Spatial economics JMPs (2019-2020)

Here’s a list of job-market candidates whose job-market papers fall within spatial economics, as defined by me when glancing at a webpage for a few seconds. Illinois has six candidates! I’m sure I missed folks, so please add them in the comments.

The annual list of trade candidates is a distinct post.

Of the 45 candidates I’ve initially listed, 18 used Google Sites, 12 registered a custom domain, 3 used GitHub, and 12 used school-provided webspace.

Here’s a cloud of the words that appear in these papers’ titles:

Trade JMPs (2019-2020)

It’s November again. Time flies, and there’s a new cohort of job-market candidates. Time really flies: I started this series a decade ago! Many members of that November 2010 cohort now have tenure or will soon.

As usual, I’ve gathered a list of trade-related job-market papers. There is no clear market leader: the most candidates from one school by my count is three (Berkeley, Maryland, UCLA). If I’ve missed someone, please contribute to the list in the comments.

A separate post lists candidates in spatial economics, broadly defined.

Of the 31 candidates I’ve initially listed, 14 registered a custom domain, 9 used Google Sites, 2 used GitHub, and only 6 use school-provided webspace.

Here’s a cloud of the words that appear in these papers’ titles:

Why your research project needs build automation

Software build tools automate compiling source code into executable binaries. (For example, if you’ve installed Linux packages, you’ve likely used Make.)

Like software packages, research projects are large collections of code that are executed in sequence to produce output. Your research code has a first step (download raw data) and a last step (generate paper PDF). Its input-output structure is a directed graph (dependency graph).

The simplest build approach for a Stata user is a “master” do file. If a project involves A through Z, this master file executes A, B, …, Y, and Z in order. But the “run everything” approach is inefficient: if you edit Y, you only need to run Y and Z; you don’t need to run A through X again. Software build tools automate these processes for you. They can be applied to all of your research code.

Build tools use a dependency graph and information about file changes (e.g., timestamps) to produce output using (all and only) necessary steps. Build automation is valuable for any non-trivial research project. Build automation can be particularly valuable for big data. If you need to process data for 100 cities, you shouldn’t manually track which cities are up-to-date and which need to run the latest code. Define the dependencies and let the build tool track everything.

Make is an old, widely used build tool. It should be available on every Linux box by default (e.g., it’s available inside the Census RDCs). For Mac users, Make is included in OS X’s developer tools. I use Make. There are other build tools. Gentzkow and Shapiro use SCons (a Python-based tool). If all of your code is Stata, you could try the project package written by Robert Picard, though I haven’t tried it myself.

A Makefile consists of a dependency graph and a recipe for each graph node. Define dependencies by writing a target before the colon and that target’s prerequisites after the colon. The next line gives the recipe that translates those inputs into output. Make can execute any recipe you can write on the command line.

I have written much more about Make and Makefiles in Section A.3 of my project template. Here are four introductions to Make, listed in the order that I suggest reading them:

What’s an “iceberg commuting cost”?

In the recent quantitative spatial economics literature, the phrase “iceberg commuting cost” appears somewhat often. The phrase primarily appears in papers coauthored by Stephen Redding (ARSW 2015, RR 2017, MRR 2018, HRS 2018), but it’s also been adopted by other authors (Fratto 2018, Gaigne et al 2018, Matt Turner’s lecture notes). However, none of these papers explicitly explains the meaning of the phrase. Why are we calling these commuting costs “iceberg”?

The phrase was imported from international economics, where the concept of “iceberg transport costs” is widely used. That idea is also explicitly defined. Alan Deardorff’s glossary says:

A cost of transporting a good that uses up some fraction of the good itself, rather than other resources. By analogy with floating an iceberg, costless except for the part of the iceberg that melts. Far from realistic, but a tractable way of modeling transport costs since it impacts no other market. Due to Samuelson (1954).

