TRADE ADJUSTMENT: WORKER-LEVEL EVIDENCE*David H. AutorDavid Dorn
Gordon H. Hanson
Jae Song

I. Introduction
Among the most significant recent changes in the global
economy is the rapid emergence of China from a technologically
backward and largely closed economy to the world’s third largest
manufacturing producer in the space of just two decades.
Between 1990 and 2000, the share of world manufacturing exports originating in China increased from 2% to 5% and then
*We thank Stéphane Bonhomme, David Card, Pinelopi Goldberg, Lawrence
Katz, Patrick Kline, Brian Kovak, and numerous seminar and conference participants for valuable comments, and Gerald Ray and David Foster of the U.S. Social
Security Administration for facilitating data access and staff research time forthis project. Jan Bietenbeck provided valuable research assistance. Dorn acknowledges funding from the Spanish Ministry of Science and Innovation(ECO2010-16726 and JCI2011-09709). Autor and Hanson acknowledge funding
from the National Science Foundation (grant SES-1227334). The findings andconclusions expressed herein are those of the authors and do not necessarily represent the views of the Social Security Administration.
! The Author(s) 2014. Published by Oxford University Press, on behalf of President
and Fellows of Harvard College. All rights reserved. For Permissions, please email:journals.permissions@oup.com
The Quarterly Journal of Economics (2014), 1799–1860. doi:10.1093/qje/qju026.
Advance Access publication on September 24, 2014.

1799

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

We analyze the effect of exposure to international trade on earnings andemployment of U.S. workers from 1992 through 2007 by exploiting industry
shocks to import competition stemming from China’s spectacular rise as amanufacturing exporter paired with longitudinal data on individual earnings
by employer spanning close to two decades. Individuals who in 1991 worked in
manufacturing industries that experienced high subsequent import growthgarner lower cumulative earnings, face elevated risk of obtaining public disability benefits, and spend less time working for their initial employers, lesstime in their initial two-digit manufacturing industries, and more time working
elsewhere in manufacturing and outside of manufacturing. Earnings losses arelarger for individuals with low initial wages, low initial tenure, and low attachment to the labor force. Low-wage workers churn primarily among manufacturing sectors, where they are repeatedly exposed to subsequent trade shocks.
High-wage workers are better able to move across employers with minimal
earnings losses and are more likely to move out of manufacturing conditionalon separation. These findings reveal that import shocks impose substantiallabor adjustment costs that are highly unevenly distributed across workers
according to their skill levels and conditions of employment in the pre-shock
period. JEL Codes: F16, H55, J23, J31, J63.

1800

QUARTERLY JOURNAL OF ECONOMICS

1. Using County Business Patterns data, we calculate that U.S. manufacturing employment was 18.3 million in 1991, 16.6 million in 2001, 13.9 million in 2007,
and 11.4 million in 2011.
2. Our approach using longitudinal data is comparable to Walker (2012) andHummels et al. (2014), and is also related to Menezes-Filho and Muendler (2011).

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016accelerated to 12% in 2007 and to 16% in 2011 (Figure I). For U.S.
manufacturing, China’s expanding role in global trade represents
a substantial competitive shock. Not only is China’s export
growth concentrated in manufacturing—the sector that still accounts for the majority of U.S. trade—but its growth in imports,
in particular from the United States and other high-income countries, has been sluggish, thus leading to large trade imbalances.
During the past decade, China’s average current account surplus
was 5% of GDP, the mirror image of the U.S. current accountdeficit over the period. As Chinese imports to the United States
surged, U.S. manufacturing employment underwent a historiccontraction. Although the level of employment in U.S.
manufacturing had been declining modestly since the start ofthe 1980s, this trend gained pace in the mid-1990s and accelerated sharply in the 2000s: the number of workers employed in
U.S. manufacturing fell by 9.7 percentage points between 1991
and 2001 and by an additional 16.1 percentage points between
2001 and 2007.1
In this article, we examine how exposure to rising competition from China affects the employment and earnings trajectories
of U.S. workers over the medium to long run. We define trade
exposure as the growth in U.S. imports from China over 1991 to2007 that occurred in a worker’s initial industry of affiliation. By
categorizing workers according to their sector of employment at
the time the shock commences, we isolate the extended consequences of exposure to import competition and avoid selection
problems arising from the postshock resorting of workers acrossindustries.2 The choice of the outcome period is dictated on thefront end by the availability of bilateral trade data that can bematched to U.S. manufacturing industries and on the back end by
the onset of the Great Recession, which severely battered U.S.
manufacturing. These years span much of China’s export boom,as the 1990s and especially the early 2000s—following China’s
entry into the World Trade Organization (WTO) in 2001—were
the years when the country’s export growth accelerated.

1801

TRADE ADJUSTMENT

5
5

0

0
1991

1996

2001
Year

2006

2011

China share of world manufacturing exports
China import penetration in US manufacturing

FIGURE I
China Share of World Manufacturing Exports and China Import Penetration in
U.S. Manufacturing, 1991–2011
The China share of world manufacturing exports is the ratio of China’s
total manufacturing exports to world total manufacturing exports as reported
in World Development Indicators (http://data.worldbank.org/). The China
import penetration ratio is U.S. manufacturing imports from China divided
by U.S. domestic absorption in manufacturing (shipments plus imports minus
exports).

Using individual-level, longitudinal data from the U.S. Social
Security Administration, we estimate the impact of exposure to
Chinese import competition on cumulative earnings, employment, movement across sectors, movement across regions, andreceipt of Social Security benefits over the period 1992 to 2007.
The data permit us to decompose worker employment spells byfirm, industry, and place of residence and examine variation intrade impacts according to worker and firm characteristics.3 To
account for possible correlation between industry imports andindustry domestic demand or productivity shocks, we instrument
3. Limitations of the SSA data include not recording hours worked, withinyear spells of unemployment, or receipt of government benefits other than throughSocial Security.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

share of world exports (%)

10

import penetration ratio (%)

10

15

1802

QUARTERLY JOURNAL OF ECONOMICS

4. Our identification strategy follows Autor, Dorn, and Hanson (2013a) andBloom, Draca, and Van Reenen (2011).
5. China’s export growth is the culmination of a sequence of reforms that began
in the 1980s. Naughton (1996) marks 1984 as the year that China’s tilt toward
exports initiated. In 1992, China launched a further wave of reforms that welcomed
foreign direct investment and promoted Special Economic Zones. China’s 2001
WTO entry solidified its most-favored-nation status in the United States, though
it had enjoyed de facto MFN status since 1980.
6. See Amiti and Davis (2012) and Hummels et al. (2014) on trade and firms;
Artuç, Chaudhuri, and McLaren (2010) and Ebenstein et al. (2011) on trade andindustries; and Chiquiar (2008), Topalova (2010), and Kovak (2013) on trade andregions.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

for the change in U.S. imports from China using import growth inother high-income countries within 397 harmonized manufacturing industries.4
Key to our identification strategy is that China’s growth overthe period appears to be driven by rapid improvements in industrial production resulting from rising total factor productivity(TFP), capital accumulation, migration to urban areas, and enhancements in infrastructure, each of which was a consequence of
its transition to a more market-oriented economy (Naughton
2007; Hsieh and Klenow 2009; Hsieh and Ossa 2011).5 Brandt,
van Biesebroeck and Zhang (2012) estimate that over the period
1998 to 2007, China had average annual TFP growth inmanufacturing of 8.0%. In the same time frame, China accounted
for an astonishing three quarters of worldwide growth inmanufacturing value added that occurred in low- and middleincome nations (Hanson 2012).
Our work contributes to the rapidly developing literature on
the labor market consequences of globalization, much of which
focuses on the consequences of international competition forwages and employment at the firm, industry, or region level.6
Concerning China’s impact on U.S. labor markets, recent findings
imply that greater import exposure results in higher rates ofplant exit and more rapid declines in plant employment(Bernard, Jensen, and Schott 2006), larger decreases inmanufacturing employment and increases in unemployment
and labor force nonparticipation rates in regions that specialize
in industries in which China’s presence is strong (Autor, Dorn,and Hanson 2013a), and larger employment declines in industries in which the threat of reversion to high import barriers
was erased by China’ joining the WTO (Pierce and Schott 2012).

TRADE ADJUSTMENT

1803

7. For structural analyses of trade shocks and labor market dynamics in thepresence of search frictions and entry and exit costs, see Helpman, Itskhoki, andRedding (2010), Helpman et al. (2012), Coşar (2011), Dix-Carneiro (2014), andCoşar, Guner and Tybout (2011).
8. See also Sullivan and von Wachter (2009), von Wachter, Song, andManchester (2009), and Couch and Placzek (2010) for recent work that uses administrative data, and see Neal (1995), Parent (2000), and Chan and Stevens (2001) for
representative work on job loss using survey data. An alternative approach to study
job loss uses the CPS Displaced Workers Survey (e.g., Addison, Fox, and Ruhm
1995; Kletzer 2000; Farber 2005).

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

The current article extends the literature on labor market
impacts of trade by shifting the focus from aggregate marketlevel reactions to adjustments at the worker level. By estimating
the difference in outcomes among workers who ex ante are observationally similar except for their industry of employment, ouranalysis captures variation in the incidence of trade-induced disruptions to earnings and employment caused by proximity to theshock.7 Implicitly, this variation in incidence arises from frictions
to moving workers between jobs, because absent such frictions,wages would equalize for similar workers at all moments of time
and we would detect no earnings differences across workers ineither the short or long run. Though our approach prevents us
from estimating the impact of trade on equilibrium employment
or wages for entire skill groups, it allows us to quantify the distribution of incidence among workers along four margins ofworker adjustment: the change in earnings at the initial employer (intensive margin), the change in earnings associatedwith job loss (extensive margin), the change in earnings associated with uptake of government benefits (transfer margin), andthe change in earnings associated with moving between employers, industries, and/or regions (reallocation margin).
Decomposing changes in earnings across these margins reveals
where in the adjustment process frictions arise and which types
of workers face larger adjustment burdens.
The research we present also relates to an influential literature on the long-run consequences of job loss. In pioneering work,
Jacobsen, LaLonde, and Sullivan (1993) draw on administrative
data to identify episodes in which plants engage in mass layoffs,
meaning that they dismiss a substantial fraction of their employees within a short time span.8 Although we follow this literature in using administrative data to examine the long-run
effects of shocks on worker outcomes, we break from it by focusing

1804

QUARTERLY JOURNAL OF ECONOMICS

9. These results contrast with earlier literature on mass layoffs, which findsthat earnings losses for affected workers are relatively uniform across worker type(e.g., Jacobson, LaLonde and Sullivan 1993; von Wachter, Manchester, and Song2009).
10. Reassuringly, Autor, Dorn and Hanson (2013b, 2013c) find that across U.S.
local labor markets, exposure to import competition and exposure to computerization are essentially uncorrelated.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

on the specific shock of China’s export growth. By identifying thesource of the shock, we see worker adjustment along intensive,extensive, transfer, and reallocation margins.
To preview the results, we find that workers more exposed to
trade with China exhibit lower cumulative earnings and employment and higher receipt of Social Security Disability Insurance(SSDI) over the sample window of 1992–2007. The difference between a manufacturing worker at the 75th percentile of industry
trade exposure and one at the 25th percentile of exposureamounts to cumulative earnings reductions of 46% of initial
yearly income and to one half of an additional month where payments from SSDI are the main source of income. Trade exposure
increases job churning across firms, industries, and sectors. Moreexposed workers spend less time working for their initial employer, less time working in their initial two-digit manufacturingindustry, and more time working elsewhere in manufacturing
and outside manufacturing altogether.
The magnitudes of job churn and adjustment in earnings andemployment differ substantially across demographic groups.
Workers with lower labor force attachment, shorter tenure, andlower earnings incur larger losses in subsequent earnings andemployment, whereas losses for workers with high initial earnings are modest.9 Distinct from their high-wage counterparts,
low-wage workers churn primarily among manufacturing industries, where they remain exposed to trade shocks.
Import competition is just one of the forces impinging on U.S.
manufacturing. Technological progress has been rapid in computer- and skill-intensive sectors (Doms, Dunne, and Troske
1997; Autor, Katz, and Krueger 1998), and to the degree this iscorrelated with industry trade exposure, it poses a potential confound for our identification strategy.10 To capture industry exposure to technical change, we control for capital and technology
intensity, as well as industry pretrends in employment andwages. We further perform falsification tests to verify that
future increases in trade exposure do not predict past changes

TRADE ADJUSTMENT

1805

II. Motivation, Identification, and Data
We examine changes in outcomes for workers over the 1992–
2007 period that are associated with exposure to growing imports
from China. The context for our analysis is one in which China
experiences productivity growth, factor accumulation, and reductions in trade costs, which lead its exports to expand. Tradetheory predicts how such shocks affect wages in China, theUnited States, and the rest of the world (e.g., di Giovanni,
Levchenko, and Zhang 2011; Hsieh and Ossa 2011). Our interest
here is reduced form and relatively narrow in focus: we seek tocapture the changes in earnings and employment that workers in
exposed industries encounter when adjusting to the shock.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

in worker outcomes. These robustness tests support the interpretation that our identification strategy isolates industry-level
shocks caused by rising import competition rather than othertemporal confounds. Our results are also robust to a broad setof alternative measures of trade exposure to China.
Our analysis complements Autor, Dorn, and Hanson (2013a),
who estimate changes in employment and average earnings
across U.S. local labor markets resulting from regional exposure
to import competition from China. Because that work uses repeated cross-sections on geographic localities, it cannot address
how individuals adjust to trade shocks. We are able to measure
worker-level consequences by focusing on the substantial differences in trade exposure that derive from the initial industry of
employment. In alternative specifications, we also control for
trade exposure operating through workers’ regions of residence,
although variation in geographic exposure to trade is less wellmeasured in our data. Consistent with Autor, Dorn, andHanson (2013a), we find little evidence that geographic mobility
is an important mechanism through which trade adjustment
operates.
We begin by documenting our approach to estimating theeffects of exposure to trade shocks and describing the data.
Section III provides estimates of the impact of trade shocks on
cumulative earnings, employment, and benefit receipts for highattachment workers. Section IV examines heterogeneity in theconsequences of trade shocks by individual characteristics andconditions of initial employment. Section V explores alternative
measures of trade exposure, and Section VI concludes.

