WORKING PAPER · NO. 2021-121

Telemigration and Development:
On the Offshorability of Teleworkable Jobs
Richard Baldwin and Jonathan I. Dingel
OCTOBER 2021

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Chicago, IL 60637
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TELEMIGRATION AND DEVELOPMENT:ON THE OFFSHORABILITY OF TELEWORKABLE JOBS
Richard Baldwin
Jonathan I. Dingel

October 2021

We thank Linghui Wu and Ka Lok (Steve) Wong for excellent research assistance, Kari
Greenswag for helpful editorial assistance, and Gene Grossman and Thomas Sampson forvaluable feedback. This paper was prepared for the book project, AI/Robots: Labour, Trade,
Growth and Welfare, edited by Lili Yan Ing and Gene Grossman for the Economic ResearchInstitute for ASEAN and East Asia (ERIA) in Jakarta. Dingel thanks the Cohen and Keenoy
Faculty Research Fund at the University of Chicago Booth School of Business.
© 2021 by Richard Baldwin and Jonathan I. Dingel. All rights reserved. Short sections of text,
not to exceed two paragraphs, may be quoted without explicit permission provided that fullcredit, including © notice, is given to the source.

Telemigration and Development: On the Offshorability of Teleworkable Jobs
Richard Baldwin and Jonathan I. Dingel
October 2021
JEL No. F1,J2

ABSTRACTThe Covid-19 pandemic has introduced huge numbers of employers and employees to remotework. How many of these newly remote jobs will go overseas? We offer a rough quantification
based on two observations: 1) offshore work is trade in services, and 2) the number oftelemigrants is the volume of this trade divided by the average wage. Combining these withgravity-model estimates, we can roughly predict the number of new telemigrants that would arise
from lower barriers to trade in services. Telemigration seems unlikely to be transformative whenit comes to the development paths of most emerging economies. The baseline service trade flows
are modest, and the standard gravity model restricts modest changes to have modest impacts.
There are no tipping points in structural gravity models. Finally, we propose a simple model oftelemigration in which small changes can have large consequences. The key is to assume that
latent comparative advantage takes a different shape than typically assumed in quantitative trademodels. Given this, small changes in trade costs can generate large and asymmetric increases inthe exports of service tasks from low-wage nations.

Richard Baldwin
Pavillard 6
CH-1009 Pully
SWITZERLAND
and CEPR
and also NBER
rbaldwin@cepr.org
Jonathan I. Dingel
Booth School of Business
University of Chicago
5807 South Woodlawn Avenue
Chicago, IL 60637
and NBER
jdingel@chicagobooth.edu

Telemigration and development: On theoffshorability of teleworkable jobs
Richard Baldwin (Graduate Institute, Geneva) and Jonathan I. Dingel (University of Chicago, Booth
School of Business)1
October 2021
Abstract
The Covid-19 pandemic has introduced huge numbers of employers and employees to remote work.
How many of these newly remote jobs will go overseas? We offer a rough quantification based on
two observations: 1) offshore work is trade in services, and 2) the number of telemigrants is thevolume of this trade divided by the average wage. Combining these with gravity-model estimates,
we can roughly predict the number of new telemigrants that would arise from lower barriers totrade in services. Telemigration seems unlikely to be transformative when it comes to thedevelopment paths of most emerging economies. The baseline service trade flows are modest, andthe standard gravity model restricts modest changes to have modest impacts. There are no tipping
points in structural gravity models. Finally, we propose a simple model of telemigration in whichsmall changes can have large consequences. The key is to assume that latent comparative advantage
takes a different shape than typically assumed in quantitative trade models. Given this, smallchanges in trade costs can generate large and asymmetric increases in the exports of service tasks
from low-wage nations.

1 Introduction
The future of globalisation is changing for one ineluctable reason. The cost of moving weightlessthings (ideas and data) is falling radically faster than the cost of moving heavy things (goods).
Telemigration – namely, working from home when home is abroad – is a small but fast-growing
aspect of globalisation’s weightless future.
Will telemigration have a measurable impact on development? The answer to this question turns on
the answer to a second question: How many service-sector jobs will be offshored from rich nations
to emerging markets? Since answering this requires all sorts of unknowable things, economists havetended to focus on a narrower question: How many jobs are offshorable?
More than a decade ago, Alan Blinder (2007) tackled the narrower question by looking at features ofjobs in the United States. His answer was based on two vectors. The first vector described theoffshorability of each occupation. The second vector was the number of US workers employed ineach occupation. The inner product of these vectors yielded the number of US jobs that could beoffshored. Blinder’s answer was: “I estimate that somewhere between 22% and 29% of all US jobs

We thank Linghui Wu and Ka Lok (Steve) Wong for excellent research assistance, Kari Greenswag for helpful
editorial assistance, and Gene Grossman and Thomas Sampson for valuable feedback. This paper was prepared
for the book project, AI/Robots: Labour, Trade, Growth and Welfare, edited by Lili Yan Ing and Gene
Grossman for the Economic Research Institute for ASEAN and East Asia (ERIA) in Jakarta. Dingel thanks the
Cohen and Keenoy Faculty Research Fund at the University of Chicago Booth School of Business.
11are or will be potentially offshorable within a decade or two. (I make no estimate of how many jobs
will actually be offshored.)”2
One goal of this paper is to argue that moving from the narrow question to the broader question
should involve switching from a focus on the nature of occupations to broader considerations thatpoint to the gravity equation as a way of roughly quantifying the value of work that might beoffshored to workers in lower-wage economies. We develop the exercise in Section 3 (theoreticalmotivation) and Section 4 (quantification exercise).
The conclusion of our rough quantification exercise is that telemigration is starting from too low of abase to allow it to become a major force for development – unless something radical changes. The
constant-elasticity gravity model we employ embeds a series of assumptions that limit the impact ofmodest declines in trade costs. Correspondingly, telemigration should not be much of a threat to theservice workers in high-wage nations. This soothing conclusion flies in the face of the anxiety thatonline offshoring evokes in high-wage nations. For example, David Wessel (2004) wrote in The WallStreet Journal: “Much of the American anxiety about outsourcing to India and China can be boiled
down to this simple question: Will there be good jobs left for our kids? ... Tens of millions ofincreasingly skilled Chinese and Indian workers are joining the global economy at a moment whentechnology can dispatch white-collar work overseas almost instantly.”
What is the source of this mismatch between popular perceptions and our quantification exercise? Is
the model we are using missing something, or does the popular anxiety reflect the familiar
disconnect between trade economists and the public regarding the role of comparative advantage in
general equilibrium? In Section 5, we explore the possibility that the mismatch might be the fault of
the model.
Canonical frameworks rule out the possibility that modest changes in trade frictions can generateradical changes in employment patterns. Quantitative Ricardian trade models, starting from Eaton
and Kortum (2002), typically assume that the pattern of comparative advantage is symmetric acrosscountries.3 More broadly, quantitative trade models typically feature a constant trade elasticity, sothat the direct effect of a symmetric decline in trade costs is a symmetric increase in exports by both
high- and low-wage nations.
In Section 5, we articulate circumstances that allow marginal changes to have radical effects. The
analytic framework features both trade in goods and telemigration, which is trade in services. In thissimple two-country model of telemigration, a symmetric decline in the costs of trading services
internationally can have very asymmetric consequences for the exports of developed and developing
economies. The key is to assume that latent comparative advantage takes a different shape thantypically assumed in quantitative trade models. In short, it is a model in which telemigration could
meaningfully shape low-wage nations’ development journeys, with attendant effects on the servicesector employment prospects of workers in high-wage economies.4

2 Offshored jobs: Beyond the two-vector approach
Blinder’s seminal calculations turned primarily on his judgement of the offshorability of particularoccupations using a four-way categorisation that ranged from non-offshorable (Category IV) tohighly offshorable (Category I). The judgement was guided by a decision tree illustrated in Figure 2.1.

The quotation is from the abstract of Blinder (2007). The paper was published as Blinder (2009).
In Eaton and Kortum (2002), productivity follows a Fréchet distribution. This distribution's location parameter,
which governs absolute advantage, is country-specific. Its shape parameter, which governs comparativeadvantage, is common across countries.
The possibility of a disruptive outcome is the focus of Baldwin (2019).
2

3

4

2

Figure 2.1: Blinder’s classification procedure.

