“The Gift of Moving: Intergenerational Consequences of a Mobility Shock,” E. Nakamura, J. Sigurdsson & J. Steinsson (2016)

The past decade has seen interesting work in many fields of economics on the importance of misallocation for economic outcomes. Hsieh and Klenow’s famous 2009 paper suggested that misallocation of labor and capital in the developing world costs countries like China and India the equivalent of many years of growth. The same two authors have a new paper with Erik Hurst and Chad Jones suggesting that a substantial portion of the growth in the US since 1960 has been via better allocation of workers. In 1960, they note, 94 percent of doctors and lawyers were white men, versus 62 percent today, and we have no reason to believe the innate talent distribution in those fields had changed. Therefore, there were large numbers of women and minorities who would have been talented enough to work in these high-value fields in 1960, but due to misallocation (including in terms of who is educated) did not. Lucia Foster, John Haltiwanger and Chad Syverson have a famous paper in the AER on how to think about reallocation within industries, and the extent to which competition reallocates production from less efficient to more efficient producers; this is important because it is by now well-established that there is an enormous range of productivity within each industry, and hence potentially enormous efficiency gains from proper reallocation away from low-productivity producers.

The really intriguing misallocation question, though, is misallocation of workers across space. Some places are very productive, and others are not. Why don’t workers move? Part of the explanation, particularly in the past few decades, is that due to increasing land use regulation, local changes in total factor productivity increase housing costs, meaning that only high skilled workers gain much by mobility in response to shocks (see, e.g., Ganong and Shoag on the direct question of who benefits from moving, and Hornbeck and Moretti on the effects of productivity shocks on rents and incomes).

A second explanation is that people, quite naturally, value their community. They value their community both because they have friends and often family in the area, and also because they make investments in skills that are well-matched to where they live. For this reason, even if Town A is 10% more productive for the average blue-collar worker, a particular worker in Town B may be reluctant to move if it means giving up community connections or trying to relearn a different skill. This effect appears to be important particularly for people whose original community is low productivity: Deyrugina, Kawano and Levitt showed how those induced out of poor areas of New Orleans by Hurricane Katrina would up with higher wages than those whose neighborhoods were not flooded, and (the well-surnamed) Bryan, Chowdhury and Mobarak find large gains in income when they induce poor rural Bangladeshis to temporarily move to cities.

Today’s paper, by Nakamura et al, is interesting because it shows these beneficial effects of being forced out of one’s traditional community can hold even if the community is rich. The authors look at the impact of the 1973 volcanic eruption which destroyed a large portion of the main town, a large fishing village, on Iceland’s Westman Islands. Though the town had only 5200 residents, this actually makes it large by Icelandic standards: even today, there is only one town on the whole island which is both larger than that and located more than 45 minutes drive from the capital. Further, though the town is a fishing village, it was then and is now quite prosperous due to its harbor, a rarity in Southern Iceland. Residents whose houses were destroyed were compensated by the government, and could have either rebuilt on the island or moved away: those with destroyed houses wind up 15 percentage points more likely to move away than islanders whose houses remained intact.

So what happened? If you were a kid when your family moved away, the instrumental variables estimation suggests you got an average of 3.6 more years of schooling and mid-career earnings roughly 30,000 dollars higher than if you’d remained! Adults who left saw, if anything, a slight decrease in their lifetime earnings. Remember that the Westman Islands were and are wealthier than the rest of Iceland, so moving would really only benefit those whose dynasties had comparative advantage in fields other than fishing. In particular, parents with college educations were more likely to be move, conditional on their house being destroyed, than those without. So why did those parents need to be induced by the volcano to pack up? The authors suggest some inability to bargain as a household (the kids benefited, but not the adults), as well as uncertainty (naturally, whether moving would increase kids’ wages forty years later may have been unclear). From the perspective of a choice model, however, the outcome doesn’t seem unusual: parents, due to their community connections and occupational choice, would have considered moving very costly, even if they knew it was in their kid’s best long-term interests.