Two bits of trivia that aren’t very relevant to the rest of the post: these should be called “grain transport costs” because von Thunen introduced the idea with oxen-pulled grain carts more than a century before Samuelson (1954) and basic physics means there are actually economies of scale in shipping ice.

Why do we use the iceberg assumption? As Deardorff highlights, it lets us skip modeling the transportation sector. By assumption, the same production function that produces the good also produces its delivery to customers. For better or worse, that means that international or long-distance transactions don’t affect factor demands or transport prices by being international or long-distance per se (Matsuyama 2007). This is one way of keeping trade models simple. Per Gene Grossman: “few would consider the ‘iceberg’ formulation of shipping costs as anything more than a useful trick for models with constant demand elasticities.”

In urban economics, saying that commuting costs take the “iceberg” form means that the model abstracts from transportation infrastructure and the transport sector. Commuters “pay” commuting costs by suffering lower utility. There is no supplier of transportation services that earns any revenues. (Given that most US roads are unpriced, this isn’t much of an abstraction.) But, just as folding transportation services into the goods-producing firm’s production function has consequences for trade models, saying that commuting enters the utility function directly has consequences for the economic content of urban models.

Given that these models do not feature a labor-leisure tradeoff, there is an equivalence between utility costs and time costs. As described by Ahfeldt, Redding, Sturm, and Wolf (2015): “Although we model commuting costs in terms of utility, there is an isomorphic formulation in terms of a reduction in effective units of labor, because the iceberg commuting cost enters the indirect utility function (5) below multiplicatively.” If the cost of commuting is mostly about the opportunity cost of time, then this modeling device captures it reasonably well in a model with homogeneous workers.

If workers are heterogeneous in their hourly wages, then their opportunity costs of time differ. Higher-wage workers have higher opportunity costs of time. In the classic model of locational choice (see Kevin Murphy’s lecture), this causes higher-wage workers to be more willing to pay for residential locations that give them shorter commutes. In the typical quantitative spatial model, however, preferences are Cobb-Douglas over housing and a tradable good. As a result, even with heterogeneous agents, the utility-cost and time-cost formulations of commuting costs are equivalent.

But what if commuting costs are paid with money? In addition to more time on the road, driving a greater distance involves burning more fuel. (Actually, in these models, it typically involves burning more of the numeraire good.) This is not equivalent to the utility formulation, because the cost of a tank of gas is not a constant proportion of one’s income. Moreover, if the car itself costs money, then lower-wage workers might take the bus. The monetary costs of accessing different commuting technologies can have big consequences for urban form, as suggested by LeRoy and Sonstelie (1983), Glaeser, Kahn, and Rappaport’s “Why do the poor live in cities?” and Nick Tsivanidis’s paper on bus-rapid transit in Bogota. The iceberg formulation of commuting costs cannot tackle these issues.

Similarly, even though transportation infrastructure is surely more capital-intensive than much of the economy, we cannot speak to that issue when we parsimoniously model transport as simply coming out of people’s utility.

“Iceberg commuting cost” is a short, three-word phrase. I hope the 600+ words above suggest what it might mean.

Market-size effects, across places and over time

The dividing line between neoclassical trade models and the now-quite-dated “new trade theory” is economies of scale. Neoclassical models feature constant (or decreasing) returns. Free trade is efficient in such settings. With the introduction of increasing returns, Brander, Spencer, Krugman, Helpman, and others “open[ed] the possibility that government intervention in trade via import restrictions, export subsidies, and so on may under some circumstances be in the national interest after all” (Krugman 1987).

The fact that “size matters” in new trade theory (size can influence the pattern of specialization because there are economies of scale) while it does not in neoclassical models became the basis for empirical investigations trying to distinguish these theories. Davis and Weinstein (2003) describe the idea behind this research strategy:

A fundamental divide may be identified between two classes of models. In the first class, unusually strong demand for a good, ceteris paribus, makes a country an importer of a good. An example would be a conventional two-sector neoclassical model with strictly downward sloping import demands. However, there is an alternative tradition within the trade literature which emphasizes an important interaction between demand conditions and production opportunities in which the production response to local demand conditions is so powerful that strong local demand for a product leads a country to export that product. When such conditions exist, the literature terms it a home market effect.