Source: Blinder (2007).

These judgements were then matched with the number of US workers employed in the relevant
occupations. The two vectors he used are shown in Table 2.1. The calculation was done under a“conservative” assumption – scenario Low in the table – that includes all jobs in category I and II
occupations, and an “aggressive” assumption – scenario High in the table – that also includes all
category III occupations. The inner product of the low scenario vector and the jobs vector yields theanswer that about 29 million jobs are offshorable, which was about 22% of the labour force in 2004.
The inner product of the high scenario vector with the jobs vector gives the answer that about 38
million jobs might be offshorable (about 29% of all jobs). Blinder refined the first vector, which
describes the offshorability of each occupation, in subsequent research (Blinder 2009; Blinder andKrueger 2013).
Some researchers came up with broadly similar results, while others found much lower figures.
McKinsey Global Institute (2005) looked at eight representative sectors in various high-wage
economies and estimated that about 11% of the jobs were offshorable to developing countries.
Bardhan and Kroll (2003) found that about 11% of all US jobs were offshorable, but they limited
themselves to occupations where some offshoring was already occurring. Van Welsum and Vickery(2005) gauged that 20% of total US employment was offshorable. Jensen and Kletzer (2010) used avery different approach that leveraged location data inside the US. Geographically concentrated
service sectors were more tradable, they suggested, because service consumers tend to bedispersed in proportion to the population. Their work implied that 38% of US workers are in
tradable, and therefore offshorable, occupations. In brief, the estimates were never negligible, butsome were less than half those of Blinder’s original estimate.

3

Table 2.1: Major Occupations Ranked by Offshorability Score (OS)Occupation

OS

Computer programmers
Telemarketers
Computer systems analysts
Billing and posting clerks and machine operators
Bookkeeping, accounting, and auditing clerks
Computer support specialists
Computer software engineers, applications
Computer software engineers, systems software
Accountants
Welders, cutters, solderers, and brazers
Helpers—production workers
First-line supervisors/managers of production and operating
workers
Packaging and filling machine operators and tenders
Team assemblers
Bill and account collectors
Machinists
Inspectors, testers, sorters, samplers, and weighers
General and operations managers
Stock clerks and order fillers
Shipping, receiving, and traffic clerks
Sales managers
Business operations specialists, and all others

I
I
I
I
I
I and II
II
II
II
II
II
II
II
II
II
II
II
III
III
III
III
IV

Scenarios
Low High
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

1
1
1
1
1
1
1
1
1

Workers
389,090
400,860
492,120
513,020
1,815,340
499,860
455,980
320,720
591,311
358,050
528,610
679,930
396,270
1,242,370
431,280
368,380
506,160
1,663,810
1,625,430
759,910
317,970916,290

Source: Authors’ elaboration of information in Blinder (2007), Table 2.

2.1 The two-vector approach
Last year, Dingel and Neiman (2020) took a two-vector approach to answer a question that seems
similar to Blinder’s: how many jobs can be done at home? Using job traits from the Occupational
Information Network (O*NET) survey, they built the first vector by classifying each occupation asable to be performed remotely or not. The second vector, as with Blinder, contained the number ofpeople working in the corresponding occupations. The inner product of the two provided the answer
to the question.
But the Dingel-Neiman and Blinder questions differ in subtle but important ways. Dingel and Neiman(2020) asked how many workers could sit in their homes during the pandemic and perform the same
job they were (overwhelmingly) performing at their employers’ offices prior to the pandemic. Quite
obviously, the main issue is the nature of the jobs: can they be performed remotely? The nature of
the workers is irrelevant as far as teleworkability is concerned because the exercise considers
whether the same workers could do the jobs from home.
Blinder (2007) answered a very different question. He asked whether, for example, a Uruguayan
political analyst could do the same job as an American political analyst by exporting the service to US
customers currently serviced by that American analyst. Here both the nature of the job and thenature of the workers would seem to matter: can the service be provided from another country andare there foreign workers who can provide it? Note that Blinder (2007) explicitly recognises thepoint, “the task is to estimate the number of jobs that are potentially offshorable, meaning that
Americans performing those jobs face potential competition from, say, Indian or Chinese workers.”
4

The following figures are not produced by code:

2.2 Additional considerations• Blinder’s classification procedure
Whether an “offshorable” job (in the sense of Blinder 2009) is actually performed offshore depends
Fromconsiderations.
WTO WTR 2019
dominance
byworkers
rich nations.
[Howdoesofthis
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of otherFirst,showing
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speakstothenumberofpotential
foreignfrom Figure B.9 in WTO’s 2019 World Trade Report? Were computations performed?]
suppliers in each occupation. Occupations that can be performed remotely tend to be skill-intensive,high-wage
the United economies
States (Dingel
and Neiman2020).
Asimport
Blinder on(2006)
discussed,
this is• Figurejobs
5.2: inDeveloping
exportmore thanthey
freelance
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part of the concern about the labour-market consequences of emerging markets exporting such
• DFStomodeland nontraded
services
developed
economies.goods
It also means that the potential foreign suppliers are not the total
foreign labour force but the number of foreign workers who possess the relevant skills, as revealed• for
Figure5.4: Nontradedtasksorinthe “service
enabled” DFS model.
by,example,educational service
attainment
current
occupation.

Second,one5.6:must
address the expansion
fact that domestic
andexports
foreign workers are still not perfect substitutes• Figure
Asymmetricof service
even when they have the same level of educational attainment or occupational title. For example,
English-speaking
Canadians might
prettygoodsubstitutes
for English-speaking Americans in many
• Figure 5.7: Equilibrium
withbefree
trade
in goods
and services.
teleworkable jobs, but Portuguese-speaking Brazilians seem less likely to be able to immediately
perform
the relevant
language-intensive
tasks.
from different countries may
Thefollowing
figures are
not produced bycodeEven
butEnglish
shouldspeakers
be:find that their interactions with customers and co-workers are subject to a variety of linguistic and• MajorOccupations
RankedbyquipO↵shorability
ScoreStates(OS)and Great Britain are two countries
culturalfrictions,as capturedby thethat the United
separated by a common language.
Figure 1: Importance of English language for teleworkable vs. non-teleworkable jobs

020Frequency
40

60

80

Figure 2.2 Importance of English language for teleworkable versus non-teleworkable
jobs

2

3
4
Importance of English Language
Not teleworkable

5

Teleworkable

Notes:
Authors’
of of
O*NET
Source:Authors’elaborationelaboration
O*NETdata.
data.The
The classification
classificationofofoccupations
occupationsasasteleworkable
teleworkableorornot
notisisfrom
fromDingel
andNeiman(2020).
Dingel and Neiman (2020).

As it turns out, such frictions may be particularly relevant in the kinds of occupations that can be
Between remotely.
2005 and The
2019,
the nominalvalue
of otherbusiness
services
China andperformed
O*NET
surveys that
Dingeland Neiman(2020)
usedexported
to classifyby
occupations
as ableto hadbe performed
remotely
also
the language intensity and importance of soft
India
both
annual growth
rates
ofcharacterize
12%.
skills in each occupation on a scale from 1 to 5. Figure 2.2 shows that teleworkable occupations
place much greater importance on command of the English language than those occupations thatcannot be performed remotely. The occupation with the highest importance of English language
score is public relations specialist (4.96 out of 5). While this job can be performed remotely, it also isan occupation in which linguistic nuance and precision are very important.

1

5

150

150

200

Figure 2: Importance of oral and written expression for teleworkable vs. non-teleworkable jobs

Figure 2: Importance of oral and written expression for teleworkable vs. non-teleworkable jobs
Frequency

2.5

3

3.5
4
Importance of Oral Expression

5

Teleworkable2

3
4
Importance of Written Expression
Not teleworkable

5

Teleworkable50

Not teleworkable

4.5

Frequency

Frequency
100

0

100
0

150

50

150
50

200 Frequency
100

100

Figure 2.3: Importance of oral and written expression for teleworkable versus nonteleworkable jobs

0

0

50

Notes: Authors’ elaboration of O*NET data. The classification of occupations as teleworkable or not is from Dingel
and Neiman (2020).

