Author Archive

Key parameters for Brexit forecasts

15 July 2016

The NBER Summer Institute hosted a panel discussion of Brexit on Tuesday. Richard Baldwin, Thomas Sampson, Helene Rey, and Anil Kashyap spoke about the consequences of Brexit for the European project, trade policy, macroeconomic growth, and London as a financial hub. I won’t try to summarize the discussion. The NBER should post video of the panel soon, and you can also learn their views from Baldwin’s twitter feed, Kashyap’s 538 piece, and Sampson’s CEP chapters.

I want to highlight three parameters that are key to forecasting Brexit’s economic consequences. They are (1) the size of the non-tariff barriers eliminated by the EU as a customs union, (2) the elasticity of real income with respect to trade, and (3) the strength of agglomeration economies in finance.

Non-tariff barriers are important because rich countries’ import tariffs are quite low. The two potential UK trading regimes people are most frequently discussing are a “Norway option” and a “WTO option“. Under the Norway option, the UK would have tariff-free access to EU markets via the EEA, but face non-tariff barriers due to leaving the customs union (e.g. rules of origin requirements and anti-dumping duties). Under the WTO option, the UK would face the EU’s MFN tariff schedule (only a few percentage points, on average) and a much wider array of non-tariff barriers due to the EU being far ahead of the WTO in reducing and/or harmonizing behind-the-border barriers and regulations.

Non-tariff barriers presumably don’t look like the iceberg trade costs frequently employed in quantitative trade models. You can find some estimates of these meaures, but they don’t seem to receive academic attention proportionate to their relevance for policy concerns like Brexit.

In contrast, the second key parameter, the elasticity of real income with respect to trade, has received considerable academic attention. However, there is not yet consensus regarding its value. In their CEP chapter, Dhingra, Ottaviano, Sampson and van Reenen review different paths one might take.

Using the class of quantitative trade models that yield the ACR formula, they estimate Brexit losses on the order of 1.3% to 2.6%. Given that the United Kingdom’s total gains from trade (relative to autarky) range from 3% to 23% in Table 4.1 of Costinot and Rodriguez-Clare’s Handbook chapter, this methodology necessarily produces numbers of this magnitude.

An alternative approach is to use reduced-form estimates of how trade changes income, presumably on the premise that there are important channels (such as dynamic effects) that are omitted from the standard quantitative models. Ed Prescott, for example, holds this view. Using Jim Feyrer’s air-vs-sea paper, which estimates that the elasticity of income with respect to trade is about one half, the CEP team infers that Brexit would reduce UK income by 6.3% to 9.5%.

The effect of trade on income is obviously important, and I expect that trade economists will always be investigating this question. At the moment, plausible predictions of Brexit-induced trade losses range widely, from 1% to 10%.

The third parameter of interest is the strength of agglomeration economies in finance. Brexit is going to reduce the role of London as a financial center, as EU-specific activities migrate to the continent. The question is whether non-EU-specific activities will follow. How complementary are these different types of financial services and how large are the relevant scale economies? I think this is an open question in urban economics, in the sense that we don’t entirely understand why the US financial sector is so concentrated in New York City. Suddenly, this has become a crucial question for London in the context of Brexit. Anil Kashyap has stressed that the financial services industry generates 11% of UK tax revenue, so strong agglomeration economies that imply an unraveling of the City of London would mean a big budget problem for the UK government.

The volatility and uncertainty of the unfolding political process makes forecasting Brexit’s consequences virtually impossible, but these are three parameters that are important to thinking about the relevant economic mechanisms.

Trade JMPs (2015-2016)

7 November 2015

It’s already November, which means it’s job-market season once again. Who’s on the market in trade? As I have for the last five years, I focus on trade papers, thereby neglecting international finance and open-economy macro papers. If I’ve missed someone, please contribute to the list in the comments.

Also, candidates should sign up at Jon Haveman’s full-featured database of trade candidates with candidate-created profiles.

Where are the jobs? Don’t look too closely

31 July 2015

Robert Manduca, a Harvard sociology PhD student, has put together a nice visualization of employment data that he titled “Where Are the Jobs?” It’s a great map, modeled after the very popular dot map of US residents by ethnicity. The underlying data come from the Longitudinal Employer-Household Dynamics (LEHD) program, which is a fantastic resource for economics researchers.

wherearethejobs

Since every job is represented by a distinct dot, it’s very tempting to zoom in and look at the micro detail of the employment geography. Vox’s Matt Yglesias explored the map by highlighting and contrasting places like Chicago and Silicon Valley. Emily Badger similarly marveled at the incredible detail.