Stepping away from trade, there’s a very different economic context in which the role of market size is also crucial: the literature on innovation. The idea dates at least to Schmookler (1966) who memorably titled two of his chapters “The amount of invention is governed by the extent of the market.” It’s also key to endogenous growth theory. Acemoglu and Linn (2004) provided empirical evidence that market size influenced innovation in a particular sector:

This paper investigates the effect of (potential) market size on entry of new drugs and pharmaceutical innovation. Focusing on exogenous changes driven by US demographic trends, we find a large effect of potential market size on the entry of nongeneric drugs and new molecular entities… Our results show that there is an economically and statistically significant response of the entry of new drugs to market size. As the baby boom generation aged over the past 30 years… the data show a corresponding decrease in the rate of entry of new drugs in categories mostly demanded by the young and an increase for drugs mostly consumed by the middle-aged.

In “The Determinants of Quality Specialization“, I showed that high-income cities manufacture higher-priced, higher-quality goods in part because they are home to more high-income households who demand such products. Quantitatively, I found that the home-market effect plays at least as large a role as the factor-abundance mechanism in quality specialization across cities of different income levels.

What does this have to do with Acemoglu and Linn (2004)? I didn’t see much of a connection when I was writing my paper. Pharmaceuticals were just one of many industries in my data on US manufacturing plants, and pharmaceutical pills are probably less sensitive to trade costs than most goods. But I now see a closer relationship between looking for home-market effects in the cross section and looking for market-size effects in the time series.

The primary bridge is a recent QJE article by Costinot, Donaldson, Kyle and Williams. They used variation in disease burdens across countries as a source of variation in demand for drugs to look for home-market effects in international pharmaceuticals production. I’ve blogged about that paper before.

The latest connection is a paper by Xavier Jaravel called “The Unequal Gains from Product Innovations: Evidence from the U.S. Retail Sector”. His article investigates the time-series analog of my cross-sectional results on quality specialization. In recent decades, income growth has been concentrated at the top of the income distribution. Did the increase in the relative size of the affluent market benefit the affluent beyond the straightforward income gains? With economies of scale, increases in demand could induce supply-side responses that favor affluent-demanded goods. That’s the home-market-effect story for why high-income cities are net exporters of high-quality products: due to increasing returns, greater demand elicits a more-than-proportionate production response. Jaravel documents the time-series equivalent for national outcomes: “(1) the relative demand for products consumed by high-income households increased because of growth and rising inequality; (2) in response, firms introduced more new products catering to such households; (3) as a result, the prices of continuing products in these market segments fell due to increased competitive pressure.”

As a result, a two-by-two matrix neatly summarizes these contributions to the empirical literature on market-size effects:

Pharmaceuticals Vertically differentiated consumer goods
Time series Acemoglu & Linn (2004) Jaravel (forthcoming)
Cross section Costinot, Donaldson, Kyle, Williams (2019) Dingel (2017)

There’s an obvious relationship between the AL and CDKW papers, as explained by CDKW:

In their original article, Acemoglu and Linn (2004) exploit such demographic variation over time within the United States to estimate the impact of market size on innovation. Here, we employ the spatial analog of this strategy, drawing on cross-sectional variation in the demographic composition of different countries in a given year, to explore how exogenous variation in demand may shape the pattern of trade.

With the benefit of hindsight, some more subtle connections between the four cells of this two-by-two matrix seem pretty clear. For example, Jaravel’s adoption of the Acemoglu (2007) terminology for “weak bias” and “strong bias” in his footnote 3 mirrors the distinction between the weak and strong versions of the home-market effect introduced by Costinot, Donaldson, Kyle, and Williams (2019).