2.5

3

3.5
4
Importance of Oral Expression

Not teleworkable

4.5

5

2

3
4
Importance of Written Expression

TeleworkableNot teleworkable

5

Teleworkable

Notes:
Authors’
of of
O*NET
Source:Authors’elaborationelaboration
O*NETdata.
data.The
The classification
classificationofofoccupations
occupationsasasteleworkable
teleworkableorornot
notisisfrom
fromDingel
andNeiman(2020).
Dingel and Neiman (2020).

Figure 2.4: Importance of persuasion and social perceptiveness for teleworkable versus

150

150

Figure 3: Importance of persuasion and social perceptiveness for teleworkable vs. non-teleworkablenon-teleworkable jobs
jobs

100

Frequency

15050
2.5
3
Importance of Persuasion

4

Teleworkable50

Not teleworkable

3.5

1000

2

Frequency

1.5

1

2

3
4
Importance of Social PerceptivenessNot teleworkable

5

Teleworkable

50

Frequency

1000

150 Frequency
50
100

Figure 3: Importance of persuasion and social perceptiveness for teleworkable vs. non-teleworkablejobs

Notes:
classification of
oror
not
is is
from
DingelSource:Authors’
Authors’elaborationelaborationofofO*NETO*NET data.
data. The
The classificationof occupations
occupationsasasteleworkable
teleworkable
notfromand Neiman (2020).

Dingel and Neiman (2020).

00More1.5broadly,
jobs thatcan 3be done3.5remotely
tend to rely
on soft skills,
which3 embed elements
that
2
2.5
4
1
2
4
5
Importance of Persuasionof Social Perceptivenessare specific to particularsocial and cultural contexts. Deming (2017) Importance
documents
a rising return toteleworkableNot teleworkable
Teleworkablesocial skills in theNotUnitedStates in Teleworkable
recent decades and emphasizes thatthe fastest growing
cognitive
occupations
been thoserequiringinterpersonal
interaction.
As shown
in FigureNotes:
Authors’haveelaborationof O*NET
data. significant
The classificationof occupations
as teleworkableor notis from2.3,
Dingeland
Neiman(2020).
2 that typically place greater importance on oral
jobs
that canbe done remotely are also occupations
expression and written expression. Figure 2.4 shows that teleworkable jobs also typically place
greater importance on persuasion and social perceptiveness. Both sales engineers and mathematicaltechnicians can telework, but they are at opposite extremes in terms of the importance of
persuasion (4.25 versus 1.66 out of 5). To the extent that social and cultural contexts vary acrosscountries, this makes it less likely that a public relations specialist or a sales engineer located in
Hanoi is a perfect substitute for one located in Seattle.
2

2.3 CAGE distance
One way to articulate the question about the substitutability of workers from different linguistic,
cultural, and social contexts is to view it as a matter of “distance” between workers. The metaphoric
6

distance encompasses a variety of dimensions. For example, activities that can be performed
remotely but must be performed synchronously will be sensitive to differences in time zones (Bahar
2020). Considering pairs of countries, Ghemawat (2007) dubs differences in these cultural,administrative, geographic, and economic factors the “CAGE” distance between them. In this sense,
French-speaking Canadians in Quebec can be considered “farther” from New York employers thanEnglish-speaking Canadians in Calgary, even though Calgary is over 3000 kilometres farther away.
This framing of the substitutability between domestic and foreign workers as a bilateral distanceleads immediately to a standard concern in trade – the existence of alternative suppliers. The
likelihood of US firms hiring foreign workers from a given country depends not just on the bilateral
CAGE distance to that country, but also on the bilateral distances to alternative sources of workers.
Thus, in addition to the two-vector approach that estimates the mass of teleworkable jobs in one
nation that might be supplied from abroad, one should consider the mass of potential suppliers inother nations and a measure of “distance” between the two. In moving from the question of “howmany developed-economy jobs are offshorable?” to “how many developed-economy jobs could go
offshore to emerging markets?”, we have added considerations about the exporting nation’s
economic mass and bilateral impediments to such potential trade in services. That points to thegravity equation for international transactions, a long-standing means of summarizing the expectedvolume of trade between economies.

2.4 Gravity is a hard habit to shake off
The simplest gravity equation predicts that bilateral trade in goods will be proportional to theeconomic mass of the origin nation times the economic mass of the destination nation divided by
the bilateral distance (Head and Mayer 2014). In thinking about occupation-level transactions, one
would need the demand for teleworkable tasks in the importing nation, a measure of potential
supply in the exporting nation, and a notion of distance. The tally of jobs in the importing nation thatcan be performed remotely is the relevant importer mass, the population of suitably skilled workers
is the relevant exporter mass, and various “CAGE” distances capture bilateral trade frictions.
As it turns out, estimation using importer and exporter fixed effects addresses the role of competingalternative suppliers and obviates the need to gather data on the mass variables, albeit at the cost ofrestricting thought experiments about potential supply. Before getting there, it is worth being
explicit about how we are thinking about telemigration as trade in services in the context of thegravity equation.

3 A first-pass gravity equation for telemigration
To guide our thinking about using gravity to study telemigration, it is useful to lay out the steps in
the model’s simplest derivation. Here we sketch the gravity model in which all economic activity is in
the service sector. This one-sector exposition follows Baldwin and Taglioni (2006).
Suppose that each potential telemigrant provides nationally differentiated labour services (theArmington assumption).5 Workers in nation o (“origin”) charge a price in market d (“destination”) of
pod. The export of services from workers in nation o to nation d is:𝑝"# 𝑥"# ≡ 𝑠ℎ𝑎𝑟𝑒"# 𝐸#

The assumption that workers in different nations produce imperfect substitutes is merely a convenient shortcut
to obtain a constant-elasticity gravity equation. The substantive assumption is that the aggregate import demandsystem is CES. For example, Eaton and Kortum (2002) provide a Ricardian model in which workers in different
nations compete to produce the same set of goods that yields a constant-elasticity gravity equation.
5

7

where shareod is the share of expenditure in market d on services supplied by workers in o. Ed is total
expenditure on teleworkable jobs in nation d. Next, we link shares to relative prices using a CES
demand function:𝑝"# 012𝑠ℎ𝑎𝑟𝑒"# = - /, 𝑤ℎ𝑒𝑟𝑒𝑃#

0/(012)<

𝑃# = 78(𝑝:# )012 >

, 𝜎>1

:=0

where R is the number of nations in the world. Assuming perfect competition, the equilibrium priceis𝑝"# = 𝜏"# 𝑤" 𝑎"where ao is the unit labour coefficient, 𝑤" is the equilibrium wage, and 𝜏"# is the bilateral iceberg
cost. Thus, exports from o to d are:𝑉"# = 𝜏"# 𝑤" 𝑎" 𝑥"# = -𝜏"# 𝑤" 𝑎" 012/𝐸#
𝑃#Here Vod is the aggregate value of telemigration payments from o to d, i.e., the bilateral value of
trade in services.
Finally, we use the general equilibrium adding-up condition to solve for prices and wages. Nation ohas income 𝑌" = 𝑤" 𝐿" (where 𝐿" is the labour supply), which in general equilibrium, must match
output, so wages have to adjust such that 𝑌" = ∑<#=0 𝑉"# . Thus:<

𝑌" = (𝑤" 𝑎" )012 8 H𝜏"# 012#=0𝐸#
𝑃# 012

I

Solving for the producer price of the services in nation o, we get:
𝑝"

012

𝑌"
=
,
𝛺"

<

𝛺" ≡ 8(𝜏"K 012

𝑤ℎ𝑒𝑟𝑒

K=0

𝐸K
𝑃K 012)

Here 𝛺" is a measure of market access; the capital-omega is a mnemonic for ‘openness’ since itmeasures the openness of nation o’s exports to world markets.
Plugging in the solution into the expression for 𝑉"# is the last step. It yields a first-pass gravity
equation for offshoring:𝑌" 𝐸#
𝑉"# = 𝜏"# 012 H
I
𝛺" 𝑃# 012
Thus, 𝑌" is the total labour income of teleworkers in origin nation o , and 𝐸# is the spending indestination nation d on labour in teleworkable jobs. This could be estimated in log-linear form.
The two core messages from Sections 2 and 3 are that 1) offshoring jobs will generate cross-border
trade in services, and 2) bilateral trade in services can be empirically modelled using the gravity
equation. To illustrate how one may use the gravity-based approach to quantify potential trade inservices, we update gravity regressions estimated by Head, Mayer, and Ries (2009). Using Eurostat
data covering 1992-2006, they found a strong negative correlation between physical distance andbilateral trade in services.