There is a lesson in the Iceland experience, as well as in the Katrina papers and other similar results: economic policy should focus on people, and not communities. Encouraging closer community ties, for instance, can make reallocation more difficult, and can therefore increase long-run poverty, by increasing the subjective cost of moving. When we ask how to handle long-run poverty in Appalachia, perhaps the answer is to provide assistance for groups who want to move, therefore gaining the benefit of reallocation across space while lessening the perceived cost of moving (my favorite example of clustered moves is that roughly 5% of the world’s Marshall Islanders now live in Springdale, Arkansas!). Likewise, limits on the movement of parolees across states can entrench poverty at precisely the time the parolee likely has the lowest moving costs.

June 2016 Working Paper (No RePEc IDEAS version yet).

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“Aggregation in Production Functions: What Applied Economists Should Know,” J. Felipe & F. Fisher (2003)

Consider a firm that takes heterogeneous labor and capital inputs L1, L2… and K1, K2…, using these to produce some output Y. Define a firm production function Y=F(K1, K2…, L1, L2…) as the maximal output that can be produced using the given vector of outputs – and note the implicit optimization condition in that definition, which means that production functions are not simply technical relationships. What conditions are required to construct an aggregated production function Y=F(K,L), or more broadly to aggregate across firms an economy-wide production function Y=F(K,L)? Note that the question is not about the definition of capital per se, since defining “labor” is equally problematic when man-hours are clearly heterogeneous, and this question is also not about the more general capital controversy worries, like reswitching (see Samuelson’s champagne example) or the dependence of the return to capital on the distribution of income which, itself, depends on the return to capital.

(A brief aside: on that last worry, why the Cambridge UK types and their modern day followers are so worried about the circularity of the definition of the interest rate, yet so unconcerned about the exact same property of the object we call “wage”, is quite strange to me, since surely if wages equal marginal product, and marginal product in dollars is a function of aggregate demand, and aggregate demand is a function of the budget constraint determined by wages, we are in an identical philosophical situation. I think it’s pretty clear that the focus on “r” rather than “w” is because of the moral implications of capitalists “earning their marginal product” which are less than desirable for people of a certain political persuasion. But I digress; let’s return to more technical concerns.)

It turns out, and this should be fairly well-known, that the conditions under which factors can be aggregated are ridiculously stringent. If we literally want to add up K or L when firms use different production functions, the condition (due to Leontief) is that the marginal rate of substitution between different types of factors in one aggregation, e.g. capital, does not depend on the level of factors not in that aggregation, e.g. labor. Surely this is a condition that rarely holds: how much I want to use, in an example due to Solow, different types of trucks will depend on how much labor I have at hand. A follow-up by Nataf in the 1940s is even more discouraging. Assume every firm uses homogenous labor, every firm uses capital which though homogenous within each firms differs across firms, and every firm has identical constant returns to scale production technology. When can I now write an aggregate production function Y=F(K,L) summing up the capital in each firm K1, K2…? That aggregate function exists if and only if every firm’s production function is additively separable in capital and labor (in which case, the aggregation function is pretty obvious)! Pretty stringent, indeed.

Fisher helps things just a bit in a pair of papers from the 1960s. Essentially, he points out that we don’t want to aggregate for all vectors K and L, but rather we need to remember that production functions measure the maximum output possible when all inputs are used most efficiently. Competitive factor markets guarantee that this assumption will hold in equilibrium. That said, even assuming only one type of labor, efficient factor markets, and a constant returns to scale production function, aggregation is possible if and only if every firm has the same production function Y=F(b(v)K(v),L), where v denotes a given firm and b(v) is a measure of how efficiently capital is employed in that firm. That is, aside from capital efficiency, every firm’s production function must be identical if we want to construct an aggregate production function. This is somewhat better than Nataf’s result, but still seems highly unlikely across a sector (to say nothing of an economy!).