In summary, market-size effects seem to be important for understanding both innovation outcomes and the geographic pattern of specialization. We’ve found market-size effects in the time series and in the cross section, for both the pharmaceutical sector and vertically differentiated manufactured goods.

Research resources that I recommend

While advising PhD students, I find myself repeatedly suggesting the same tools and tricks. Since these are general-purpose technologies, the following list of resources that I regularly recommend to my students might interest others as well. Going forward, I’ll update this webpage, not this blog post.




The job market

  • One year before you’ll be on the market, read John Cawley’s very comprehensive Guide and Advice For Economists on the US Junior Academic Job Market. The process will be more coherent and less intimidating if you see the big picture from the beginning.
  • Give a full draft of your paper to your advisors in June. Sharing something in September is too late.

Spatial economics JMPs (2018-2019)

Last year, I comingled spatial-economics candidates with the trade JMCs. This year, I’m offering a separate list. Thanks to those who suggested spatial-economics candidates in response to my tweet. Since people working on spatial economics come from a variety of fields, I’ve listed candidate’s self-identified fields in brackets after their JMP title. I’m sure I missed folks, so please add them in the comments.

  • Clare-Balboni (LSE) – In Harm’s Way? Infrastructure Investments and the Persistence of Coastal Cities [Environmental, Trade, Development]
  • Chiara Fratto (Chicago) – The reallocative effects of mobility restrictions on workers and firms. An application to the West Bank [applied macro, urban, productivity, trade]
  • Xuan Fei (UC Davis) – Land Market Misallocation in China [International, Urban, Macro, and Chinese Economy]
  • Laurel Wheeler (Duke) – Property Rights, Place-Based Policies, and Economic Development [labor and development]
  • Dennis McWeeny (Wisconsin) – Spatial Competition in the Airline Industry [industrial organization, health, and regional]
  • Yanjun (Penny) Liao (UCSD) – How Hurricanes Sweep Up Housing Markets: Evidence from Florida [Environmental, Public]
  • Xueying Lu (UCSD) – Housing Markets and Automobile Policy [Environmental, Urban, Energy]
  • Cailin Slattery (Virginia) – Bidding for Firms: Subsidy Competition in the US [public finance, urban, industrial organization]
  • Mesay M. Gebresilasse (Boston U) – Rural Roads, Agricultural Extension, and Productivity [development and applied microeconomics]
  • Tatjana Kleineberg (Yale) – Can We Save the American Dream? A Dynamic General Equilibrium Analysis of the Effects of School Financing on Local Opportunities [Macro, Economic Development, and Urban]
  • Donghyuk Kim (Yale) – Government Incentives and Firm Location Choices [Industrial Organization, Urban, and Labor]
  • Max Perez Leon (Yale) – Inducing Labor Mobility? Evidence from Peruvian Teacher Reallocation [Labor, Urban, and Personnel]
  • Nicholas Li (Berkeley) – Housing Market Channels of Segregation [Labor, Urban, Development]
  • Qing Zhang (Columbia) – Sunlight, Development Restrictions, and Urban Density [Development, Political Economy, Urban]
  • Ben Klopack (Stanford) – One Size Fits All? The Value of Standardized Retail Chains [Industrial Organization, Urban, Public]
  • Zhezhi Hou (Binghamton) – Growing from Spillovers: A Semiparametric Varying Coefficient Approach [econometrics and applied microeconomics]
  • Soeren Henn (Harvard) – Complements or Substitutes: State Presence and the Power of Traditional Leaders [development and political economy]
  • Jessica Brown (Princeton) – Does Public Pre-K have Unintended Consequences on the Child Care Market for Infants and Toddlers? [public and labor]
  • Matthew Davis (Wharton) – The Distributional Impact of Mortgage Interest Subsidies: Evidence from Variation in State Tax Policies [Real Estate, Public Finance]
  • Simon Franklin (Oxford/LSE) – The demand for government housing: evidence from a lottery for 200,000 homes in Ethiopia [Development, Labour, and Urban]

What share of US manufacturing firms export?