1806

QUARTERLY JOURNAL OF ECONOMICS

II.A. Industry Trade Shocks

ð1Þ

IPj; ¼

UC
Mj;

Yj;91 þ Mj;91  Ej;91;

where for U.S. industry j, MjUC is the change in imports from
China over the period 1991 to 2007 and Yj0 + Mj0 – Ej0 is initialabsorption (measured as industry shipments, Yj0, plus industry
imports, Mj0, minus industry exports, Ej0). We choose 1991 as the
11. An online theoretical appendix provides the formal analysis underlying this
discussion.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

As theoretical motivation, consider an economy with two sectors, one that is directly exposed to trade shocks in the rest of theworld and one that is not.11 In the long run, but not necessarily
the short run, labor is mobile between sectors, equalizing wages
for similarly skilled workers. Implicitly, nonlabor factors are immobile across sectors, as in the specific factors model (Feenstra
2004). Suppose that productivity growth abroad causes productdemand to fall for the economy’s trade exposed industry. The
immediate impact is to reduce the industry’s demand for labor,
causing its nominal wages to fall and inducing some of its workers
to relocate to the unexposed sector. If there are frictions inmoving labor between industries, adjustment will be slow, forcing
nominal wages in the exposed industry to remain below those in
the nonexposed industry during the transition. Over time, labor
will continue to exit the exposed sector until interindustry wages
are again equilibrated. Summing worker earnings over the preshock, shock, and postshock periods, nominal and real cumulative incomes for workers initially employed in the exposed sector
will be less than for workers initially employed in the unexposed
sector. The resulting earnings differences, attributable entirely tothe transitional phase, are increasing in the extent of labor immobility across sectors in both the short and medium run. Thiseffect on cumulative earnings, and the associated churning inworkers across employers, industries, and possibly regions, arethe focus of our empirical analysis.
Empirically, we capture shocks to foreign export supply
using changes in China’s presence in the U.S. market. Our baseline measure of trade exposure is the change in the import penetration ratio for a U.S. industry over the period 1991 to 2007,
defined as

1807

TRADE ADJUSTMENT

ð2Þ

IPOj ¼

OC
Mj;

Yj;88 þ Mj;88  Xj;88;

OC
is the change in imports from China from 1991 towhere Mj;
2007 in non-U.S. high-income countries, based on the industry inwhich the worker was employed in 1988, three years prior to thebase year.12 We use industry of employment in 1988, rather than1991, to account for worker sorting across industries in anticipation of future trade with China. The motivation for the instrument in equation (2) is that high-income economies are similarly
exposed to growth in Chinese imports that is driven by supply
shocks originating in China.13 Thus, the identifying assumption
is that industry import demand shocks are weakly correlated
across high-income economies. In an Online Appendix, we regress
the value in equation (1) on the value in equation (2), which isequivalent to the first-stage regression in the estimation without
controls. The coefficient is 0.855 and the t-statistic and R-squared
are 9.20 and 0.34, indicating the strong predictive power ofimport growth in other high-income countries for U.S. import
growth from China.
A potential threat to our identification strategy is that product demand shocks are correlated across high-income countries,
implying that our instrumental variables (IV) estimates may be

12. These countries are Australia, Denmark, Finland, Germany, Japan, New
Zealand, Spain, and Switzerland, which are the high-income countries for which we
can obtain disaggregated bilateral HS trade data back to 1991.
13. An alternative identification strategy would be to consider specific policy
shocks that may have contributed to China’s export growth, as in Bloom, Draca and
Van Reenen (2011), who exploit the end of the Multifibre Arrangement, or Pierce
and Schott (2012), who exploit China’s accession to the WTO.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

initial year because it is the earliest period for which we havedisaggregated bilateral trade data for a large number of country
pairs that we can match to U.S. manufacturing industries.
A natural concern with equation (1) as a measure of trade
exposure is that observed changes in the import penetration ratio
may in part reflect domestic shocks to U.S. industries. Even if thefactors driving China’s export growth are internal supply shocks,
U.S. industry import demand shocks may still contaminate observed bilateral trade flows. To capture the China supply-driven
component in U.S. imports from China, we instrument for trade
exposure in equation (1) with the variable

1808

QUARTERLY JOURNAL OF ECONOMICS

II.B. Measuring Trade Exposure
There is immense variation in import growth across industries. We use data on trade flows from UN Comtrade, concorded
from HS product codes to 397 four-digit SIC manufacturing industries (see the Data Appendix). A combination of these datawith information on shipments by U.S. four-digit industry fromthe NBER Productivity Database (Bartelsman, Becker, and Gray
2000) yields the industry-level trade exposure measure of equation (1). Figure II plots on the horizontal axis the change inindustry import penetration from China from 1991 to 2007 andon the vertical axis the share of production workers in industry
employment in 1991—meant to capture industry dependence onless skilled labor. Each four-digit industry is a point on the graph.
We use common symbols for industries that fall within 10 broad
14. Recent evidence (e.g., Bloom, Draca, and Van Reenen 2011; Autor, Dorn,and Hanson 2013c) suggests that automation and related changes in technology are
not what is behind rising import penetration from China.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

contaminated by correlation between import growth and unobserved components of product demand. This would tend to bias
a negative impact of trade exposure on earnings and employment
toward zero. To help address this concern, we alternatively measure the change in trade exposure using a gravity-based strategy,
which captures changes in China’s industry productivity relative
to changes in U.S. industry productivity, akin to the change in
China–U.S. comparative advantage. The gravity approach neutralizes demand conditions in importing countries by using thechange in China’s exports relative to U.S. exports within destination markets, helping isolate supply and trade cost–drivenchanges in China’s export performance. Our gravity and IV estimates end up being very similar, suggesting that correlated
import demand shocks across countries are not driving our
results.
An additional threat to identification is that growth in imports from China may be due to technology shocks affecting all
high-income countries that have shifted employment away fromapparel, footwear, furniture, and other labor-intensive industries. We address this issue in the estimation by employing an
extensive set of initial-year industry controls that potentially account for confounding technology shocks. We discuss additional
robustness tests in the sections that follow.14

1809

0

20

40
60
Growth of Import Penetration 1991-2007

food/tobacco (0.6)
wood/furniture (15.2)
chemical/petroleum (1.4)
metal/metal products (6.1)
transportation (1.7)

80

100

textile/apparel/leather (16.7)
paper/print (1.4)
plastic/rubber/glass (7.0)
machines/electrical (15.2)
toys/other (32.6)

FIGURE II
Trade Exposure and Production Worker-Intensity by Industry
Numbers in parentheses in the legend indicate average growth of importpenetration within industry group, weighted by 1991 employment. Values for
growth of import penetraton are Winsorized at 100 in this figure.

sectors, where each sector consists of industries that have relatively similar production-worker employment shares.
Focusing first on changes in import penetration at the sector
level, which are reported in the legend to Figure II, the sectors
with the largest increase in exposure from 1991 to 2007—whose
component industries include points located on the far right of
the figure—tend to be those intensive in the use of production
workers. These highly trade exposed sectors include toys, sports
equipment, and other products (whose industries appear as
squares), with a 32.6 percentage point increase in import penetration; apparel, leather (footwear), and textiles (shown as hollow
diamonds), with a 16.7 percentage point increase; and furniture
and wood products (shown as triangles), with a 15.2 percentage
point increase. The four-digit industries within these broad sectors are located relatively far up the vertical axis, indicating ahigh level of production worker intensity. Also exposed to trade ismachinery, electrical machinery, and electronics, where import

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Share of Production Workers 1991
.2
.4
.6
.8
1

TRADE ADJUSTMENT

1810

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

penetration grew by 15.2 percentage points. In the United States,
this sector contains both more and less skill-intensive industries(e.g., semiconductors in the former and computer peripherals in
the latter group), evident in the wide vertical range of the plus
plotting symbols in Figure II. In China, the dominant industries
within this sector include finished computers and telecommunications equipment (e.g., cell phone handsets), within which thecountry specializes in the processing of components and final assembly. The least exposed sectors in Figure II—food products,
beverages, and tobacco; chemical and petroleum products; andtransportation equipment—each have changes in import penetration of less than 2 percentage points and thus their industries
are clustered vertically close to the y-axis. These sectors have incommon intensive use of natural resources or physical capital.
Overall, the broad patterns of sectoral import growth are consistent with China’s strong comparative advantage in laborintensive activities (Amiti and Freund 2010; Huang, Ju, andYue 2011).
Notwithstanding the differences in trade exposure by broad
sector, factor intensity is not the whole story behind China’s
export growth. Visible in Figure II is variation in the change inindustry import penetration within broad sectors. The location of
the hollow diamonds high on the vertical axis confirms the apparel and textile industries’ dependence on production workers.
Yet within the sector, the most exposed industries see changes inimport penetration of over 90 percentage points, whereas theleast exposed see changes of less than 10 percentage points.
Within-sector variation in trade exposure is consistent with theidiosyncratic nature of comparative advantage (Schott 2004).
Factor intensity determines high-level patterns of specialization(Romalis 2004), with other factors shaping which goods countries
produce. In China’s case, the factors affecting within-broad-sector
export performance include the ease of offshoring (Feenstra andHanson 2005), proximity to suppliers or buyers (Koopman, Wang,
and Wei 2012), and the phase-out of rules favoring state-owned
firms in exporting (Khandelwal, Schott, and Wei 2013). In theempirical analysis, we include controls for the 10 broad sectors
indicated in Figure II, meaning that our identification strategy
exploits variation in import growth among industries with similar skill intensities.
Notably, China’s export production also relies heavily on imported inputs. During the sample period, approximately half ofTRADE ADJUSTMENT

1811

II.C. Worker-Level Data
We draw on the Annual Employee-Employer File (EE) extract from the Master Earnings File (MEF) of the U.S. Social
Security Administration to study longitudinal earnings histories
for a randomly selected 1% of workers in the United States. Mostof our analysis explores the impact of import competition on
workers’ career outcomes during the years 1992 and 2007,
while using data since 1972 as control variables and for robustness checks. For each worker and year, we observe total annual earnings, an employer identification number (EIN), anddetailed industry code (SIC) for the worker’s main employer.15
We augment the EE data using additional Social Security
Administration data files that provide basic demographic characteristics of workers, their income obtained from Social Security
benefits and from self-employment, and information on total
employment and payroll for each firm represented in thedata.16 The data lamentably do not contain information on
15. For workers who have multiple jobs in a given year, we aggregate earnings
across all jobs and retain the EIN and SIC of the employer that accounted for thelargest share of the worker’s earnings.
16. We lack information on receipt of other forms of government benefits.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

China’s manufacturing exports were produced by export processing plants, which import parts from abroad and assemble these
inputs into final export goods (Feenstra and Hanson 2005). The
importance of processing plants in China’s exports suggests thatthe gross value of its exports overstates actual value added in the
country. Recent evidence suggests, however, that the domestic
content of China’s exports is substantial and rising. Koopman,
Wang, and Wei (2012) find that the share of domestic value
added in China’s total exports rose from 50% in 1997 to over
60% in 2007. Even within the highly specialized export processing sector, domestic value added rose from 32% of gross exports in2000 to 46% in 2006 (Kee and Tang 2012). Our IV strategy does
not require that China is the sole producer of the goods it ships
abroad but that the growth of its manufacturing exports is driven
largely by factors internal to China. To account for how input
trade may affect the transmission of trade shocks in China to
U.S. industries, we use six alternative measures of changes inimport competition, alongside our principal measure in equation(1), discussed in Section V.

