Tracking Trump’s trade policy

23 January 2017 by

The start of the Trump administration means that trade policy is in the headlines far more than it has been for at least a decade. While the trade-policy blogosphere remains pretty quiet (partly because I haven’t updated my blogroll in a few years), there’s a flurry of activity on trade-policy Twitter. You can follow me @TradeDiversion.

Here are some highlights from around the web, most of which I discovered via trade twitter:

  • The Peterson Institute (@PIIE) has been providing fantastic coverage across the board. Gary Hufbauer provided an authoritative brief on the presidential powers that would allow Trump to take protectionist actions without much congressional oversight, and Chad Bown outlined the implications of denying China “market economy” status.  I expect PIIE’s February 1 event on border tax adjustments to be highly informative.
  • The International Economic Law and Policy Blog (@WorldTradeLaw) hasn’t slowed down and remains an essential source of news and analysis.
  • Twitter is the fastest way to see the text of the TPP withdrawal order Trump signed today, learn that Sen. Mike Lee wants to limit Trump’s power to raise tariffs, or ask  the experts what withdrawing from NAFTA without repealing the NAFTA Implementation Act might entail. Shawn Donnan of the FT (@sdonnan) is highly engaged on Twitter. And Brad Setser (@Brad_Setser) recently returned from a long blogging hiatus.
  • One of my MBA students recently pointed me to a story noting that Apple wants to build a US data center in a “foreign-trade zone” exempt from import tariffs. Those foreign-trade zones are the subject of Matthew Grant’s job-market paper. I’m sure Trump will have to something to say about them once he learns they exist.

Home-Market Effects, Weak and Strong

13 November 2016 by

Does size matter? In international trade, market size can influence the pattern of specialization when there are economies of scale. A number of papers written in the late 1990s and early 2000s, surveyed by Keith Head and Thierry Mayer in a 2004 Handbook chapter, looked at the connection between market size and the pattern of specialization and trade, using countries’ total expenditure as the relevant measure of size.

The recent literature linking patterns of trade to countries’ income levels has spurred a number of economists to pay more attention to the role of product quality and non-homothetic preferences in international trade. In particular, relative country size and relative demand are only necessarily synonymous when preferences are homothetic. When expenditure shares vary with income levels, the composition of income influences the composition of demand. Two places with the same number of consumers will have very different demands for high-quality products if one place is populated by high-income households and the other is not. In elegant theoretical settings, Fajgelbaum, Grossman, and Helpman (2011) and Matsuyama (2015) show that economies of scale and trade costs can cause higher-income locations to specialize in the income-elastic products that are in greater relative demand.

Whether a country’s income level influences its product mix and export basket through this demand channel is a classic question in international trade. The hypothesis dates to a monograph by Staffan Burenstam Linder published in 1961. He wrote “the range of exportable products is determined by internal demand. It is a necessary, but not a sufficient condition, that a product be consumed (or invested) in the home country for this product to be a potential export product.” “The level of average income… has… a dominating influence on the structure of demand.” Linder’s economic mechanism – entrepreneurial discovery in bringing products to market – was not presented as a mathematical model, but he made clear empirical predictions about the pattern of trade that could be taken to data: “The more similar the demand structure of two countries, the more intensive, potentially, is the trade between these two countries.”

A formal model in which the pattern of demand influenced the pattern of production did not arrive until 1980, when Paul Krugman introduced a very special model in which there are “home market” effects on the pattern of trade. Consider two countries with identical technologies, homothetic preferences, and different population sizes. Suppose there are two sectors, one producing with increasing returns and trade costs, the other producing with constant returns and costless trade. Krugman (1980) and Krugman and Helpman (1985) showed that “if two countries have the same composition of demand, the larger country will be a net exporter of the products whose production involves economies of scale.”

There are important differences between Linder (1961) and Krugman (1980). En route to formal results, the role of income levels was lost. With homothetic preferences, market size and total expenditure are synonymous. Thus, the empirical work summarized by Head and Mayer used this notion of size. Fajgelbaum, Grossman, and Helpman (2011) bridged this gap between Linder and Krugman by using a demand system in which the composition of income matters for market size. My job-market paper, now available at the Review of Economic Studies, showed that better market access to higher-income consumers results in manufacturing plants specializing in higher-quality products.

There is another gap between Linder (1961) and Krugman (1980). Linder said internal demand was necessary for production and thus exporting. Krugman (1980) predicts that greater demand elicits such a strong production response that the location is a net exporter. Following Linder, I focused on the pattern of specialization and exports in early drafts of my JMP. Some discussants and referees told me that they wanted an empirical result for net exports because “the home-market effect is a prediction about net exports.” I found that proximity to higher-income consumers predicts the composition of exports but not the composition of imports, so my results did characterize net exports. But I remained a bit puzzled by the gap between Linder and Krugman.

