American Economic Review: Papers & Proceedings 2013, 103(3): 1–8
http://dx.doi.org/10.1257/aer.103.3.1

The Geography of Trade and Technology Shocks
in the United States †By David H. Autor, David Dorn, and Gordon H. Hanson*

The United States and many European countries have experienced growing income inequality and increasing employment polarization(i.e., concentration of employment in the highest
and lowest paid occupations) over the past several decades (Autor, Katz, and Kearney 2008;
Dustmann, Ludsteck, and Schönberg 2009;
Goos, Manning, and Salomons 2012). The
two most prominent potential causes for these“effects’’ are rapid technological change (e.g.,
the computer revolution) and expanding international trade (e.g., the rise of China). Thereis also a growing sense that trade and technology are a unified force affecting labor markets.
Commencing with the work of Blinder (2009),
economists have posited that job tasks that aresuitable for automation may also be suitable foroffshoring.1 However, not all work processes
are equally susceptible to trade and technology.
Many labor-intensive tasks that have proved
challenging to automate can nevertheless bereadily performed overseas. Consequently, substantial pieces of production chains have already
moved to the developing world. But there aremany labor-intensive tasks, such as janitorialservices and package delivery, that must be
performed in person or in close proximity tocustomers and, hence, are not readily susceptible to international trade. Thus, for example,
while short order cooks at restaurants face little
competitive threat from overseas workers, it is
now commonplace for grocery stores to carry
prepared meals that are cooked and packaged
overseas.
The objective of this paper is to explore thegeographic overlap of trade and technology
shocks in US local labor markets. If the overlap is extensive, there would be a strong casefor viewing trade and technology as phenomena
whose consequences cannot be distinguished.
However, if the evidence reveals only limited
overlap, trade and technology may be playing
substantively different roles in shaping labormarket developments in the United States andother rich countries. Focusing on Commuting
Zones (CZs) that approximate US local labormarkets, we examine whether the CZs that are
most exposed to rising trade penetration are also
those most impacted by computerization. On
the technology front, we follow Autor and Dorn
(forthcoming) who use data on occupation mix
by CZ and data on job tasks by occupation tomeasure the degree to which CZs are specialized
in routine job activities well suited to computerization. On the trade front, we follow Autor,
Dorn, and Hanson (forthcoming) in identifying
trade shocks using cross-industry and cross-CZ
variation in import competition stemming from
China’s rapidly rising productivity and fallingbarriers to foreign trade and investment.2* Autor: MIT Department of Economics, 50 Memorial
Drive, E52-371, Cambridge, MA 02142 and NBER (e-mail:dautor@mit.edu); Dorn: CEMFI, Casado del Alisal 5, 28014
Madrid, Spain (e-mail: dorn@cemfi.es); Hanson: IR/PS
0519, University of California, San Diego, 9500 Gilman
Drive, La Jolla, CA 92093 and NBER (e-mail: gohanson@ucsd.edu). Acknowledgements: Autor and Hanson
acknowledge funding from the National Science Foundation(grant SES-1227334). Dorn acknowledges funding fromthe Spanish Ministry of Science and Innovation (award
JCI2011-09709 and grant ECO2010-16726).
†
To view additional materials, and author disclosurestatement(s),visit the article page at
http://dx.doi.org/10.1257/aer.103.3.1.
1
The reasoning here is that tasks that follow explicit codifiable procedures (what Autor, Levy, and Murnane 2003 call“routine’’ tasks) are both well suited to automation because
they can be computerized, and are well suited to offshoring
because they can be performed at a distance without substantial loss of quality.

2
While we focus on labor market exposure to computerization and goods trade, a number of papers consider theroles of both computerization and offshoring (e.g., Autorand Dorn forthcoming; Goos, Manning, and Salomons 2012;
Firpo, Fortin, and Lemieux 2012; Oldenski 2012; Michaels,
Natraj, and Van Reenen forthcoming).

