially, she gives a cleanly identified and historically important example of long-run effects of institutions a la Acemoglu and Robinson, but the effect she finds is that “bad” institutions in the colonial era led to “good” outcomes today. The mechanism by which historical institutions persist is not obvious and must be examined on a case-by-case basis.

Today’s paper is about another critical issue: the Mexican drug war. Over 40,000 people have been killed in drug-related violence in Mexico in the past half-decade, and that murder rate has been increasing over time. Nearly all of Mexico’s domestic drug production, principally pot and heroin, is destined for the US. There have been suggestions, quite controversial, that the increase in violence is a result of Mexican government policies aimed at shutting down drug gangs. Roughly, some have claimed that when a city arrests leaders of a powerful gang, the power vacuum leads to a violent contest among new gangs attempting to move into that city; in terms of the most economics-laden gang drama, removing relatively non-violent Barksdale only makes it easier for violent Marlo.

But is this true? And if so, when is it true? How ought Mexico deploy scarce drugfighting resources? Dell answers all three questions. First, she notes that the Partido Acción Nacional is, for a number of reasons, associated with greater crackdowns on drug trafficking in local areas. She then runs a regression discontinuity on municipal elections – which vary nicely over time in Mexico – where PAN barely wins versus barely loses. These samples appear balanced according to a huge range of regressors, including the probability that PAN has won elections in the area previously, a control for potential corruption at the local level favoring PAN candidates. In a given municipality-month, the probability of a drug-related homicide rises from 6 percent to 15 percent following a PAN inauguration after such a close election. There does not appear to be any effect during the lame duck period before PAN takes office, so the violence appears correlated to anti-trafficking policies that occur after PAN takes control. There is also no such increase in cases where PAN barely loses. The effect is greatest in municipalities on the border of two large drug gang territories. The effect is also greatest in municipalities where detouring around that city on the Mexican road network heading toward the US is particularly arduous.

These estimates are interesting, and do suggest that Mexican government policy is casually related to increasing drug violence, but the more intriguing question is what we should do about this. Here, the work is particularly fascinating. Dell constructs a graph where the Mexican road network forms edges and municipalities form vertices. She identifies regions which are historical sources of pot and poppyseed production, and identifies ports and border checkpoints. Two models on this graph are considered. In the first model, drug traffickers seek to reach a US port according to the shortest possible route. When PAN wins a close election, that municipality is assumed closed to drug traffic and gangs reoptimize routes. We can then identify which cities are likely to receive diverted drug traffic. Using data on drug possession arrests above $1000 – traffickers, basically – she finds that drug confiscations in the cities expected by the model to get traffic post-elections indeed rises 18 to 25 percent, depending on your measure. This is true even when the predicted new trafficking routes do not have a change in local government party: the change in drug confiscation is not simply PAN arresting more people, but actually does seem like more traffic along the route.

A second model is even nicer. She considers the equilibrium where traffickers try to avoid congestion. That is, if all gangs go to the same US port of entry, trafficking is very expensive. She estimates a cost function using pre-election trafficking data that is fairly robust to differing assumptions about the nature of the cost of congestion, and solves for the Waldrop equilibrium, a concept allowing for relatively straightforward computational solutions to congestion games on a network. The model in the pre-election period for which parameters on the costs are estimated very closely matches actual data on known drug trafficking at that time – congestion at US ports appears to be really important, whereas congestion on internal Mexican roads doesn’t matter too much. Now again, she considers the period after close PAN elections, assuming that these close PAN victories increase the cost of trafficking by some amount (results are robust to the exact amount), and resolves the congestion game from the perspective of the gangs. As in the simpler model, drug trafficking rises by 20 percent or so in municipalities that gain a drug trafficking route after the elections. Probability of drug-related homicides similarly increases. A really nice sensitivity check is performed by checking cocaine interdictions in the same city: they do not increase at all, as expected by the model, since the model maps trafficking routes from pot and poppy production sites to the US, and cocaine is only transshipped to Mexico via ports unknown to the researcher.

So we know now that, particularly when a territory is on a predicted trafficking route near the boundary of multiple gang territories, violence will likely increase after a crackdown. And we can use the network model to estimate what will happen to trafficking costs if we set checkpoints to make some roads harder to use. Now, given that the government has resources to set checkpoints on N roads, with the goal of increasing trafficking costs and decreasing violence, where ought checkpoints be set? Exact solutions turn out to be impossible – this “vital edges” problem in NP-hard and the number of edges is in the tens of thousands – but approximate algorithms can be used, and Dell shows which areas will benefit most from greater police presence. The same model, as long as data is good enough, can be applied to many other countries. Choosing trafficking routes is a problem played often enough by gangs that if you buy the 1980s arguments about how learning converges to Nash play, then you may believe (I do!) that the problem of selecting where to spend government counter-drug money is amenable to game theory using the techniques Dell describes. Great stuff. Now, between the lines, and understand this is my reading and not Dell’s claim, I get the feeling that she also thinks that the violence spillovers of interdiction are so large that the Mexican government may want to consider giving up altogether on fighting drug gangs.

http://econ-www.mit.edu/files/7484 (Nov 2011 Working Paper. I should note that this year is another example of strong female presence at the top of the economics job market. The lack of gender diversity in economics is problematic for a number of reasons, but it does appear things are getting better: Heidi Williams, Alessandra Voena, Melissa Dell, and Aislinn Bohren, among others, have done great work. The lack of socioeconomic diversity continues to be worrying, however; the field does much worse than fellow social sciences at developing researchers hailing from the developing world, or from blue-collar family backgrounds. Perhaps next year.)

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