Category Archives: Development

“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. (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.)

“The Gifts of Athena,” J. Mokyr (2003)

“Accelerating growth since 1750 has affected the world more than all other social and political changes taken together (p. 297).” From the dawn of mankind to the mid-1800s, the range of real incomes between the poorest regions at their poorest times and the richest regions at their richest times was probably no more than a factor of three of four, and certainly any deviation from that range was supremely short-lived. We know this because the minimum real income is subsistence only, and the maximum can be estimated from consumption in certain wealthy areas (early Ming China, the height of Rome, Holland in the 1500s, Venice in its heyday, etc.). Following the Industrial Revolution, real incomes have risen in many areas to more than 100 times their pre-IR level. Such gains are not only in terms of income: many sub-Saharan countries today have literacy rates, infant mortality rates and life expectancies better than the most prosperous countries in the world as of 1850 (and even 1900!). Given such massive effects, knowing why the IR took place is perhaps the most important question a historian could answer; many simple explanations do not hold water (e.g., early rates of high literacy are not associated with early income growth).

Mokyr provides what seems to me the most cogent answer. Roughly, the IR is special not only because new techniques were invented – things were being invented continuously throughout history – but because the process of cumulative invention did not peter out. He suggests that there are two types of knowledge, prescriptive and propositional, which tell us how to do something and why that how works. The two types of knowledge feed back onto one another: seeing a machine work gives us reason to search for why, and knowing why a process operates lets us develop new techniques using that process. The Industrial Revolution exploded with greatest force when there was a process for doing scientific research, whose results were accepted as “true”, whose results were communicated to the broader politic, which were then transformed into products by tinkerers and other non-research inventors. Much of Mokyr’s book, especially chapters 2 and 3, provide low-level and heavily-cited evidence for such claims. You should read the whole thing, so the rest of this post is just notes on other arguments I found interesting.

1) Knowledge being “tight” and well-justified helps science become accepted, but such tightness is not necessary for progress. Consider sanitation in the 1800s: cleanliness helped reduce germ-borne illness, especially from the water supply, but the justification for cleanliness campaigns was by and large the now-discredited miasma theory (“sickness is in the air”).

2) Selection of “true” techniques may operate sometimes on firms, but certainly won’t operate on households. Households need to be persuaded that a given fact is true in order to change behavior. Cue Latour and Ziman on socially constructed facts. Beyond households, non-market selection of technology is also prevalent in many other areas since politics shapes market outcomes. For instance, in 2001, the Netherlands got 4% of power from nuclear, versus 56% in next-door Belgium.

3) Useful new technology is often resisted, and not for Mancur Olson style rent-seeking reasons. Law is often xenophobic (Ming and Qing-era China), corporate leaders can be conservative while having market power (Henry Ford did not like radial tires), etc.

4) Sometimes resistance is justified by uncertainty. Consider this amazing anecdote about the engineer Thomas Midgley, from General Motors. In 1921, Midgley invented tetraethyl lead for gasoline, which helped engine performance; of course, it also polluted terribly. In 1928, he invented CFCs, a miracle chemical which also turned out to be awful for the environment. After being stricken by polio, Midgley invented a series of pulleys to help himself get out of bed – well, you know how this ends: the poor guy ended up strangling himself with his own invention by accident!

5) “Caldwell’s Law” says that creative states are only super creative for a short time. This isn’t merely a result of rent-seeking taking over, though. Top-down invention systems like those in Song-era China can be very productive, while political fragmentation may halt invention due to wars and instability (Sweden after 1700, Netherlands after 1580). Rather, certain types of inventions may be more amenable to certain types of institutions, and if institutions are rigid over time, a given state can be super inventive in one era and less so in another. And institutions are not rigid arbitrarily: “Institutions are there for a reason; they were not imposed on poor countries by an evil spirit (p. 283).”

This is a book well worth reading. (Google Books preview – grab the full text from your nearest library!)

“Balancing Growth with Equity: The View from Development,” E. Duflo (2011)

Esther Duflo recently presented this paper at the Jackson Hole monetary policy conference. In the “perfect world” model of the economy, there is no role for inequality to play in growth: investment depends on marginal returns, not on who has the cash. A good prospective company gets their money, earns their return, and pays back whoever lent. Marginal propensity to save matters, of course, and is affected by inequality, so that in a Kaldor world high inequality leads to higher savings leads to higher growth. But are we forgetting some constraints? Indeed.

