Archive for the ‘Measures, Statistics & Technicalities’ Category

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.


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.

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.

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.

“Ricardian Productivity Differences and the Gains from Trade”

18 November 2013

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.

Melitz and Redding on heterogeneous firms and gains from trade

6 June 2013

In a recent VoxEU column, Marc Melitz and Stephen Redding describe the logic of Melitz (Ecma, 2003) and Arkolakis, Costinot, and Rodriguez-Clare (AER, 2012). Those should be familiar to Trade Diversion readers (e.g. ACR 2010 wp, Ossa 2012 wp). They then explain their new paper:

In Melitz and Redding (2013b), we show that firm-level responses to trade that generate higher productivity do in fact represent a new source of gains from trade.

  • We start with a model with heterogeneous firms, then compare it to a variant where we eliminate firm differences in productivity while keeping overall industry productivity constant.

We also keep all other model parameters (such as those governing trade costs and demand conditions) constant.

  • This ‘straw man’ model has no reallocations across firms as a result of trade and hence features no productivity response to trade.

Yet it is constructed so as to deliver the same welfare prior to trade liberalisation. We then show that, for any given reduction in trade costs, the model with firm heterogeneity generates higher aggregate welfare gains from trade because it features an additional adjustment margin (the productivity response to trade via reallocations). We also show that these differences are quantitatively substantial, representing up to a few percentage points of GDP. We thus conclude that firm-level responses to trade and the associated productivity changes have important consequences for the aggregate welfare gains from trade.

How can these findings be reconciled with the results obtained by Arkolakis, Costinot, and Rodriguez-Clare (2012)? Their approach compares models that are calibrated to deliver the same domestic trade share and trade elasticity (the sensitivity of aggregate trade to changes in trade costs). In so doing, this approach implicitly makes different assumptions about demand and trade costs conditions across the models that are under comparison (Simonovska and Waugh 2012). By assuming different levels of product differentiation across the models, and assuming different levels of trade costs, it is possible to have the different models predict the same gains from trade – even though they feature different firm-level responses. In contrast, our approach keeps all these ‘structural’ demand and cost conditions constant, and changes only the degree of firm heterogeneity (Melitz and Redding 2013b). This leads to different predictions for the welfare gains from trade.

One potential criticism of our approach is that one can estimate the trade elasticity (the sensitivity of aggregate trade to changes in trade costs) using aggregate trade data only – without requiring any specific assumptions about the firm-level responses to trade. Whatever assumptions are made about those firm-level responses (and the demand and trade-cost conditions), they should then be constructed so as to match that estimated aggregate elasticity. However, recent empirical work has shown that those underlying assumptions radically affect the measurement of the aggregate trade elasticity, and that this trade elasticity varies widely across sectors, countries, and the nature of the change in trade costs (see for example Helpman et al. 2008, Ossa 2012, and Simonovska and Waugh 2012). There is thus no single empirical trade-elasticity parameter that can be held constant across those different models.

Given the lack of a touchstone set of elasticities, we favour our approach to measuring the gains from trade arising from different models; one that maintains the same assumptions about demand and trade costs conditions across those models.

“Large cities” in the EU and US, redux

12 October 2012

The Economist is six months late to the party, but the latest print edition has a piece on that McKinsey comparison of American and Europe cities. I have some quibbles, again.

I don’t understand the piece’s opening, though it has little to do with what follows. It begins:

AMERICA is full of vast, empty spaces. Europe, by contrast, seems chock-a-block with humanity, its history shaped by a lack of continental elbowroom. Ironically, Europe’s congestion partly reflects the fact that its large cities suck up relatively few people.

Moving people across cities wouldn’t change the (unweighted) average population density of the US or EU, so what does this comparison mean? Europe is going to be full of humanity because the land area of the EU is roughly half that of the continguous US (1.7m vs 3.1m square miles). Since larger cities are generally denser, the population-weighted density of Europe would rise if its large cities had higher population shares.

Never mind the elbowroom. The Economist continues:

Although America and the euro zone have similar total populations, America’s 50 largest metropolitan areas are home to 164m people, compared with just 102m in the euro area. This striking disparity has big consequences.

Differences in metropolitan populations may help explain gaps in productivity and incomes. Western Europe’s per-person GDP is 72% of America’s, on a purchasing-power-parity basis. A recent study by the McKinsey Global Institute, the consultancy’s research arm, reckons that some three-quarters of this gap can be chalked up to Europe’s relatively diminutive cities. More Americans than Europeans live in big cities: there is a particular divergence in the size of each region’s “middleweight” cities, those that teem just a little less than the likes of New York and Paris (see chart). And the premium earned by Americans in large cities relative to those in the countryside is larger than that earned by urban Europeans.