Why, then, do empirical exercises using, say, aggregate Cobb-Douglas seem to give such reasonable parameters, even though the above theoretical results suggest that parameters like “aggregate elasticity of substitution between labor and capital” don’t even exist? That is, when we estimate elasticities or total factor productivities from Y=AK^a*L^b, using some measure of aggregated capital, what are we even estimating? Two things. First, Nelson and Winter in their seminal book generate aggregate date which can almost perfectly be fitted using Cobb-Douglas even though their model is completely evolutionary and does not even involve maximizing behavior by firms, so the existence of a “good fit” alone is, and this should go without saying, not great evidence in support of a model. Second, since ex-post production Y must equal the wage bill plus the capital payments plus profits, Felipe notes that this identity can be algebraically manipulated to Y=AF(K,L) where the form of F depends on the nature of the factor shares. That is, the good fit of Cobb-Douglas or CES can simply reflect an accounting identity even when nothing is known about micro-level elasticities or similar.

So what to do? I am not totally convinced we should throw out aggregate production functions – it surely isn’t a coincidence that Solow residuals for TFP match are estimated to be high in places where our intuition says technological change has been rapid. Because of results like this, it doesn’t strike me that aggregate production functions are measuring arbitrary things. However, if we are using parameters from these functions to do counterfactual analysis, we really ought know better exactly what approximations or assumptions are being baked into the cake, and it doesn’t seem that we are quite there yet. Until we are, a great deal of care should be taken in assigning interpretations to estimates based on aggregate production models. I’d be grateful for any pointers in the comments to recent work on this problem.

Final published version (RePEc IDEAS. The “F. Fisher” on this paper is the former Clark Medal winner and well-known IO economist Franklin Fisher; rare is it to find a nice discussion of capital issues written by someone who is firmly part of the economics mainstream and completely aware of the major theoretical results from “both Cambridges”. Tip of the cap to Cosma Shalizi for pointing out this paper.

Debraj Ray on Piketty’s Capital

As mentioned by Sandeep Baliga over at Cheap Talk, Debraj Ray has a particularly interesting new essay on Piketty’s Capital in the 21st Century. If you are theoretically inclined, you will find Ray’s comments to be one of the few reviews of Piketty that proves insightful.

I have little to add to Ray, but here are four comments about Piketty’s book:

1) The data collection effort on inequality by Piketty and coauthors is incredible and supremely interesting; not for nothing does Saez-Piketty 2003 have almost 2000 citations. Much of this data can be found in previous articles, of course, but it is useful to have it all in one place. Why it took so long for this data to become public, compared to things like GDP measures, is an interesting one which sociology Dan Hirschman is currently working on. Incidentally, the data quality complaints by the Financial Times seem to me of rather limited importance to the overall story.

2) The idea that Piketty is some sort of outsider, as many in the media want to make him out to be, is very strange. His first job was at literally the best mainstream economics department in the entire world, he won the prize given to the best young economist in Europe, he has published a paper in a Top 5 economics journal every other year since 1995, his most frequent coauthor is at another top mainstream department, and that coauthor himself won the prize for the best young economist in the US. It is also simply not true that economists only started caring about inequality after the 2008 financial crisis; rather, Autor and others were writing on inequality well before date in response to clearer evidence that the “Great Compression” of the income distribution in the developed world during the middle of the 20th century had begun to reverse itself sometime in the 1970s. Even I coauthored a review of income inequality data in late 2006/early 2007!

3) As Ray points out quite clearly, the famous “r>g” of Piketty’s book is not an explanation for rising inequality. There are lots of standard growth models – indeed, all standard growth models that satisfy dynamic efficiency – where r>g holds with no impact on the income distribution. Ray gives the Harrod model: let output be produced solely by capital, and let the capital-output ratio be constant. Then Y=r*K, where r is the return to capital net of depreciation, or the capital-output ratio K/Y=1/r. Now savings in excess of that necessary to replace depreciated assets is K(t+1)-K(t), or

Y(t+1)[K(t+1)/Y(t+1)] – Y(t)[K(t)/Y(t)]

Holding the capital-output ratio constant, we have that savings s=[Y(t+1)-Y(t)]K/Y=g[K/Y], where g is the growth rate of the economy. Finally, since K/Y=1/r in the Harrod model, we have that s=g/r, and hence r>g will hold in a Harrod model whenever the savings rate is less than 100% of current income. This model, however, has nothing to do with the distribution of income. Ray notes that the Phelps-Koopmans theorem implies that a similar r>g result will hold along any dynamically efficient growth path in much more general models.