Unfortunately, at this super-fine geographical resolution, some of the data-collection details start to matter. The LEHD is based on state unemployment insurance (UI) program records and therefore depends on how state offices reporting the data assign employees to business locations. When an employer operates multiple establishments (an establishment is “a single physical location where business transactions take place or services are performed”), state UI records don’t identify the establishment-level geography:

A primary objective of the QWI is to provide employment, job and worker flows, and wage measures at a very detailed levels of geography (place-of-work) and industry. The structure of the administrative data received by LEHD from state partners, however, poses a challenge to achieving this goal. QWI measures are primarily based on the processing of UI wage records which report, with the exception of Minnesota, only the employing employer (SEIN) of workers… However, approximately 30 to 40 percent of state-level employment is concentrated in employers that operate more than one establishment in that state. For these multi-unit employers, the SEIN on workers’ wage records identifies the employing employer in the ES-202 data, but not the employing establishment… In order to impute establishment-level characteristics to job histories of multi-unit employers, non-ignorable missing data model with multiple imputation was developed.

These are challenging data constraints. I have little idea how to evaluate the imputation procedures. These things are necessarily imperfect. Let me just mention one outlier as a way of illustrating some limitations of the data underlying the dots.

Census block 360470009001004 (that’s a FIPS code; “36” is New York “36047” is Kings County, and so forth) is in Brooklyn, between Court St and Adams St and between Livingston St and Joralemon St. The Borough Hall metro station is on the northern edge of the block. (Find it on the Census Block maps here). A glance at Google Maps shows that this block is home to the Brooklyn Municipal Building, Brooklyn Law School, and a couple other buildings.

brooklynbridge

360470009001004

What’s special about census block 360470009001004 is that it supposedly hosted 174,000 jobs in 2010, according to the LEHD Origin-Destination Employment Statistics (ny_wac_S000_JT01_2010.csv). This caught my eye because it’s the highest level in New York and really, really high. The other ten census blocks contained in the same census tract (36047000900) have less than 15,000 jobs collectively. This would be a startling geographic discontinuity in employment density. The census block with the second highest level of employment in the entire state of New York has only 48,431 employees.

A glance at the Brooklyn Municipal Building shows that it’s big, but it sure doesn’t make it look like a place with 174,000 employees.

municipalbuilding

And other data sources that do report employment levels by establishment (rather than state employer identification number) show that there aren’t 174,000 jobs on this block. County Business Patterns, a data set that is gathered at the establishment level, reports that total paid employment in March 2010 in ZIP code 11201, which contains this census block and many others,  was only 52,261. Looking at industries, the LODES data report that 171,000 of the block’s 174,000 jobs in 2010 were in NAICS sector 61 (educational services). Meanwhile, County Business Patterns shows only 28,117 paid employees in NAICS 61 for all of Brooklyn (Kings County) in 2010. I don’t know the details of how the state UI records were reported or the geographic assignments were imputed, but clearly many jobs are being assigned to this census block, far more than could plausibly be actually at this geographic location.

So you need to be careful when you zoom in. Robert Manduca’s map happens to not be too bad in this regard, because he limits the geographic resolution such that you can’t really get down to the block level. If you look carefully at the image at the top of this post and orient yourself using the second image, you can spot the cluster of “healthcare, education, and government” jobs on this block near Borough Hall just below Columbus Park and Cadman Plaza Park, which are jobless areas. But with 171,000 dots on such a tiny area, it’s totally saturated, and its nature as a massive outlier isn’t really visible. In more sparsely populated parts of the country, where census blocks are physically larger areas, these sorts of problems might be visually evident.

“Where Are The Jobs?” is an awesome mapping effort. It reveals lots of interesting information; it is indeed “fascinating” and contains “incredible detail“. We can learn a lot from it. The caveat is that the underlying data, like every other data source on earth, have some assumptions and shortcomings that make them imperfect when you look very, very closely.

P.S. That second-highest-employment block in New York state? It’s 360470011001002, across the street from the block in question. With 45,199 jobs in NAICS sector 48-49, Transportation and Warehousing. But all of Kings County reported only 18,228 employees in NAICS 48 in 2010 in the County Business Patterns data.

The NAFTA trucking dispute in 2015

8 May 2015

I’ve been blogging the NAFTA trucking dispute since 2008. Under NAFTA, Mexican trucks were supposed to be able to operate in Texas, California, New Mexico, and Arizona by December 1995. That never happened, and it took a dozen years for a pilot program to be started, only to be aborted in 2009. Another pilot took place 2011-2014.

Nearly twenty years late, the US government may be close to fulfilling its NAFTA obligations. In January 2015, the US DOT announced “that Mexican motor carriers will soon be able to apply for authority to conduct long-haul, cross-border trucking services in the United States.” I’m not immediately finding confirmation that this has actually started, so I won’t call this case closed quite yet. We’ll see if it hits the 20-year mark this December.

