Category Archives: Urban Economics

“The Determinants and Welfare Implications of US Workers’ Diverging Location Choices by Skill: 1980-2000,” R. Diamond (2012)

Rebecca Diamond, on the market from Harvard, presented this interesting paper on inequality here on Friday. As is well-known, wage inequality increased enormously from the 1970s until today, with the divergence fairly well split between higher wages at top incomes and higher incomes to higher educated workers. There was simultaneously a great amount of locational sorting: the percentage of a city’s population which is college educated ranges from 15% in the Bakersfield MSA to around 45% in Boston, San Francisco and Washington, DC. Those cities that have attracted the highly educated have also seen huge increases in rent and housing prices. So perhaps the increase in wage inequality is overstated: these lawyers and high-flying tech employees are getting paid a ton, but also living in places where a 2,000 square foot house costs a million dollars.

Diamond notes that this logic is not complete. New York City has become much more expensive, yes, but it’s crime rate has gone way down, the streets are cleaner, the number of restaurants per capita has boomed, and the presence of highly educated neighbors and coworkers is good for your own productivity in the standard urban spillover models. It may be that wage inequality is underestimated using wage alone if better amenities in cities with lots of educated workers more than compensates for the higher rents.

How to sort this out? If you read this blog, you know the answer: data alone cannot tell you. What we need is a theory of high and low education workers’ location choice and a theory of wage determination. One such theory lets you do the following. First, find a way to identify exogenous changes in labor demand for some industry in cities, which ceteris parabis will increase the wages of workers employed in that industry. Second, note that workers can choose where to work, and that in equilibrium they must receive the same utility from all cities where they could be employed. Every city has a housing supply whose elasticity differs; cities with less land available for development because of water or mountains, and cities with stricter building regulations, have less elastic housing supply. Third, the amenities of a city are endogenous to who lives there; cities with more high education workers tend to have less crime, better symphonies, more restaurants, etc., which may be valued differently by high and low education workers.

Estimating the equilibrium distribution of high and low skill workers takes care. Using an idea from a 1991 paper by Bartik, Diamond notes that some shocks hit industries nationally. For instance, a shock may hit oil production, or hit the semiconductor industry. The first shock would increase low skill labor demand in Houston or Tulsa, and the second would increase high skill labor demand in San Jose and Boston. This tells us what happens to the labor demand curve. As always, to identify the intersection of demand and supply, we also need to identify changes in labor supply. Here, different housing supply elasticity helps us. A labor demand shock in a city with elastic housing supply will cause a lot of workers to move there (since rents won’t skyrocket), with fewer workers moving if housing supply is inelastic.

Estimating the full BLP-style model shows that, in fact, we are underestimating the change in well-being inequality between high and low education workers. The model suggests, no surprise, that both types of workers prefer higher wages, lower rents, and better amenities. However, the elasticity of college worker labor supply to amenities is much higher than that of less educated workers. This means that highly educated workers are more willing to accept lower after-rent wages for a city with better amenities than a less educated worker. Also, the only way to rationalize the city choices of highly educated workers over the time examined is with endogenous amenities; if well-being depends only on wages and rents, then highly educated workers would only have moved where they ended moving if they didn’t care at all about housing prices. Looking at smaller slices of the data, immigrant workers are much more sensitive to wages: they spend less of their income on housing, and hence care much more about wages when deciding where to live. In terms of spillovers, a 1% increase in the ratio of college educated workers to other workers increases college worker productivity by a half percentage point, and less educated worker productivity by about .2 percentage points.

Backing out the implies value of amenity in each MSA, the MSAs with the best amenities for both high and low education workers include places like Los Angeles and Boston; the least desirable for both types include high-crime Rust Belt cities. Inferred productivity by worker type is very different, however. While both types of workers appear to agree on which cities have the best and worst amenities, the productivity of high skill workers is highest in places like San Jose, San Francisco and New York, whereas productivity for low skill workers is particularly high in San Bernardino, Detroit and Las Vegas. The differential changes in productivity across cities led to re-sorting of different types of workers, which led to differential changes in amenities across cities. The observed pattern of location choices by different types of workers is consistent with a greater increase in well-being between high and low education workers, even taking into account changes in housing costs, than that implied by wage alone!