You may wonder, then, how we can have r>g and yet not have exploding income held by the capital-owning class. Two reasons: first, as Piketty has pointed out, r in these economic models (the return to capital, full stop) and r in the sense important to growing inequality, are not the same concept, since wars and taxes lower the r received by savers. Second, individuals presumably also dissave according to some maximization concept. Imagine an individual has $1 billion, the risk-free market return after taxes is 4%, and the economy-wide growth rate is 2%, with both numbers exogenously holding forever. It is of course true true that this individual could increase their share of the economy’s wealth without bound. Even with the caveat that as the capital-owning class owns more and more, surely the portion of r due to time preference, and hence r itself, will decline, we still oughtn’t conclude that income inequality will become worse or that capital income will increase. If this representative rich individual simply consumes 1.92% of their income each year – a savings rate of over 98 percent! – the ratio of income among the idle rich to national income will remain constant. What’s worse, if some of the savings is directed to human capital rather than physical capital, as is clearly true for the children of the rich in the US, the ratio of capital income to overall income will be even less likely to grow.

These last couple paragraphs are simply an extended argument that r>g is not a “Law” that says something about inequality, but rather a starting point for theoretical investigation. I am not sure why Piketty does not want to do this type of investigation himself, but the book would have been better had he done so.

4) What, then, does all this mean about the nature of inequality in the future? Ray suggests an additional law: that there is a long-run tendency for capital to replace labor. This is certainly true, particularly if human capital is counted as a form of “capital”. I disagree with Ray about the implication of this fact, however. He suggests that “to avoid the ever widening capital-labor inequality as we lurch towards an automated world, all its inhabitants must ultimately own shares of physical capital.” Consider the 19th century as a counterexample. There was enormous technical progress in agriculture. If you wanted a dynasty that would be rich in 2014, ought you have invested in agricultural land? Surely not. There has been enormous technical progress in RAM chips and hard drives in the last couple decades. Is the capital related to those industries where you ought to have invested? No. With rapid technical progress in a given sector, the share of total income generated by that sector tends to fall (see Baumol). Even when the share of total income is high, the social surplus of technical progress is shared among various groups according to the old Ricardian rule: rents accrue to the (relatively) fixed factor! Human capital which is complementary to automation, or goods which can maintain a partial monopoly in an industry complementary to those affected by automation, are much likelier sources of riches than owning a bunch of robots, since robots and the like are replicable and hence the rents accrued to their owners, regardless of the social import, will be small.

There is still a lot of work to be done concerning the drivers of long-run inequality, by economists and by those more concerned with political economy and sociology. Piketty’s data, no question, is wonderful. Ray is correct that the so-called Laws in Piketty’s book, and the predictions about the next few decades that they generate, are of less interest.

A Comment on Thomas Piketty, inclusive of appendix, is in pdf form, or a modified version in html can be read here.

On Gary Becker

Gary Becker, as you must surely know by now, has passed away. This is an incredible string of bad luck for the University of Chicago. With Coase and Fogel having passed recently, and Director, Stigler and Friedman dying a number of years ago, perhaps Lucas and Heckman are the only remaining giants from Chicago’s Golden Age.

Becker is of course known for using economic methods – by which I mean constrained rational choice – to expand economics beyond questions of pure wealth and prices to question of interest to social science at large. But this contribution is too broad, and he was certainly not the only one pushing such an expansion; the Chicago Law School clearly was doing the same. For an economist, Becker’s principal contribution can be summarized very simply: individuals and households are producers as well as consumers, and rational decisions in production are as interesting to analyze as rational decisions in consumption. As firms must purchase capital to realize their productive potential, humans much purchase human capital to improve their own possible utilities. As firms take actions today which alter constraints tomorrow, so do humans. These may seem to be trite statements, but that are absolutely not: human capital, and dynamic optimization of fixed preferences, offer a radical framework for understanding everything from topics close to Becker’s heart, like educational differences across cultures or the nature of addiction, to the great questions of economics like how the world was able to break free from the dreadful Malthusian constraint.