4 Quantifying potential growth in trade in services
The combined forces of rapid technological progress in digital communication tools (Baldwin 2019)
and an extended experiment with remote work imposed by the coronavirus pandemic (Dingel and8Neiman 2021) may substantially reduce the importance of physical proximity for trade in services
relative to the turn of the century. We use the gravity regression to illustrate the scope for greater
services trade if physical distance becomes less relevant and discuss the frictions associated with
cultural, linguistic, and social differences between trading nations.
Unfortunately, data on trade in services is quite crude relative to data on trade in goods – andextremely crude compared to the occupation-level data used in Blinder (2007) and Dingel andNeiman (2020) and shown in Figures 2.2 through 2.4. There are more than 800 distinct 6-digit
occupations in those data sets. The ILO provides employment counts for 40 distinct 2-digit
occupational groups for more than 80 countries. By contrast, bilateral trade flows for services arereported for only a dozen or so broad categories of services. Head, Mayer, and Ries (2009) report
estimates for only four categories of services trade.
The advantage of using reported flows of services trade is that it allows one to assess the role ofinternational business frictions without having to construct a model of the relevant economic
masses. As mentioned, these masses are absorbed in importer-year and exporter-year fixed effects.
The consequences of differences in language, cultural and physical proximity are estimated using
variation in services trade across pairs of countries.

4.1 Data
We estimate gravity service regressions using data released since Head, Mayer, and Ries (2009) –henceforth HMR – wrote their study. We use trade flows for 12 service sectors in 2005-2019, asreported by the OECD-WTO Balanced Trade in Services (BaTIS) dataset, and flows for 11 service
sectors in 1995-2006, as reported in the previous BaTIS edition (Fortanier et al. 2017; Liberatore andWettstein 2021). In sharp contrast to the data employed by HMR, these services trade data sets
report very few zeros.6 Following HMR, we include log physical distance, differences in time zones,
and dummy variables indicating a colonial relationship, shared language, and a common legal systemfor each pair of countries.7
While one might combine estimates of the frictions impeding international services trade withproxies for latent occupation-level demand and supply for teleworkable tasks, here we confine
ourselves to discussing the direct effects associated with the estimated elasticities of the gravity
regression.

4.2 Gravity estimates of bilateral frictions for trade in services
Table 4.1 shows our results for total services. The first 5 columns are for the 1995-2006 period(corresponding to the original HMR period). The signs and magnitudes of the point estimates are allreasonable in light of their prior findings. Like HMR, we find that distance has a large, negative
coefficient that is more negative than what is typically found for goods trade. Given that the totalservices data are such a grab-bag of phenomena – everything from airline tickets and online gameplaying to pipeline fees and call centres – it is hard to characterize all the roles bilateral distance may
play. It may seem counterintuitive that distance should matter more for trade in services, which isless connected to the transportation of physical objects. Total services, however, includes manyflows for which face-to-face interaction may be important for setting up, maintaining, or operating
the international exchange. For instance, distribution services, tourism, transportation, and financial
services are all major services categories and likely require managers and specialists to move among

See Liberatore and Wettstein (2021) for a detailed description of the relevant data construction and imputation
procedures underlying the BaTIS data.
We obtain the distance, colonial, and language covariates from the CEPII website associated with Head,Mayer, and Ries (2010) and Head and Mayer (2014). We obtain the legal-origin covariate from La Porta,
Lopez-de-Silanes, and Shleifer (2008). We use time-zone data posted by Herman Wong.
6

7

9

facilities. The coefficients on time-zone differences, shared language, colonial link, and shared legal
origin are all significant in the full sample.
Table 1: Total services,various country
samples,linear samples,
fixed e↵ect
model
Table 4.1 Total-services
gravity regressions:variouscountry
linearfixedeffects model
(1)
Full

(2)
EU

(3)
OECD

(4)
G20

(5)
EU/OECD

2005 - 2019
(6)
EU/OECD

Distance (log)-1.155a(0.007)-1.763a(0.046)-1.505a(0.034)-0.746a(0.034)-1.475a(0.033)-1.249a(0.025)Distance (log) ⇥ trend-0.009a(0.001)0.027a(0.006)0.009b(0.004)-0.004(0.005)0.009b(0.004)0.003(0.002)Time zone di↵.

0.080a(0.001)-0.310a(0.030)0.105a(0.006)-0.026a(0.004)0.091a(0.006)

0.057a(0.004)

Shared language

0.473a(0.007)-0.331a(0.058)0.037(0.030)0.372a(0.036)0.089a(0.028)0.205a(0.022)

Colonial link

1.198a(0.020)0.104c(0.054)0.192a(0.043)0.254a(0.045)0.273a(0.040)0.344a(0.032)

Shared legal origin

0.106a(0.004)0.360a(0.022)0.272a(0.015)0.095a(0.023)0.228a(0.015)0.180a(0.011)

SampleAllo,d in EU o,d in OECD o,d in G20 o,d in EU/OECD o,d in EU/OECD
Obs.
382,552
7,792
13,456
4,104
15,976
23,510
R2
0.845
0.925
0.926
0.914
0.917
0.914
Notes: Regressions include origin-year and destination-year fixed e↵ects. Statistical significance at the 1%,5%, and 10% levels is indicated by a, b and c.

The coefficients vary substantially across estimation samples defined by different sets of countries.
This could reflect substantial heterogeneity within the “total services” set of activities. Trade inservices within the EU, for instance, likely has a very different composition than trade among theG20 nations.8 Beyond such compositional effects, measurement error and other data features may
also vary across the samples. Finally, the relevant elasticities may not be global constants.9
Figure 4: The relative efficiency curve with Eaton-Kortum assumptions
The sixth column of Table 4.1 reports estimates for more recent years (2005-2019) for the set of
nations where both are in the EU and/or the OECD. The estimated effects of distance, shared legal
origin, and time-zone are smaller, and the distance trend becomes insignificant, but the effect ofshared language more than doubles. The coefficient on the dummy for a colonial link also increases
in magnitude. We refrain from overinterpreting these differences in coefficients, since the nature of
trade in services shifted considerably between 2005 and 2019, let alone between the 1990s and thelater period.
To address some of the concerns about heterogeneity within total services, we present the same
regressions for different types of services in Table 4.2. The sample of nations is the EU/OECD sample
and the 2005-2019 period, so the first column of Table 4.2 is identical to the sixth column of Table
4.1.
✓

⇤

✓

Notes: Authors’ elaboration; the Fréchet distributions are F (s) = e 2s , F (s⇤ ) = e s. We randomly sample
5000 draws from these Fréchet distributions, compute the unit-cost ratios, and plot those ratios in rank order.

The G20 countries include members of the European Union, the G7 (France, Germany, Italy, Britain, US,
Canada, and Japan), and the BRICS (Brazil, Russia, India, China, and South Africa), plus Argentina, Australia,
Indonesia, South Korea, Mexico, Saudi Arabia, and Turkey.
83

Indeed, the notably smaller distance elasticity for the G20 sample is driven by observations in the left tail of thebilateral-distance distribution, suggesting a rejection of the constant-elasticity specification.
910

servicegravity
aggregates,
linear fixede↵ectmodel, EU/OECD
2005-2019.
Table Various
4.2 Sectoral
regressions:linearfixed-effects
model, sample,
EU/OECD
sample,2005-2019.
(1)
Total

(2)
OCS

(3)
Finance

(4)
IT

(5)
OBS

Distance (log)-1.249a(0.025)-1.285a(0.030)-1.517a(0.039)-1.253a(0.035)-1.219a(0.033)Distance (log) ⇥ trend

0.003(0.002)0.006b(0.003)-0.001(0.004)0.012a(0.004)-0.002(0.003)Time zone di↵.