As I explained back in April, the MGI report does not say Europeans would reach American prosperity levels if the population shares of their large cities reached American levels:

The gap in per capita GDP between the US and Europe is about 35%, according to the MGI figures in Exhibit 2. The “large city” premia in the United States and Europe of 34% and 30% are virtually the same. That means that the difference in per capita income attributable to the difference in “large city” population shares is the large city premium (~30pp) times the difference in large city population shares (22pp). The six to seven percentage points explained by this difference in population shares is at best one-fifth of the 35% gap between US and EU incomes. You can confirm this quick calculation by studying the decomposition in MGI’s Exhibit 2. Moving more people into large cities wouldn’t meaningfully reduce the US-EU per capita income gap.

Look at Exhibit 2 for yourself:

The Economist mentions the big-city population share and big-city premium components. They neglect that 53 of those 74 percentage points are strictly attributable to the difference in average income. Differences in metropolitan populations are not at the heart of the story.

After citing all the advantages of cities, the Economist considers two reasons why European cities aren’t as large as US cities: regulatory barriers and incomplete integration. While the former might matter, I put a lot of stock in the latter. As I explained in my prior post, Zipf’s law holds at the country level. Since no European state has a population close to 300 million, we should not expect any European city to approach the size of NYC or LA. Until intra-European mobility looks anything like intra-US mobility, I think we should expect Zipf’s law to hold at the country level. And since MGI used a common cutoff of population > 150,000 for defining a “large city”, it’s not at all surprising that a larger share of the US population lives in its large cities. I wrote before:

Given the UK population, increasing the fraction of UK residents who live in “large cities” with populations greater than 150,000 would require the emptying out of smaller metropolitan areas. While such migration is entirely possible, it would violate the expected city size distribution… If you know the populations of New York and London and are familiar with Zipf’s law, then it’s not at all surprising that a greater fraction of the US population is found in metropolitan areas above some common population threshold. I don’t think that tells us much about the economic mechanisms determining the role of US cities in the global economy.

Update: Related to my comparison of US and UK city-size distributions, see Henry Overman on the details of Zipf’s law for UK cities.

Atkin & Donaldson – Who’s Getting Globalized? The Size and Nature of Intranational Trade Costs

10 July 2012

David Atkin and Dave Donaldson are presenting this paper tomorrow afternoon at the NBER summer institute:

This paper uses a newly collected dataset on the prices of narrowly defined goods  across many dispersed locations within multiple developing countries to address the  question, How large are the costs that separate households in developing countries from the  global economy? Guided by a flexible model of oligopolistic intermediation with variable mark-ups, our analysis proceeds in four steps. first, we measure total intranational trade costs (ie marginal costs of trading plus mark-ups on trading) using price gaps over space within countries—but we do so only among pairs of locations that  are actually trading a good by drawing on unique data on the location of production  of each good. Second, we estimate, separately by location and commodity, the passthrough rate between the price at the location of production and the prices paid by inland consumers of the good. Our estimates imply that incomplete pass-through—and therefore, intermediaries’ market power—is a commonplace, and that pass-through is especially low in remote locations. Third, we argue that our estimates of total trade costs (Step 1) and pass-through rates (Step 2) are sufficient to infer the primitive effect  of distance on the marginal costs of trading; after correcting for the fact that mark-ups  vary systematically across space we find that marginal costs are affected by distance  more strongly than typically estimated. finally, we show that, in our model, the estimated pass-through rate (Step 2) is a sufficient statistic to identify the shares of social  surplus (ie the gains from trade) accruing to inland consumers, oligopolistic intermediaries, and deadweight loss; applying this result we find that intermediaries in  remote locations capture a considerable share of the surplus created by intranational  trade.

You can listen to a podcast of Donaldson presenting a much earlier version of this work from the International Growth Centre. He does a really nice job of summarizing the issues involved in inferring trade costs from price data.

How big are the gains from trade?

28 May 2012

One of the most-mentioned trade papers of the last couple years is “New Trade Models, Same Old Gains?” by Arkolakis, Costinot & Rodriguez-Clare, now published in the AER. Their theoretical work shows that, for a broad class of theoretical models that includes the Armington, Eaton and Kortum (2002), and Melitz-Chaney approaches, the gains from trade are characterized by a formula involving only two numbers – the domestic expenditure share and the trade elasticity. The former can be straightforwardly obtained from the data. The latter needs to be estimated, which is more involved but feasible. ACR shows that their formula says that US welfare is about 1% higher than it would be under autarky.