Poor countries are a world of special constraints: credit constraints, nonexistent insurance markets, nonoptimal education decisions, etc. Duflo asks, given these constraints, might pro-equality policies also be pro-growth? Or, what types of pro-growth policies might we favor if we take effects on the poorest into special consideration? Unsurprising if you know Duflo’s work, evidence in this paper is drawn strictly from well-identified micro-studies rather than cross-country regressions or the like; I won’t go into this choice further here, except to say that awful quality of aggregate data on the third world alone seems reason enough to me to support Duflo’s position.

A few stylized facts, first. The gap between interest rates for lenders and borrowers in the third world is enormous: 30 to 40 percentage points per year is not strange. This is perhaps due to poor legal systems which necessitate extensive spending on loan recovery. It is not due to high default rates. Marginal returns to investment in small and medium-sized businesses in the third world is often huge: 50 to 100 percent returns have been found in well-identified studies. Misallocation of capital due to trust issues is widespread. Saving is “difficult” due to expensive (as a percentage of annual income) fees on savings accounts and threats of theft with informal savings mechanisms. Insurance markets, such as weather insurance for farmers, often don’t exist, and take-up rates are very low for actuarially fair insurance; the reasons why are numerous. Parents often undereducate children, both because they do not value childhood welfare equal to their own (as in a Becker model), because education systems are very poor, and because they misunderstand returns to education (it is log-linear, not “lumpy”, but parents often think only kids smart enough to get a government job or similar are worth educating). Many of these constraints combine to keep labor mobility across space suboptimally low, primarily by keeping too many peasants in rural areas.

Pro-growth and pro-poor policies can mitigate many of these problems. A number of studies have shown interventions in primary education that improve student outcomes in poor countries. Subsidized crop insurance may lead to higher growth by letting farmers grow crops with higher return by higher variability. Microcredit may not be that useful (if the production function has thresholds, small loans may allow for less effort while not allowing for high marginal return investments that can pull the poor out of poverty) whereas loan schemes to medium-sized businesses with smaller lend-borrow interest rate gaps may be particularly useful. Property right clarification a la Hernando de Soto can allow the poor to invest more freely, escaping poverty traps. The point of this paper is less to provide precise policies than to show where useful future research may lie concerning pro-poor growth.

One final note on Duflo’s RCT-style development work. Duflo (and Banerjee) could win a Nobel this year and nobody should complain: they would fully deserve it. That said, as useful as their work is, there is an obvious bubble in identification-heavy micro-level development work. I don’t think “replicate studies in 10 different villages in 10 countries” is in any way a good form of testing for external validity, and I also don’t think it’s good that the huge majority of young economists going into development are focusing their attention away from the “big picture” questions. There is a middle ground: identification with theoretically-sound structural models. That is, we take rigorous data collection and careful work on causality, and combine it with “big picture” theory that allows us to compare results across regions and to learn how interconnected aspects of the market function. And I think development is already moving in that direction! (Indeed, precisely these comments apply to a lot of experimental economics, but that’s a discussion for another day…) (Aug 2011 KC Fed Jackson Hole Conference final version)

“The O-Ring Theory of Economic Development,” M. Kremer (1993)

The O-Ring theory is unquestionably one of the most influential papers in applied theory of the past 20 years. The basic idea is simple: Write a Cobb-Douglas utility function where instead of labor entering as a lump of homogenous efficiency units, n units of labor for n tasks must be supplied by n individuals. This represents situations where quantity cannot be substituted for quality. A football team can only have one quarterback, and a kitchen only one head chef. With probability 1-q(i), a given agent fails at his task – all tasks are identical – and destroys 1-q(i) of the final production. This is analagous to the space shuttle Challenger, which exploded despite having only one bad part, the famous O-Ring. For now, assume no uncertainty about skill.

What happens in equilibrium? As in Becker’s marraige model, there is perfect sorting: all workers in a given firm are of the same quality. Each firm makes zero profit by assumption. Since quality enters multiplicatively in the production function, high quality workers are most valuable to firms already employing other high quality workers. The distribution of wages is far more extreme than the distribution of quality, since the marginal product of high quality workers is calculated given their employment at high quality firms. This fact perhaps explains why janitors and secretaries at high productivity firms are paid more than janitors and secretaries at low productivity firms: making a mistake is so costly at the high productivity firms that those companies are willing to pay a very high salary to someone who is even slightly better at their secretary job. The story is less plausible when it concerns janitors – surely interfirm equity issues of some kind provide a better explanation – but the theory is clever nonetheless.