1812

QUARTERLY JOURNAL OF ECONOMICS

17. The 722 CZs, which cover the entire U.S. mainland, are clusters of counties
that share strong within-cluster and weak between-cluster commuting ties in the1990 census.
18. To mitigate these problems, we impute a worker’s 1991 residential location(see Data Appendix) and incorporate tabulations of industry employment by county
from the 1990 County Business Patterns.
19. Observations from the first period are necessary to construct equation (2)and for the second period to construct equation (1).

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

hours worked or on spells of unemployment less than one calendar year in duration.
From 1993 forward, we also observe an individual’s county ofresidence and can therefore match the worker to the commuting
zone (CZ) in which he or she resides.17 Measuring trade exposurein the local labor market is complicated by a lack of geographic
information for earlier years and for workers without employment in a given year; further, the 1% extract of the SSA data
provides an imprecise measurement of local industry composition.18 In light of these limitations, we examine the regional
dimension of worker exposure to trade shocks in extensions to
the main results but not in the baseline specification. Since thecorrelation between industry-level and region-level trade exposure is small in the data (correlation coefficient of 0.12), themain results for worker adjustment to industry-level trade
shocks are not sensitive to controlling for variation in geographic
trade exposure.
We focus on workers who were born between 1943 and 1970and study their outcomes over the period 1992–2007, during
which these individuals were between 22 and 64 years old. We
use two samples in the estimation. Our primary sample of508,129 workers consists of workers with high labor force attachment in each year from 1988 to 1991 (i.e., prior to the outcome
period), with earnings that equal or exceed the equivalent of
1,600 annual hours of work at the real 1989 federal minimum
wage. The full sample, containing 880,465 workers, adds workers
with low labor force attachment and comprises all working-age
individuals who had positive earnings (and a valid industry code)
for at least one year each during 1987 through 1989 and 1990
through 1992.19

TRADE ADJUSTMENT

1813

III. Impacts of Trade Exposure on Earnings andEmployment

ð3Þ

0E~ ij ¼ 0 þ 1 IPj þ 2 IPj;91 þ Xij;03 þ Z0j;0 4 þ eij ;where E~ ij ¼

Pt¼07 Eijt
t¼92 E it

is cumulative earnings over 1992 to 2007,

0

normalized by average annual earnings over 1988–1991, forworker i employed in industry j in 1991. Cumulative earnings
embody the sum of labor market activity over the sample
period. Normalizing cumulative earnings by workers’ initial (preshock) earnings E it0 provides a natural metric for assessing theeffect of shocks on the evolution of earnings. Relative to the conventional approach of taking the logarithm of earnings to provide
a proportional scale, this normalization has two virtues: baseline
earnings are constructed with preshock data only and so are notcontaminated by postshock outcomes, and scaling postshockearnings by the preshock baseline circumvents the problem
20. Because the sample is individuals of working age, the vast majority of workers who report Social Security benefits receive them in the form of SSDI, rather
than Social Security Retirement Income (whose primary recipients are aged 65 orolder) or SSI, whose primary recipients do not have sufficient prior work history toqualify for SSDI.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

We begin by examining the impact of trade exposure on total
earnings and employment, and then consider worker adjustment
to trade shocks through transitions between jobs, regions, andreceipt of benefits. We study five main worker outcomes overthe sample period: total labor earnings, the number of years
with positive labor earnings, earnings per year for years with
nonzero earnings, total self-employment income, and total SSDI
benefits received.20 Table I describes variation in these outcomes
across workers. For the main sample of workers with high labor
force attachment, the average worker had positive labor earnings
in 14.2 of the 16 years, cumulatively earned 19.2 times his initialaverage annual wage (measured as the average of the annual
wage between 1988 and 1991), earned an average of 1.3 initialannual earnings in years in which earnings were nonzero, andspent 0.3 years (4 months) in which SSDI benefits were themain income source. Among individuals initially employed in
manufacturing, the average increase in import penetrationfrom China was 7.7 percentage points.
Our baseline model takes the form:

1814

QUARTERLY JOURNAL OF ECONOMICSTABLE IDESCRIPTIVE STATISTICS
Main Sample

Extended Sample

Manuf
WorkersAll
Workers

Manuf
Workers

1.60(7.05)[2.72, 0.00][0.00, 0.00]
0.11(0.94)

7.72(13.85)[24.74, 0.06][7.30, 0.62]
0.54(2.00)

1.30(5.85)[1.99,0.00][0.00,0.00]
0.11(0.88)

6.30(11.59)[18.75, 0.04][6.28, 0.38]
0.48(1.78)

1,918.4(1,181.7)
1422.3(342.1)130.0(69.9)
34.3(168.3)46.4(175.9)1,808.9(1,025.7)
1428.6(335.7)122.4(59.7)
43.3(188.6)37.7(152.2)n/an/a1326.0(428.3)n/a1355.8(403.6)n/a

44.4(200.0)59.8(200.4)50.9(211.4)47.0(170.2)

0.431
0.207
0.077
0.208
0.269
0.368
0.169
0.194
0.231
0.237
0.246
0.286
10.46
508,129

0.312
0.200
0.086
1.000
0.236
0.355
0.187
0.221
0.153
0.289
0.290
0.267
10.55
105,625

0.475
0.236
0.085
0.173
0.418
0.301
0.129
0.153
0.257
0.231
0.210
0.302
9.23
880,465

0.359
0.235
0.097
0.836
0.425
0.283
0.134
0.158
0.199
0.285
0.251
0.265
9.60
181,900

Panel A: trade exposure, 1991–2007( imports from China to U.S.)/U.S.
consumption91
P90, P10 interval
P75, P25 interval(1991 imports from China to U.S.)/U.S.
consumption91

Panel B: main outcome variables, 1992–2007
100*cumulative earnings (in mult of avg
annual wage 88–91)
100*number of years with earnings > 0
100*cumul earn/yrs with earn > 0(in mult of avg ann wage 88–91)
100*number of years with mainincome from SSDI
100*number of years with main income
from self-employment
Panel C: worker characteristics in 1991
FemaleNonwhite
Foreign-born
Employed in manufacturing
Tenure 0–1 years
Tenure 2–5 years
Tenure 6–10 years
Tenure 11+ years
Firm Size 1–99 employees
Firm Size 100–999 employees
Firm Size 1,000–9,999 employees
Firm Size 10,000+ employees
Average log wage 1988–1991
Sample size

Notes. The main sample in the first two columns includes all workers who had at least full-time minimum wage earnings during each of the years 1988 to 1991. The extended sample in the second two columns
includes all workers who had a positive income in at least one year between 1987 and 1989 and one year
between 1990 and 1992. Trade exposure for this sample and control variables for manufacturing employment, tenure, and firm size are computed and averaged over all years between 1990 and 1992 during which
the worker is employed. Average log wage for this sample is computed based on years with positive earnings
between 1988 and 1991. The outcome variables for main income sources are defined for the years 1993-2007
in the extended sample. Column (4) in Panel B provides statistics for the subset of workers from theextended sample who were employed in manufacturing during at least one year between 1990 and 1992.

that log earnings are undefined when income from some period orsource is zero.
We model the cumulative shock due to trade exposure inequation (3) as a function of import penetration in 1991 (IPj,91)

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

All
Workers

TRADE ADJUSTMENT

1815

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

plus the growth in import penetration from 1991 to 2007, which isequivalent to the initial condition plus the average annual change(IPj, as defined in equation (1)). The vector Xij0 contains controls for the worker’s gender, birth year, race, and foreign-born
status, average log annual earnings over 1988 to 1991 and its
interaction with worker age, lagged earnings growth from 1988
to 1991, indicators for labor market experience and for tenure asof 1991 in the worker’s primary firm, the level and growth of the
primary firm’s average wage between 1988 and 1991, and indicators for the size of the primary firm. The vector Xj,0 controls for
economic conditions in industry j in 1991, discussed in moredetail later. While trade exposure and industry characteristics
vary at the level of workers’ four-digit industries in 1991, standard errors are clustered at the level of three-digit industries,
thus allowing for correlation in error terms among workers whoare initially employed in the same or in closely related industries.
Implicitly, our analysis compares workers with similar demographic characteristics, employment histories, and averagefirm and industry characteristics, some of whom work in industries subject to import competition from China and some of whom
do not. If labor markets are frictionless, such that workers cancostlessly change industries and obtain identical compensation inalternative lines of work, we will see no earnings or employment
impacts from exposure to China trade—though we should still beable to detect interfirm and interindustry mobility induced bytrade exposure. If growing imports from China cause wages tochange for entire groups of similarly skilled workers, our approach would also fail to identify such adjustments in earnings
or employment, since in this case the wage effects would not befirm or industry-specific. Conversely, we will find that trade impacts worker outcomes if trade shocks induce exposed firms to cut
wages and employment, and either firm or industry-specifichuman capital make it costly for workers to change employers
or industries (Neal 1995), or search and matching frictions
reduce the gains from changing jobs in the event of separation(Helpman, Itskhoki, and Redding 2010; Rogerson and Shimer2011).
A further consideration is that individuals will be exposed to
trade not just through their initial industry of employment but
also through their region of residence. Regions more vulnerable toimport competition from China—by virtue of being specialized in
sectors in which China has a strong comparative advantage—

1816

QUARTERLY JOURNAL OF ECONOMICS

III.A. Cumulative Earnings, Years Worked, and Earnings per
Year
Table II presents baseline estimates of equation (3) using thesample of individuals with high labor force attachment forthe outcome period 1992–2007. We initially include only the
Chinese import penetration measure and a full set of birthyear dummies to account for life cycle variation in earnings.
The regression in column (1) is estimated by ordinary leastsquares (OLS), whereas the regression in column 2 is estimatedby two-stage least squares (2SLS), using the variable described inequation (2) as an instrument for the change in import penetration given in equation (1). In both cases, there is a negative andstatistically significant relationship between the change inimport penetration and cumulative earnings over 1992 and2007. Greater exposure to imports from China based on a worker’s initial industry of employment is associated with lower total
earnings over the subsequent 16-year period.
To interpret the coefficient estimates, we compare a
manufacturing worker at the 75th percentile of the change intrade exposure (7.30 percentage points) with a manufacturing
worker at the 25th percentile (0.62 percentage points, see
Table I).21 The implied differential reduction in earnings overthe 16-year outcome period for the worker at the 75th percentile
is 20% (2.94  (7.30 – 0.62)) of initial annual earnings in column(1), and 38% (5.73  (7.30 – 0.62)) of initial annual earnings incolumn (2). The 2SLS estimate is roughly twice the magnitude of
the OLS estimate, which is consistent with there being a positive
correlation between U.S. industry import demand shocks and21. Nonmanufacturing workers are not a useful comparison group since bydefinition they have zero trade exposure.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

have undergone larger reductions in overall employment andearnings (Autor, Dorn, and Hanson 2013a). We also address
how workers are affected by an import shock to the industriesof other workers in their local labor market, in addition to the
own-industry trade shock. The local labor market trade exposure
for worker i is measured by averaging the value of equation (1)across all other workers in the same initial CZ of residence,
whereas the instrument for local labor market import
exposure is the corresponding average of other workers’ valuesof equation (2).

TABLE II2.939*(1.150)yes

5.726**(1.425)yes(2)
6.017**(1.475)
yes
yes

(3)
6.099**(1.395)
yes
yes
yes

(4)

(6)
6.436**(2.450)
yes
yes
yes
yes
yes

2SLS
6.883**(1.351)
yes
yes
yes
yes

(5)
5.695*(2.326)
yes
yes
yes
yes
yes
yes

(7)

(9)
6.864**(2.477)
yes
yes
yes
yes
yes
yes
yes
yes

(8)
6.728**(2.392)
yes
yes
yes
yes
yes
yes
yes

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Notes. Dependent variable: 100  earnings 1992–2007 (in multiples of initial annual wage). N = 508,129. All regressions include a constant and a full set of birth year dummies.
Demographic controls in column (3) include dummies for female, nonwhite, and foreign-born. Employment history controls in column (4) include dummies for tenure at 1991 firm (0–1, 2–5, 6–10 years), experience (4–5, 6–8, 9–11 years), and size of 1991 firm (1–99, 100–999, 1,000–9,999 employees). Earnings history controls in column (5) include the worker’s
annual log wage averaged over 1988–1991, an interaction of intial wage with age, and the change in log wage between 1988 and 1991, as well as the level and trend of the 1991
firm’s log mean wage for the period 1988–1991. Columns (6)–(9) add controls for a worker’s 1991 manufacturing industry, starting with initial trade penetration by Chinese and nonChinese imports in column (6). Column (7) adds dummies for 10 manufacturing subindustries, column (8) adds 1991 levels for employment share of production workers, log average
wage, capital/value added, and 1990 levels for computer investment, share of investment allocated to high-tech equipment, and fraction of intermediate goods among imports in
1990; column (9) additionally controls for changes in industry employment share and log average wage level during the preceding 16 years (1976–1991). Robust standard errors inparentheses are clustered on start-of-period three-digit industry. p  .10, *p  .05, **p  .01.