0.057a(0.004)0.063a(0.005)0.114a(0.007)0.023a(0.006)

0.058a(0.006)

Shared language

0.205a(0.022)0.188a(0.025)0.226a(0.039)-0.000(0.032)0.197a(0.030)Colonial link

0.344a(0.032)

0.234a(0.037)0.220a(0.051)0.295a(0.045)0.163a(0.042)

Shared legal origin

0.180a(0.011)0.190a(0.013)0.226a(0.020)0.199a(0.017)0.234a(0.016)o,d in EU/OECD
23,5100.914

o,d in EU/OECD
23,5030.891

o,d in EU/OECD
22,3960.843

o,d in EU/OECD
23,2630.851

o,d in EU/OECD
23,3550.859

SampleObs.
R2

Notes: Regressions include origin-year and destination-year fixed e↵ects. Statistical significance at the 1%,5%, and 10% levels is indicated by a, b and c. The service sectors are Other Commercial Services (“OCS”,
which is non-government services that are not transportation or travel), Finance (financial services including
those delivered locally by foreign branches, etc), IT (includes telecommunications and software services), andOther Business Services (“OBS”, which is trade related services, operational leasing/rentals, and miscellaneous business, professional and technical services such as legal, accounting, management consulting, public
relations services, advertising, market research and public opinion polling, research and development services,architectural, engineering, and other technical services, agricultural, mining and on-site processing).

The results show that there is meaningful variation in the point estimates across service categories,
but most of them retain the expected sign and not too dissimilar magnitudes. Contrasting Tables 4.1
and 4.2 shows that the sample of countries used has a much greater influence on the estimatedcoefficients than breaking out total services trade into component categories.
When thinking about telemigration, the category that corresponds most clearly to the model wehave in mind is ‘Other Business Services’ (OBS) shown in column 5. Since the EU/OECD sample does
not include India – a key player in telemigration – we present analogous estimates for recent years
for a sample of nations that includes all the G20 nations as origin and destination countries in Table
4.3.

5

11

TableTable
4.3 Sectoral
gravitylinearfixed
fixed-effects
model,
sample,20053: Various
serviceregressions:aggregates, lineare↵ect model,
G20G20sample,2005-2019
2019.
(1)
Total

(2)
OCS

(3)
Finance

(4)
IT

(5)
OBS

Distance (log)-0.775a(0.026)-0.696a(0.028)-0.831a(0.039)-0.901a(0.032)-0.707a(0.029)Distance (log) ⇥ trend

0.004(0.003)0.003(0.003)0.001(0.004)0.009a(0.003)0.004(0.003)Time zone di↵.

-0.011a(0.003)-0.014a(0.003)0.007(0.005)-0.003(0.004)-0.001(0.003)

Shared language

0.431a(0.024)0.477a(0.027)0.558a(0.039)0.384a(0.035)0.396a(0.029)Colonial link

0.185a(0.034)0.054(0.042)-0.150b(0.059)0.087b(0.042)-0.036(0.035)

Shared legal origin

0.067a(0.015)0.074a(0.020)-0.046b(0.023)0.126a(0.021)0.173a(0.021)o,d in G20
5,130
0.926

o,d in G20
5,130
0.925

o,d in G20
5,119
0.913

o,d in G20
5,130
0.910

o,d in G20
5,129
0.921

SampleObs.
R2

Notes: Regressions include origin-year and destination-year fixed e↵ects. Statistical significance at the 1%,5%, and 10% levels is indicated by a, b and c. For service sector definitions, see notes of previous table.

The point estimates for the OBS column of Table 4.3 are the ones we use in the quantification
exercise. The next step is to think about how to relate the flow of services to jobs.

4.3 Translating services flows into jobs
Embracing the bold approximation that OBS services imports are payments to foreign service
workers, the bilateral service trade flow can be interpreted as the “wage bill” for telemigrants
between pairs of countries. This is crude, but it allows us to tackle order-of-magnitude questions
when it comes to the impact of making telemigration easier.
Put simply, emerging economies currently export very modest volumes of other business services. In
2019, China and India’s total OBS exports were $68 billion and $51 billion, respectively. These small
initial volumes would need to expand massively to be associated with meaningful numbers of new
telemigrant jobs. If telemigrants earned $10,000 per
6 year ($5 an hour for 2000 hours per year), a
doubling of OBS exports would only create a few million telemigrants in these economies.
Consider how many jobs might come from increased bilateral exports to the US or EU as a result of
lower service trade costs. Since physical distance is the canonical shifter of bilateral trade costs, weconsider the consequences of reducing its negative impact on bilateral services trade. Per Table 4.3,
the estimated impact of distance on trade became less negative during 2005–2019. However, thistrend is neither statistically nor economically significant. The estimated coefficient on the interactionof log distance and a linear time trend (0.004) is less than 1% of the log-distance coefficient (-0.707).
At that rate, decades would have to pass for an appreciable change in trade volumes to arise. Rather
than tracing out the impact of this trend, we choose to consider the consequences of a more radical
shift.
Our thought experiment is to consider the numbers of emerging-market jobs that might be createdby a 25% decline in the bilateral distance between economies. Imagine the reduced cost of physical12

distance applying to one pair of countries in isolation. For example, it is about 14,000 kilometresfrom Palo Alto to Bangalore; slicing off a quarter of this distance would put Bangalore near Taipei.
The distance elasticity of -0.707 implies export would be about 18% higher. We then translate thischange in export value into a number of telemigrants by our crude assumption about wages. This
calculation overstates the increase in bilateral exports and the number of telemigrants to the extent
that it neglects substitution between export destinations and general-equilibrium effects on wages
that would dampen the response.
The first line of Table 4.4 reports the value of US OBS imports in 2019 from four partners: China,
India, Brazil, and Canada. The United States is the largest destination for Brazilian, Canadian, andIndian exports of OBS. It is China's second largest destination after Hong Kong. The figures are allvery modest: about $11 billion from India and Canada and even less from China and Brazil. Thus, any
export growth will be starting from low initial values. Consolidated across all EU members, China andIndia do export more OBS to the EU, but these values are again modest. These small flows explain
why even seemingly large changes in the impact of distance on OBS flows will have rather modest
effects on jobs.
We convert the import values to a baseline of foreign jobs using a proxy for the relevant foreign
wage: the average hourly wage of an IT worker. US workers in computer and mathematicaloccupations average $41.51 per hour (Dingel and Neiman 2020). Canadian wages are about threequarters of that, while those in Brazil, China and India are notably lower.10 The resulting numbers oftelemigrant jobs suggested by the calculation are 190,000 in Canada, 400,000 in Brazil, 360,000 in
China, and 2.38 million in India.
As a result, the jobs increases associated with an 18% increase in US OBS imports are very modest.
For India, the number is 420,000 more telemigrant jobs. The figure is less than 70,000 for the threeother economies. The job increases associated with increased exports to the EU are similarly
modest.

Hourly wages for “natural and applied sciences and related occupations” in Canada are 37.87 CAD (StatCan
Table 14-10-0340-01 for 2019 full-time employees). Average monthly earnings for information,communication, financial, and professional occupations in Brazil were 3,445 BRL in 2019 (Q27 in Pesquisa
Nacional Por Amostra De Domicílios contínua, Instituto Brasileiro de Geografia e Estatística, 2012-2020).
Urban male “technicians & associate professionals” in India average 28,923 INR per month (Table 55, Annual
Report, Periodic Labour Force Survey, 2019-20). Average earnings for “information transmission, software, andinformation technology” workers in China were 161,352 CNY per year in 2019 (Table 4-15, China Statistical
Yearbook 2020). We convert these to US dollars at (roughly) 1 CAD = 0.79 USD, 1 BRL = 0.19 USD, 1 CNY =0.15 USD, and 1 INR = 0.013 USD. We assume 2,000 hours of work per year.
1013TableIncreasesinin telemigration
telemigration associatedreductionin bilateral
distance
Table
4.4:4:Increases
associatedwith
with25%25%
reductionin bilateral
distance
CanadaBrazil

China

India

Telemigrants selling to US
US OBS imports in 2019 (billions USD)
Hourly wage
Base jobs (millions)
Percent increase in US imports of OBS
Jobs increase (millions)

11.37
30.00
0.19
0.18
0.03

3.19
3.96
0.40
0.18
0.07

8.85
12.45
0.36
0.18
0.06

11.13
2.34
2.38
0.18
0.42

Telemigrants selling to EU
EU OBS imports in 2019 (billions USD)
Hourly wage
Base jobs (millions)
Percent increase in EU imports of OBS
Jobs increase (millions)

5.52
30.00
0.09
0.18
0.02

4.18
3.96
0.53
0.18
0.09

22.31
12.45
0.90
0.18
0.16

18.32
2.34
3.91
0.18
0.69

Notes: This table computes the jobs increases associated with the bilateral export increases implied by bilateral distance declining by 25%. OBS imports in 2019 are from
WTO data. Hourly wages for IT workers are from payscale.com. The distance elasticity
is from Table 4.3 column 5. We assume 2,000 hours of work per year. Remaining numbers
are authors’ calculations.