What the Heckscher-Ohlin theorem is and is not about

18 January 2015

In a piece for Bloomberg, Noah Smith wrote:

No. 9. The Heckscher-Ohlin theorem

This is a theory about trade. It says that countries with more capital — industrialized countries such as the U.S. or Japan — will tend to make things that are more capital-intensive. And countries with more labor — such as India — will tend to make things that are more labor-intensive. That’s why the U.S. makes a lot of semiconductors (which require huge fabrication plants), and India makes a lot of clothes.

Tyler Cowen says Noah Smith oversimplified/misrepresented the theorem. He raises four objections, concluding with:

I continue to believe most economists don’t have such a clear sense of the Hechscker-Ohlin theorem. There are so many tricks to HOT I wouldn’t be surprised if I slipped up somewhere myself in this post.

Indeed, I do think Tyler slipped up a bit. He’s right that identifying “effective units” of capital and labor is the relevant exercise and also very difficult (objection #2), and of course the Heckscher-Ohlin theorem is all about ratios, not absolute quantities (objection #3).1

But I want to defend Noah a bit from Tyler’s first complaint, which was:

The HOT proposition is about exports being relatively capital- or labor-intensive, not about production per se. Even for a popular audience, I think that substitution should have been easy enough.

Is that so? Here’s how Ron Jones and Peter Neary stated the theorem in question in their 1984 Handbook chapter, which surely was not aimed at a popular audience:

Heckscher-Ohlin theorem. A country has a production bias towards, and hence tends to export, the commodity which uses intensively the factor with which it is relatively well endowed.

Why does production composition determine net export composition in this model? Well, the factor-abundance theory is a story about economies’ endowments determining the pattern of trade. To talk only about endowments (and thus only about supply-side elements), we have to neuter demand by assuming identical, homothetic preferences.2 Given commodity-price equalization and homothetic preferences, each country has a consumption vector that is proportionate to its share of world income. With no differences in the composition of consumption, differences in the composition of production translate into differences in the composition of net exports, which are simply production minus consumption.

Thus, the prediction about the pattern of trade simply falls out of the prediction about the pattern of production. Here’s how Jones and Neary explained it:

The final core proposition is the Heckscher-Ohlin theorem itself, but this in fact is closely related to the Rybczynski theorem. Consider two countries with different relative factor endowments and the same technology for producing both goods. If both countries face the same commodity prices then, by the Rybczynski theorem, the country with the greater relative endowment of capital will produce relatively more of the capital-intensive good… Provided this production bias is not offset by a demand bias, the relatively capital-abundant country will export the relatively capital-intensive good. When it is expressed in terms of a physical definition of factor abundance, the Heckscher-Ohlin theorem is thus a simple corollary of the Rybczynski theorem…

In terms of the canonical theorem, I think that Noah got that part right. And in a meta sense, Tyler was right as well.


1. When he cites the Leontief paradox, he’s getting into more complicated territory, see Leamer (JPE 1980).
2. In reality, of course, I think that the composition of demand matters!

When international shipping is cheaper than domestic

26 December 2014

The Washington Post headlined this “The Postal Service is losing millions a year to help you buy cheap stuff from China“:

This strange consequence of postal law was less significant when the mail was mostly personal correspondence. But as Chinese companies began logging on to Web marketplaces like eBay, Amazon, and Alibaba, they started taking advantage of the shipping deal to sell directly to American consumers. And so it’s never been easier to get something cheap and Chinese delivered to your door for a startlingly low price: $4.64 for a digital alarm clock; $2.50 for a folding knife; $1.88 for an iPhone cable — all with shipping included…

Countries used to provide this forwarding service to each other for free, but in 1969 an update to this postal treaty called for small fees (called terminal dues) on each mail piece. Since then the dues have grown, and the payment system has become labyrinthine. In most cases, however, postal services still charge each other less than they would charge their own citizens for moving a package across the country.

According to the terms set out in Universal Postal Union treaty, the USPS in 2014 gets paid no more than about $1.50 for delivering a one-pound package from a foreign carrier, which makes it hard to cover costs. [1] The USPS inspector general’s office estimated that the USPS lost $79 million in fiscal year 2013 delivering this foreign treaty mail. (The Postal Service itself declined to provide specific figures.) …

At the latest round of negotiations in 2012, countries did agree to raise fees slightly. The United States will get to charge about 13 percent more every year from 2014 to 2017. Under the new terms, the inspector general’s office believes that the USPS will start to lose less money on inbound mail. [3]

All this should be a reminder that any trade deal has winners and losers and unintended consequences. Internet commerce suddenly made the terms of a long-standing mail treaty a competitive advantage for Chinese merchants, and U.S. importers like the McGraths have been feeling the squeeze. But this same system also means that average Americans can get a really sweet deal on an iPhone case shaped like an Absolut bottle.