1

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I. Measurement

AQ 1

Our concept for local labor markets is
Commuting Zones (CZs) developed by Tolbert
and Sizer (1996), who use county-level datafrom the 1990 census data to create clusters ofcounties that are characterized by strong commuting ties within a cluster, and weak commuting ties across clusters. The 722 CZs in our
analysis cover the entire mainland United States.
Following an extensive literature, we conceiveof automation as taking the form of a decline in
the cost of computerizing routine tasks, suchas bookkeeping, clerical work, and repetitive
production and monitoring activities, thereby
potentially displacing the workers performing
these tasks. To measure the degree to which
CZs were historically specialized in these routine, codifiable job activities that were intrinsically amenable to computerization, we proceed
in two steps. Using data from the Dictionary of
Occupational Titles (1977), we create a summary measure of the routine task–intensity RTIof each occupation, calculated as:(1)

RT​Ik​​ = ln ​( ​T​  Rk, 1980​ ​)​ − ln ​( ​T​  M
k, 1980​ ​)​− ln ​( ​T​  Ak, 1980​ ​)​,

where ​T​  Rk​  ​, ​T​  M​ ​  Mk​  ​, and Tk​  ​are, respectively, the routine, manual, and abstract task inputs in each
occupation k in 1980.3 This measure is rising in
the importance of routine tasks in each occupation and declining in the importance of manualand abstract tasks. To measure cross-market
variation in employment in routine-intensive
occupations, we classify as routine occupations
those that fall in the top third of the employment-weighted distribution of the RTI measure
in 1980. Using this classification, we then assign
to each commuting zone a routine employment
share measure (RS​H​jt​) equal to the fraction of
CZ employment at the start of a decade that falls
in routine task–intensive occupations:3
Tasks are measured on a zero to ten scale. For the 5
percent of microdata observations with the lowest manualtask score, we use the manual score of the fifth percentile. A
corresponding adjustment is made for abstract scores.

P20134402.indd 2(2)(

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K)

RS​Hjt​​ = ​ ​∑ ​​L​ ​jkt​ · 1​[ RT​Ik​​ > RT​I​P66​  ]​  ​

(

k=1

K

)

−1

× ​​ ​∑ ​​L​ jkt​ ​  ​​ ​.
k=1

Here, L​ ​jkt​ is the employment in occupation k
in commuting zone j at time t, and 1 [ · ] is theindicator function, which takes the value of one
if the occupation is routine-intensive by ourdefinition.
The rapid growth in US imports from lowincome countries since the early 1990s is driven
largely by China, whose transition to a marketoriented economy has yielded rapid rates of
productivity growth arising from massive ruralto-urban migration, industries gaining access tolong banned foreign technologies and inputs,
and multinational enterprises being permitted to operate in the country (Naughton 2007).
Compounding the effects of these internal
reforms is China’s accession to the World Trade
Organization (WTO) in 2001, which further
expanded the country’s access to foreign markets.
Following the empirical specification derived
by Autor, Dorn, and Hanson (forthcoming), ourmain measure of local-labor-market exposure
to import competition is the change in Chinese
import exposure per worker in a CZ, whereimports are apportioned to the CZ according to
its share of national industry employment:(3)​ ​​L​ijt​ Δ​Mucjt​​​   ​ ​ _
ΔIP​Wu​it​ =​∑ _
 ​
.
​Lujt
​ ​ ​Lit​​j

In this expression, L​i​t​ is the start of period
employment (year t) in CZ i and Δ​Mucjt​ ​ is theobserved change in US imports from China
in industry j between the start and end of theperiod. The difference in ΔIP​Wuit​ ​ across local
labor markets stems entirely from variation in
local industry employment structure at the startof period t. This variation arises from differential concentration of employment in manufacturing versus nonmanufacturing activities andspecialization in import-intensive industries
within local manufacturing. Differences in manufacturing employment shares are not the primary source of variation, however; in a bivariate
regression, the start-of-period manufacturing
employment share explains less than 25 percentof the variation in ΔIP​Wuit​ ​.