The data requirements and econometric skill involved in this model is considerable, but it should allow a lot of other interesting questions in urban policy to be answered. I asked Rebecca whether she looked at the welfare impacts of housing supply restrictions. Many cities that have experienced shocks to high education labor demand are also cities with very restrictive housing policies: LA, San Francisco, Boston, DC. In the counterfactual world where DC allowed higher density building, with the same labor demand shocks we actually observed, what would have happened to wages? Or inequality? She told me she is working on a similar idea, but that the welfare impacts are actually nontrivial. More elastic housing supply will cause more workers to move to high productivity cities, which is good. On the other hand, there are spillovers: housing supply restrictions form a fence that makes a city undesirable to low education workers, and all types of workers appear to both prefer highly educated workers and the amenities they bring. Weighing the differential impact of these two effects is an interesting next step.

November 2012 working paper (No IDEAS version). Fittingly on the week James Buchanan died, Diamond also has an interesting paper on rent extraction by government workers on her website. Roughly, government workers like to pay themselves higher salaries. If they raise taxes, private sector workers move away. But when some workers move out, the remaining population gets higher wages and pays lower rents as long as labor demand slopes down and housing supply slopes up. If housing supply is very inelastic, then higher taxes lead to workers leaving lead to a large decrease in housing costs, which stops the outflow of migration. So if extractive governments are trading off higher taxes against a lower population after the increase, they will ceteris parabis have higher taxes when housing supply is less elastic. And indeed this is true in the data. Interesting!

“Are Big Cities Bad Places to Live?,” D. Albouy (2012)

Are certain cities nice places to live, “consumer cities” in the words of Ed Glaeser, or are they Dickensian, crime-filled hubs useful solely for their aggregation externalities on the production side? Economists have, no surprise, tried to solve that debate. On the one hand, if cities were so great, then why are wages so high, even for unskilled labor? Surely this is a sign that workers are being paid extra to cover the disutility they face from living in a city. On the other hand, if cities were so awful, then why is residential land so expensive? Surely, workers are only willing to pay a premium for an identical house if they enjoy its location.

Economics helps sort out the puzzle. Assume workers can take a job in any city they wish. In equilibrium, their utility, including the utility they receive from non-market quality of life factors, must equate across cities. A place with high quality of life must have relatively high local costs (such as housing) relative to the local wage, once we adjust for demographic and industry characteristics, or more workers would move there. This exercise has been done for decades now, and the results are puzzling: a 2003 estimate of quality of life by states had Wyoming and South Dakota in first and second place. This doesn’t appear to match our intuition. Further, large cities (in the sense of urban areas, not in the sense of core cities) are very strongly associated with low quality of life.

David Albouy has reestimated quality of life in places across the United States, with a few adjustments. First, he adjusts wages for the progressivity of the tax code. High-wage cities are, because of higher marginal tax rates, not so much richer than low-wage cities, hence the unobserved quality of life component of utility in high-wage cities is higher than in an estimate without this adjustment. Second, he notes that the share of spending on local goods (like housing) is higher than that used in previous estimates of quality of life. This means that previous estimates of quality of life are biased against cities with high cost-of-living. Third, he accounts for labor wages per household in a slightly more sophisticated way.

These changes make a big difference. The cities with the highest quality of life, in labor market equilibrium, roughly match your intuition: Honolulu, Santa Barbara, Monterey, San Francisco, San Luis Obispo, Santa Fe, non-metro Hawaii, Cape Cod, San Diego and rural Colorado are highest, while Decatur, IL, Beaumont, TX and Kokomo, IN bring up the rear. Hawaii, California, Vermont and Colorado have the highest QOL among states, while West Virginia, Mississippi, Michigan and Texas have the least. Most of the difference in the quality of life measure is predicted solely by measures of temperate weather, sun, coastal proximity and hilly topography. Large and dense cities, overall, have no lower quality of life than small cities or rural areas; that is, cities are no longer Dickensian, and their cultural amenities balance their congestion-based disamenities. Adjusting these figures for moving costs or heterogeneity makes the above even starker, as both adjustments imply that faster growing cities are, all else equal, higher quality of life than slower growing cities, the intuition being that with heterogeneous tastes for quality-of-life amenities, the next potential migrant values the amenities less than the migrants who have already arrived, hence wages must be higher in faster growing cities with the same quality of life as slower growing cities.

It’s not totally clear to me how we use the results of this estimation, though knowledge for its own sake is certainly worthwhile. It is a great example, though, of how we can use a bit of economic theory to answer seemingly impossible questions like “how valuable is it to live by the sea?” Answers that don’t account for labor market general equilibrium effects, or that rely on survey responses, are far less interesting to me than the type of answer Albouy provides. My strong hunch is that clever empiricists with a good bit of theoretical training can tell us much more about the value of non-market goods that we currently understand.