Hat tip to Corinne Low.

Shipping costs are endogenous

30 November 2014

Cost to transport a 20′ container from major US ports to various foreign ports:

Cost to transport a 20' container from major US ports to various foreign ports

From Jose Asturias and Scott Petty.

Trade JMPs (2014-2015)

18 November 2014

It’s already that time of year again, and I’m a little late. Who’s on the job market this year with a paper on international trade?

As in prior years, I focus on trade papers, thereby neglecting international finance and open-economy macro papers. If I’ve missed someone, please contribute to the list in the comments.

Here are folks listing international trade as a field with a JMP in economic geography:

Also, Jon Haveman is making my annual compilation obsolete by offering a full-featured database of trade candidates with candidate-created profiles: Job Candidate Database.

Recently on Twitter

11 July 2014

While I’ve fallen behind on blogging, I do a better job of staying active on Twitter. In the last two weeks, @TradeDiversion has tweeted about:

How not to estimate an elasticity

29 June 2014

The Cato Institute’s Randal O’Toole claims to debunk a recent paper suggesting a “fundamental of road congestion”.

In support of the induced-demand claim, Mann cites research by economists Matthew Turner of the University of Toronto and Gilles Duranton of the University of Pennsylvania. “We found that there’s this perfect one-to-one relationship,” Mann quotes Turner as saying. Mann describes this relationship as, “If a city had increased its road capacity by 10 percent between 1980 and 1990, then the amount of driving in that city went up by 10 percent. If the amount of roads in the same city then went up by 11 percent between 1990 and 2000, the total number of miles driven also went up by 11 percent. It’s like the two figures were moving in perfect lockstep, changing at the same exact rate.” If this were true, then building more roads doesn’t make traffic worse, as the Wired headline claims; it just won’t make it any better.

However, this is simply not true. Nor is it what Duranton & Turner’s paper actually said. The paper compared daily kilometers of interstate highway driving with lane kilometers of interstates in the urbanized portions of 228 metropolitan areas. In the average metropolitan area, it found that between 1983 and 1993 lane miles grew by 32 percent while driving grew by 77 percent. Between 1993 and 2003, lane miles grew by 18 percent, and driving grew by 46 percent.

That’s hardly a “perfect one-to-one relationship.”

The paper also calculated the elasticities of driving in relationship to lane kilometers. An elasticity of 2 would mean a 10 percent increase in lane miles would correspond with a 20 percent growth in driving; an elasticity of 1 would mean that lane miles and driving would track closely together. The paper found that elasticities were very close to 1 with standard errors of around 0.05. Even though this is contradicted by the previously cited data showing that driving grew much faster than lane miles, this is the source of Turner’s “perfect one-to-one relationship.”

My prior belief is that results published in the American Economic Review are unlikely to be debunked by a couple of paragraphs in a blog post. In this case, it’s fairly straightforward to explain why the average growth rates of lane kilometers and vehicle-kilometers traveled are not informative about the elasticity.

The lane-kilometer elasticity of VKT describes how changes in VKT relate to changes in lane kilometers. O’Toole tries to say something about this relationship by noting the average value of each. But describing the average growth rates does not say whether cities that experienced faster growth in lane kilometers also experienced faster growth in vehicle-kilometers traveled. It’s entirely possible for both averages to be positive and the elasticity relating them to be negative! Here are a few lines of Stata code to generate an example in which the averages are 32% and 77%, while the elasticity is -1.

clear
set obs 228
gen delta_lane = .32 + rnormal(0,.2)
gen delta_VKT = (.77 +.32) - delta_lane + rnormal(0,.2)
twoway (scatter delta_VKT delta_lane) (lfit delta_VKT delta_lane), graphregion(color(white))

That yields a figure like this:

otoole1

Having made this econometric point, one can grab the data used in the Duranton and Turner paper to  note the average values and appropriately estimate the elasticity, revealing no contradiction whatsoever between these two moments.

use "Duranton_Turner_AER_2010.dta", clear
gen delta_VKT = log(vmt_IHU_93) - log(vmt_IHU_83)
gen delta_lane = log(ln_km_IHU_93) - log(ln_km_IHU_83)
summ delta*
reg delta_VKT delta_lane
twoway (scatter delta_VKT delta_lane) (lfit delta_VKT delta_lane), graphregion(color(white))

otoole2

Across MSAs, the average VKT change was a 61 log-point increase, while the average lane kilometers change was a 25 log-point increase. That’s a ratio greater than two, but the estimated elasticity is 0.955. Hence Matt saying that he and Gilles found a one-to-one relationship. Their paper deals with various types of roads and instrumenting to infer the causal relationship, but I don’t need to describe those issues here. I’ve written enough to demonstrate why O’Toole’s blog post does not debunk the Duranton-Turner findings.


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