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trade and technology

Panel A. Routine Employment Share by Commuting
Zone in 1990

Lowest quartile (least routine)
2nd quartile
3rd quartile
Highest quartile (most routine)

3

Panel B. Trade Exposure by Commuting Zone,
1990–2007

Lowest quartile (least exposed)
2nd quartile
3rd quartile
Highest quartile (most exposed)

Figure 1. Routine Employment and Trade Exposure by Commuting Zone

II. Results

Are the CZs that are most exposed to rising trade penetration also those most impacted
by computerization? To explore this question,
Figures 1 and 2 illustrate the geography of trade
and technology exposure at the Commuting
Zone level. Each panel of the figures presents
a map of the 48 contiguous US states with all
722 CZ boundaries outlined in gray. In Figure
1, panel A, the interior of each CZ is shaded
to indicate its quartile rank within the distribution of CZs in the fraction of workers that were
employed in routine task–intensive occupationsin 1990.4 Darker colors correspond to higher
quartiles of RSH, with the lightest color denoting CZs in the lowest quartile, and the darkest
color denoting CZs in the fourth quartile.
Evident from this figure is that the CZs
with the highest employment shares in routine task–intensive occupations constitute a
mixture of manufacturing-intensive locations(e.g., Cleveland, Detroit, Milwaukee, andMinneapolis) and human capital–intensive
population centers such as New York, Chicago,
Dallas, and Los Angeles). This pattern reflects
the dual sources of routine task–intensive
occupations: blue-collar production occupations associated with capital-intensive manufacturing; and white-collar office, clerical, andadministrative support occupations associated4
Rankings are unweighted, and, hence, each quartile
contains roughly one-fourth of the 722 total CZs.

P20134402.indd 3

with banking, insurance, finance, and otherinformation-intensive sectors.
Figure 1, panel B presents analogous information for exposure to import competition from
China. In this panel, the lightest shading indicates CZs in the lowest quartile of trade exposure increase between 1990 and 2007 (measured
as the change in real dollars of imports per
worker), and the darkest color indicates CZs
that are in the highest quartile of trade exposure
increase. As expected, many manufacturingintensive regions appear among the most tradeexposed CZs, including substantial parts of
the Northeast and South Central United States,
where labor-intensive goods manufacturing,such as furniture, rubber products, toys, apparel,
footwear, and leather goods, is concentrated.
A comparison of the two panels of Figure 1
indicates both clear overlaps and pronounced
differences among the sets of CZs with hightrade exposure and those with high technology
exposure. Most notable, however, is that trade
exposure is geographically more concentrated.
A substantial fraction of the top quartile of tradeexposed CZs are located in a small cluster ofstates, including Tennessee, Missouri, Arkansas,
Mississippi, Alabama, Georgia, North Carolina,
and Indiana. By contrast, routine task–intensive
CZs are more dispersed throughout the UnitedStates.
Table 1 highlights the contrasting geographyof trade and technology exposure by summarizing our two exposure measures for the eight
major US census divisions that make up the contiguous US states. Growth in import exposure per
worker differs by more than a factor of six across

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Table 1—Trade and Technology Exposure
by Census Division

East South Central
Mid-Atlantic
East North Central
New EnglandSouth AtlanticWest North Central
West South Central
Pacific
Mountain

I. Trade exposure

II. Tech exposure

Growth imports
per worker($1,000s)% employees inroutineoccupations

4.00
2.55
2.37
2.28
2.15
1.60
1.56
1.21
0.62

29.76
30.85
31.31
29.70
29.80
27.51
27.39
27.87
26.51

Notes: The table shows unweighted averages of commuting zone technology and trade exposure within census divisions. Routine employment share is averaged over 1990 and
2000, and ten-year equivalent growth in imports per worker
in real 2007 dollars (expressed in $1,000) is averaged over
1990–2000 and 2000–2007.