The headline result from these calculations is that the job-impact numbers are very small when wecompute growth from initially small bilateral export values. India’s labour force is in the region of ahalf-billion workers. The job impact suggested by the rough calculations in Table 4.4 is tiny comparedto that. The job increases for Brazil and China are similarly modest.
Of course, physical distance is not the only source of trade costs. New digital tools andorganizational practices might diminish the frictions associated with time-zone or linguistic
differences. One could compute such consequences similar to our distance-driven scenario above.
But an important reason the jobs numbers in Table 4.4 are small is that the 2019 exports of otherbusiness services are small, implying a small number of new telemigrants associated with any
modest shift in trade costs.

4.4 Modest changes are insufficient for a big development impact
Our rough quantification exercise could be greatly refined, but refinements of the elasticities
estimated in Section 4.2 are essentially immaterial to the basic conclusion. The current volume of
7 representing payments to telemigrants is justservice trade of the type that could be construed as
too small for a doubling or tripling to have meaningful consequences for emerging markets’
development trajectories. A radical increase in trade flows would be necessary to produce many
new service-sector jobs in emerging markets. This is not impossible.11 For example, between 2005
and 2019, the nominal value of Other Business Services exported by China and India both had annual
growth rates of 12%. Such growth also reflects supply shifts, not merely declining trade costs.
The next section explores the economic logic of a model in which modest changes in the frictional
barriers to trade in services could lead to more radical changes than those suggested by ourquantification exercise.

Indeed, Kehoe and Ruhl (2013) emphasize the contribution of growth in previously least-traded goods to total
trade growth in episodes of structural transformation and trade liberalization.
11

14

5 A simple model of telemigration: Could comparativeadvantage work differently with services?
Telemigration evokes anxiety in rich nations. “This year’s mass experiment with remote working has,for some, triggered a prickling sense of unease: if I can do my job from home in London, Brooklyn or
Canberra, could someone else do it more cheaply from Sofia, Mumbai or Manila?” wrote SarahO’Connor (2020) in the Financial Times. The fear is that digitally enabled telemigration will beasymmetric: It will expose tens of millions of office workers based in rich nations to foreigncompetition without providing them additional, offsetting export opportunities.
Is this anxiety well-founded? The conventional gravity approach would suggest that it is not. In thesequantitative models, the elasticity of trade flows with respect to trade costs is constant. As a result,the direct effect of a common decline in the costs of trading services is a common proportionate
increase in the exports of services by all economies. Even neglecting developed economies’ potential
export expansion, the quantification of their increased imports from emerging economies suggeststhat the number of jobs at stake is modest. There is some nuance in terms of general-equilibrium
effects, but the assumptions of the gravity equation that make the pattern of comparativeadvantage symmetric and the trade elasticity constant essentially rule out highly asymmetricresponses to a common decline in trade costs. On the other hand, we have little evidence to support
restricting attention to these particular functional forms.
Two intriguing empirical patterns suggest that it may be fruitful to build a model that allows us toillustrate the possibility behind the anxiety. First, most rich nations run a trade surplus in services
and so would seem to have a comparative advantage in services (WTO 2019, Figure B.9). Specifically,
developed nations account for about 75% of global service exports, and they export more than they
import. Most of this services trade, about 70%, takes place in so-called traditional services.
Specifically, these are distribution services (20% of world total), financial services (19%),
telecommunications, computer and audio-visual services (13.2%), transportation (12%), and tourism(8%).12 Correspondingly, developing countries as a whole are net importers of services. In a
conventional gravity model, a decline in the cost of trading services would reinforce this initialpattern of comparative advantage.
The second fact is that developing economies have the edge in office and professional services thatare exported via digital platforms (ILO 2021). These categories are currently much smaller than
traditional services (Other Business Services and professional services account for 4% and 3% ofworld services trade, respectively), but they are growing faster. The fact that rich nations are running
trade deficits in one of the fastest-growing service-trade segments raises the possibility that further
advances in digital technology may not simply reinforce past patterns of trade in services.
To evaluate the possibility that the future expansion of service trade may be asymmetric, we need ageneral-equilibrium trade model that allows us to address two key questions. Why are today’s
services exports largely “running uphill,” from high-wage nations to low-wage nations? Why is therea presumption that reducing the costs of telemigration will lead to an asymmetric rise in crossborder flows that reverses this pattern? Along the way, the model will allow us to consider a few
other issues such as: “How do low trade costs for goods interact with higher trade costs forservices?” and “If international relative wages are mostly determined today by things likeinternational labour productivity differences and country size, what happens to relative wages whentelemigration gets much easier?”
To address these questions, we use the Dornbusch, Fischer, and Samuelson (1977) – henceforth DFS– model as a basis for thinking about telemigration. The model is familiar, so we move through the

122017 values from Figure B.2 of WTO (2019).

15

analysis quickly. To fix ideas and introduction notation, we first present the well-known DFS modelfor trade in goods.

5.1 The DFS model for goods
The world economy has two nations (North and South). Markets are perfectly competitive. There is acontinuum of goods, indexed by z. Production employs only labour and exhibits constant returns toscale. Labour endowments in North and South are L and L*, respectively. Unit costs for good z are:𝑤𝑎(𝑧),𝑤 ∗ 𝑎 ∗ (𝑧),𝑧 = 0…1∗( )where 𝑎(𝑧) and 𝑎 𝑧 are the unit labour input coefficients for North and South, and w and w* aretheir respective wages. North, as the rich nation, is assumed to have an absolute advantage in every
sector, but only relative efficiencies, 𝑎*(𝑧)⁄𝑎(𝑧), matter for the pattern of trade (Ricardo 1817).
Indexing the sectors so that North’s productivity advantage is greatest in low-z sectors, we have:𝐴(𝑧) =𝑎∗ (𝑧)> 1,𝑎(𝑧)𝐴Q (𝑧) < 0,

𝜔=

𝑤
,
𝑤∗

𝑧 = 0…1

North’s wage relative to South’s wage is ω. Preferences are identical across people and nations and
YQ
are Cobb-Douglas with b(z) as the expenditure share for good z. Also, 𝐵(𝑧′) = ∫Z 𝑏(𝑧)𝑑𝑧 and𝐵(1) = 1.
Adding trade costs

Trade is subject to symmetric, iceberg trade costs, so firms must ship τ>1 units to sell one unit in theother nation; domestic trade is costless. Since North goods pay 𝜏 to get inside South’s market, theSouth-market threshold or borderline good, which we label as 𝑧[ , is defined by the equal pricecondition 𝜏𝑤𝑎(𝑧[ ) = 𝑤 ∗ 𝑎∗ (𝑧[ ); likewise the North-market threshold good, 𝑧\ , is defined by𝑤𝑎(𝑧\ ) = 𝜏𝑤 ∗ 𝑎∗ (𝑧\ ).
Closing the model requires the relative wage, and this is determined by the North labour market
clearing condition. Given the preferences, it is: 𝑤𝐿 = 𝑤 ∗ 𝐿∗ 𝐵(𝑧[ ) + 𝑤𝐿𝐵 (𝑧\ ) because North
consumers buy North-made goods in the 0… 𝑧\ range while South consumers spend only on North
export goods, 0…𝑧[ . To recap, the three equilibrium conditions are:𝜏𝜔 = 𝐴(𝑧[ ),𝜔 = 𝜏𝐴(𝑧\ ),𝜔=

𝐵(𝑧\ ) 𝐿∗
1 − 𝐵(𝑧[ ) 𝐿(1)

It is convenient to have a simple, two-dimensional diagram, so we collapse the 3-equation system
into a 2-equation system by assuming explicit functional forms. We assume A(z)=1/z, and b(z)=1 forall z, so 𝐵(𝑧′) = 𝑧′. Additional simplification comes from supposing L=L*. With these, 𝑧\ = 𝑧[ 𝜏 _ andthe equilibrium conditions are:
𝜏
𝑧`
(2)
𝜔= ,
𝜔=
𝜏_
𝑧`
1 − 𝑧`The equilibrium 𝜔, 𝑧` are defined by the intersection of the two conditions in the left panel of Figure
5.1.
The right panel shows the classic non-traded goods analysis in DFS. The upward sloped line is theratio of North to South production costs; it is linear due to A(z)=1/z.
•••

When North’s relative cost is low enough, it is competitive inside the South market despite
the trade cost. This is true for the range zero to 𝑧[ ; North exports these goods.
When North’s relative cost is high enough, South goods are competitive in North despite thetrade costs. This is true for the range from 𝑧\ to unity; South exports these goods.
Goods between the two thresholds are non-traded.
16

Note that neither of the two key questions is clarified by this diagram. The trade is not particularly“uphill” (exports and imports necessarily balance in value terms), and a reduction of 𝜏 would bring
forth more North and more South exports in the same proportion (an outcome that is indeed
guaranteed by the equilibrium conditions).

