Author Archives: jdingel

Bhagwati Award and Lösch Prize

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.

Spatial economics JMPs (2021-2022)

Here’s a list of job-market candidates whose job-market papers fall within spatial economics, as defined by me quickly skimming webpages. I’m sure I missed folks, so please add them in the comments.

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

Zahin Haque (NYU) – The Production Engel
Yuta Suzuki (Penn State) – Local Shocks and Regional Dynamics in an Aging Economy
Yao Wang (Syracuse University) – Linguistic Distance, Internal Migration and Welfare: Evidence from Indonesia
Xian Jiang (Duke) – Information and Communication Technology and Firm Geographic Expansion
Vladimir Avetian (Sciences Po) – Consider the Slavs: Overt Discrimination and Racial Disparities in the Rental Housing
Vinicios Sant’Anna (UIUC) – Send Them Back? The Real Estate Consequences of Repatriations
Vinayak Iyer (Columbia) – What Drives the Efficiency in Ridesharing Markets? Evidence from Austin, Texas
Victor Yifan Ye (BU) – Simulating Endogenous Global Automation
Vasily Rusanov (NYU) – Internal Migration and the Diffusion of Schooling in the US
Valentin Lindlacher (Munich) – Digital Infrastructure and Local Economic Growth: Early Internet in Sub-Saharan Africa
Taylor Mackay (UC Irvine) – Source of Income Discrimination and the Housing Choice Voucher Program
Tanner Regan (LSE) – Ask a local: Improving public pricing in urban Tanzania
Tal Roitberg (USC) – Can’t Wait? Urgency with Strategic Commuters and Tolled Express Lanes
Sung-Yup Jung (BU) – Industrial Parks and Regional Development: Evidence from South Korean Industrial Park Policy
Sarah Schneider-Strawczynski (PSE) – When is Contact Effective? Evidence on Refugee-Hosting and Far-Right Support in France
Rui Yu (Wharton) – Returns to Political Contributions in Local Housing Markets
Rolando Campusano (Toronto Rotman) – Startup Location, Local Spillovers, and Neighborhood Sorting
Philip Mulder (Wharton) – Mismeasuring Risk: The Welfare Effects of Flood Risk Information
Petr Martynov (Berkeley Haas) – Welfare Effects of Zoning: Density Constraints and Heterogeneous Agglomeration
Pawel Janas (Kellogg) – Public goods under financial distress: evidence from cities in the great depression
Paul J. Fisher (Arizona) – The Role of Property Tax in California’s Housing Crisis
Palak Suri (Maryland) – Public Transit Infrastructure and Employment Accessibility: The Benefits of the Mumbai Metro
Nikhil Datta (UCL) – Local Monopsony Power
Motaz Al-Chanati (Columbia) – Residential Segregation and the Demand for Schooling
Mingxi Li (UC Davis) – Firm Foundations: Legal Systems and Economic Performance in Colonial Shanghai, 1903-1934
Michael Pollmann (Stanford) – Causal Inference for Spatial Treatments
Matthew Lilley (HBS) – The Long Run Effects of Right to Work Laws
Mason Reasner (Purdue) – Agglomeration and Congestion Spillovers: Evidence from Base Realignment and Closure
Martin Jégard (PSE) – An Optimal Distribution of Polluting Activities Across Space
