Cost to transport a 20′ container from major US ports to various foreign ports:
From Jose Asturias and Scott Petty.
Cost to transport a 20′ container from major US ports to various foreign ports:
From Jose Asturias and Scott Petty.
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?
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.
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:
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:
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))
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.
I’m happy to say that I will become an assistant professor at Chicago Booth in July.
In the course of researching my job market paper, I read a lot of old or obscure literature related to the Linder hypothesis. It yielded some real gems. Unfortunately, I also unearthed some big disappointments. You’ll see what I mean in a moment.
For the moment, here’s the abstract of McPherson, Redfearn and Tieslau – “International trade and developing countries: an empirical investigation of the Linder hypothesis” in Applied Economics (2001), an article with 44 citations in Google Scholar:
This paper presents empirical evidence in support of the Linder hypothesis for five of the six East African developing countries studied here: Ethiopia, Kenya, Rwanda, Sudan and Uganda. This finding implies that these countries trade more intensively with others who have similar per capita income levels, as predicted by Linder. The contributions of this research are three-fold. First, new information is provided on the Linder hypothesis by focusing on developing countries. Second, this is one of very few analyses to capture both time-series and cross-section elements of the trade relationship by employing a panel data set. Third, the empirical methodology used in the analysis corrects a major shortcoming in the existing literature by using a censored dependent variable in estimation.
Now, here’s the abstract of Bukhari, Ahmad, Alam, Bukhari, and Butt – “An Empirical Analysis of the Linder Theory of International Trade for South Asian Countries” in The Pakistan Development Review (2005), with zero citations in Google Scholar:
This paper presents empirical evidence in support of the Linder theory of international trade for three of the South Asian countries, Bangladesh, India, and Pakistan. This finding implies that these countries trade more intensively with countries of other regions, which may have similar per capita income levels, as predicted by Linder in his hypothesis. The contribution of this research is threefold: first, there is new information on the Linder hypothesis by focusing on South Asian countries; second, this is one of very few analyses to capture both time-series and cross-section elements of the trade relationship by employing a panel data set; third, the empirical methodology used in this analysis corrects a major shortcoming in the existing literature by using a censored dependent variable in estimation.
It continues like this, paragraph for paragraph. Finally, we arrive at Table 2 of each paper. Here’s McPherson, Redfearn and Tieslau:
And here’s Bukhari, Ahmad, Alam, Bukhari, and Butt:
That’s Bangladesh-Kenya, India-Ethiopia, and Pakistan-Uganda with identical rows. The same thing occurs in Table 3. It continues, all the way through the concluding paragraphs.
You’ll recall that Ralph Ossa emphasized sectoral heterogeneity in trade elasticities as one reason the ACR formula might understate the gains from trade. I haven’t read it yet, but this new NBER WP by Andrei Levchenko and Jing Zhang also emphasizes the importance of sectoral heterogeneity in thinking about this topic:
[T]he simpler formulas that do not use information on sectoral trade volumes understate the true gains from trade dramatically, often by more than two-thirds. The error in the formulas across countries is strongly negatively correlated to the strength of Ricardian comparative advantage: the one-sector formula-implied gains understate the true gains from trade by more in countries with greater dispersion in sectoral productivity. The model-based exercise thus reinforces the main result of the paper that accounting for sectoral heterogeneity in productivity is essential for a reliable assessment of the gains from trade.
It’s that time of year again. Who’s on the job market this year with a paper on international trade?
As resident blogger, I’m going to exercise a point of personal privilege to note that I am on the job market this year. Please tell your friends who are on hiring committees.
Jonathan Dingel (Columbia): “The Determinants of Quality Specialization”
With that important piece of information out of the way, here are this year’s trade candidates:
Paul Krugman’s 1980 AER paper formally introduced the home-market effect. In introducing his result, he mentions (p.955):
Notice that this argument is wholly dependent on increasing returns; in a world of diminishing returns strong domestic demand for a good will tend to make it an import rather than an export. But the point does not come through clearly in models where increasing returns take the form of external economies (see W. M. Corden). One of the main contributions of the approach developed in this paper is that by using this approach the home market can be given a simple formal justification.
I doubt that very many people have looked at the Corden reference, as it appeared in a 1970 conference volume titled Studies in international economics. Monash Conference papers. Here’s an excerpt from the surprisingly prescient three-page note:
A note on economies of scale, the size of the domestic market and the pattern of trade
Professor Grubel suggests that a country will tend to produce and export those products or ‘styles’ of products for which it has a relatively large domestic market. He explains this in terms of economies of scale. This is essentially the ‘Linder hypothesis’ which has obtained some empirical support, as well as being intuitively plausible. But it does raise an interesting theoretical question which has not, to my knowledge, been explored, in a simple static two-product two-country model with no transport costs, with economies of scale and with the demand patterns differing between the two countries it does not follow that a country will export that product to which its own demand pattern is biased. In that sort of model, as is well-known, one can say only that at least one of the two countries, and possibly both, will specialise, but one cannot say which country will specialise in which good. From the point of view of maximising potential world income there will be an optimum pattern of specialisation, but this will not depend in any simple predictable way on differences between the demand patterns of the two countries. Thus we cannot obtain the Linder hypothesis from this simple model. The question then is: What else must we put into the model? Is it transport costs, or is it rather something ‘dynamic’ ? In order to focus on the main point I shall now assume that the two countries are of equal size, that their factor endowments and production functions are identical, and that any differences between the factor-intensities of the two products are not large. Hence the two countries have identical convex production transformation curves. They differ only in their demand patterns. Country A’s demand pattern is biased towards product X and country B’s towards product Y. Needless to say, the discussion to follow is very tentative…
A third approach might be to introduce transport costs. Transporting goods from one country to another uses up resources, and from the point of view of maximising world income it will pay to minimise transport costs. Given that in the final equilibrium both countries will specialise, each country should then specialise on the good for which it has the relatively greater demand, since this will minimise transporting. This seems obvious. Provided we do not introduce other complications, trade along Linder lines will maximise potential world income. But it does not seem so easy to prove that trade will actually assume that pattern. Suppose that, for some reason, one starts with the trade flow in the opposite direction. One might explain this in terms of some dynamic considerations. Will there then be a natural tendency for the pattern of specialisation and hence the flow of trade to reverse itself? It does not seem obvious that this would be so. There is scope for further theoretical explorations here.
As Krugman himself has commented: “Now it is always tricky to reread old texts in the light of subsequent information; knowing what actually happened, you can probably find a prophecy of Nostradamus that fits the event, and knowing subsequent developments in economic theory, you can probably find most of it hinted at in Ibn Khaldun.” Still, I think Corden was onto something in 1970.
In August 1935, Gottfrieb Haberler wrote (Theory of International Trade, Preface to the English Edition):
[I]t seems to me that the theory of international trade, as outlined in the following pages, requires further development, in two main directions. The theory of imperfect competition and the theory of short-run oscillation (business cycle theory) must be applied to the problems of international trade. It will soon be possible to do this in a systematic way, since much progress has been made in both fields in recent years.
With regard to the first of these questions, there is the literature which centres around the two outstanding books, Monopolistic Competition by Professor E. Chamberlin and Imperfect Competition by Mrs. Joan Robinson. In the second field where further development is required, it is not so easy to refer to a body of accepted theory.