Today, the fact that labor can augment itself with education is taken for granted, which is a huge shift in how economists think about production. Becker, in his Nobel Prize speech: “Human capital is so uncontroversial nowadays that it may be difficult to appreciate the hostility in the 1950s and 1960s toward the approach that went with the term. The very concept of human capital was alleged to be demeaning because it treated people as machines. To approach schooling as an investment rather than a cultural experience was considered unfeeling and extremely narrow. As a result, I hesitated a long time before deciding to call my book Human Capital, and hedged the risk by using a long subtitle. Only gradually did economists, let alone others, accept the concept of human capital as a valuable tool in the analysis of various economic and social issues.”

What do we gain by considering the problem of human capital investment within the household? A huge amount! By using human capital along with economic concepts like “equilibrium” and “private information about types”, we can answer questions like the following. Does racial discrimination wholly reflect differences in tastes? (No – because of statistical discrimination, underinvestment in human capital by groups that suffer discrimination can be self-fulfilling, and, as in Becker’s original discrimination work, different types of industrial organization magnify or ameliorate tastes for discrimination in different ways.) Is the difference between men and women in traditional labor roles a biological matter? (Not necessarily – with gains to specialization, even very small biological differences can generate very large behavioral differences.) What explains many of the strange features of labor markets, such as jobs with long tenure, firm boundaries, etc.? (Firm-specific human capital requires investment, and following that investment there can be scope for hold-up in a world without complete contracts.) The parenthetical explanations in this paragraph require completely different policy responses from previous, more naive explanations of the phenomena at play.

Personally, I find human capital most interesting in understanding the Malthusian world. Malthus conjectured the following: as productivity improved for some reason, excess food will appear. With excess food, people will have more children and population will grow, necessitating even more food. To generate more food, people will begin farming marginal land, until we wind up with precisely the living standards per capita that prevailed before the productivity improvement. We know, by looking out our windows, that the world in 2014 has broken free from Malthus’ dire calculus. But how? The critical factors must be that as productivity improves, population does not grow, or else grows slower than the continued endogenous increases in productivity. Why might that be? The quantity-quality tradeoff. A productivity improvement generates surplus, leading to demand for non-agricultural goods. Increased human capital generates more productivity on those goods. Parents have fewer kids but invest more heavily in their human capital so that they can work in the new sector. Such substitution is only partial, so in order to get wealthy, we need a big initial productivity improvement to generate demand for the goods in the new sector. And thus Malthus is defeated by knowledge.

Finally, a brief word on the origin of human capital. The idea that people take deliberate and costly actions to improve their productivity, and that formal study of this object may be useful, is modern: Mincer and Schultz in the 1950s, and then Becker with his 1962 article and famous 1964 book. That said, economists (to the chagrin of some other social scientists!) have treated humans as a type of capital for much longer. A fascinating 1966 JPE [gated] traces this early history. Petty, Smith, Senior, Mill, von Thunen: they all thought an accounting of national wealth required accounting for the productive value of the people within the nation, and 19th century economists frequently mention that parents invest in their children. These early economists made such claims knowing they were controversial; Walras clarifies that in pure theory “it is proper to abstract completely from considerations of justice and practical expediency” and to regard human beings “exclusively from the point of view of value in exchange.” That is, don’t think we are imagining humans as being nothing other than machines for production; rather, human capital is just a useful concept when discussing topics like national wealth. Becker, unlike the caricature where he is the arch-neoliberal, was absolutely not the first to “dehumanize” people by rationalizing decisions like marriage or education in a cost-benefit framework; rather, he is great because he was the first to show how powerful an analytical concept such dehumanization could be!