May 2012 Working Paper (IDEAS version). Albouy notes on his site that this is currently under review for the JPE. (I, and perhaps I alone, find it interesting how “typecast” the major journals in economics are. If you told me this particular paper was potentially coming out at a top journal and asked me to guess, I would tell you “JPE” without a second’s thought. Shall we say that Econometrica is the high theory, AER is diverse but with a recent heavy bias toward experimental in all its guises, ReStud is my favorite kind of old-school applied theory, JPE are articles that the New York Times might write about, and QJE tilts very much toward the empirical style popular at Harvard and MIT?)

“Startups by Recent University Graduates and their Faculty,” T. Astebro, N. Bazzazian & S. Braguinsky (2012)

Since the Bayh-Dole Act of 1980, universities that receive federal research funding have been encourage to patent their research and license it to private industry.  The benefits of the patenting rule are still very much in dispute, but surely everyone can agree that the spread of academic research into real firms has been a net positive?  At many universities, faculty are even explicitly encouraged to commercialize their research, through start-ups and other means.

Astebro et al point out that something is missing here, though.  Google is a university spinoff, but spun off by two PhD students at Stanford.  Facebook and Microsoft are university spinoffs, but spun off by undergraduate students.  Indeed, faculty don’t seem to be very entrepreneurial at all: the median top-100 US university in any given year has zero such spinoffs.

Using data from a (somewhat) longitudinal survey of university undergraduates, along with existing faculty spinoff data, the authors point out that students, within a few years of graduating, are twice as likely as faculty during that period to form their own business.  Since there are so many more students, this means that 24 times more startups come from recent university grads than from faculty.  And these are not low-quality startups or disguised unemployment: 36 percent of the businesses are still around in a follow-up survey two years later.  Earnings from these startups is particularly strong for those students who claim their business is in an area related to their studies, and for students at research universities in the NRC top 10 for doctoral research.  

There is very little in the way of identification in this paper, so read this as only a first go through the data. Nonetheless, the basic point is clear: if you are a region who wants to leverage universities for new business growth, developing better and more entrepreneurial students seems to trump encouraging faculty to run businesses. Indeed, to the extent that faculty create the human capital these students will use in their businesses, such faculty-biased policies may be counterproductive. The authors discuss a couple case studies, including the interesting E-school program at Chalmers in Gothenberg, Sweden, which perhaps provide a way forward here. 

A broader takeaway, which hopefully is well-known already: the link between urban policy and invention/entrepreneurship policy is ridiculously important.

http://www.andrew.cmu.edu/user/sbrag/ABB.pdf (Draft – final version in Research Policy 41.4 (2012). No IDEAS page available.)

“Information Technology and Economic Change: The Impact of the Printing Press,” J. Dittmar (2011)

There is something of a paradox when it comes to invention, particularly before the 18th century in what might be termed The Malthusian Era. We had many prominent and apparently massively influential inventions like writing, bronze, the wheel, agriculture and the gun, yet historians find little evidence of these inventions on incomes in the areas where they were invented. What might explain this? Do we simply not have good enough data to notice an effect? Is every bit of growth being swallowed up by a larger population, as in the standard Malthus explanation?

Jeremiah Dittmar considers the case of the Gutenberg press. Although printed works represented a very small segment of the 15th and 16th economy, and therefore the direct economic effects of the press are surely too small to be noticed, the indirect effects are often thought to be large. Merchants’ tables and mathematical techniques disseminated widely, literacy increased as Bibles spread in the protestant world, and atlases such as those of Blaeu were made available. Dittmar reexamines the case for economic effects of the printing press by looking at growth in city population – a good proxy for economic growth in the 1500s – in cities that had the press versus those that did not. He finds cities that got the press grew quite a bit faster than those did not. The city population data is nice in that it uses urban agglomeration data, rather than administrative boundary data; would that all urban economics handled this point properly!

There is obvious endogeneity, of course. Those cities that are booming would import a press first. To handle this issue, Dittmar runs an IV regression using distance from Mainz, Germany as an instrument. The press was invented in Mainz, and the techniques needed to build it were a secret known only to a guild in that city. For this reason, the press disseminates more or less in concentric circles from Mainz over time, but nonetheless there is some variability. The effect of press adoption before 1500 on city population growth from 1500-1600 is positive and significant. If an alternative instrument is used – say, distance from Amsterdam or Wittenberg – the effect of the press is no longer seen. Also, the cities that adopted the press, in the IV regression, grew no faster than cities that did not in the pre-1450 century.