What share of US manufacturing firms export? That’s a simple question. But my answer recently changed by quite a lot. While updating one of my class slides that is titled “very few firms export”, I noticed a pretty stark contrast between the old and new statistics I was displaying. In the table below, the 2002 numbers are from Table 2 of Bernard, Jensen, Redding, and Schott (JEP 2007), which reports that 18% of US manufacturing firms were exporters in 2002. The 2007 numbers are from Table 1 of Bernard, Jensen, Redding, and Schott (JEL 2018), which reports that 35% of US manufacturing firms were exporters in 2007.

NAICS Description Share of firms Exporting firm share Export sales share of exporters
2002 2007 2002 2007 2002 2007
311 Food Manufacturing 6.8 6.8 12 23 15 21
312 Beverage and Tobacco Product 0.7 0.9 23 30 7 30
313 Textile Mills 1.0 0.8 25 57 13 39
314 Textile Product Mills 1.9 2.7 12 19 12 12
315 Apparel Manufacturing 3.2 3.6 8 22 14 16
316 Leather and Allied Product 0.4 0.3 24 56 13 19
321 Wood Product Manufacturing 5.5 4.8 8 21 19 09
322 Paper Manufacturing 1.4 1.5 24 48 9 06
323 Printing and Related Support 11.9 11.1 5 15 14 10
324 Petroleum and Coal Products 0.4 0.5 18 34 12 13
325 Chemical Manufacturing 3.1 3.3 36 65 14 23
326 Plastics and Rubber Products 4.4 3.9 28 59 10 11
327 Nonmetallic Mineral Product 4.0 4.3 9 19 12 09
331 Primary Metal Manufacturing 1.5 1.5 30 58 10 31
332 Fabricated Metal Product 19.9 20.6 14 30 12 09
333 Machinery Manufacturing 9.0 8.7 33 61 16 15
334 Computer and Electronic Product 4.5 3.9 38 75 21 28
335 Electrical Equipment, Appliance 1.7 1.7 38 70 13 47
336 Transportation Equipment 3.4 3.4 28 57 13 16
337 Furniture and Related Product 6.4 6.5 7 16 10 14
339 Miscellaneous Manufacturing 9.1 9.3 2 32 15 16
Aggregate manufacturing 100 100 18 35 14 17


Did a huge shift occur between 2002 and 2007? No. The difference between these two tables is due to a change in the data source used to identify whether a firm exports. In their 2007 JEP article, BJRS used a question about export sales in the Census of Manufactures (CM). In their 2018 JEL article, BJRS used customs records from the Longitudinal Firm Trade Transactions database (LFTTD) that they built. Footnote 23 of the latter article notes that “the customs records from LFTTD imply that exporting is more prevalent than would be concluded based on the export question in the Census of Manufactures.”

This is a bit of an understatement: only about half of firms that export in customs records say that they export when asked about it in the Census of Manufactures! [This comparison is inexact because the share of exporting firms may have really increased from 2002 to 2007, but BJRS (2018) say that they “find a relatively similar pattern of results for 2007 as for 2002” when they use the CM question for both years.] The typical three-digit NAICS industry has the share of firms that export roughly double when using customs data rather than the Census of Manufactures survey response. Who knows what happened in “Miscellaneous Manufacturing” (NAICS 339), which had 2% in the 2002 CM and 35% in the 2007 LFTTD.

I presume that the customs records are more reliable than the CM question. More firms are exporters than I previously thought!

Trade JMPs (2018-2019)

It’s already November again. Time flies. As I do annually, I’ve gathered a list of trade-related job-market papers. The market leader in trade this year is Penn State, which offers seven candidates. If I’ve missed someone, please contribute to the list in the comments. A few schools (e.g., UCLA, Yale) have not yet posted candidates.

[Nov 11 update: I’ve added a number of candidates since this was posted Nov 5. Now listing 40 people. I didn’t recompute stats nor word cloud.]