Manuela Puente Beccar (Bocconi) – Sorting and health: understanding health inequalities
Luca Perdoni (Yale) – The Effects of Federal “Redlining” Maps: A Novel Estimation Strategy
Kristina Komissarova (NYU) – Location Choices over the Life Cycle: The Role of Relocation for Retirement
Kohei Takeda (LSE) – On the Geography of Structural Transformation: Impact on Inequality and Upward Mobility
Juan Pablo Uribe (Brown) – Subsidies and Market Equilibrium: Evidence from a Notch in the Colombian Housing Market
Josh Morris-Levenson (Chicago) – The Origins of Regional Specialization
Jose Morales-Arilla (HKS) – Autocrats in crisis mode: Strategic favoritism during economic shocks
John Morehouse (Oregon) – Carbon Taxes in Spatial Equilibrium
Jaehee Song (Yale) – The Effects of Residential Zoning in U.S. Housing Markets
Iain Bamford (Columbia) – Monopsony Power, Spatial Equilibrium, and Minimum Wages
Hyun Yeol Kim (Rochester) – Internal U.S. Migration and Consumption Dynamics: A Panel Data Analysis
Georgios Tsiachtsiras (Barcelona) – Transportation Networks and the Rise of the Knowledge Economy in 19th Century France
Filippo Tassinari (Barcelona) – Low emission zones and traffic congestion: evidence from Madrid Central
Fernando Stipanicic (TSE) – The Creation and Diffusion of Knowledge: Evidence from the Jet Age
Fernanda Rojas-Ampuero (UCLA) – Sent Away: The Long-Term Effects of Slum Clearance on Children and Families
Elisa Facchetti (Queen Mary University of London) – Police Infrastructure, Police Performance and Crime: Evidence from Austerity Cuts
Dzhamilya Nigmatulina (LSE) – Misallocation and State Ownership: Evidence from the Russian Sanctions
Dheeraj Chaudhary (Maryland) – Trade, Financial Development, and Inequality: Evidence from US Railroads in the 19th Century
Derek Christopher (UC Irvine) – Homeownership in the Undocumented Population and the Consequences of Credit Constraints
Cory Briar (Oregon) – Rent Control and Gentrification in San Francisco: A Simulation Approach
Cora Wigger (Northwestern) – Decoupling Homes and Schools
Christopher M. Hair (Kellogg) – The Local Effects of Spatially Targeted Public Policies: Evidence from California School Finance Reform
Bunyada (Mos) Laoprapassorn (Michigan) – Entry and Spatial Competition of Intermediaries: Evidence from Thailand’s Rice Market
Benjamin Freyd (UCLA) – Labor Market Polarization and the Growth of Service Employment: Routinization or Consumption Spillovers?
Barthélémy Bonadio (Michigan) – Ports vs. Roads: Infrastructure, Market Access and Regional Outcomes
Arman Khachiyan (UCSD) – The Impacts of Fracking on Microspatial Residential Investment
Antoine Levy (MIT) – Tax Policy and Spatial Investment Behavior
Anomita Ghosh (UIUC) – Developing incentives to move new physicians: Longitudinal evidence from a local supply-side reform
András Jagadits (UPF) – Emigration and Local Structural Change: Evidence from (Austria)-Hungary in the Age of Mass Migration
Abhishek Rai (Penn State) – Temporary Migration in Spatial Economy