( China imports)/U.S.
consumption91
Birth year dummies
Demographic controls
Employment history
Earnings history
Initial import levels
10 mfg industry dummies
Industry characteristics
Pretrends ind emp and wage

(1)
OLS

IMPORTS FROM CHINA AND CUMULATIVE EARNINGS, 1992–2007: OLS AND 2SLS ESTIMATES

TRADE ADJUSTMENT

1817

1818

QUARTERLY JOURNAL OF ECONOMICS

22. A second difference between these specifications is that the OLS model uses
1991 industry affiliation in its exposure measure, whereas the the 2SLS model uses
1988 industry affiliation for the instrument. Since the first-stage relationship will
be tighter for workers who remain in the same industry in 1988 and 1991, differential effects of trade shocks by worker attachment to the initial industry may also
contribute to the greater magnitude of the 2SLS point estimates.
23. Data on firms’ total wage bill and total employment is drawn from the full
SSA Master Earnings File instead of the 1% sample.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

U.S. industry labor demand that biases the OLS estimate toward
zero.22
In column (3), we add dummies for female, nonwhite, andforeign-born; column (4) controls for individuals’ work history
by adding dummies for tenure at the 1991 firm (0–1, 2–5, 6–10
years), experience (4–5, 6–8, 9–11 years), and size of the 1991 firm
(1–99, 100–999, 1,000–9,999 employees); and column (5) controls
for worker earnings histories, including the annual log wage averaged over 1988–1991, an interaction of initial wage with age,the change in log wage between 1988 and 1991, and the level andtrend of the 1991 firm’s log mean wage for 1988 to 1991.23 These
additional controls modestly increase the magnitude and precision of the coefficient of primary interest.
Industries that are subject to greater import competition
may also be exposed to other economic shocks that are confounded with China trade. To capture overall industry exposure
to trade, column (6) includes controls for initial trade penetration
by Chinese and non-Chinese imports in the worker’s industry.
Column (7) adds dummies for 10 manufacturing sectors, meaningthat the regression models compare outcomes for manufacturing
workers who are initially employed in different subindustries ofthe same sector. To address differential rates of technologicalprogress across manufacturing industries, in column (8) we
include controls for the 1990 level of computer investment, theshare of investment allocated to high-tech equipment, the capital/value added ratio, and the 1991 employment share of production
workers and industry log average hourly wage of production
workers. This column further includes the share of imported intermediate inputs in material purchases (from Feenstra andHanson 1999) to capture overall industry exposure to offshoring.
To account for preexisting trends in industry growth, column (9)adds measures of changes in industry employment shares and log
average wage levels during the preceding 16 years, 1976 to 1991.

TRADE ADJUSTMENT

1819

24. The drop in employment years for a manufacturing worker at the 75th percentile of exposure relative to a worker at the 25th percentile is 3.6% of a year(0.54  6.7), or about two weeks, during the 16-year outcome period.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

The additional controls in columns (6) through (9) have little
substantive impact on the estimated impact of import penetration. The coefficient of 6.86 on the import penetration in the
final column is about 20% larger than in the initial 2SLS specification (column (2)). This suggests that conditional on demographic measures, workers with somewhat higher potential
earnings are initially employed in industries that subsequently
experience sharper rises in trade exposure. Accounting for thesesources of heterogeneity thus leads to a slightly larger estimate of
the earnings losses that workers experience due to trade exposure. To gauge the economic magnitude of this point estimate, weagain compare the implied impact on earnings of a manufacturing worker at the 75th versus 25th percentile of the change intrade exposure. Multiplying by the point estimate in column (9),we find a reduction of cumulative earnings by approximately one
half of an initial annual wage for the more exposed worker(6.86  (7.30 – 0.62) = 45.8).
The upper panel of Table III considers two additional labormarket outcomes. Column (2) estimates the impact of trade exposure on the number of years between 1992 and 2007 in which
the worker has nonzero labor earnings. This measure of the extensive margin of employment is coarse: an individual who works
a single day in a year will have nonzero earnings, so even prolonged periods of nonemployment will go undetected unless theyspan a full calendar year. The point estimate of 0.53 in column(2) is negative, suggesting that increases in industry trade exposure reduce subsequent years of employment. But this coefficient
is not statistically significant and it implies only a modest effect oftrade exposure on years with positive earnings.24 The thirdcolumn of Table III considers the impact of trade exposureon earnings per year of employment (in multiples of the initial annual wage) for years in which labor earnings are nonzero.
The point estimate of 0.39 (t = 2.8) suggests that trade
exposure depresses future earnings: specifically, earnings are differentially reduced by 2.6% per year (0.39  6.7) for a worker
initially employed in an industry at the 75th percentile of exposure relative to a worker at the 25th percentile of exposure.
In combination, the estimates in columns (2) and (3) reveal that

1820

QUARTERLY JOURNAL OF ECONOMICSTABLE IIIIMPORTS FROM CHINA 1992–2007 AND CUMULATIVE EARNINGS, YEARS WITH EARNINGS,AND EARNINGS PER YEAR 1992–2007 AND 1976–1991: 2SLS ESTIMATES(2)
Years w/
Earnings > 0(3)
Earnings/Year6.864**(2.477)

0.535(0.505)

0.393**(0.140)0.432(1.996)0.695(0.856)

0.064(0.112)

Panel A: treatment period 1992–2007( China imports)/U.S. consumption91
Panel B: preperiod 1976–1991( China imports)/U.S. consumption91

Notes. Dependent variables: 100  cumulative earnings (in multiples of initial annual wage); 100  years
with earnings; 100  earnings per year of employment (in multiples of initial annual wage), N = 508,792 inPanel A and N = 301,490 in Panel B, except N = 506,339 and N = 300,239 in column (3), where the dependent
variable is not defined for individuals who are never employed during the entire outcome period. Regressions
in Panel A include the full control vector from column (9) of Table II. Regressions in Panel B include the same
controls except tenure, experience, and firm size; industry-level controls are measured either for 1975 or for1972 (intermediate imports, computer investment, and high-tech equipment). Robust standard errors in parentheses are clustered on start-of-period three-digit industry. p  .10, *p  .05, **p  .01.

the net reductions in cumulative earnings seen in column (9) of
Table II (and replicated in Table III, column (1)) stem primarily
from reductions in within-year earnings rather than from additional years with zero earnings. These within-year earnings declines are in turn a combination of reduced earnings per hour andreduced hours worked, the relative contributions of which wecannot disentangle with our data.
Could these results merely reflect the secular decline oflabor-intensive U.S. manufacturing employment rather thanthe period-specific effects of exposure to China trade? We explore
this concern in Panel B of Table III by testing whether the growth
in import competition from China in the 1990s and 2000s ‘‘predicts’’ earnings and employment outcomes for an earlier cohort ofworkers that was not directly exposed to Chinese competition.25
25. We draw on an extended version of the Social Security data to construct
cumulative earnings from 1976 to 1991 for workers who were between 22 and 64
years of age during this earlier period, and we use these data to examine whether
their employment outcomes during 1976 through 1991 are correlated with later,
post-1991 changes in Chinese import penetration that subsequently occurred intheir initial industries. The sample for the analysis of the 1976–1991 period uses thesame sampling criteria as our main sample, and hence comprises the workers whoearned at least the equivalent of $8,193 at 2007 values in each of the four years
preceding the outcome period.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

(1)
Cumulative
Earnings

TRADE ADJUSTMENT

1821

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

This falsification test provides scant evidence of the hypothesized
confound. Column (1) estimates a negative relationship between
cumulative earnings and future industry-level China trade exposure occurring during the 1990s and 2000s, but the point estimate
is insignificant and less than one-tenth the magnitude of theanalogous contemporaneous estimate in Panel A. Column (2)finds a weakly positive relationship between years of nonzero
labor income and subsequent industry trade exposure, opposite
to Panel A. Finally, column (3) finds a small, negative, and insignificant relationship between annual wages in years with nonzero earnings and subsequent trade exposure. In net, future trade
exposure is a poor predictor for past earnings and employmentoutcomes for workers, suggesting that our main findings are notplausibly attributable to industry-specific trends that predate therise of import competition from China.
Figure III provides a dynamic view of these findings by plotting the estimated effect of import exposure on worker cumulative outcomes calculated on a rolling annual basis for each year
from 1991 to 2007. The estimating equations underlying the top
panel of the figure are identical to those in our baseline regression(Table II, column (9)) except that in place of workers’ cumulative
earnings over the entire period 1992–2007, we use cumulative
earnings up through the year indicated on the horizontal axis.
Trade exposure remains the total change over the 1991–2007
period, such that the figure depicts how the impact of trade exposure amasses over time. The figure reveals a significant adverse
effect of trade exposure on cumulative earnings in every year
between 1992 and 2007. The impact coefficients become progressively more negative over the 1990s and then grow even more
rapidly after 2001; the 2001–2007 total decrease is nearly twicethat for 1992–2001.
The path of earnings evident in the upper panel of
Figure III encompasses multiple possible channels of adjustment.
If workers’ initial sectoral affiliations are relatively durable,
the monotonic decrease in earnings may be a consequence ofextended exposure to the rise in import competition.
Alternatively, trade shocks may increase churning across jobs,
impairing workers’ ability to find secure positions or obtain equivalently high-quality matches with new employers, as would beconsistent with labor market scarring effects. We present initialevidence of the connection between import competition and job
churn in the bottom panel of Figure III, which follows the

0

-1

-2

-3

Coefficient-6 -5 -4

-7

-8

-9

88

19

19

89

19

90

91

19

92

19

19
93

19
94

19
95

19
96

97

19
99
00

20
01

20
02

20
03

20
04

20
05

20
06

20

FIGURE III

07

20

19
88

19
89

19
90

19
91

19
92

19
93

19
94

19
95

19

96

97

19

Year

19

98

19

99

20

00

20

01

20

02

20

03

20

04

Cumulative Firm-Firm and Emp-NonEmp Transitions since 1991

Cumulative Earnings and Cumulative Job Churning since 1991

98

19

Year

19

Cumulative Earnings since 1991

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

20

05

20

06

20

07

Each panel plots regression coefficients and 90% confidence intervals obtained from 20 regressions that relate the indicatedoutcome variables to the 1991–2007 trade exposure of a worker’s 1991 industry. The outcome in the left panel is the cumulative
earnings that a worker obtained from 1991 through the year indicated on the x-axis, expressed in percentage points of the worker’s
average annual earnings in 1988–1991. Coefficients for years prior to 1992 refer to cumulative earnings between the year indicated on
the x-axis and 1991. The outcome variable in the right panel is the cumulative number of times a worker has changed employers or
moved between employment and nonemployment between 1991 and the year indicated on the x-axis, multiplied by 100. This outcome
variable takes negative values prior to 1991, such that positive coefficients pre-1991 indicate lower churning for workers whose 1991
industry was trade-exposed. All regressions include the vector of control variables from column (9) of Table I.

-11 -103.5
3
2.5
Coefficient
1.5
2
1
.5
0

1822
QUARTERLY JOURNAL OF ECONOMICSTRADE ADJUSTMENT

1823

III.B. Worker Mobility across Firms, Industries, and Sectors
A strength of the SSA longitudinal data is that they permit
us to observe the earnings, employment, and job reallocation
margins by which workers and, indirectly, their employers,adjust to changes in import penetration. We analyze this reallocation process by decomposing the total worker-level effect oftrade exposure seen in Table III into a set of additive, mutually
exclusive channels that include employment observed at theworker’s initial employer; employment at other firms within
the worker’s initial two-digit industry; employment at firms

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

structure of the top panel but replaces the dependent variablewith workers’ cumulative number of changes in employers andemployment to nonemployment transitions as of a given year.
From 1992 forward, the coefficients are uniformly positiveand significantly so for all years after 1997. More trade exposed workers have a larger number of job changes, where thesum of these changes steadily grows as the trade shock becomesfully expressed. We subsequently explore whether these more
frequent job changes also entail greater mobility across sectors
or regions.
The time pattern in the top panel of Figure III highlights animportant nuance in interpreting our impact estimates: the risein China trade exposure is an ongoing process that builds momentum in the early 1990s as China embraces export-led development, and then accelerates after 2001 as the country further
opens its markets. Thus, the time pattern of coefficients in
Figure III does not constitute an ‘‘event study’’ as in traditional
mass layoff analyses—that is, a discrete shock followed by a time
path of adjustments—but depicts the interaction between two
economic forces, rising import penetration and ongoing worker
adaptation. We similarly find that at the industry level, increases
in trade exposure exhibit strong serial correlation: no sizable industry experiences a large increase in trade competition in the
2000s that did not also experience one in the 1990s, or vice versa.
Industries that are in the top tercile of trade exposure in one
decade but the bottom tercile in the other account for just 1% of
all manufacturing workers in our sample. These attributes of thedata motivate our parameterization of the rise in China’s importpenetration in equation (3) as a single long change over the period1991 through 2007.