Kremer further considers sequential production. He notes that Rembrandt’s newest assistants prepared the canvas, his advanced assistants painted most of the work, and Rembrandt came in at the end to paint the face and hands. When making a mistake destroys all of the product, the worker least likely to make a mistake performs her task last. If each stage of the sequential production is thought of as a firm who sells their intermediate product onward, the 7th step of production produces profit for the firm of the price of the object sold up to the 8th stage (q) times the probability the 7th step is done correctly (p) minus the price of the good bought from the 6th intermediate company (p’) minus the skill-specific wage. By zero profit condition, this means that the skill specific wage is qp-p’. Since the value of the good increases in every stage, p>p’, and therefore the wage schedule is steeper in skill at higher stages of production. That is, workers who are slightly better get a much higher wage. This is similar in flavor, if not derivation, to Rosen’s tournament model. The argument here ignores the fact that higher-skill workers generally perform more difficult tasks (this point was made to me by a professor here at NW).

Skill can also be endogenized. In the first period, workers choose a level of education which stochastically generates skill. Their true skill is only observed imperfectly by a test score. The worker payoff is her wage minus the cost of her education. Each worker is paid precisely her expected skill. Note the strategic complementarity here: if other workers have a lot of skill, I will also try to get educated, but if other agents do not get much education, then I will not either. These multiple equilibria exist even when looking only at pure strategic symmetric equilibria. Because actual skill level is only observed imperfectly, in a symmetric equilibria, workers end up pooled at firms with other workers at the same test score. Kremer claims this can be a model of statistical discrimination: with multiple equilibria, if firms expect a certain ethnic group to be of low education, then workers in that group will not get a lot of education, confirming the firm hypothesis. Because of the multiplier on wage discussed earlier, the return to education in the low equilibria group will be lower than in the high equilibria group.

The discrimination argument relies heavily on the fact that errors on education and on test scores are normally distributed, hence no matter what the true quality, test scores have full support. If this weren’t the case, a worker in the discriminated group can always get the same amount of education as in the nondiscriminated group, obtain a higher posterior on his test score, and end up matched in the good equilibrium. Essentially, the labor market is not separated. There are multiple equilibria, but there is not necessarily a sorting equilibrium. Indeed, generating a sorting equilibrium would seem to require, for instance, different costs of education.

Today, Kremer is doing a lot of work about replacing (or perhaps reinforcing) the patent system with rewards. That series of papers should be much more influential than they are! (Final QJE version)

“Multinationals and Anti-Sweatshop Activism,” A. Harrison and J. Scorse (2010)

In the mid-1990s, activists took aim at the low pay of sweatshop footwear and apparel producers for companies like Nike, many of which were located in Indonesia. Luckily, Indonesia requires firms to fill out an annual survey reporting wages, worker characteristics (education level, sex, etc.) and profits. Harrison and Scorse carefully consider, using difference-in-differences, whether activism raised worker wages, and further whether they decreased employment among exporting firms over the period 1990-1996. They find that export-oriented firms increased wages quicker, despite no major change in the characteristics (sex, experience, education, etc.) of their workers, and further than total employment did not fall among these firms. These results held particularly for low-paid unskilled workers, not for managers. Therefore, it appears that activism can positively affect third world wages by either reducing profits/skilled wages or by some sort of Card/Kreuger efficiency wage argument.

Despite the care of the work, I am skeptical. First, though employment does not fall by 1996, that’s a very short time horizon. By 2000, and certainly by 2010, there is a massive decline in export-oriented footwear and apparel production in Indonesia (much of which heads to places like Vietnam and Bangladesh with lower quality-adjusted wages). Second, the fact that export oriented apparel production wages rise from 1990 to 1996 while other wages do not, even if the authors note that there is no such divergence in the 1980s before sweatshop activism, can be implied by any number of other reasons; principally, other competitors for labor supply in SE Asia saw massive income gains from 1990-1996, often greater than that of Indonesia, and increasing wage and employment numbers in Indonesia may simply reflect a shift of apparel production to Indonesia from those countries. That is, wages may have risen for supply reasons that have nothing to do with activism. In general, issues like these two tend to be very hard to parse, hence my preference for natural experiments rather than straightforward regressions, even if the data is as good as it happens to be in this Indonesia example. (Link to final WP – final version in AER 2010.1)

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