A new paper by Costinot, Donaldson, Kyle, and Williams, which Dave presented at last week’s SCID-IGC conference, has now cleared up this confusion about “the home-market effect”. They introduce a “distinction between the weak home-market effect, which focuses on gross exports, and the strong home-market effect, which focuses on net exports.” Economies of scale are necessary for both. “By lowering the price of goods with larger domestic markets, economies of scale can instead create a positive relationship between exports and domestic demand.” “A strong home-market effect arises if economies of scale are strong enough to dominate the direct effect of domestic demand on imports.”

Why has this distinction not been stated previously? It turns out that the formal models in Krugman (1980) and other accounts assume functional forms such that any home-market effect is always strong. The notion of a weak home-market effect, stated very clearly in Linder’s 1961 book, disappeared due to a modeling choice. We can now see it again, in clear mathematical terms, thanks to CDKW.

Simplifying assumptions are a double-edged sword. The role of market access only received its due attention after Krugman’s formalization, for which he won a Nobel Prize. But there were elements in Linder’s account of home-market effects that we are only recovering half a century later. Fajgelbaum, Grossman, and Helpman (2011) revived the role of income composition, and now CDKW have revived the weak home-market effect.

No one appreciates these trade-offs in modeling more than Paul Krugman himself. As he wrote in his 1980 contribution: “The analysis in this section has obviously been suggestive rather than conclusive. It relies heavily on very special assumptions and on the analysis of special cases.” Unfortunately, economists have spent many, many years using only this special case. The weak home-market effect was lost due to assumptions embedded in the very tractable functional forms Krugman employed.

In particular, the weak home-market effect was lost to a modeling quirk that linked economies of scale with the price elasticity of demand. Peter Neary warned about this particular assumption in his great 2001 JEL piece, “Of Hype and Hyperbolas“. Section 4, “Limitations of the Model”, describes “a number of special features that make it less suitable for addressing some issues” and the fact that consumers’ elasticity of substitution and price elasticity of demand winds up as an index of returns to scale is first on his list. With hindsight, we know that the weak home-market effect was one of those issues left unaddressed.

This seems a classic case of a phenomenon Krugman highlighted in his meditation on economic methodology: “an extended period in which improved technique actually led to some loss in knowledge”. Gradually, though, the rigor of formal theory leads to better understanding. Now we have home-market effects, weak and strong.

Trade JMPs (2016-2017)

9 November 2016 by

Another November, another job market. Who’s on the market in trade this year? As I have for the last six years, I focus on trade, neglecting international finance and open-economy macro. The distribution is a bit uneven this year — some schools have zero candidates, while UC Davis has six. If I’ve missed someone, please contribute to the list in the comments.

Measuring rules of origin

25 October 2016 by

In the modern global economy, most barriers to trade do not come in the form of tariffs or quotas. Indeed, as early as 1970, Robert Baldwin described non-tariff protection as a big challenge following the Kennedy Round: “lowering of tariffs has, in effect, been like draining a swamp. The lower water level has revealed all the snags and stumps of non-tariff barriers that still have to be cleared away.” In fact, as Chad Bown notes, draining the swamp may have not just revealed non-tariff barriers, it “may have stimulated growth in levels of old and new forms of nontariff protection”.

This fact about modern protectionism is a bit inconvenient for economists. It’s pretty straightforward to teach the partial-equilibrium economics of tariffs and quotas to students. The supply-and-demand story can be taught with one diagram containing a few rectangles and triangles, like in this video. Moreover, the analysis of an ad valorem tariff is not sensitive to the sector or good being discussed. Given supply and demand elasticities, a tax is a tax, whether it’s applied to apples or autos. Technical barriers to trade like product regulations are necessarily sector-specific. A discussion of the fact that US automobiles must have amber front turn signals while in the EU those lamps are white does not necessarily yield general principles that could be applied to other sectors.

This difficulty also pops up in research. A lot of trade-policy theory treats tariffs as the relevant instruments. For example, Grossman and Helpman’s “Protection for Sale” model describes a government that may impose trade taxes and subsidies. In their empirical assessments of this theory, Goldberg and Maggi and Gawande and Bandyopadhyay used non-tariff barriers as their measures of protection rather than tariff rates, because tariffs are negotiated at the WTO, not determined unilaterally. But non-tariff barriers come in many different forms and therefore raise a host of measurement issues (what is the tariff-equivalent of requiring amber vs white turn-signal lamps?), particularly for making cross-sector comparisons (does comparing the fraction of two sectors’ products covered by any non-tariff measures reveal their relative restrictiveness?). I think we would see a lot more research on non-tariff barriers if they were as easy to measure as tariff rates.

Another prominent feature of modern trade policy is the huge role played by preferential trade agreements. Proposed US trade agreements like the TPP and TTIP mostly concern non-tariff issues like intellectual property rights and regulatory harmonization, not the single-digit ad valorem tariffs that remain for most manufactures. But the preferential tariff rates that define PTAs like NAFTA, customs unions like the EU, and GSP schemes like AGOA rely on a non-tariff barrier called “rules of origin”.