1824

QUARTERLY JOURNAL OF ECONOMICS

26. Prior to 2000, industry information is available for 96–97% of all workers ina given year. However, industry codes are missing for most firms that were incorporated in or after 2000. The Social Security data very rarely record changes infirms’ industry affiliation, and worker outcomes at the initial firm (column (2) ofTable IV) thus correspond to outcomes in the initial industry.
27. In Panels A and B (cumulative earnings and cumulative years of employment), summing the coefficients in columns (2) through (6) produces the value incolumn (1). This adding-up property does not apply to Panel C because the outcome
variable, earnings per year employed, is a conditional value.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

within manufacturing but outside the worker’s initial industry;
employment outside of manufacturing entirely; and employment at new firms whose industry is unrecorded in the data.
This analysis encompasses the direct impact of rising importcompetition on workers’ tenure and earnings at their initial
employers as well as any subsequent, potentially offsetting
effects of moves across employers, across subsectors of
manufacturing, and between the manufacturing and nonmanufacturing sectors.26
Table IV presents the results, beginning in Panel A with cumulative earnings. The first column (1) replicates our main estimate for the impact of trade exposure on cumulative earnings
among high attachment workers, while columns (2) through (6)decompose this net earnings effect into its constituent additive
parts: initial firm; new firm in same industry and sector; new industry in same sector; new sector; and sector missing.27 The negative and significant coefficients in columns (2) and (3) reveal thatworkers initially employed in industries that undergo substantial
increases in trade exposure experience both sharply reduced
earnings at their initial employers and lower subsequent income
from other firms in the same two-digit industry. Do these reductions stem from changes at the extensive margin (reduced years ofwork), the intensive margin (reduced earnings per year), or both?
The estimates in columns (2) and (3) in Panel B, which consider
employment rather than earnings, indicate that the answer issome of both. Comparing across panels, cumulative earnings fallby about 50% more than cumulative employment at both the initial employer and in the initial industry. Columns (2) and (3) in
Panel C confirm this implication: trade exposure significantly reduces earnings per year of employment at the initial firm and at
subsequent firms in the same industry.
These impacts at the initial firm and industry may of course
be offset by gains in other sectors. Indeed, we know from the small

1825

TRADE ADJUSTMENTTABLE IV

IMPORTS FROM CHINA AND EARNINGS AND EMPLOYMENT BY FIRM, INDUSTRY, AND
SECTOR, 1992–2007: 2SLS ESTIMATES
(1)

(2)

All Employers

(4)

(5)
Other
Sector

N/A

No
No

No
No

N/A
No

5.914*(2.326)

2.253(3.049)1.579*(0.800)4.654**(1.655)1.451(1.983)1.600*(0.625)Same Sector
Yes
Yes

Yes
No

(6)

Panel A: cumulative earnings (in initial annual wage*100)( China imports)/
U.S. consumption91

6.864**(2.477)

9.107** 2.998
(3.395)(1.671)Panel B: cumulative employment (in years*100)( China imports)/
U.S. consumption91

0.535(0.505)

6.204*(2.566)

2.036(1.278)Panel C: earnings per year of emp (in initial annual wage*100)( China imports)/
U.S. consumption91

0.393**(0.140)0.283(0.093)

0.543 0.551
(0.304)(0.300)0.655* 0.606*(0.291)(0.245)

Notes. Dependent variables: 100  cumulative earnings; 100  years with earnings; 100  earnings per
year of employment. N = 508,129 in Panels A and B. N = 506,339, 424,027, 155,993, 263,158, 112,002,
119,989 in columns (1)–(6) of Panel C. Column (6) measures employment and earnings in firms withmissing industry information. A large majority of these firms are new firms that have been incorporated
between 2000 and 2007. All regressions include the full vector of control variables from column (9) of
Table II. Robust standard errors in parentheses are clustered on start-of-period three-digit industry.
p  .10, *p  .05, **p  .01.

and insignificantly negative net estimate of trade exposure on
years of employment (column (1), Panel B) that employment
losses are almost entirely offset: trade-impacted workers makeback their employment losses in the initial firm and industry
through employment outside of the original two-digit industry.
Column (4) in Panel B indicates that trade-exposed workers
spend more years employed at firms that belong to a different
two-digit industry within their initial sector of employment.
But these offsetting employment gains from reallocation toother two-digit industries within manufacturing are only justover half as large as the losses incurred with the original em4:65¼ 0:56), indicating thatployer and two-digit industry (6:20þ2:04
trade exposure in a worker’s initial firm reduces the worker’s
total manufacturing employment net of mobility within the
sector.28 Columns (5) and (6) in Panel B complete the employment
28. Column (6), Panel B shows moderate employment gains at firms with a
missing industry code, a large majority of which were incorporated in the years
2000–2007, when a new data collection process no longer recorded information on

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Same 2-digit Industry
Same Firm

(3)

1826

QUARTERLY JOURNAL OF ECONOMICS

industry. Even if one assumes that the new firms that employ former manufacturing employees all operate in the manufacturing sector, there is still a sizable negative effect of trade exposure on manufacturing employment or earnings, which
may be seen by summing the coefficients across columns (2), (3), (4), and (6) of
Panel A or B.
29. The results in Panel C must be interpreted with care, however, because theearnings-per-year effects combine variation stemming from changes in weeks
worked, hours worked per week, and earnings per hour of work.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

picture by considering employment outside of the worker’s initial
sector and at firms whose industry could not be identified.
Workers who are initially employed in trade-exposed industries
experience offsetting employment gains in both categories,
though results for employment outside of manufacturing are imprecisely estimated.
Although trade-exposed workers appear to offset most of
their employment losses in the initial firm and two-digit industry
through employment further afield, they do not fully offset lostearnings in subsequent employment. In particular, Panel A demonstrates that earnings gains in other manufacturing industries
are only half as large as the losses incurred with the original
5:91
employer and industry (9:11þ3:00¼ 0:49), whereas earnings gains
outside of manufacturing are on net close to zero. Thus, to thedegree that trade-impacted manufacturing workers succeed inoffsetting initial earnings losses, this primarily occurs throughsubsequent earnings gains with other manufacturing firms outside of the immediate two-digit industry. This result is surprisingbecause U.S. manufacturing is a comparatively small and rapidly
contracting sector throughout the time period of our study(see note 5). Panel C finally demonstrates that the discrepancy
between the large trade-induced reduction in cumulative earnings and the insignificant trade-induced reduction in employment
years is explained by lower earnings per year of employment bothat the initial firm and at subsequent employers within and outside of manufacturing.29
Why don’t employment transitions allow initially tradeexposed workers to fully recoup declines in earnings with theinitial employer? The literature on job loss provides one potential
answer (e.g., Neal 1995): displacement destroys industry-specifichuman capital, leaving affected workers in positions for which
they are poorly suited relative to nondisplaced workers. A parallel explanation is that workers’ specific skills cause them to seek
positions in which they remain exposed to import competition,

TRADE ADJUSTMENT

1827

III.C. Do Workers Adjust to Trade Exposure through Geographic
Mobility?
Transitions across employers and industries are one mechanism by which workers adapt to the consequences of import competition. Moving between geographic locations is another. The
literature provides mixed evidence on mobility responses tolabor market shocks. The flow of labor across U.S. cities andstates following changes in regional labor demand appears to besluggish and incomplete (Topel 1986; Blanchard and Katz 1992;
Glaeser and Gyourko 2005). This sluggishness is most pronounced among less educated workers, who make up a
30. The correlations compare the 1991–2007 growth of import penetration between the industry that employed a worker in 1991 and the industry that employeda worker in the subsequent year indicated on the y-axis. This correlation is 1.0 byconstruction in 1991.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

notwithstanding the predilection of trade impacted workers toexit their original two digit sector. Figure IV provides insight
into this latter mechanism by depicting the correlation between
workers’ trade exposure at their initial employers and at theircurrent employers for each year between 1991 and 2007.30 In
the years immediately following 1991, few workers have yet separated from their original firms, and hence the correlation remains close to 1. Over time, the correlation between initial andcurrent firm trade exposure falls, as job transitions proceed
apace, but remains strongly positive, leveling off at 0.43 in the
final year (2007). As a benchmark against which to evaluate thepersistence of trade exposure, Figure IV also plots counterfactual
correlations in which trade exposure at any new employer is set to0, such that the reported series summarizes the cumulative likelihood of having left the initial firm as of a given year. Logicallythis counterfactual correlation also declines over time, reflecting
the rising likelihood of having departed from the original placeof work. But the counterfactual decline is far more rapid than
the actual series and ends up at the much lower level of 0.17
in 2007. By implication, were trade-exposed workers to exit
manufacturing immediately after the first job separation, their
net subsequent exposure would be 60% lower than in the actual
data. Thus, even after changing employers, initially tradeexposed workers appear likely to remain in high-exposure industries, which are subject to further trade shocks.

1828

.4
.2
0

07

06

20

05

20

04

20

03

20

02

20

01

20

00

20

99

20

98

19

97

19

96

19

95

19

94

19

93

19

92

19

91

19

19

Year
Conditional correlation in trade exposure between 1991 firm and firm in indicated year
Counterfactual with zero trade exposure in all firms except 1991 firm

FIGURE IV
Persistence of Trade Exposure since 1991
The graph plots regression coefficients and 90% confidence intervals obtained from 2  16 regressions that relate the 1991–2007 trade exposure of aworker’s industry in the year indicated on the x-axis to the 1991–2007 trade
exposure of the worker’s intial 1991 industry. The counterfactual data series
sets trade exposure to 0 for all firms except the worker’s initial employer. It
refers to a hypothetical scenario in which no worker joins a trade-exposed firm
after separating from their initial firm, and so all persistence in trade exposureis due to workers who have not separated from their initial firm.

disproportionate share of manufacturing employment (Boundand Holzer 2000; Notowidigdo 2013). Consistent with limited mobility responses, Autor, Dorn, and Hanson (2013a) find little
impact of regional trade exposure on changes in regional population. We revisit the issue of geographic relocation by extending
our analysis to consider whether workers initially employed in
more trade-exposed industries are more likely to change theirplace of residence during the sample period.
The SSA data provide a county code for the residence address
associated with each individual record in a given year. Because
individuals may reside in one location and work in another, residence may be a noisy indicator of the employment location.
However, because we designate geographic units to be CZs—
which are defined precisely to be the regions within which

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Coefficient
.6
.8

1

1.2

QUARTERLY JOURNAL OF ECONOMICSTRADE ADJUSTMENT

1829

31. We are able to determine the 1993 county code of residence for 94% of workers in our main sample.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016individuals usually both live and work—there is a built-in buffer
against such noise. Other limitations of individual addresses arethat they do not appear in the data until 1993, are not available
for all workers, and are never observed for individuals who are
not employed in a given year.31
We match a worker’s county of residence in 1993 to the corresponding CZ and define a worker to have changed locations if
the CZ of residence subsequently changes. Despite its limitations,
the SSA data appear to accord well with other sources in capturing the frequency of location changes. Molloy, Smith and Wozniak(2011) use the census to calculate that 12.9% of workers changed
their CZ between 1995 and 2000; in our SSA sample over thesame time frame, the mobility rate is 14.4%. Because geographic
information is not available until 1993, when analyzing regional
mobility we define the outcome period to be 1994–2007 instead of
1992–2007. As before, we define the change in import penetration
using equation (1) and continue to use the instrument defined inequation (2).
Table V presents results on the regional dimension ofadjustment to trade shocks. Column (1) repeats the baseline specifications for the shorter outcome period 1994–2007, whichcauses estimates to differ slightly (but not qualitatively) fromthose in Panel A of Table III. We examine the consequences oftrade exposure on geographic mobility in columns (2) through (4)in Panel A by decomposing the earnings that a worker accumulated from 1994 to 2007 according to whether the earnings were
accrued in the initial CZ, in a different CZ, or, in 6% of cases, inlocations that are missing a CZ identifier.
If workers respond to trade shocks by moving between regions, import exposure will have a negative effect on total earnings in the initial CZ in which a worker resides—shown in column(2)—which would indicate reduced labor supply in the location
where a worker is employed when trade exposure starts to rise.
But there would be a positive effect on total earnings in future
CZs of residence—shown in column (3)—indicating a shift inlabor supply across regions in response to the shock. This pattern
is nowhere present in the data. In Panel A, the impact of trade
exposure on cumulative earnings as a share of initial period earnings is negative not just for workers’ initial CZs (column (2), Panel

1830

QUARTERLY JOURNAL OF ECONOMICSTABLE V

IMPORTS FROM CHINA AND EARNINGS AND EMPLOYMENT BY GEOGRAPHIC LOCATION,
1994–2007: 2SLS ESTIMATES(1)
All CZ(2)
Initial CZ(3)Other CZ

(4)
N/A CZ( China imports)/U.S.
consumption91

6.649**(2.417)

3.558*(1.396)

2.307
(1.281)

0.785(0.542)

0.491(0.539)

0.154(0.253)

0.741*(0.369)

1.010*(0.398)Panel B: cumulative employment (in years*100)( China imports)/U.S.
consumption91

0.653(0.513)

0.008(0.599)Panel C: earnings/year (in initial annual wage*100)( China imports)/U.S.
consumption91

0.418**(0.152)

0.344**(0.124)

Notes. Dependent variables: 100  cumulative earnings; 100  years with earnings; 100  earnings per
year of employment. N = 508,129, except smaller samples in Panel C. Column (1) shows aggregate resultsfor earnings and employment. Columns (2)–(4) subdivide results into employment and earnings obtained
while residing in the 1993 CZ of residence (column (2)), in all CZs other than the 1993 CZ of residence(column (3)), and in CZs than cannot be classified because either the 1993 location or subsequent locationof the worker is unknown (column (4)). All regressions include the full control vector from column (9) of
Table II, and a dummy for the 6% of workers whose 1993 CZ is unknown. Robust standard errors inparentheses are clustered on start-of-period three-digit industry. p  .10, *p  .05, **p  .01.