Trade JMPs (2021-2022)

For the 12th year running, I’ve gathered a list of trade-related job-market papers. It’s in reverse-alphabetical order by first name. If I’ve missed someone, please contribute to the list in the comments.

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


Zachary Kiefer (Oregon) – Extracting the Costs of International Internet Communcation
Yuting Gao (Indiana) – Lobbying for Trade Liberalization and its Policy Influence
Yuta Suzuki (Penn State) – Local Shocks and Regional Dynamics in an Aging Economy
Yu-Siang Wu (Michigan State) – Import Exposure and STEM Major Choice: Evidence from the U.S.
Youssef A. Benasser (Oregon) – Measuring Trade Policy Uncertainty and Its Impact on Financial Market Volatility
YongKi Hong (UCLA) – Related-Party Trades in Vertical Integration
Yoko Shibuya (Stanford) – Firm Size and Complementarity between Geography and Products
Yogeshwar Bharat (Michigan State) – Credit Constraints, Bank Incentives, and Firm Export: Evidence from India
Yao Wang (Syracuse University) – Linguistic Distance, Internal Migration and Welfare: Evidence from Indonesia
Xiaohan Zhang (Minnesota) – Increased degree of openness, technology capital and structural change in the U.S.
Torsten Jaccard (Toronto) – Who Pays for Protectionism? The Welfare and Substitution Effects of Tariff Changes
Tianchen Song (Rochester) – Multinationals Expansion, Shareholding Choice, and Local Know-How
Thu Tran (Vanderbilt) – Financial constraints and trade intermediation
Shihangyin (Frank) Zhang (Maryland) – Quantitative Effects of Trade Shocks Under Global Supply Chains
Ron Yang (HBS) – (Don’t) Take Me Home: Home Bias and the Effect of Self-Driving Trucks on Interstate Trade
Roman Merga (Rochester) – International Trade, Volatility, and Income Differences
Prakrati Thakur (UIUC) – Welfare Effects of International Trade in Waste
Nicholas Rowe (Michigan State) – When Does Finance Help Trade? Banking Structures and Export in the Macroeconomy
Mélina London (Aix-Marseille) – Trade Networks and Natural Disasters: Diversion, not Destruction
Md. Deen Islam (BU) – The Geography of Automation
Mayara Felix (MIT) – Trade, Labor Market Concentration, and Wages
Mathilde Munoz (PSE) – Trading Non-Tradables: The Implications of Europe’s Job Posting Policy
Maria-Jose Carreras-Valle (Minnesota) – Increasing Inventories: The Role of Delivery Times
Lydia Cox (Harvard) – The Long-Term Impact of Steel Tariffs on U.S. Manufacturing
Lidia Smitkova (Cambridge) — Competitiveness, ‘Superstar’ Firms and Capital Flows: The North-South Divide in Pre-Crisis Europe
Kairong Chen (Indiana) – Trade Policy Uncertainty: Measurement and Impacts on US firms in Global Value Chains
Jun-Tae Park (Michigan State) – The Welfare Effects of Labor Switching Costs in a Trade Economy
José-Luis Cruz (Princeton) – Global Warming and Labor Market Reallocation
John Finlay (Yale) – Exporters, Credit Constraints, and Misallocation
Jiacheng Feng (Harvard) – Signaling Export Quality under Firm Heterogeneity
Jeffrey Wang (Harvard) – Robots, Trade, and Offshoring: Perspective from US Firms
Jaedo Choi (Michigan) – Technology Adoption and Late Industrialization
Heehyun Rosa Lim (Maryland) – Trade in Intermediates and US Manufacturing Emissions
Hamza Zahid (Houston) – Global Footprints of US Energy Innovations: Energy Efficiency and Shale Revolution
Giulia Sabbadini (Graduate Institute) – Firm-Level Prices, Quality, and Markups: The Role of Immigrant Workers
Fernando Stipanicic (TSE) – The Creation and Diffusion of Knowledge: Evidence from the Jet Age
Evgenii Fadeev (Harvard) – Creative Construction: Knowledge Sharing in Production Networks
Elijah Coleman/ (Vanderbilt) – Patent Placement: Evaluating the Impact of the WTO/TRIPS on International Patenting Behavior
Conor Foley (UCLA) – Flexible Entry/Exit Adjustment for Price Indices
Chujian Shao (Washington) – International Trade, Immigration and Macroeconomic Dynamics
Brian Pustilnik (UCLA) – Trade Policy on a Buyer-Seller Network
Bin Zhao (Cornell) – Input Specificity, Firm-to-Firm Trade, and Firm Growth
Barthélémy Bonadio (Michigan) – Ports vs. Roads: Infrastructure, Market Access and Regional Outcomes
Aycan Katitas (Princeton) – Politicizing Trade: How Economic Discontent and Identity Politics Shape Anti-Trade Campaign Appeals

J. Peter Neary (1950-2021)

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.

A reminder about the definition of trade diversion

It’s likely been more than a decade since a Trade Diversion blog post actually mentioned trade creation and trade diversion. Having missed numerous opportunities in recent years, I won’t pass up commenting on the following paragraph in Noah Smith’s recent post about experts and public policy:

Nor was this the only form of deception that economists employed in defense of free trade. Economists have known for many decades that some countries as a whole can be hurt by free trade. If a multilateral trade agreement — like the WTO, for example — admits new member countries, existing countries who compete directly with the newcomer nations can become poorer. This is called “trade diversion”, and it follows directly from the same simple classical economic theories of comparative advantage that economists typically use to justify free trade.

That paragraph is neither the most important nor most interesting part of his recent post, but it is salient for the owner of the leading trade-diversion-related domain name on the internet. Simon Lester, David DeRemer, and Michael Lane already pointed out on Twitter that Noah’s description of trade diversion has problems. Michael’s thread explained it clearly, but for posterity’s sake, here’s a blog post about the definition of trade diversion.

The concepts of trade creation and trade diversion concern the economic consequences of preferential trade agreements. They were introduced by Jacob Viner in a 1950 book titled The Customs Union Issue (that was, surprisingly, reprinted with a new introduction in 2014). Alan Deardorff’s glossary contains a concise definition of trade diversion: “Trade that occurs between members of a PTA that replaces what would have been imports from a country outside the PTA. Associated with welfare reduction for the importing country since it increases the cost of the imported good.”