Figure 5.1: DFS model and non-traded goods

Source: Authors’ elaboration.

We laid out this goods-only model to fix ideas and notation. It also gives us a departure point wheremost trade is in goods, so 𝜔 is predominately determined by goods trade.

5.2 A simple model of telemigration: Adding services and service-linked trade costs
Adding services is simple if we embrace a two-tier Cobb-Douglas preferences structure with 𝛾 as theexpenditure share on goods (gamma is a mnemonic for goods). Here we are taking the task
approach to services akin to the model of Grossman and Rossi-Hansberg (2006).
We label the relative productivity curve for service ‘tasks’ as:𝑆(𝑡) =𝑠 ∗ (𝑡)> 1,𝑠(𝑡)𝑆 Q (𝑡) < 0,

𝜏[ 𝜔 = 𝑆(𝑡[ ),𝜔 = 𝜏[ 𝑆(𝑡\ )where s(t) and s*(t) are the unit labour input coefficients for North and South service “tasks” (s and t
are mnemonics for services and tasks respectively). The final two expressions define the thresholdtasks, 𝑡\ and 𝑡[ , which delineate the non-traded services range. The new labour clearing market
condition, which now must include spending on services, is: 𝑤𝐿 = 𝛾{𝑤 ∗ 𝐿∗ 𝑧[ + 𝑤𝐿𝑧\ } +(1 − 𝛾){𝑤 ∗ 𝐿∗ 𝑡[ + 𝑤𝐿𝑡\ }. This simplifies to:𝜔=𝛾𝑧[ + (1 − 𝛾)𝑡[1 − {𝛾𝑧\ + (1 − 𝛾)𝑡\ }Assumptions reflecting the peculiarities of services trade: The “hockey stick”

We add two elements to the model to adapt it to the services context. First is that the trade costs inservices are very high – so high that there is little cross-border trade in services (compared to thesize of the sector). This echoes the fact that service provision often requires, or is much easier, when
the service provider and buyer are in the same room at the same time (and moving humans is
expensive compared to moving goods). Second, we assume that the pattern of comparativeadvantage explains the fact that much of this “mode-1” services trade consists of Northern exportsof highly sophisticated services (finance, engineering, communications, etc).
The first feature – high service trade costs – does not require a modification of the model, just theapplication of a different parameter value. To get the second feature, however, requires more
substantial changes. Specifically, we assume the 1/S(t) curve has the “hockey-stick” shape shown in
Figure 5.2.
17

Figure 5.2: Non-traded service tasks in the “service-enabled” DFS model.
North relative
cost of service
tasks

”

Source: Authors’ elaboration.

Before thinking about the microfoundations of the hockey stick shape, consider what the S(t) curve
would look like under Eaton and Kortum (2002) assumptions.13 When 1/𝑠(𝑡) and 1/𝑠 ∗ (𝑡) are Fréchet
distributed, as in Eaton and Kortum (2002), the 𝑆(𝑡) curve has the shape shown in Figure 5.3. Notethat since the support of the Fréchet distribution is unbounded, there will always be extreme values
that can overcome any finite level of trade costs. Thus, every country is predicted to exports sometasks, regardless of sectoral-level average productivities. To put it more graphically, the S(t)generated by the Fréchet assumption has two blades: one pointing up and one pointing down.14
Moreover, the patterns of manifest comparative advantage (in tasks actually traded) and latentcomparative advantage (in non-traded tasks) are the same. That assumption delivers a constanttrade elasticity.
By contrast, we consider the single-blade pattern of relative productivities depicted in Figure 5.2.
Why might relative productivities exhibit this “hockey-stick” shape? We briefly step outside themodel to think about continuous technological diffusion that tends to narrow extreme productivity
differences across economies on a task-by-task basis.
Without innovation, there is a natural tendency for the seepage of know-how to flatten the relative
productivity curve. Imagine that products and processes are invented in the advanced economy, soa(z) tends to be less than a*(z), but the cross-sectional differences will tend to fade in a Vernon(1966)-like fashion. That is, things such as good roads, institutions, and trust can explain why
German workers are more productive than, say, Turkish workers in every sector, but unless German
industry keeps innovating, the German-Turkish productivity ratios will tend towards a constant in all
sectors. In other words, this mechanism allows for persistent absolute advantages but only shorterlived comparative advantages. The disappearance of comparative advantage is accelerated if thepace of know-how seepage increases with the size of the productivity gap. In this conceptualisation,
the A(z) curve gets its slope from the fact that German industry innovates faster in high-technology
sectors. The left part of the A(z) curve is continuously pulled upward by fresh innovations even as
the gap between a(z) and a*(z) is continuously narrowed by technological diffusion. In this story,
Germany has a comparative advantage in its most innovative sectors.

Eaton and Kortum (2002) note that the Kortum (1997) and Eaton and Kortum (1999) models of innovation anddiffusion deliver a Fréchet distribution of productivities. Costinot, Donaldson, and Komunjer (2012) andCaliendo and Parro (2015) provide multi-sector models in which productivities within each sector follow the
Fréchet distribution. These models generate constant-elasticity gravity equations for sectoral trade flows.
00
When g(h) and g ∗(h) are Fréchet distributed with the same shape parameter, then the log difference in unit costs,
13

14

ln (𝑆(𝑡)), follows the logistic distribution.

18

Figure 5.3: The relative efficiency curve with Eaton-Kortum assumptions

lm

∗ lm

Source: Authors’ elaboration; the Fréchet distributions are 𝐹(𝑠) = 𝑒 1_g , 𝐹(𝑠 ∗ ) = 𝑒 1g𝑠, 𝑠 ∗ ≥ 0. We
randomly sample 5,000 draws from these Fréchet distributions, compute the unit-cost ratios, and plot thoseratios in rank order.

Why might S(t) look like a hockey stick in services but not goods?

Moving this line of thinking to the service sector, things change since innovation in the service sector
is famously slow. This leads to what is known as Baumol’s cost disease. A recent University of
Chicago Booth School of Business survey of leading economists framed it as: “Because labour
markets across different sectors are connected, rising productivity in manufacturing leads the cost oflabour-intensive services to rise.” Nordhaus (2008) documents the fact.
The point is that slow innovation results in a very flat relative productivity curve as the slow
innovation gives technological diffusion enough time to reduce differences in the cross-sector profile
of relative labour productivities (i.e., to flatten the S-curve). The exceptions to this are those service
tasks that are high-tech. They involve rapid innovation and coordination of many complex things.
This is what turns what otherwise would be a “baseball-bat” shaped 1/S(t) curve into a “hockeystick” shaped 1/S(t) curve. In particular, it adds the “blade” that indicates that the North has amarked comparative advantage in sophisticated services where innovation is rapid.
Implications of the hockey-stick productivity profile