A) but also for cumulative earnings in other CZs (column (3),Panel A), and for cumulative earnings in CZs that we cannot
classify because information on a worker’s initial or subsequent
location is missing (column (4), Panel A). More trade-exposed
workers thus receive less in total earnings from all local labormarkets in which they reside, suggesting that geographic mobility is not a primary mechanism for adjusting to trade shocks.
Results for cumulative years of nonzero earnings in Panel B
also fail to indicate that trade exposure causes geographic mobility to increase. Workers initially employed in industries subject togreater import competition have lower cumulative years with
nonzero earnings not just in their initial CZ (column (2)) but
also in other CZs (column (3)) and in unidentified CZs (column(4)), though none of these effects is significant. The decline incumulative earnings by location in Panel A is instead driven largely by reduced earnings per year of employment in all CZs inwhich a worker resides (columns (2)–(4), Panel C). In sum, theresults of Table V give further indication that regional mobility isof limited import as a mechanism through which workers adjust
to changes in trade exposure.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Panel A: cumulative earnings (in initial annual wage*100)

TRADE ADJUSTMENT

1831

III.D. Social Security Benefits as a Channel of Adjustment

32. To become insured by the SSDI program, an individual must have worked in
at least 5 of the 10 most recent years in covered employment. Prior literature has
established that enrollment in the SSDI program is generally countercyclical, andlocal economic shocks can induce sharp rises in SSDI applications and subsequent
awards (Black, Daniel, and Sanders 2002; Autor and Duggan 2003; Autor, Dorn,and Hanson 2013a).

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Alongside changes in employment and earnings, a complementary channel by which workers may adjust to employment
shocks is through job search, retraining and transfer programs,
such as the federal Trade Adjustment Assistance (TAA) program,state unemployment insurance programs, and numerous needbased transfer programs. One such adjustment program that isobservable in our data is the federal SSDI, which provides income
transfers and Medicare coverage to workers who have developed aphysical or mental disability that prevents them from being gainfully employed.32 Since workers cannot obtain SSDI if they are
employed, it is plausible that the trade-induced declines in employment and earnings seen in Table III may have a counterpart
in increased SSDI participation. We explore this possibility in
Table VI by analyzing the impact of trade exposure on SSDI enrollment along four margins: the number of years receiving SSDI
as a primary income source, cumulative income from SSDI, theprobability of receiving SSDI at any point, and the number of years
with positive SSDI income for those who receive any benefits.
The first panel of Table VI shows the effect of trade exposureon the primary source of income during each year that is observedin our data: labor income (column (1)), self-employment income(column (2)), SSDI income (column (3)), and no recorded income(column (4)). Echoing the results in Table III, column (2) in Panel
A shows a negative but insignificant relationship between trade
exposure and years with labor earnings as the main source ofincome: among the workers in our main sample (with strong initial labor force attachment), spending a full calendar year out ofwork is uncommon. In Table VI, Panel A, column (2), trade exposure is also negatively but not significantly correlated with total
years in which self-employment is the primary income source,
indicating that transitions into self-employment are not an important mechanism for worker adjustment to trade shocks.
However, column (3) finds that trade exposure predicts a1832

QUARTERLY JOURNAL OF ECONOMICSTABLE VI

IMPORTS FROM CHINA AND NONWAGE INCOME, 1992–2007: 2SLS ESTIMATES(1)
Wage
Earnings(2)
Self-Emp.
Income(3)
SSDI
Income(4)
No Recorded
Income( China imports)/U.S.
consumption91

0.493(0.498)

0.127(0.207)0.562*(0.268)0.057(0.367)

Panel B: Cumulative Income from Indicated Source (in Initial Annual
Wage*100)( China imports)/U.S.
consumption91

6.864**(2.477)

0.007(0.348)0.352*(0.164)n/aPanel C: Years with SSDI Income: Intensive vs Extensive Margin( China imports)/U.S.
consumption91

100*Dummy(Yrs SSDI > 0)

100*Yrs SSDI
if Yrs SSDI > 00.082*(0.035)0.687(1.298)Notes. Dependent variables 100  cumulative years with main income from indicated source, 100  cumulative earnings from indicated source, 100  probability of ever receiving SSDI benefits,
100  cumulative years with SSDI benefits among individuals with positive SSDI receipts. All regressions
include the full vector of control variables from column (9) of Table II. Robust standard errors in parentheses are clustered on start-of-period three-digit industry. p  .10, *p  .05, **p  .01.

significant increase in years receiving SSDI as the primary
income source. Applying our 75th and 25th percentile comparison
among manufacturing workers, the more trade-exposed worker
spends an additional half month receiving SSDI benefits as theprimary income source during the 16-year outcome window. Thisis a modest effect. By way of context, the average manufacturing
worker spends approximately five months (0.43 year) over the
sample period with SSDI benefits as his or her main income
source.33
In untabulated results, we repeat this analysis while replacing receipt of SSDI with receipt of any type of Social Security
benefit, which includes SSDI, Social Security Retirement Income,and Supplemental Security Income. The results are nearly

33. Workers may exit the labor force and obtain SSDI in the same calendar year
without having both sources of income concurrently. In addition, SSDI recipientsare permitted to work up to a Substantial Gainful Activity threshold (currently$1,010 per month for nonblind adults) without jeopardizing their SSDI benefits.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Panel A: Cumulative Years with Main Income from Indicated SourceTRADE ADJUSTMENT

1833

34. Our data indicate that SSDI payments average 47% of initial average labor
earnings for years in which workers in our sample receive SSDI.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

identical to those Table VI, consistent with our main sample containing working-age individuals with high attachment to thelabor force who are unlikely to qualify for other types of Social
Security payments.
Panel B of Table VI studies more closely the impact of trade
exposure on income (rather than employment) by considering
self-employment and SSDI income alongside wage income.
Column (2) finds a negligible impact of trade exposure on selfemployment income. Commensurate with the increased durationof SSDI benefit receipts documented in Panel A, column (3), Panel
B shows a positive and statistically significant effect of trade exposure on receipt of SSDI income (measured in percentage pointsof the initial average wage). The point estimate of 0.35 implies
that a manufacturing worker at the third quartile of exposure
receives an additional 2.3% of initial annual earnings in SSDI
benefits relative to a manufacturing worker at the first quartileof exposure. Thus, SSDI benefits only replace a small fraction—
about 5%—of the income lost to trade exposure.34
Panel C decomposes the increased SSDI benefit receipts into
intensive and extensive margins. Although trade exposure significantly increases the likelihood that a worker receives SSDI atsome point in the next 16 years, the effect is modest. Comparing
the 75th and 25th percentile manufacturing worker, the increment to the probability of any SSDI receipt over 16 years is only
0.6 percentage point, with no significant effect on years of receipt
conditional on receiving SSDI. This pattern is also evident in thebottom panel of Online Appendix Figure A.2, which shows thatthe effect of trade exposure on the incidence of SSDI receipt cumulates slowly over the 16-year outcome window. Thus, disability plays an important role on the extreme extensive margin—
among workers who exit the labor force altogether—but the majority of trade-exposed workers with an initial strong labor force
attachment remain attached to the labor market, albeit at reduced earnings. As subsequent analysis shows, SSDI is a more
important adjustment margin for less attached workers.

1834

QUARTERLY JOURNAL OF ECONOMICS

IV. Heterogeneity in Worker Adjustment
to Trade Exposure

IV.A. High versus Low Labor Force Attachment Workers
Table VII expands our sample to include workers who had
any labor income in at least one year during 1987–1989 and one
year during 1990–1992, which brings our sample size from508,129 to 880,465. The additional workers of the extended
sample either had zero earnings or earned less than the salaryof a full-time minimum wage job in at least one of the years 1988
to 1991. Although our data do not contain information on hours
worked, it is likely that the expanded sample contains many morepart-time and intermittent workers.35 For consistency with thebroadened sample definition, trade exposure is now measured as
the average exposure of the industries that employed a worker
during the years 1990 to 1992, while the instrument is based on
industry affiliation during the years 1987 to 1989. All firm- andindustry-specific control variables are also averaged over 1990
through 1992, and outcomes are measured over the subsequent
period of 1993 to 2007.

35. The minimal preperiod earnings of workers in the extended sample arelikely a poor proxy of their earnings potential, and they complicate an analysis of
future earnings that are expressed in multiples of initial earnings as in Table II. We
therefore focus on employment outcomes, as in Table V.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

We have so far followed the literature on mass layoffs bylimiting our sample to workers with high labor force attachment.
This exclusive focus on high-attachment workers may miss animportant dimension of heterogeneity in adjustment to adverse
shocks. If workers with weaker attachment are at greater risk ofjob displacement or face fewer outside employment options, they
may be differentially harmed by adverse trade shocks. Alternatively, if these workers are particularly likely to exit the labormarket regardless of circumstance, adverse trade shocks may
not alter their career trajectories. We assess these differencesby comparing results from Table VI to estimates for the fullsample that includes workers with low labor force attachment.
We then extend the analysis to explore heterogeneity in adjustment according to workers’ firm tenure, age, and earnings levels.

1835

TRADE ADJUSTMENTTABLE VII

IMPORTS FROM CHINA AND CUMULATIVE YEARS OF EMPLOYMENT AND SSDI RECEIPT BY
WORKER SAMPLE: 2SLS ESTIMATES
(1)
(2)
(3)
(4)
Cumulative Years with Main Income from
SelfEmploymt

SSDI
Benefits

No
Income100*Dummy(Yrs SSDI > 0)

Panel A: main sample of high LF attachment workers( China imports)/U.S.
consumption91

0.613(0.415)

0.152(0.157)0.438*
0.327(0.202)(0.330)N = 508,129

0.056*(0.026)

Panel B: extended sample with high and low LF attachment workers( China imports)/U.S.
consumption91

0.991
(0.569)

0.694*(0.301)1.137**
0.548(0.431)(0.426)
N = 880,465

0.125**(0.043)

Notes. Dependent variables: 100  cumulative years with income from indicated sources and100  dummy for SSDI income, 1993–2007. Panel A includes workers who earned at least the equivalentof a full-time minimum wage job in each of the four years 1988 to 1991. Panel B adds workers with low
annual incomes or interrupted careers who were employed at least in one year during 1987–1989, andone year during 1990–1992. All regressions include the full vector of control variables from column (9) of
Table II, except that all industry- and firm-related variables are averaged over those years in which aworker was employed during 1990 to 1992. Robust standard errors in parentheses are clustered on startof-period three-digit industry. p  .10, *p  .05, **p  .01.

We begin in Panel A of Table VII by replicating the main
specification of Table VI for the original high labor force attachment sample, using suitably modified variable definitions. Thesechanges in variable definitions have no substantive effect on themain estimates. Combining high and low labor force attachment
workers in Panel B magnifies trade’s negative impact on years
worked and positive impact on SSDI reliance. In column (1),Panel B of Table VII, the negative coefficient on total years
with main income from labor earnings is 60% larger in absolutevalue than the high attachment coefficient in Panel A (and is
now marginally statistically significant), implying that among
low-attachment workers, adjustment at the (extreme) extensive
margin of full-year nonemployment is comparatively common.
In column (2), the reduction in years with main income fromself-employment is larger in absolute value terms in the fullsample than in the high attachment sample.
Columns (3) and (5) capture the differential responsivenessof SSDI take-up in the two samples. Extending the sample toinclude low-attachment workers increases the coefficient on cumulative years with SSDI as the main income source by 160%
(column (3)) and the coefficient on ever receiving SSDI income by

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Wage
Earnings

(5)

1836

QUARTERLY JOURNAL OF ECONOMICS

120% (column (5)). Because low-attachment workers are more
likely to exit the labor force after a trade shock, as seen incolumn (1), they are also more likely to take up SSDI, which isunavailable while workers are gainfully employed. Thus, greater
usage of SSDI benefits and larger extensive margin employment
adjustments go hand in hand.