Here’s a longer explanation from Richard Lipsey (Economica, 1957):

A is a small country whose inhabitants consume two commodities, wheat and clothing. A specialises in the production of wheat and obtains her clothing by means of international trade. Being small, she cannot influence the price of clothing in terms of wheat. Country C offers clothing at a lower wheat price than does B, so that, in the absence of country-discriminating tariffs, A will trade with C, exporting wheat in return for clothing. It is assumed that A levies a tariff on imports but that the rate is not high enough to protect a domestic clothing industry, so that she purchases her clothing from C. Now let country A form a customs union with country B, as a result of which B replaces C as the supplier of clothing to A. B is a higher cost producer of clothing than is C but her price without the tariff is less than C’s price with the tariff. Hence, the union causes trade diversion, A’s trade being diverted from C to B.

The language of economics was a bit different in 1957, to say the least.

The welfare losses that might result from trade diversion are a consequence of lost government revenue: consumer expenditure switches to the higher-cost supplier because it does not face an import tariff. Viner wrote “This is a shift which the protectionist approves, but it is not one which the free trader who understands the logic of his own doctrine can properly approve.” If one treats consumer surplus and government tariff revenue as equally valuable, trade diversion may cause a net loss. A typical diagram:


Returning to the blog post that sparked this one, Noah’s paragraph is odd for at least two reasons. First, it’s unusual to think of the World Trade Organization as a preferential trade agreement. WTO members are obliged to charge each other most-favored-nation (MFN) duties. For the WTO to be a preferential agreement associated with trade diversion, Noah must have in mind lower-cost non-WTO suppliers. But China wasn’t joining an exclusive club. Back in 2001, Vietnam and Afghanistan were not yet WTO members, but something like 140 nations already were and Vietnam already had MFN access to the US market.

Second, import duties on Chinese goods were not lowered when it became a WTO member. The United States had charged MFN rates on imported Chinese goods since 1980 (these were annually renewed; China’s WTO accession made them permanent). A policy change that does not lower import duties cannot cause trade diversion in classical trade theory. Of course, trade policy uncertainty was reduced, but that story is a bit far from the theory of comparative advantage taught in undergraduate economics classes.

Bottom line: When thinking about whether the United States was hurt by China joining the WTO, you don’t need to contemplate trade diversion.

Spatial economics JMPs (2020-2021)

Here’s a list of job-market candidates whose job-market papers fall within spatial economics, as defined by me quickly skimming webpages. I’m sure I missed folks, so please add them in the comments.

Of the 27 candidates I’ve initially listed, 12 registered a custom domain, 8 used Google Sites, 3 used GitHub, 3 used school-provided webspace, and 2 used Weebly.