census divisions, increasing, for example, by anaverage of 4,000 dollars per worker per decadein the East South Central division between 1990
and 2007 versus a relatively modest 620 dollars
per worker per decade in the Mountain division.
The geography of technology exposure is, bycontrast, far less regionally concentrated; themost and least exposed regions differ by only afew percentage points in their share of employment in routine intensive occupations. Thispattern does not, however, reflect a paucity ofgeographic variation in the RSH. Indeed, among
CZs with populations exceeding 750,000, RSH
varies by as much as 10 percentage points within
the state of California alone.
Figure 2 facilitates a direct comparison of
exposure to technology and trade by dividing
CZs into three groups: those in the highest quartile of both trade and technology exposure; thosein the lowest quartile of both trade and technology exposure; and the remainder. If trade andtechnology exposure were perfectly positively
correlated across locations, one-fourth of CZs
would be found in each of the first two groups.
If instead they were uncorrelated, roughly 6
percent (one-sixteenth) of CZs would be in thehigh-high and low-low groups, with the remaining seven-ninths in the remaining category. In
reality, 9 percent of CZs are in the top quartileof both trade and technology exposure, and 14

P20134402.indd 4Intermediate trade exposure orintermediate routine share
Low trade exposure, low routine share
High trade exposure, high routine share

Figure 2. The Joint Geographic Distribution of Tradeand Technology Exposure

percent are in the bottom quartile of both trade
and technology exposure. A simple populationweighted correlation between the trade andtechnology exposure variables finds that there
is almost no relationship between the two: thecorrelation is −0.02 for the 1990 to 2000 period
and 0.01 for the 2000 to 2007 period.
Table 2 contrasts employment patterns among
the full sample of CZs and the CZs that are in
the top quartile of either technology or trade
exposure. Highly trade-exposed and technology-exposed CZs are substantially above average in their manufacturing employment shares.
However, these CZs differ substantially intheir occupational composition. Most notably,
technology-exposed CZs exceed the nationwide average of employment in “abstract’’managerial and professional occupations and in“routine’’ clerical occupations, whereas tradeexposed CZs fall below the national average inboth these occupational categories. This patternreflects the fact that CZs with high technology
exposure are variously specialized in a mixtureof manufacturing and information-intensive production activities (e.g., finance and insurance).
By contrast, the US manufacturing industries
that are most exposed to China trade, such as
shoes, textiles, and furniture, are comparatively
labor intensive and tend to employ few workers
in abstract or in routine clerical occupations. In
short, highly trade-exposed and highly technology-exposed CZs are dissimilar both from average CZs and from one another.
A summary answer to our question regarding
the geography of trade and technology exposureis that the sets of heavily trade-exposed CZs and2/27/13 9:46 AM

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trade and technology

Table 2—Industry and Occupation Composition ofCommuting Zones with High Technology or Trade
Exposure

All CZ(1)

Top quartile Top quartile
technology
trade
exposure
exposure(2)(3)

Share of working age pop. employed in:
Manufacturing
11.8
16.2
industry
Nonmanufacturing
56.6
55.3
industry
Abstract occupation
20.3
23.1
Routine occupation,
14.1
16.3
clericalRoutine occupation,
7.9
9.9
production
Manual occupation
26.1
22.3

17.5
50.9
19.0
13.7
11.3
24.5

Notes: Column 1 shows unweighted statistics for the fullsample of commuting zones. Columns 2 and 3 show corresponding statistics for the quarter of CZs with highest technology or trade exposure. “Abstract” occupations include
managers, professionals, technicians, financial sales, andpublic security occupations. “Clerical routine” occupations
include clerical and retail sales occupations, while “production routine” occupations include precision production andoperator occupations. “Manual” occupations include service, transportation, repair, construction, and agriculturaland mining occupations.

5III. Conclusions

There is a wide agreement among economists that technological change and expanding
international trade have led to changing skill
demands and growing inequality or polarization of labor market outcomes in the UnitedStates and in other rich countries. This paper
highlights important differences in the exposure of local labor markets to the impacts oftechnology and trade. Regional exposure totechnological change, as measured by specialization in routine task–intensive production andclerical occupations, is largely uncorrelatedwith regional exposure to trade competition
from China. While the impacts of technology
are present throughout the United States, theimpacts of trade tend to be more geographically concentrated, owing in part to the strong
spatial agglomeration of labor-intensive manufacturing. Our findings suggest that it should bepossible to separately identify the impacts ofrecent changes in trade and technology on US
regional economies.
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