Low-tenure workers make up a second segment of the labor market omitted by mass layoff studies. Seniority-based employment rules may insulate older workers from external shocks
while exposing younger workers to greater risk of career disruption (Oreopoulos, von Wachter, and Heisz 2012). Alternatively,
workers with higher tenure may suffer greater losses from trade
exposure because their ability to adapt to new employers may belimited (Jacobson, LaLonde, and Sullivan 1993).
In the first two panels of Table VIII, we compare the impactof trade exposure on employment and earnings for workers withlow job tenure at their initial firms (defined as less than five years
as of 1991) to those with high initial tenure (five-plus years in1991).36 Condensing the specification in Table IV, we disaggregate worker outcomes according to three mutually exclusive employment venues: the initial employer (column (2)), subsequent
employers in manufacturing (column (3)), and employers outside
manufacturing or with a missing industry code (column (4)).37
For reference, the Online Appendix displays the average trade
exposure of manufacturing workers in both high- and low-tenure
subsamples, as well as the exposure for other subsamples that we
explore subsequently. Although average trade exposure for lowtenure workers is about one-fourth larger than for high-tenure
workers, there is little difference in the timing of exposure within
the 1991–2007 period. In each subsample that we consider, about
one quarter of the increase in trade competition occurs 1991 to
1999, and three quarters in 1999 to 2007, when import growthfrom China accelerates strongly. The different coefficient
36. The low- and high-tenure groups are drawn from our primary sample withhigh-attachment workers, and the five-year tenure cutoff splits this sample roughly
in half.
37. For workers initially employed in manufacturing, ‘‘same sector’’ and ‘‘othersector’’ denote manufacturing and nonmanufacturing, respectively.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

IV.B. High-versus Low-Tenure Workers, Younger versus
Older Workers

TABLE VIII

SameSector15.53**(5.77)
9.03*(3.88)
0.60**(0.20)

1.28(5.02)
1.99(3.14)
1.65**(0.60)

1.74
(0.94)
1.27*(0.59)
0.05(0.06)
4.99*(2.08)
4.18*(1.86)
0.07(0.06)

0.06(0.99)
0.17(0.76)
0.01(0.08)

Panel A2: workers w/ firm tenure  5 years

15.06**(5.53)
0.84(0.99)
0.95**(0.34)

Panel A1: workers w/ firm tenure < 5 years

Initial
Firm

(2)
(3)
Outcomes: Overall and by Employer

3.20(1.98)
2.74(1.68)
0.13(0.11)

1.75(6.76)
7.88
(4.63)
1.22*(0.51)

Oth.
Sect./NA(4)

Same
Sector8.98**(3.40)
4.43*(2.09)
0.56**(0.18)

2.45(3.37)
2.49(1.78)
1.06*(0.50)

5.63**(2.03)
1.38
(0.76)
0.25**(0.09)

8.39**(3.28)
6.84*(2.80)
0.09(0.08)

0.08(1.94)
1.42(1.43)
0.47*(0.21)

Panel B2: workers born in 1943–1956

8.71*(3.52)
0.94(0.64)
0.61**(0.23)

Panel B1: workers born in 1957–1970

InitialFirm

2.84(2.67)
4.04(2.49)
0.25(0.17)

2.18(4.09)
2.88(2.47)
0.68*(0.31)

Oth.
Sect./NA

(6)
(7)
(8)
Outcomes: Overall and by Employer

All
Firms

(5)

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Notes. Dependent variables: 100  cumulative earnings (in multiple of initial annual wage); 100  years with earnings; 100  earnings per year of employment (in multiples of
initial annual wage). N = 293,816, 214,313, 236,136, 249,921 for Panels A1, A2, B1 and B2, except smaller samples for cumulative earnings per year of employment. Columns (4)and (8) report outcomes at firms outside the initial sector and at firms with missing industry information (most of which have been incorporated between 2000 and 2007). All
regressions include a constant and the full vector of control variables from column (9) of Table II. Robust standard errors in parentheses are clustered on start-of-period three-digit
industry. p  .10, *p  .05, **p  .01.

Cumul. earn./year

Cumul. yrs. emp.

Cumul. earnings

Cumul. earn./year

Cumul. yrs. emp.

Cumul. earnings

All
Firms

(1)

IMPORTS FROM CHINA AND EARNINGS AND EMPLOYMENT BY FIRM TENURE AND AGE, 1992–2007: 2SLS ESTIMATES

TRADE ADJUSTMENT

1837

1838

QUARTERLY JOURNAL OF ECONOMICS

IV.C. Differences in Adjustment by Earnings Capacity
The clear pattern emerging from Tables VII and VIII is thatworkers who have a weaker foothold in the labor market suffer thelargest proportionate earnings losses from trade shocks. This

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

estimates for trade impacts on earnings and employment acrosssubsamples of workers cannot therefore be explained by a differential timing of import shocks.
Akin to the Table VII estimates for low-attachment workers,
Panels A1 and A2 of Table VIII reveal that the negative impactsof trade exposure on cumulative earnings and on earnings per
year worked are substantially larger for the low tenure group.
In Panel A1, the effect of trade exposure on cumulative earnings
for low-tenure workers is 8.7 times as large as the estimatedeffect for high-tenure workers (Panel A2). The difference between
low- and high-tenure workers begins at the initial employer. Lowtenure workers experience more than twice the reduction in
employment years as high-tenure workers and more than threetimes the loss in earnings at the initial firm (Panels A1 and A2,column (2)). Low-tenure workers make up little of this loss
through earnings from other employers (Panel A1, columns (3)and (4)). The comparatively modest but still negative and significant cumulative earnings losses for high-tenure workers at theinitial firm are offset—by a factor of nearly two-thirds—throughgains at employers outside the initial sector (Panel A2, column(4)). Earnings losses for low-tenure workers are not explained bylost years of employment; in fact, the estimated cumulative employment effect is weakly positive. It appears instead that theirannual earnings in subsequent employment lies substantiallybelow their earnings in the original firm.
One reason low-tenure workers may be more affected bytrade is simply that they are young, with employers preferring
to sever ties first with those having less labor market experience.
Panels B1 and B2 of Table VIII separate workers by age, with theyounger cohort being 22–35 years old in 1992 and the older cohort
being 36–49 years old in that year. Coefficient estimates are modestly larger for the younger workers, but the difference between
the two groups is slight and statistically insignificant. Thus, thelarger effects of trade on low-tenure workers in Panels A1 and A2
appear to be driven more by their brief attachment to their initial
employer than by their youth.

1839

TRADE ADJUSTMENTTABLE IXIMPORTS FROM CHINA AND EARNINGS AND EMPLOYMENT BY SUBGROUP OF WORKERS:
2SLS ESTIMATES(1)

InitialFirm(4)

Oth Firm,
Other
Same Sector Sector/NA

Panel A: initial wage in bottom tercile of cohort
Cumulative earnings

18.27**(5.76)
Cumulative years employed 0.20(1.30)
Cumulative earnings/year1.16**(0.34)

8.78*(3.84)
4.10
(2.23)
0.44
(0.24)

0.85(5.21)
4.50
(2.73)
2.71**(0.85)

10.34*(4.52)
0.60(2.68)
1.34**(0.45)

2.73(2.38)
2.84
(1.65)
0.67
(0.35)

4.16(3.17)
1.22(1.88)
0.67*(0.31)

2.24(2.36)
1.10(1.59)
0.07(0.29)

6.69(5.38)
6.28(4.29)
0.03(0.22)

Panel B: initial wage in middle tercile of cohort
Cumulative earnings
Cumulative years employed
Cumulative earnings/year9.93**(3.65)
0.37(0.65)
0.61**(0.22)

8.50**(3.27)
4.43*(2.03)
0.51**(0.17)

Panel C: initial wage in top tercile of cohort
Cumulative earnings
Cumulative years employed
Cumulative earnings/year0.22(2.07)
0.41(0.55)
0.03(0.14)

9.15
(4.69)
7.79
(4.29)
0.04(0.12)

Notes. Dependent variables: 100  cumulative earnings (in multiples of initial annual wage); 100  years
with earnings; 100  earnings per year of employment (in multiples of initial annual wage). N = 169,386,
169,357, 169,386 for Panels A, B, C, except smaller samples for cumulative earnings per year. Earnings andemployment outcomes are reported cumulatively over all firms that employ a worker during the outcome period(column (1)), and separately for the initial firm (column (2)), other firms of the same sector (column (3)), andfirms that are either outside the initial sector or have missing industry information (column (4)). All regressions
include a constant and the full vector of control variables from column (9) of Table II. Robust standard errors inparentheses are clustered on start-of-period three-digit industry. p  .10, *p  .05, **p  .01.

suggests more broadly that the adverse effects of trade shocks may
be greatest for workers with low earnings capacity, a possibility
that we explore in Table IX by separately estimating the impact oftrade on workers subdivided into terciles of average annual preexposure earnings (1988 through 1991) relative to others in theirage cohort. By making within-cohort comparisons, this sample
split captures differences in earnings capacity that are likely to

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

All
Firms

(2)
(3)
Worker Outcomes:
Overall and by Employer

1840

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

stem from factors such as education and ability—not directly observable in our data—rather than experience and seniority.
Table IX confirms that the adverse impacts of trade exposure
on worker outcomes are inversely monotone in initial earnings.
Column (1) of the top panel finds a large and highly significant
negative estimate of the impact of trade exposure on cumulative
earnings of bottom-tercile workers. Scaling by the interquartile
range of trade exposure, the point estimate implies that a lowwage worker in manufacturing at the 75th percentile of exposure
loses 1.2 (18.3  6.7) additional years of initial annual earnings
over the subsequent 16 years relative to a worker at the 25th
percentile of exposure. This effect is nearly three times as large
as the impact for the full sample (Table II, column (9)). In comparison, the estimated impact for middle-tercile workers (PanelB) is only half as large as the impact for bottom tercile workers,
whereas the estimated impact for top-tercile workers (Panel C) isessentially 0.
Why are the adverse effects larger for low-wage than for
high-wage workers? One possibility is that import competitiondifferentially reduces firms’ demand for lower skill workers
while leaving high-wage workers relatively unscathed. The subsequent rows of Table IX do not support this conjecture. High-,
middle-, and low-wage workers experience similarly large declines in earnings obtained from the initial employer (column(2)). What differs between these groups is their subsequent
labor market adjustment. High-wage workers recover from initialshocks by moving out of the impacted sector. Middle- and lowwage workers are less able to offset those losses through sectoral
mobility. High-wage workers on average make up one-fourth oftheir loss with the initial employer through additional earnings
from other firms in the same sector (column (3)), and they recoup
most of the remaining three-fourths at employers outside ofmanufacturing (column (4)). Middle-wage workers, by contrast,
replace around one-third of earnings lost with the initial employer through earnings elsewhere in manufacturing, and they
suffer further losses outside of the original sector. At the otherextreme, low-wage workers experience no offsetting increase inearnings within the same sector, and they accrue even larger
earnings declines outside of the original sector.
The second rows of each panel in Table IX offer further insight into the adjustment process by focusing on years of employment rather than cumulative earnings. Workers at all wage

TRADE ADJUSTMENT

1841

38. Among low-wage workers, the implied interquartile differential loss inearnings per year amounts to 7.8% (1.2  6.7) annually, whereas for middlewage workers, the in-year earnings losses are only approximately half as large.
This result appears to stand in contrast with the local labor market evidence from
Autor, Dorn, and Hanson (2013a), who find that CZ-level trade exposure reduces
the log weekly earnings of currently employed college and noncollege workers bysimilar (modest) amounts. Why do the earnings effects of trade exposure measured
at the worker level differ substantially by skill group while those measured at the
CZ level do not? Noting that our SSA annual earnings data indicate that only asmall percentage of primary sample workers has zero earnings over the course of afull-year, we hypothesize that the fall in within-year earnings detected in the SSA
data (Table IX) is reflected primarily in a rise in the odds of nonparticipation during
the survey reference week in the census and American Community Survey data
used in the Autor, Dorn, and Hanson (2013a) analysis. Consistent with this supposition, Autor, Dorn, and Hanson (2013a, Table 5) find that trade shocks induce amuch sharper fall in the probability of labor force participation during the survey
reference week among noncollege than college workers: –0.83 versus –0.30 percentage points per thousand dollars of CZ import exposure. The small drop in weekly
earnings of low-education workers detected by Autor, Dorn, and Hanson (2013a),combined with a large increase in their nonparticipation, is potentially consistent
with the significant decline in the cumulative annual earnings of low-wage workers
detected above.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

levels experience substantial reductions in years of employment
at the initial employer. However, the magnitude of this reduction
is nearly twice as great for high-wage relative to middle or lowwage workers. Although all three groups appear to largely offset
lost earnings years (though not lost earnings) at the initial firm
through additional employment, their subsequent employment
venues differ substantially. Low-wage workers primarily offset
lost employment at the initial firm with employment elsewherein the manufacturing sector; middle-wage workers offset losses
with extra employment both inside and outside manufacturing;and high-wage workers appear primarily to obtain new employment outside manufacturing—highlighting that these workers
are particularly mobile across industries. Thus, while workers
at all earnings levels exhibit heightened job churning as a consequence of trade shocks, workers at different earnings levels differ
substantially in how they adjust to these shocks.
The third rows of each panel in Table IX reveal an additional
contrast among earnings groups: low- and middle-wage workers
experience substantial declines in earnings per year both at theinitial firm and at subsequent employers. High-wage workers, bycontrast, exhibit no adverse earnings effects, even as they moveacross firms and sectors.38 What explains the contrast in earnings