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

Aaron Weisbrod (Brown) – Housing Booms and Urban Frictions: The Impact of the 1917 Halifax Explosion on Local Property Values
Aleksandar Petreski (Jönköping University, Sweden)
Spatial-temporal asymmetry, shock and memory: housing transaction prices in Sweden
Andrew Simon (Michigan) – Public Good Spillovers and Fiscal Centralization: Evidence from Community College Expansions
Avichal Mahajan (Geneva) – Highways and segregation
Björn Brey (Nottingham) – The long-run gains from the early adoption of electricity
Brendan Shanks (LMU Munich) – Land Use Regulations and Housing Development: Evidence from Tax Parcels and Zoning Bylaws in Massachusetts
Christoph Albert (CEMFI) – Immigration and Spatial Equilibrium: the Role of Expenditures in the Country of Origin
Desen Lin (Penn) – Housing Search and Rental Market Intermediation
Dmitry Sedov (Northwestern) – How Efficient are Firm Location Configurations? Empirical Evidence from the Food Service Industry
Eduardo Fraga (Yale) – Drivers of Concentration: The Roles of Trade Access, Structural Transformation, and Local Fundamentals
Eunjee Kwon (USC) – Why Do Improvements in Transportation Infrastructure Reduce the Gender Gap in South Korea?
Ewane Theophile (UQAM) – Trade costs, prices and connectivity in Rwanda
Ezequiel Garcia-Lembergman (Berkeley) – Multi-establishment Firms, Pricing and the Propagation of Local Shocks: Evidence from US Retail
Franklin Qian (Stanford) – The Effects of High-skilled Firm Entry on Incumbent Residents
Gregor Schubert (HBS) – House Price Contagion and U.S. City Migration Networks
Ian Herzog (Toronto) – The City-Wide Effects of Tolling Downtown Drivers: Evidence from London’s Congestion Charge
Jacob Krimmel (Wharton) – Reclaiming Local Control: School Finance Reforms and Housing Supply Restrictions
Jan David Bakker (UCL) – Trade and Agglomeration: Theory and Evidence from France
Joanna Venator (Wisconsin) – Dual Earner Migration, Earnings, and Unemployment Insurance
John Pedersen (Binghamton) – Voting for Transit: The Labor Impact of Public Transportation Improvements
Jonathan Moreno-Medina (Duke) – Local Crime News Bias and Housing Markets
Kate Pennington (Berkeley ARE) – Does Building New Housing Cause Displacement?: The Supply and Demand Effects of Construction in San Francisco
Kenneth Tester (Kentucky) – The Effect of Taxes on Where Superstars Work
Magdalena Domínguez (Barcelona) – Sweeping Up Gangs: The Effects of Tough-on-crime Policies from a Network Approach
Marcos Ribeiro Frazao (Yale) – Brand Contagion: The Popularity of New Products in the United States
Margaret Bock (WVU) – Unintended Consequences of the Appalachian Development Highway System on Mortality
Mariya Shappo (Illinois) – The Long-Term Impact of Oil and Gas Extraction: Evidence from the Housing Market
Matthew Gross (Michigan) – The Long-Term Impacts of Rent Control on Renters
Meng Li (Queen’s) – Within-city Income Inequality, Residential Sorting, and House Prices
Miguel Zerecero (TSE) – The Birthplace Premium
Pablo E. Warnes (Columbia) – Transport Infrastructure Improvements, Intra-City Migration, and Spatial Sorting: Evidence from a BRT system in Buenos Aires
Pedro Tanure Veloso (Minnesota) – Housing Supply Constraints and the Distribution of Economic Activity: The Case of the Twin Cities
Piyush Panigrahi (Berkeley) – Endogenous Spatial Production Networks: Quantitative Implications for Trade and Productivity
Prottoy Aman Akbar (Pittsburgh) – Who Benefits from Faster Public Transit?
Rizki Nauli Siregar (UC Davis) – Global Prices, Trade Protection, and Internal Migration: Evidence from Indonesia
Sarah Thomaz (UC Irvine) – Investigating ADUS: Determinants of Location and Their Effects on Property Values
Sebastian Ellingsen (Pompeu Fabra) – Free and Protected: Trade and Breaks in Long-Term Persistence
Sebastian Ottinger (UCLA Anderson) – Immigrants, Industries and Path Dependence
Shiyu Cheng (Kentucky) – High-Speed Rail Network and Brain Drain: Evidence from College Admission Scores in China
Sydney Schreiner (Ohio State) – Does Gentrification Stop at the Schoolhouse Door? Evidence from New York City
Tianyun Zhu (Syracuse) – Estimating the Implicit Price Elasticity of the Demand for Neighborhood Amenities: A Hedonic Approach
Tillman Hönig (LSE) – The Legacy of Conflict: Aggregate Evidence from Sierra Leone
Timur Abbiasov (Columbia) – Do Urban Parks Promote Racial Diversity in Social Interactions? Evidence from New York City
Xiao Betty Wang (Wharton) – Housing Market Segmentation
Yiming He (Stanford) – The Economic Impacts of Slum Demolition on the Displaced: Evidence from Victorian England
Zibin Huang (Rochester) – Peer Effects, Parental Migration and Children’s Human Capital: A Spatial Equilibrium Analysis in China

Trade JMPs (2020-2021)

For the 11th year running, I’ve gathered a list of trade-related job-market papers. It’s in reverse-alphabetical order by first name. If I’ve missed someone, please contribute to the list in the comments.

Of the 38 candidates I’ve initially listed, 15 registered a custom domain, 13 used Google Sites, 4 used GitHub, 3 used Weebly and only 3 use school-provided webspace.

