We’ve written a new paper: “Market Size and Trade in Medical Services” with Josh Gottlieb, Maya Lozinski, and Pauline Mourot.

There’s a long-running discussion in health policy about “spatial mismatch”: are doctors and clinics too concentrated in big cities? Our paper emphasizes that you need to quantify the trade-off between local increasing returns and trade costs to answer this question. If the division of labor is limited by the extent of the market, there’s an upside to geographically concentrating production. The downside depends on how difficult it is for patients to travel.

See the paper for the gory details of how we use gravity regressions to estimate the distance elasticity and each region’s service quality and then estimate the regional production function for medical services. Or check out the less technical summary or the podcast interview, courtesy of the Becker Friedman Institute.

One of the heartening lessons for trade economists is that our conventional tools, which have been overwhelmingly developed for and applied to trade in manufactured goods, do a pretty good job of describing medical services. The economic logic and mechanisms are familiar, even if the magnitudes are different in important ways. I’ve learned a lot from my first foray into health economics, and it’s also been a lot of fun to apply classic insights from international and urban economics to a really important service sector.

I am delighted that my research has recently garnered two prizes, one in international trade and one in regional science.

The Journal of International Economics granted the 2022 Bhagwati Award to “The Comparative Advantage of Cities” (joint with Don Davis). This award recognizes the best trade paper published in the JIE during the past two years.

The City of Heidenheim and the August Lösch Association awarded me the 2022 August-Lösch Prize, recognizing three of my papers published in 2020 and 2021 (“The Comparative Advantage of Cities”; “Cities, Lights, and Skills in Developing Economies”; “How Many Jobs Can be Done at Home?”). This prize recognizes outstanding academic research in the field of Regional Science.

Peter Neary passed away two weeks ago. He was a great economist and a great human being. His enthusiasm for economics and ideas was infectious, and he was extremely kind to all. He’ll be deeply missed.

As a first-year MPhil student keen on international trade, I was very lucky that Peter Neary also arrived in Oxford in 2006. I met him at a departmental social function early in the autumn term. As many others have testified, Peter made a habit of graciously introducing himself to newcomers. I no longer recall much of that first conversation – beyond my insufferably geeky initial query, “are you the Neary of Anderson and Neary?” – but Peter made us feel welcome from the start. We would all do well to emulate him.

As one might expect given his vivid and lucid argumentation, Peter was an excellent teacher. His lectures on international trade were well organized and illuminating. His enthusiasm and witty asides made even dry material lively. One of my classmates, who was not keen on trade per se, said at the time that Peter’s first lecture was the best we had had in Oxford.

Peter’s scholarly contributions are well known. He worked on hard, important problems and made seminal contributions. His Irish compatriots mention him alongside Edgeworth, Geary, and Gorman, which would delight him as an enthusiast of the history of economic thought. Peter also devoted time to professional service throughout his career. University College Dublin hosted a very nice event celebrating Peter Neary two months ago, now available on YouTube.

Rest in peace, Peter. I’ll miss you.

Brent Neiman and I wrote a paper that tackles a simple question: “How Many Jobs Can be Done at Home?” The latest draft (April 16) is here. The full replication package is available on GitHub.

We estimate that 37% of US jobs, accounting for 46% of wages, can be performed entirely at home. Applying our occupational classifications to 85 other countries reveals that lower-income economies have a lower share of jobs that can be done at home.

This simple question is suddenly very important during this pandemic. See the Economist and Wall Street Journal for their reactions. I did an video interview with CEPR about our paper, which includes some thoughts about offshoring and the future of telecommuting. My comments to Vice appeared in a story titled “You’re Not Going Back to Normal Office Life for a Long, Long Time“.

I tweeted this, but a blog post seems more appropriate (screenshots of URLs are unhelpful, tweets aren’t indexed by Google, etc).

The top five journals in economics permit authors to either post the published PDF on their personal website or provide a free-access link to the published article.

• American Economic Review: “Authors are permitted to post published versions of their articles on their personal websites.”
• Econometrica: “Authors receive a pdf copy of the published article which they can make available for non-commerial use.”
• Journal of Political Economy: “Authors may also post their article in its published form on their personal or departmental web.”
• Quarterly Journal of Economics and Review of Economic Studies: “Upon publication, the corresponding author is sent a free-access link to the online version of their paper. This link may be shared with co-authors and interested colleagues, and posted on the author’s personal or institutional webpage.”

Thus, articles in the top five economics journals are accessible to the general public at no fee, provided that the authors of those articles make the effort to share them. Other journals may not be so accessible. A lot of field journals are published by Elsevier, which has less generous sharing policies.

If you are a student interested in earning an economics PhD, you should consider working as a research assistant before starting graduate school. Working on someone else’s research projects is an opportunity to learn a lot about the research process that is never taught in PhD courses. Learning by doing is a powerful force.

I’m hiring people to start working with me in summer of 2018.  Apply here: http://www.nber.org/jobs/Dingel_Chicago%20Booth.pdf. More generally, you can find a list of such opportunities on the NBER website.

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.

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.

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.