That’s not a bad little piece of economic history. Actually, Dittmar appears to have a handful of other interesting looking urban history working papers on his website. One suggests that Zipf’s Law did not hold until the era of modern economic growth began. A second uses divergence from power law distribution of cities to identify heavily-distorted economies in the modern world, which is both a clever idea and one I wish I’d thought of first!

http://www.jeremiahdittmar.com/files/Printing-QJE-Final.pdf (Final WP – forthcoming in QJE)

“Housing Externalities,” R. Owens, E. Rossi-Hansberg, P. Sarte (2010)

“Urban renewal” has been a dominant theme of the past 60 years, and not only in the US. The basic idea is that housing values depend on the value of nearby houses, in that poorly-kept houses lower nearby house values, and well-groomed lawns do the opposite. That is, the quality of housing stock has a large externality component. Of course, the magnitude of this effect is very difficult to measure. If I renovate my home, and you – the analyst – see house prices increase in my neighborhood, there is a ton of endogeneity; perhaps I renovated because there was some secular gentrifying trend within my neighborhood already present.

The authors of this recent JPE use a unique dataset to get around this problem. In the 1990s, the city of Richmond, Virginia gave grant money to CDCs to renovate housing stock in four poor neighborhoods in that city; millions of dollars in each neighborhood were given. Of note, a fifth neighborhood was considered and left out of the program because it was in the same city council district as a neighborhood already chosen. Using over 100,000 residential house sales, the authors strip housing prices down to land value using a semiparametric technique, then investigate what happens to land prices in the city after the program ends. The result is striking: home prices very close the the center of CDC work increases 6-10% faster per year than elsewhere in the city, the effect was noticeable at a distance of half a mile. Discounting future tax revenue, the housing externalities in run-down neighborhoods near the city center were so strong as to almost pay for the renovation themselves.

(I particularly like this paper because I spent a good chunk of time when I was at the Richmond Fed working with this data. The takeaway for my own future work is that nonparametric estimation, when done smartly, can be used even with very large datasets, and that urban data (land value, crime rates, etc.) are far too “lumpy” to be captured with the simple econometric techniques often deployed in urban econ. A paper with one of the authors of this JPE that was published in a Fed journal a few years ago shows this quite plainly when it comes to land values in a metro area.

http://199.169.211.101/research/economists/bios/pdfs/sarte_housing_externalities_revisedpaper.pdf (like to NBER WP version. Final version published in JPE 118.3)

“Ability Sorting and Consumer City,” S. Lee (2010)

It is commonly noted that there is an urban wage premium, perhaps because of agglomeration effects on productivity, or to compensate (in utility terms) for higher traffic and land costs. S. Lee presents evidence that, at least in some fields, there is a negative urban wage premium for high skill workers. The story is thus: high skill workers have greater taste for variety in consumption, and cities provide such variety; if the extra utility from consumption variety grows faster than rents, and utility is equal in rural and urban areas, then urban areas could show lower wages for high skill workers. Since high skill workers are relatively cheap in cities, they will be used in firms in higher proportion than low skill workers. Data on the medical profession bears this out: hospitals use a higher ratio of doctors to nurses in cities, the doctors are paid less in real terms in cities, and the nurses are paid more in real terms in cities than in rural areas. Further, these urban doctors tend to work in higher skill specialties, and be graduates of higher ranked medical schools, than higher paid rural doctors. This is yet more evidence of the consumer-driven rationale for cities: production is not everything.

I should note, however, that the data in the paper violates my major urban econ pet peeve by using MSA level data. Look at a map of MSAs in the US: they bear very little resemblance to what we might call “cities”, and it’s tough to argue that a worker 50 miles from the city center is somehow taking lower wages in exchange for urban amenities. I understand that a lot of data is given at the MSA level, but we really should be focusing on tighter definitions; my preference is the Census “urbanized area”.

http://strategy.sauder.ubc.ca/lee/papers/ability_sorting_and_consumer_city.pdf (Final WP – published version in JUrbE 68 (2010))

“The New Economic Geography, now Middle Aged,” P. Krugman (2010)