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

Ziho Park (Chicago) – Trade Adjustment: Establishment-Level Evidence
Zachary Kiefer (Oregon) – Extracting the Costs of International Internet Communication
Yuta Watabe (Penn State) – Triangulating Multinationals and Trade
Yusuke Kuroishi (LSE) – Value of Trademarks: Micro Evidence from Chinese Exports to Africa
Yoonseon Han (Kentucky) – Determinants of Export Earnings Volatility
Yang Zhou (Minnesota) – The US-China Trade War and Global Value Chains
Xiaochen Xie (Penn State) – Export Dynamics: Evidence from the Global Mobile Phone Industry
Xiao Ma (UC San Diego) – College Expansion, Trade, and Innovation: Evidence from China
Vu Thanh Chau (Harvard) – International Portfolio Investments with Trade Networks
Trang Hoang (Vanderbilt) – The Dynamics of Global Sourcing
Tomas Dominguez-Iino (NYU) – Efficiency and Redistribution in Environmental Policy: An Equilibrium Analysis of Agricultural Supply Chains
Todd Messer (Berkeley) – Foreign Currency as a Barrier to International Trade: Evidence from Brazil
Tanmay Belavadi (Penn State) – Informality, Inequality and Trade
Swapnika Rachapalli (Toronto) – Learning Between Buyers and Sellers Along the Global Value Chain
Sen Ma (Illinois) – Can Foreign Direct Investment Increase the Productivity of Domestic Firms? Identifying FDI Spillovers from Borders of Chinese Dialect Zones
Sebastian Ellingsen (Pompeu Fabra) – Free and Protected: Trade and Breaks in Long-Term Persistence
Samuel Bailey (Minnesota) – Competition and Coordination in Infrastructure: Port Authorities’ Response to the Panama Canal Expansion
Roza Khoban (Stockholm University) – The Impact of Trade Liberalization in the Presence of Political Distortions
Ross Jestrab (Syracuse) – Do Multilateral and Bilateral Trade Agreements Share the Same Motive? An Empirical Investigation
Rizki Nauli Siregar (UC Davis) – Global Prices, Trade Protection, and Internal Migration: Evidence from Indonesia
Priyam Verma (Houston) – Optimal Infrastructure after Trade Reform in India
Piyush Panigrahi (Berkeley) – Endogenous Spatial Production Networks: Quantitative Implications for Trade and Productivity
Paul Ko (Penn State) – Dissecting Trade and Business Cycle Co-movement
Monika Khan (Kentucky) – Finance and Trade: The Role of Stock Markets and Importers
Marius Faber (Basel) – Robots and Reshoring: Evidence from Mexican Labor Markets
Marijn Bolhuis (Toronto) – Financial Linkages and the Global Business Cycle
Lucas Zavala (Yale) – Unfair Trade: Market Power in Agricultural Value Chains
Kendrick Morales (UC Irvine) – Religious hostilities: A consequence of international trade?
Jan David Bakker (UCL) – Trade and Agglomeration: Theory and Evidence from France
Haruka Takayama (Virginia) – Greenfield or Brownfield? FDI Entry Mode and Intangible Capital
Haishi Harry Li (Chicago) – Multinational Production and Global Shock Propagation in the Great Recession
Gustavo Gonzalez (Chicago) – Commodity Price Shocks, Factor Utilization, and Productivity Dynamics
Ezequiel Garcia-Lembergman (Berkeley) – Multi-establishment Firms, Pricing and the Propagation of Local Shocks: Evidence from US Retail
Eduardo Fraga (Yale) – Drivers of Concentration: The Roles of Trade Access, Structural Transformation, and Local Fundamentals
Ebehi Iyoha (Vanderbilt) – Estimating Productivity in the Presence of Spillovers: Firm-level Evidence from the US Production Network
Daniel Bonin (Purdue) – To Greener Pastures: the Domestic Migration Response to Social Policies and Its Impact on Political Polarization
Daisuke Adachi (Yale) – Robots and Wage Polarization: The Effects of Robot Capital across Occupations
Christoph Albert (CEMFI) – Immigration and Spatial Equilibrium: the Role of Expenditures in the Country of Origin
Chenying Yang (UBC) – Location Choices of Multi-plant Oligopolists: Theory and Evidence from the Cement Industry
Bérengère Patault (CREST-Ecole Polytechnique) – How valuable are business networks? Evidence from sales managers in international markets
Bruno Conte (UAB) – Climate change and migration: the case of Africa
Brett McCully (UCLA) – Immigrants, Legal Status, and Illegal Trade
Arnold Njike (Université Paris Dauphine) – Trade in value-added and the welfare gains of international fragmentation
Armen Khederlarian (Rochester) – Inventories, Input Costs and Productivity Gains from Trade Liberalizations
Alexander Wise (Princeton) – Global Dynamics of Structural Change

Thought experiments that exact hat algebra can and cannot compute

Among other things, I’m teaching the Eaton-Kortum (2002) model and “exact hat algebra” to my PhD class tomorrow. Last year, my slides said “this model’s counterfactual predictions can be obtained without knowing all parameter values by a procedure that we now call ‘exact hat algebra’.” Not anymore. Only some of its counterfactual predictions can be attained via that technique.

As I reviewed in a 2018 blog post, when considering a counterfactual change in trade costs (and no change in exogenous productivities nor population sizes), the exact-hat-algebra calculation requires only the trade elasticity and initial trade flows in order to solve for the endogenous proportionate wage changes associated with any choice of exogenous proportionate trade-cost changes.