Of the 33 candidates I’ve initially listed, 16 use Google Sites, 8 registered their own domain, and only 5 use school-provided webspace (3 use Weebly; 1 GitHub).

Here’s a cloud of the words that appear at least twice in these papers’ titles:


Why I encourage econ PhD students to learn Julia

Julia is a scientific computing language that an increasing number of economists are adopting (e.g., Tom Sargent, the NY FRB). It is a close substitute for Matlab, and the cost of switching from Matlab to Julia is somewhat modest since Julia syntax is quite similar to Matlab syntax after you change array references from parentheses to square brackets (e.g., “A(2, 2)” in Matlab is “A[2, 2]” in Julia and most other languages), though there are important differences. Julia also competes with Python, R, and C++, among other languages, as a computational tool.

I am now encouraging students to try Julia, which recently released version 1.0. I first installed Julia in the spring of 2016, when it was version 0.4. Julia’s advantages are that it is modern, elegant, open source, and often faster than Matlab. Its downside is that it is a young language, so its syntax is evolving, its user community is smaller, and some features are still in development.

A proper computer scientist would discuss Julia’s computational advantages in terms of concepts like multiple dispatch and typing of variables. For an unsophisticated economist like me, the proof of the pudding is in the eating. My story is quite similar to that of Bradley Setzler, whose structural model that took more than 24 hours to solve in Python took only 15 minutes using Julia. After hearing two of my computationally savvy Booth colleagues praise Julia, I tried it out when doing the numerical simulations in our “A Spatial Knowledge Economy” paper. I took my Matlab code, made a few modest syntax changes, and found that my Julia code solved for equilibrium in only one-sixth of the time that my Matlab code did. My code was likely inefficient in both cases, but that speed improvement persuaded me to use Julia for that project.

For a proper comparison of computational performance, you should look at papers by S. Boragan Aruoba and Jesus Fernandez-Villaverde and by Jon Danielsson and Jia Rong Fan. Aruoba and Fernandez-Villaverde have solved the stochastic neoclassical growth model in a dozen languages. Their 2018 update says “C++ is the fastest alternative, Julia offers a great balance of speed and ease of use, and Python is too slow.” Danielsson and Fan compared Matlab, R, Julia, and Python when implementing financial risk forecasting methods. While you should read their rich comparison, a brief summary of their assessment is that Julia excels in language features and speed but has considerable room for improvement in terms of data handling and libraries.

While I like Julia a lot, it is a young language, which comes at a cost. In March, I had to painfully convert a couple research projects written in Julia 0.5 to version 0.6 after an upgrade of GitHub’s security standards meant that Julia 0.5 users could no longer easily install packages. My computations were fine, of course, but a replication package that required artisanally-installed packages in a no-longer-supported environment wouldn’t have been very helpful to everyone else. I hope that Julia’s 1.0 release means that those who adopt the language now are less likely to face such growing pains, though it might be a couple of months before most packages support 1.0.

At this point, you probably should not use Julia for data cleaning. To be brief, Danielsson and Fan say that Julia is the worst of the four languages they considered for data handling. In our “How Segregated is Urban Consumption?” code, we did our data cleaning in Stata and our computation in Julia. Similarly, Michael Stepner’s health inequality code relies on Julia rather than Stata for a computation-intensive step and Tom Wollmann split his JMP code between Stata and Julia. At this point, I think most users would tell you to use Julia for computation, not data prep. (Caveat: I haven’t tried the JuliaDB package yet.)

If you want to get started in Julia, I found the “Lectures in Quantitative Economics” introduction to Julia by Tom Sargent and John Stachurski very helpful. Also look at Bradley Setzler’s Julia economics tutorials.

Trade economists might be interested in the Julia package FixedEffectModels.jl. It claims to be an order of magnitude faster than Stata when estimating two-way high-dimensional fixed-effects models, which is a bread-and-butter gravity regression. I plan to ask PhD students to explore these issues this fall and will report back after learning more.