In the words of Ralph Ossa, “either the gains from trade are small for most countries or the workhorse models of trade fail to adequately capture those gains.” Different people come down on different sides of that choice. Ed Prescott, for example, is clearly in the latter camp.

Ossa has a new paper, “Why Trade Matters After All“, aimed at reconciling this divide:

I show that accounting for cross-industry variation in trade elasticities greatly magnifies the estimated gains from trade. The main idea is as simple as it is general: While imports in the average industry do not matter too much, imports in some industries are critical to the functioning of the economy, so that a complete shutdown of international trade is very costly overall…

I develop a multi-industry Armington (1969) model of international trade featuring nontraded goods and intermediate goods and show what it implies for the measurement of the gains from trade…

Loosely speaking, the exponent of the aggregate formula is therefore the inverse of the average of the trade elasticities whereas the exponent of the industry-level formula is the average of the inverse of the trade elasticities which is different as long as the elasticities vary across industries.

allowing for cross-industry heterogeneity in the trade elasticities substantially increases the estimated gains from trade for all countries in the sample. For example, the estimated gains from trade of the US increase from 6.4 percent to 42.0 percent if I do not adjust for nontraded goods and intermediate goods and from 3.8 percent to 23.5 percent if I do…

the 10 percent most important industries account for more than 80 percent of the log gains from trade on average.

Thinking about the firm-size distribution

27 May 2012

[Note: This post isn’t about international economics. I’ll use an example from trade to comment on a feature of the US real-estate market.]

In a letter to the Economist, the president of the National Association of Realtors writes:

[I]t is not true that large brokers dominate the industry. In fact, the real-estate industry consists mostly of independent contractors and small firms. Eight out of ten realtors work as independent contractors for their firms.

The second sentence appears to be a non-sequitur, unless one thinks that existence is informative about dominance. It’s not. In their first glance, antitrust authorities would look at concentration ratios or Herfindahl–Hirschman indices, because dominance is about economic outcomes, such as market shares, not mere existence.

According to Bernard, Jensen, Redding, and Schott’s JEP survey, four percent of the 5.5 million US firms export. That makes 220,000 exporters. The top ten percent, just 22,000 exporters, are responsible for 96% of US exports. Would we say that “larger exporters do not dominate exporting because the exporting set of firms consists mostly of small exporters”? Of course not.

When thinking about the sales distribution, we care about the exponent of the power law characterizing it, not merely the fact that its support includes small sizes.

US abandons zeroing (for now?)

7 February 2012

I’m seeing a lot of news about the US federal government dropping its practice of zeroing in calculating antidumping duties. The WTO news item is uninformative. I don’t have time this week to follow the latest developments, so I’ll just drop links:


The US has reached deals with the European Union and Japan to drop a contentious practice in its anti-dumping calculations known as “zeroing”, ending a longstanding international trade dispute in order to prevent retaliation against American products. The agreements, signed in Geneva, will close the books on a fight that began in 2003 when the EU first filed a case against the US at the World Trade Organisation.

USTR press release:

After the WTO found that the United States had not brought its antidumping methodologies into compliance, the EU and Japan requested authorization to impose hundreds of millions of dollars of trade retaliation. Had these agreements not been reached today, substantial volumes of U.S. exports could have been closed out of markets in the EU and Japan, resulting in job loss for U.S. workers and financial loss for U.S. farms and businesses…

Under the agreements signed today, the United States will complete the process – which began in December 2010 – of ending the zeroing practices found in these disputes to be inconsistent with WTO rules. In return, the EU and Japan will drop their claims for trade retaliation.

Politics-oriented coverage from The Hill includes this detail: “the Obama administration said it will try to negotiate a future deal at the WTO to permit the practice.” Here’s Scott Lincicome on the news.

What we don’t learn from looking at exports/GDP

27 January 2012

I’m afraid that I found “The Quiet Driver of Economic Growth: Exports“, a NYT Economix post by Binyamin Appelbaum, to be more confusing than illuminating. In this post, I’ll try to explain why one must be careful in interpreting a number of economic statistics.

Appelbaum writes:

The estimates of the nation’s economic performance last year, released Friday, highlight a striking trend: Exports have never been more important.