In this recent lecture to the Association of American Geographers, Krugman offers a brief defense of a mathematical model investigation of geography (as opposed to the “discursive” atheoretical style in vogue among non-economist geographers today), and counters the criticism that New Economic Geography is irrelevant to the world today. While NEG generates its main results through transport costs, and does a good job of explaining industrial clusters, the US and Europe do not seem to be defined by industrial clusters as much today as in the past (“Pittsburgh was a steel city, Atlanta today is a…what?”). To explain modern cities, information seems to be the critical element (in addition to consumer cities a la Glaeser). Nonetheless, traditional NEG does do a good job explaining the developing world, such as China; this is unsurprising, since economically these countries look a lot like the US in 1900, which NEG was basically invented to explain.

http://www.princeton.edu/~pkrugman/aag.pdf

“High Speed Rail: Lessons for Policy Makers from Experiences Abroad,” D. Albalate & G. Bel (2010)

The US is considering the construction of high speed rail corridors similar to those in place in France, Germany, Japan, Italy and, most recently, China, whose fastest current route (Guangzhou-Wuhan) covers 700 miles in about 3 hours. What can we learn? First, only France runs a profitable (in the sense of social welfare) system at present, the economic impacts of HSR are fairly limited (though difficult to measure), the environmental impact of HSR is limited (it produced similar CO2 to cars), and HSR optimally connects regions at distances between 100 to 500 miles that will see over 8 million trips per year. Also, cities with poor internal public transit, or whose rail stations are outside the city center, see less benefit from HSR. Given that around 1/3 to 1/2 of air travel is replaced by HSR after it is introduced (as well as limited amounts of car travel), California and Washington DC to Boston via NY would seem to be viable candidates for profitable high speed rail.

http://www.ub.edu/irea/working_papers/2010/201003.pdf

“How Transport Costs Shape the Spatial Pattern of Economic Activity,” J-F. Thisse (2009)

A brief review of the literature on how changes in transport costs can change the spatial makeup of economies. Consider a world with positive transport costs between regions, and constant returns to scale technology: there is no reason for trade, since transport is costly, and everything can be produced at the same cost in each region. Therefore, each household will be an autarky. To generate cities and trade, then, we need some sort of increasing returns to scale in production, whether than be directly in the factor, through some aggregation of knowledge, or whatever. Given that premise, nonobvious results are widespread in this theory. For instance, imagine a poor and a rich country decrease the transport costs between the two (i.e., the EU builds a railroad from Germany to Poland). In a spatial model, this will generally lead to more aggregation of high-value production in the wealthier country, due to greater efficiency in the high-value sector resulting from increasing returns to scale.

Clearly, production is not the only reason cities exist – Glaeser, among others, has pointed out that consumption externalities, like access to opera, exist as well – but nonetheless understanding the economics of cities in a world of declining transport and communication prices is critical for current urban policy.

http://www.internationaltransportforum.org/jtrc/DiscussionPapers/DP200913.pdf

“Causes of Sprawl: A Portrait from Space,” M. Burchfield et al (2006)

Using 30 meter by 30 meter satellite and high altitude photography from 1976 and 1992, the authors study the nature of sprawl in the United States by exactly noting what parts of the US are built up or paved. They note that only 1.9% of the continental US is built up or paved, that even within urban areas there is enormous amounts of open space, and that, by one definition, the percent of built up land around a given house in an urban area has not increased from 1976 to 1992, since the new housing built on the urban fringe is evenly weighed when infill development increases the density of existing houses in already built up areas. The authors regress a number of explanatory variables on their density measure, and find that, among other things, geography (the presence of mountains, the ruggedness of the terrain, and the existence of aquifers suitable for wells) cannot be ignored. There is also a citation-rich summary of the extant models of monocentric and polycentric cities.

(As an aside, some claims in this paper should, as the authors note, be taken with a grain of salt; this gives me a good excuse to link to a 2007 paper of my own on density in the US. The “Sprawl from Space” paper suggests that sprawl has not increased from 1976 to 1992, and second, that counter-intuitively, western cities like SF and Phoenix are not particularly sprawling. Our paper, on the contrary, showed declining population density over each decade, over any measure of a city (municipal, urban area, MSA, etc.) you want, and further pointed out that a quirk in the definition of “metropolitan area” means that MSAs are totally unsuitable for work on density. Some rectification on the population density claim results from the fact that sprawl appears to have been much quicker in the 50s-70s than during the 80s.)

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.139.6796&rep=rep1&type=pdf

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