Rules of origin are the criteria used to define where a good was produced. Preferential trade policies necessitate defining goods’ origins so that imports from preferred partners are eligible for lower tariff rates while imports from non-members cannot qualify through mere transshipment. But when goods are produced using intermediate inputs, saying “where” a good was made can get quite difficult. In dictating how to determine the national source of a product, rules of origin can discourage firms from using intermediates imported from sources that aren’t eligible for preferential tariffs. That is, “they prevent final good producers from choosing the most efficient input suppliers around the world, in order to avoid losing ‘origin status’ and the tariff preference it confers.”

We suspect that rules of origin matter. When they’re absent, transshipment occurs. Rotunno, Vezina, and Wang attribute a surge of African textile exports to AGOA’s weak rules of origin, which led Chinese textile manufacturers to exploit AGOA-eligible countries as transshipment corridors to the US. When rules of origin are present, firms find them costly. In a survey of manufacturing firms in developing economies, rules of origin and related paperwork represented the most troublesome type of non-tariff barrier for exporters.

But there has been little research quantifying these rules’ consequences, since measuring rules of origin seems a daunting task. A recent paper by Conconi, Garcia-Santana, Puccio, and Venturini tackles the measurement challenge:

First, the rules contained in the NAFTA agreement are written at a disaggregated level, with specific rules for each product (defined at the heading or sub-heading level of the Harmonized Schedule). Second, they are mostly defined in terms of change of tariff classification, with few instances in which these rules are combined with valued added rules. These features allow us to construct a unique dataset, which maps the input-output linkages embedded in NAFTA RoO. For every final good, we can trace all the inputs that are subject to RoO requirements. Similarly, we can link every intermediate good to the final goods that impose RoO restrictions on its sourcing.

They find that rules of origin matter:

Our results show that NAFTA RoO on final goods led to a significant reduction in Mexican imports of intermediate goods from non-NAFTA countries. As expected, the magnitude of this effect depends on whether the sourcing restrictions were strict or flexible (i.e. whether change in tariff classification rules were combined with alternative value added rules) and on the extent to which Mexican producers had incentives to comply with them (i.e. on the size of the preference margin and the importance of NAFTA export markets).

Here’s a VoxEU column summarizing their research.

Colombia’s port-of-entry restrictions on textile imports

30 July 2016 by

Here’s an unusual non-tariff barrier from a 2006 WTO complaint brought by Panama (mentioned in Eaton, Jinkins, Tybout, Xu):

Second, Panama considers that, through three specific resolutions, Colombia has established a requirement that all goods falling under Chapters 50 to 64 of Colombia’s Customs Tariff (textile and footwear products) that originate in, and/or are imported from, Panama or China shall enter into Colombia only through specified ports of entry. This restriction on the ports of entry applies only to relevant goods coming from Panama or China and not to goods imported directly from third countries or customs territories. Panama claims that the restriction on the ports of entry appears to be inconsistent with Colombia’s obligations under Articles I:1, V:6, XI:1 and XIII:1 of the GATT 1994.

Third, Panama considers that, through a specific resolution, Colombia has established a requirement that commercial invoices of goods coming from the Free Zone of Colon shall include, in addition to the regular requirements, the name of the buyer in Colombia, his address and his Tax Identification Number (“NIT”). This requirement applies only to goods coming from the Free Zone of Colon and not to goods originating in third countries or customs territories. Panama claims that this requirement appears to be inconsistent with Colombia’s obligations under Articles I:1, V:6, XI:1 and XIII:1 of the GATT 1994.

While Panama and Colomiba reached a mutual agreement to solve these issues in December 2006, a closely related July 2007 complaint revisited exports from the Free Zone of Colon, which again faced port-of-entry restrictions:

In relation to restrictions on ports of entry, Panama’s request for consultations is directed at a resolution of June 2007 which provides that all goods classifiable in Chapters 50-64 of the Customs Tariff coming from the Free Zone of Colon in Panama shall be entered and imported exclusively through the jurisdictions of the Special Customs Administration of Bogota and the Barranquilla Customs Office. This requirement does not apply to goods arriving directly from third countries. The regulation provides that with respect to these goods, the authorization of the customs transit procedure will not be appropriate. Furthermore, the import declaration applicable to these imports shall be presented prior to their arrival in the national customs territory but not more than 15 days in advance. If an importer does not comply with these requirements, it is subject to special procedures under Colombia’s Customs Code, including the detention of goods.

Panama considers that these restrictions are inconsistent with Colombia’s obligations pursuant to Articles XI:1, XIII:1, V:2, V:6 and I:1 of the GATT 1994.

Colombia lost this WTO case and conformed to the DSB’s ruling in 2010.

Key parameters for Brexit forecasts

15 July 2016 by

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 by

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 by

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.


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.



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.


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 by

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 by

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!