In Section 6.1 of Eaton and Kortum (2002), the authors consider two counterfactual scenarios that speak to the gains from trade. The first raises trade costs to their autarkic levels (“dni goes to infinity”). The second eliminates trade costs (“dni goes to one”). Exact hat algebra can be used to compute the first counterfactual; see Costinot and Rodriguez-Clare (2014) for a now-familiar exposition or footnote 42 in EK (setting α = β = 1). The second counterfactual cannot be computed by exact hat algebra.

One cannot compute the “zero-gravity” counterfactual of Eaton and Kortum (2002) using exact hat algebra because this would require one to know the initial levels of trade costs. To compute the proportionate change in trade costs associated with the dni=1 counterfactual, one would need to know the values of the “factual” dni. The exact hat algebra procedure doesn’t identify these values. Exact hat algebra allows one to compute proportionate changes in endogenous prices in an underidentified model by leveraging implicit combinations of parameter values that rationalize the observed initial equilibrium without separately identifying them.

Exact hat algebra requires only the trade elasticity and the initial trade matrix (including expenditures on domestically produced goods). That’s not enough to identify the model’s parameters. (If these moments alone sufficed to identify bilateral trade costs, the Head-Ries index that only computes their geometric mean wouldn’t be necessary.) Thus, one can only use exact hat algebra to compute outcomes for counterfactual scenarios that don’t require full knowledge of the model’s parameter values. One can express the autarky counterfactual in proportionate changes (“d-hat is infinity”), but one cannot define the proportionate change in trade costs for the “zero-gravity” counterfactual without knowing the initial levels of trade costs. There are some thought experiments that exact hat algebra cannot compute.

Update (5 Oct): My comment about the contrast between the two counterfactuals in section 6.1 of Eaton and Kortum (2002) turns out to be closely related to the main message of Waugh and Ravikumar (2016). They and Eaton, Kortum, Neiman (2016) both show ways to compute the frictionless or “zero-gravity” equilibria when using additional data (namely, prices or price deflators). See also footnote 7 of Sposi, Santacreu, Ravikumar (2019), which is attached to the sentence “Note that reductions of trade costs (dij − 1) require knowing the initial value of dij.”

Who is working at home during the pandemic?

In late March, Brent Neiman and I posted a paper addressing a straightforward and suddenly pressing question: How Many Jobs Can be Done at Home?

Our aim was to describe what is feasible. Looking at pre-2020 practices, one would not have observed many high-school teachers working from home, but the global pandemic changed that. We used information on job characteristics to estimate which occupations could be performed entirely at home. Of course, this supply-side trait is only one important ingredient when thinking about jobs during the crisis. Demand-side considerations, such as designating a job as “essential”, are clearly important too. Couriers and messengers cannot work from home, but this industry has seen robust employment growth in recent months.

Enough time has passed that we are now learning who has been working at home during the pandemic. In a recent Economics Observatory column (What has coronavirus taught us about working from home?) and the latest version of our paper, Brent and I discuss some of this evidence. The initial evidence suggests that our classification of occupations is quite sensible.

In the United States, Alexander Bick, Adam Blandin, and Karel Mertens have been conducting a Real-Time Population Survey, an online survey of adults designed to mimic the Current Population Survey. Last week, they released a paper called “Work from Home After the COVID-19 Outbreak“. They report that 35 percent of their US respondents worked entirely from home in May 2020. Their Figure 1 shows that the share of respondents in an industry working from home in May is highly correlated with our estimate of the feasible share for that industry.

In Europe, the EU’s Eurofound launched an e-survey, Living, working and COVID-19, “to capture the most immediate changes during the pandemic and their impact.” Last month, they released first results on the impact of the pandemic on work and teleworking. As we report in our latest draft, there is a close correspondence between our country-level estimates of feasibility and what has occurred during the crisis.

Finally, while the latest update of the relevant paper hasn’t been posted online yet, in the video presentation below, Ed Glaeser reports that the industry-level variation in the share of jobs reported as being performed at home in a survey of small businesses is highly correlated with our industry-level feasible shares.

We classified the feasibility of working from home based on pre-pandemic conditions. Over time, I expect businesses to adapt their practices and leverage new tools to reallocate tasks and change the nature of jobs. A pressing question, which I briefly discussed at the end of a recent seminar presentation, is whether this temporary surge in remote work will have permanent consequences for the future of work.