“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)

“How Does Family Income Affect Enrollment?,” N. Hilger (2012)

Nate Hilger is on the market from Harvard this year. His job market paper continues a long line of inference that is probably at odds with mainstream political intuition. Roughly, economists generally support cash rather than in-kind transfers because people tend to be the best judges of the optimal use of money they receive; food stamps are not so useful if you really need to pay the heat bill that week. That said, if the goal is to cause some behavior change among the recipient, in-kind transfers can be more beneficial, especially when the cash transfer would go to a family while the in-kind transfer would go to a child or a wife.

Hilger managed to get his hands on the full universe of IRS data. I’m told by my empirically-minded friends that this data is something of a holy grail, with the IRS really limiting who can use the data after Saez proved its usefulness. IRS data is great because of the 1098T: colleges are required to file information about their students’ college attendance so that the government can appropriately dole out aid and tax credits. Even better, firms that fire or layoff workers file a 1099G. Finally, claimed dependents on the individual tax form let us link parents and children. That’s quite a trove of data!

Here’s a question we can answer with it: does low household income lower college attendance, and would income transfers to poor families help reduce the college attendance gap? In a world with perfect credit markets, it shouldn’t matter, since any student could pledge the human capital she would gain as collateral for a college attendance loan. Of course, pledging one’s human capital turns out to be quite difficult. Even if the loans aren’t there, a well-functioning and comprehensive university aid program should insulate the poor from this type of liquidity problem. Now, we know from previous studies that increased financial aid has a pretty big effect on college attendance among the poor and lower middle class. Is this because the aid is helping loosen the family liquidity constraint?

Hilger uses the following trick. Consider a worker who is laid off. This is only a temporary shock, but this paper and others estimate a layoff lowers discounted lifetime earnings by an average of nearly $100,000. So can we just propensity match laid off and employed workers when the child is college age, and see if the income shock lowers attendance? Not so fast. It turns out that matching on whatever observables we have, children whose fathers are laid off when the child is 19 are also much less likely to attend college than children whose fathers are not laid off, even though age 19 would be after the attendance decision is made. Roughly, a father who is ever laid off is correlated with some nonobservables that lower college attendance of children. So let’s compare children whose dads are laid off at 17 to children whose dads are laid off from a similar firm at age 19, matching on all other observables. The IRS data has so many data points that this is actually possible.

What do we learn? First, consumption (in this case, on housing) spending declines roughly in line with the lifetime income hypothesis after the income shock. Second, there is hardly any effect on college attendance and quality: attendance for children whose dads suffer the large income shock falls by half a percentage point. Further, the decline is almost entirely borne by middle class children, not the very poor or the rich: this makes sense since poor students rely very little on parental funding to pay for college, and the rich have enough assets to overcome any liquidity shock. The quality of college chosen also declines after a layoff, but only by a very small amount. That is, the Engel curve for college spending is very flat: families with more income tend to spend roughly similar amounts on college.

Policy-wise, what does this mean? Other authors have estimated that a $1000 increase in annual financial aid increases college enrollment by approximately three percentage points (a particularly strong effect is found among students from impoverished families); the Kalamazoo experiment shows positive feedback loops that many make the efficacy of such aid even higher, since students will exert more effort in high school knowing that college is a realistic financial possibility. Hilger’s paper shows that a $1000 cash grant to poor families will likely improve college attendance by .007 to .04 percentage points depending on whether the layoff is lowering college attendance due to a transitory or a permanent income shock. That is, financial aid is orders of magnitude more useful in raising college attendance than cash transfers, especially among the poor.

November 2012 working paper (No IDEAS version). My old Federal Reserve coworker Christopher Herrington is also on the job market, and has a result suggesting the importance of Hilger’s finding. He computes a DSGE model of lifetime human capital formation, and considers the counterfactual where the US has more equal education funding (that is, schools that centrally funded rather than well-funded in rich areas and poorly-funded in poor areas). Around 15% of eventual earnings inequality – again taking into account many general equilibrium effects – can be explained by the high variance of US education funding. As in Hilger, directly altering the requirement that parents pay for school (either through direct payments at the university level, or by purchasing housing in rich areas at the primary level) can cure a good portion of our growing inequality.