Foreign buyers purchased more than $2 trillion in goods and services, the first time exports have topped that threshold. And those exports accounted for almost 14 percent of gross domestic product, the largest share since at least 1929.

We usually talk about exports alongside its opposite number, imports, and since the United States buys much more than it sells – our “trade deficit” — the general impression is that foreign trade is a drag on the economy. But that tends to obscure the importance of exports, which have accounted for about 10 percent of G.D.P. over the last two decades and, since the recession, considerably more…

Much of the rise in exports is a consequence of domestic problems… This is a good thing on the whole. The ability of American companies to make money in foreign markets is helping to offset the pain of those domestic problems.

I found this confusing in three senses:

  1. In purely accounting terms, GDP depends on net exports, not gross exports.
  2. In compositional terms, the export-GDP ratio doesn’t tell you if international commerce is offsetting domestic problems.
  3. The key phrase “exports accounted for X% percent of GDP” is meaningless at best and misleading at worst.

National accounts

To the extent that gross domestic product is our measure of economic performance, should we think about net exports or gross exports? Recall the expenditure definition of GDP:

Y = C + I + G + X – M

If you want to talk about — in accounting terms, not causal terms — what’s happening to US GDP, then net exports are informative. They appear on the right-hand side. When you talk about gross exports while holding gross imports constant, you are losing information. If X and M both increased by the same amount, then GDP would not increase, but X/GDP would rise.

Suppose that I were describing a firm’s economic performance to you. If we had an accounting definition of our performance that said

corporate profits = revenues – costs,

would the following claim seem reasonable? “We usually talk about revenues alongside their opposite numbers, costs, and since the company buys much more than it sells — our ‘negative profits’ — the general impression is that doing business is a drag on the firm. But that tends to obscure the importance of revenues, which have accounted for 110 percent of (the absolute value of) net profits…”

If you care about the international component of GDP, you are losing information when you switch from looking at net exports to solely considering exports. If you look at the BEA press release, you learn that real exports grew 4.7% and real imports grew 4.4%. But recall that we run a trade deficit, so real imports are growing from a larger base. If we go to table 3 of the full BEA announcement (pdf), my reading is that net exports moved from -$562b in the third quarter to -$582b in the fourth quarter. If I’m reading the table correctly, then net exports actually fell. That means that the way we usually talk about trade, which looks at net exports, tells you something different than what is implied by looking at gross exports (implicitly holding imports constant).

Are exports offsetting domestic problems?

Exports as a share of GDP is a ratio. If exports stay the same while GDP shrinks, the exports-to-GDP ratio will rise. Is that a sign of exports offsetting domestic problems? I suppose that it is if the counterfactual is that exports would shrink at the same rate of GDP. But if net exports actually fell, then the increase in the trade deficit “reduced” US GDP (in accounting, not causal, terms), so exports/GDP seems like the wrong statistic to study.

What do exports “account for”?

Applebaum writes that “exports accounted for almost 14 percent of gross domestic product” and that we’ve negleted “the importance of exports, which have accounted for about 10 percent of G.D.P. over the last two decades.” I do not know what the phrase “accounts for” means in these statements.

It’s true that Y = C + I + G + X – M, so exports are a component of GDP. But when I read “accounts for”, I imagine a decomposition of GDP into pieces that sum to 100%. That’s not true when you talk about gross exports. We’re back to the distinction between gross values and value-added measures that I have repeatedly emphasized. What would it mean to say that “exports account for 223% of Hong Kong’s GDP“?

I would suggest that exports/GDP is meaningless as a measure of how international commerce has benefited the US economy during the last quarter. At worst, the “accounts for” language might cause readers to interpret the measure as representing a decomposition of GDP’s components.


Be careful with economic statistics! There are important differences between gross exports and net exports and between gross value and value added.

I’ve tried to be careful here, but I may have read Applebaum’s post or written my post too quickly, so if I’ve made an error in handling statistical definitions or the BEA data, please point it out in the comments. Thanks!


I’ve tried to write carefully, but there’s a danger that readers might think I’m treating “net exports” as a scorecard for US economic performance. I am certainly not saying that exports are good and imports are bad. Remember that the current account deficit is the amount by which domestic investment exceeds domestic savings. These outcomes are jointly determined in general equilibrium. My story about “net profits” was an accounting analogy, not an economic analogy.

Addendum (28 Jan, 12pm): Here’s my Twitter exchange with Appelbaum. It doesn’t change anything I said above.


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