In the short run, using pre-pandemic job characteristics to classify which jobs can be done at work has aligned well with who has actually been working at home during the pandemic.

Spatial Economics for Granular Settings

Economists studying spatial connections are excited about a growing body of increasingly fine spatial data. We’re no longer studying country- or city-level aggregates. For example, many folks now leverage satellite data, so that their unit of observation is a pixel, which can be as small as only 30 meters wide. See Donaldson and Storeygaard’s “The View from Above: Applications of Satellite Data in Economics“. Standard administrative data sources like the LEHD publish neighborhood-to-neighborhood commuting matrices. And now “digital exhaust” extracted from the web and smartphones offers a glimpse of behavior not even measured in traditional data sources. Dave Donaldson’s keynote address on “The benefits of new data for measuring the benefits of new transportation infrastructure” at the Urban Economics Association meetings in October highlighted a number of such exciting developments (ship-level port flows, ride-level taxi data, credit-card transactions, etc).

But finer and finer data are not a free lunch. Big datasets bring computational burdens, of course, but more importantly our theoretical tools need to keep up with the data we’re leveraging. Most models of the spatial distribution of economic activity assume that the number of people per place is reasonably large. For example, theoretical results describing space as continuous formally assume a “regular” geography so that every location has positive population. But the US isn’t regular, in that it has plenty of “empty” land: more than 80% of the US population lives on only 3% of its land area. Conventional estimation procedures aren’t necessarily designed for sparse data sets. It’s an open question how well these tools will do when applied to empirical settings that don’t quite satisfy their assumptions.

Felix Tintelnot and I examine one aspect of this challenge in our new paper, “Spatial Economics for Granular Settings“. We look at commuting flows, which are described by a gravity equation in quantitative spatial models. It turns out that the empirical settings we often study are granular: the number of decision-makers is small relative to the number of economic outcomes. For example, there are 4.6 million possible residence-workplace pairings in New York City, but only 2.5 million people who live and work in the city. Applying the law of large numbers may not work well when a model has more parameters than people.

Felix and I introduce a model of a “granular” spatial economy. “Granular” just means that we assume that there are a finite number of individuals rather than an uncountably infinite continuum. This distinction may seem minor, but it turns out that estimated parameters and counterfactual predictions are pretty sensitive to how one handles the granular features of the data. We contrast the conventional approach and granular approach by examining these models’ predictions for changes in commuting flows associated with tract-level employment booms in New York City. When we regress observed changes on predicted changes, our granular model does pretty well (slope about one, intercept about zero). The calibrated-shares approach (trade folks may know this as “exact hat algebra“), which perfectly fits the pre-event data, does not do very well. In more than half of the 78 employment-boom events, its predicted changes are negatively correlated with the observed changes in commuting flows.

The calibrated-shares procedure’s failure to perform well out of sample despite perfectly fitting the in-sample observations may not surprise those who have played around with machine learning. The fundamental concern with applying a continuum model to a granular setting can be illustrated by the finite-sample properties of the multinomial distribution. Suppose that a lottery allocates I independently-and-identically-distributed balls across N urns. An econometrician wants to infer the probability that any ball i is allocated to urn n from observed data. With infinite balls, the observed share of balls in urn n would reveal this probability. In a finite sample, the realized share may differ greatly from the underlying probability. The figure below depicts this ratio for one urn when I balls are distributed across 10 urns uniformly. A procedure that equates observed shares and modeled probabilities needs this ratio to be one. As the histograms reveal, the realized ratio can be far from one even when there are two orders of magnitude more balls than urns. Unfortunately, in many empirical settings in which spatial models are calibrated to match observed shares, the number of balls (commuters) and the number of urns (residence-workplace pairs) are roughly the same. The red histogram suggests that shares and probabilities will often differ substantially in these settings.

Balls and 10 urns: Histogram of realized share divided by underlying probability

Balls and 10 urns: Histogram of realized share divided by underlying probability

Granularity is also a reason for economists to be cautious about their counterfactual exercises. In a granular world, equilibrium outcomes depend in part of the idiosyncratic components of individuals’ choices. Thus, the confidence intervals reported for counterfactual outcomes ought to incorporate uncertainty due to granularity in addition to the usual statistical uncertainty that accompanies estimated parameter values.

See the paper for more details on the theoretical model, estimation procedure, and event-study results. We’re excited about the growing body of fine spatial data used to study economic outcomes for regions, cities, and neighborhoods. Our quantitative model is designed precisely for these applications.