It would take substantial empirical investigation to verify that the relative productivity profile has a“hockey-stick” shape. Before economists invest in such research, however, it is useful to show that if
it were true, then it would imply intriguing possibilities relative to the standard logistic-shaped curvethat is assumed in the conventional gravity model analysis. To that end, we resume the analysis
taking the hockey-stick shape as given.
The equal price conditions that define the threshold tasks, 𝑡\ and 𝑡[ , are 𝜏𝑤𝑠(𝑡[ ) = 𝑤 ∗ 𝑠 ∗ (𝑡[ ) and𝑤𝑠(𝑡\ ) = 𝜏𝑤 ∗ 𝑠 ∗ (𝑡\ ), which imply:𝜏[ 𝜔 = 𝑆(𝑡[ ),𝜔 = 𝜏[ 𝑆(𝑡\ )As drawn in Figure 5.2, the North’s productivity edge is only high enough to overcome the services
barriers for a small range of services tasks. Thus, North exports services tasks indexed by zero to 𝑡[ .
The inverse S(t) curve, however, flattens out, so the lower South wage (which is largely determined19

by goods market conditions) is not low enough to allow South service providers to be competitive
inside the North market. In other words, South exports no services to the North because 𝜔 >𝜏[ 𝑆(1): there are no services tasks where the South’s relatively low wage is low enough to overcome
its productivity disadvantage given the high trade costs. Thus, South exports some goods, and it
imports goods and services from North.
The goods sector is crucial to this story. Obtaining the asymmetric trade in services result requires agoods sector where trade is fairly free and comparative advantage is fairly symmetric. Without this,the usual “Ricardian wage equilibration” would drive the relative wage to a point where both Northand South were competitive in some service tasks.
The final change is to adapt the equilibrium labour-market condition to incorporate services. For thegeneral case, where both North and South export some service tasks, we will have two service
thresholds. In the Figure 5.2 case, however, there is only one, so: 𝑤𝐿 = 𝛾{𝑤 ∗ 𝐿∗ 𝑧[ + 𝑤𝐿𝑧\ } +(1 − 𝛾){𝑤 ∗ 𝐿∗ 𝑡[ + 𝑤𝐿}, which simplifies to:𝜔=𝛾𝑧[ + (1 − 𝛾)𝑡[𝛾 (1 − 𝑧\ )(3)

When 𝑡[ is small, the second expression shows that the relative wage is mostly determined by tradein goods as in the classic DFS model.

5.3 Telemigration and development
Here we use the model to guide thinking about how telemigration could affect the future of
globalisation. In the initial situation, South service tasks are not competitive inside North, but there
is an incipient arbitrage opportunity. If a North firm could – via some new digital technology –purchase tasks in South without incurring the trade costs, they would do so for most tasks. For
example, if something held the relative wage fixed, but the 𝜏[ became unity for a particular serviceproducing firm in the North, that firm would find it cheaper to source all tasks from t” to 1 in theSouth (see Figure 5.2). This could be the source of the anxiety that is often associated withtelemigration. Based on partial-equilibrium thinking, which takes relative wages as fixed, digitaltechnology that makes telemigration easier would seem to bring many more service-sector workers
in high-wage nations into direct wage competition with low-wage service workers.
Trade consequence of lower service trade costs

Figure 5.4 shows what a substantial reduction in the service trade costs would do to the services
trade pattern. The blue dashed lines show the new situation with 𝜏g falling to 𝜏g ′. This would greatly
expand South service exports and only slightly increase North service exports, so the relative
demand for North labour would fall – bringing the equilibrium relative wage down from 𝜔p to 𝜔p ′.
A twist on this thought experiment that may have some additional contact points with reality
considers asymmetric changes in telemigration frictions.
Consider the impact of an asymmetric adoption of the necessary digital technology by North firms
but not South firms. For example, we might imagine that the general economic and digital conditionsin the North make it easy for South workers to telemigrate Northwards, but the “digital divide”
makes it hard for North workers to telemigrate Southwards. In this case, it becomes much easier forSouth to exports services since, for instance, the North digital coverage and sophistication makes it
easier for North firms to integrate remote workers in their service value chains.
The main message from this model is that digital technology could open much greater export
opportunities for South-based telemigrants than North-based telemigrants. In other words, theanxiety in the popular debate can be rationalised by a profile of comparative advantage in servicesof the hockey-stick shape.
20Figure 5.4: Asymmetric expansion of service exports

Source: Authors’ elaboration.

Given this pattern of productivities, for many office workers and professionals in high-wage nations,telemigration would be a new source of competition, while it would be a new source ofopportunities for rather fewer high-wage workers. With the “twin-blades” model of Figure 5.3, rapid
technological advances that made remote workers less remote create vast new export
opportunities. In other words, if the pandemic-induced experience of “work from home” portends ashift to “work from anywhere” but substantially increased trade in services mimics the current
pattern of trade in services, then developed economies will experience substantial services export
growth.
For completeness, we solve for the equilibrium when there is costless trade in both goods andservices.15

6 Concluding remarks
The Covid-19 pandemic has introduced huge numbers of employers and employees to remote work.
Remote-work arrangements that had been technologically feasible for years but not broadly
employed were rapidly adopted in the absence of any alternative. As the economy returns tonormal, employers are likely to re-evaluate the kinds and numbers of workers that they employ.
With free trade, there is only one threshold in goods and one in services, which we label as 𝑧 p andqY r s(01q)h r
𝑡 p . The equilibrium conditions are: 𝜔p = 𝐴(𝑧 p ), 𝜔p = 𝑆 (𝑡 p ), 𝜔p =rr . To provide
1501{qY s(01q)h }closed-form solutions we can assume that the S(t) curve takes the form 𝑆(𝑡) = 1/√𝑡 . This does not
fully reflect the hockey stick shape, but it makes calculations simple and thus transparent. Imposing000functional forms: 𝜔p = r , 𝜔p = (h r)u/v , 𝛾 = .
Y

_

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Some may return to their pre-pandemic practices, but massive disruptions produce opportunities topivot, and some temporary shocks have permanent consequences. Some investments in remote
work capacity are very likely to stick (Barrero, Bloom, and Davis 2021).
It seems inevitable that some of the tasks that can be done remotely will be done by telemigrants
rather than domestic workers. Again, this will not be due to a massive change in the frontier ofpossibilities – telemigration is already widespread in sectors like web design and customized
software development. It will, if it happens, be due to many more firms changing practices to adopt
these technologies. In short, it would seem that the pandemic has brought forward the date of the“Next Industrial Revolution” that Alan Blinder (2006) famously contemplated a decade and a halfago.
This paper is an attempt to think about how we might evaluate the size of the increased offshoringof office and professional jobs from high-wage to low-wage nations. The starting point is torecognize that many jobs that can be performed remotely require soft skills that make domestic andforeign workers imperfect substitutes. The equilibrium number of telemigrants, therefore, depends
on the number of potential foreign suppliers of these tasks and the bilateral frictions that impede
trade in these services.
Our quantification is based on two simple ingredients. The first is interpreting trade in certain
services as payments to telemigrants so that the gravity equation for trade flows can be used todescribe how the volume of transactions will respond to lower trade costs. We updated gravity
regressions estimated by Head, Mayer, and Ries (2009) to ballpark these magnitudes. The second
ingredient is to assess the development potential of this trade by crudely translating trade flows into
numbers of jobs using average wages and the approximation that payments for these services aremainly payments to workers. This is clearly not true for some types of services trade – fees for using
oil pipelines, for instance – but it seems sensible for categories like Other Business Services, whichcontains many of the activities that would be transformed by remote work going global.
Our quantification exercise yields a simple message: the number of offshored jobs is unlikely to betransformative when it comes to the development paths of most emerging economies. Thisconclusion stems from a point of fact and a point of economic logic. First, the current baseline oftrade in Other Business Services is very small. Multiplying these flows by a factor of two or threewould not translate to many jobs in emerging markets. Second, quantitative trade models thatassume constant elasticities cannot generate very large responses to modest declines in trade costs.
There are no “tipping points” in canonical structural gravity models.
The final contribution of our paper is to propose a simple model of telemigration in which modestchanges in trade costs can have large effects. The key ingredient is that the pattern of comparativeadvantage has a hockey-stick shape rather than the symmetric shape typically assumed inquantitative trade models. In this account, emerging markets have tremendous potential to export
services that is not evident in the current pattern of trade flows.
Our paper raises many questions for future research. It suggests that making the optimistic case fortelemigration-led development will be harder than one might have thought. At the very least, itsuggests that making the case would require substantial departures from the standard gravity-based
account typically employed in empirical exercises.

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