1842

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

consequences among skill groups? One proximate explanation
focuses on the differences in their sectoral mobility identified in
the second row of Table IX. High-wage workers primarily offset
losses at their initial firm by moving out of manufacturing.
Low-wage workers, however, disproportionately remain in
manufacturing, where their subsequent earnings are subject torapidly accelerating trade pressure throughout the 1990s and2000s. A potential cause of this low rate of intersectoral
mobility is that low-wage workers may be earning rents in
manufacturing—that is, wages or net surplus that exceed
their alternative opportunities outside of manufacturing (Katzand Summers, 1989; Gittleman and Pierce, 2011). As trade pressure erodes these rents, workers may therefore choose to accept
lower pay in manufacturing rather than seek employment outside of it.
The Online Appendix investigates whether these differencesin reallocation of workers across firms and sectors also operate
through differential geographic mobility. Import exposure does
indeed shift the employment of high-wage workers from the initial CZ toward other regions, while the opposite pattern is observed for workers with middle or low wages. The greater
geographic mobility response of high-wage workers is consistent
with previous research on U.S. domestic migration (Wozniak
2010; Notowidigdo 2013), but the increase in employment years
outside the initial CZ is not significant either for high-wage workers or for any of the previously studied tenure and age subsamples. Across all groups of workers, trade exposure inducesconsiderably more mobility across firms and sectors than acrossspace.
In the SSA data, workers with low earnings are differentiallyaffected by trade shocks. No distinctions between low- and highwage workers appear in the literature on mass layoffs, however,where low- and high-skill workers fare equally poorly subsequent
to large contractions in their initial firms. In the Online
Appendix, we examine when and how workers separate fromtheir initial employer in response to changes in trade exposure.
Not surprisingly, more trade-exposed workers are more likely toseparate from their initial place of work, with the impact of trade
exposure on separation being larger for low-wage workers thanfor high-wage workers. When we categorize job exits by theirproximity to a mass layoff at the initial firm, we find that thecontribution of mass layoff separations to total separations is

TRADE ADJUSTMENT

1843

V. Alternative Measures of Trade Exposure
In this section, we consider alternative measures of workers’
exposure to trade with China. We explore specifications that measure exposure not only to trade competition in the worker’s industry, but also in the worker’s local labor market of residence.
We then reestimate the regressions for cumulative earnings, cumulative employment years, and earnings per year of employment from Panel A of Table III for six alternative measures ofindustry-level trade shocks.
V.A Industry-Level and Region-Level Trade Exposure
Our main analysis finds that workers who were employed insubsequently import-exposed industries accumulate substantially lower earnings over their careers than comparable workers
in industries with little trade competition, a result consistent
with labor market frictions preventing workers from smoothly
adjusting to trade shocks. If trade-exposed industries werehighly segregated across local labor markets, these results
could be consistent with moving frictions inhibiting flows acrosslocal labor markets, even while reallocation within these markets
is frictionless. This would imply that workers in trade-exposed

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016highest among low-wage and lowest among high-wage workers.
Separations from the initial employer immediately before a mass
layoff have the opposite pattern, being relatively high among
high-wage workers. Although trade exposure significantly increases the likelihood that a low-wage worker leaves the initial
firm during a mass layoff, the only significant effect among
high-wage workers is for separations before mass layoffs. Thisdifference in pre–mass layoff separations between low- andhigh-wage workers is naturally absent in the mass layoff literature because that work focuses exclusively on workers who haveseparated during a mass layoff (e.g., Jacobson, LaLonde, andSullivan 1993; von Wachter, Song, and Manchester 2009). In contrast to existing literature that concludes that mass layoffs haveequally scarring effects for workers of all skill levels, our results
suggest instead that high-wage workers are less exposed to mass
layoffs and are more likely to leave their initial firm in a prelayoff
period when presumably a larger fraction of separations isvoluntary.

1844

QUARTERLY JOURNAL OF ECONOMICS

39. To obtain the employment weights of all industries in a CZ, we draw on the1990 County Business Patterns (CBP), as in Autor, Dorn, and Hanson (2013a).
The CBP provides more precise measures of industry employment structure at
the county level than our main data, a 1% random sample of SSA data. The SSA
data provide workers’ county of residence as of 1993. We impute 1991 location,
taking advantage of the fact that most workers did not change employers, andpresumably did not change region, between 1991 and 1993. The Data Appendix
provides a detailed description of the imputation procedure.
40. In combining both industry and regional exposure to trade, our approachcomplements McLaren and Hakobyan (2010). Though our use of panel data contrasts with their use of census data on repeated cross-sections of individuals, ourresults that industry exposure is more consequential than regional exposure are
qualitatively similar to theirs.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016industries would obtain lower earnings than those in lowexposure industries primarily because they are located in differentially trade-exposed regions. We explore this hypothesis bytesting how our main results are affected by controlling forimport exposure in a worker’s initial CZ of residence, where a
CZ’s industry exposure is defined as the average growth ofChinese import penetration in the industries of all other workers
who reside in the individual’s initial CZ.39
Table X follows Table V by analyzing the impact of tradecompetition on earnings, employment, and geographic mobility
during the shortened outcome period of 1994 to 2007. Column (1)shows the impact of industry-level trade exposure on earnings
and employment (replicating column (1) of Table V), column (2)regresses the same outcomes on CZ–level trade exposure and aset of indicator variables for the nine census divisions that absorb
regional trends, and column (3) combines the two exposure measures.40 Columns (2) and (3) of Panel A show that workers initially residing in CZs with larger increases in trade exposure hadsignificantly lower cumulative earnings over the 1994–2007
period. Results for CZ trade exposure are similar either with(column (2), Panel A) or without (column (3), Panel A) initial
own-industry trade exposure in the regression; in parallel fashion, results for own-industry trade exposure change only modestly when CZ trade exposure is added (columns (1) and (3),Panel A). The regions of residence of workers in trade-exposedindustries are sufficiently dispersed across space that the correlation coefficient between industry-level and CZ-level trade exposure is only 0.12. The industry-level effects reported earlier are
hence not primarily capturing geographic variation in worker’s
trade exposure. The negative earnings effect of a trade-exposed

TABLE X

0.42**(0.15)

0.65(0.51)

6.65**(2.42)

1.66**(0.58)

2.26
(1.23)

20.30**(6.72)

I. All CZ(2)

0.37**(0.14)
1.62**(0.59)

0.65(0.50)
2.33
(1.22)

6.07**(2.16)
19.68**(6.79)

0.30**(0.12)
1.59**(0.52)

0.14(0.65)
0.42(3.13)

3.31**(1.24)
16.88**(4.59)

0.66(0.59)
2.24(1.38)
0.14(0.31)
0.76(0.71)
0.97**(0.36)
1.33
(0.81)

0.38(0.58)
1.15(2.74)
0.71*(0.36)
1.02
(0.58)

IV. N/A CZ(6)

2.09
(1.25)
0.56(4.88)(3)(4)(5)
Outcomes Measured at
II. Initial CZ III. Other CZ

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Notes. Dependent variable: 100  cumulative earnings, years of employment, or earnings/year, 1994–2007. N = 508,129, except smaller samples in Panel C. The CZ average ofimport exposure for worker i in CZ j is defined as the average trade exposure of all other workers –i located in CZ j, according to the industry employment structure obtained fromthe 1990 County Business Patterns. Workers’ 1991 CZ of residence is not observed in the data and thus imputed based on available location and employment information. For mostworkers, it corresponds to the observed 1993 CZ of residence. All regressions include the full control vector from column (9) of Table II and regressions in columns (2)–(6) include adummy for observations with missing 1993 CZ and dummies for 1991 census divisions. Robust standard errors in parentheses are two-way clustered on start-of-period three-digit
industry and on imputed 1991 CZ of residence. p  .10, *p  .05, **p  .01.

CZ avg of [( imports from China to U.S.)/U.S.
consumption91]( Imports from China to U.S.)/U.S. consumption91

Panel C: earnings per year of employment

CZ avg of [( imports from China to U.S.)/U.S.
consumption91]( Imports from China to U.S.)/U.S. consumption91

Panel B: cumulative years of employment

CZ avg of [( imports from China to U.S.)/U.S.
consumption91]( Imports from China to U.S.)/U.S. consumption91

Panel A: cumulative earnings(1)

EFFECT OF EXPOSURE TO CHINESE IMPORTS AT THE INDUSTRY AND COMMUTING ZONE LEVEL ON EARNINGS AND EMPLOYMENT: 2SLS ESTIMATES

TRADE ADJUSTMENT

1845

1846

QUARTERLY JOURNAL OF ECONOMICS

41. These results reflect the fact that trade exposure of manufacturing workers
varies much more at the industry level (P75–P25 differential of 6.68) than at the CZ
level (P75–P75 differential of 0.83).
42. We multiply the P75–P25 differential of CZ exposure across all workers withthe coefficient in column (2), Panel A of Table X to obtain a cumulative earnings
loss of 0.86  (20.30) = –17.50. Assuming that the annual earnings loss increases
linearly over time (as roughly suggested by Figure III), we calculate that the final
2
year 2007 contributes 14
to the total earnings loss over the 14-year outcome period,
2
corresponding to a decline of annual income by 14 (17.50) percentage points ofan initial annual income between start and end of the sample period. In Autor,
Dorn, and Hanson (2013a), the difference between the import exposure of a CZ at
the 75th and 25th percentile is 1.11 (with import exposure measured in $1,000 ofimports per worker, and averaged over the periods 1990–2000 and 2000–2007), andmultiplication with the estimated effect of import exposure on average household
wage income per adult yields a differential earnings decline of 1.11  (2.14) =–2.36% of base-year earnings.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016industry, conditional on trade exposure of the local labor market,
is consistent with the presence of labor market frictions that prevent a smooth relocation of workers across industries within local
labor markets.
To interpret the magnitude of the effects of trade exposure,
we use the estimates of column (3), Panel A to compare
manufacturing workers who are at the 75th versus 25th percentiles of industry trade exposure or at the 75th versus 25th percentile of regional trade exposure. Over the period 1994–2007, theworker in the more exposed industry received 40.5% lower cumulative earnings valued in terms of initial average annual earnings, whereas the worker in the more exposed local labor market
earned 19.7% less than a peer in a less exposed location.41 The
results on local labor market exposure accord with Autor, Dorn,and Hanson (2013a), who find that CZs more exposed to China
trade had lower growth in household wage income. To comparewith their estimates, we calculate the decline in annual wage
income per worker between the start and end of our sample for
CZs at the 75th versus 25th percentiles of trade exposure, and use
a corresponding calculation based on findings from Autor, Dorn,and Hanson (2013a). From Table X, we estimate that the annual
earnings in the more exposed CZs decline by 2.5% of workers’
initial annual earnings during the sample period, while we estimate a reduction of average household income per adult of 2.4%
between 1990 and 2007 using estimates from Autor, Dorn, andHanson (2013a).42

TRADE ADJUSTMENT

1847

V.B. Alternative Measures of Industry-Level Trade Exposure
We use alternative measures of industry exposure to importcompetition to reestimate regressions from Panel A of Table II.
Table XI contains the results, with baseline estimates from
Table III shown in Panel A. The alternative measures of trade
exposure are as follows.
1. Gravity-Based Measure of Trade Exposure. Our primarystrategy for identifying the impact of trade exposure as measured

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Panels B and C show the impact of greater regional trade
exposure on cumulative years with positive earnings (columns(2) and (3)) and average earnings per year employed (columns(2) and (3), Panel C). While earnings per year fall with greater
local trade exposure (column (1), Panels A and B), the impact on
years worked is, surprisingly, positive. Given the large negative
effect of trade exposure on earnings, the slight corresponding increase in total years of employment may be an income effect. It
also bears noting that these estimates use our main sample ofworkers with high labor force attachment. Although Table VII
indicates that industry-level trade shocks particularly reduce employment of less attached workers, we are unable to include thelow-attachment subsample in the Table X analysis because wecannot measure workers’ residential locations when they are
not employed.
The final three columns of Table X disaggregate the earnings
and employment outcomes in column (3) according to the CZ ofresidence in which workers accumulated their earnings and employment. Almost all earnings losses due to local labor market
exposure occur in the initial, trade-exposed CZ, whereas losses
due to industry exposure are more dispersed across locations(columns (4) to (6), Panel A). Workers who are initially locatedin a trade-exposed region do not appear to spend significantly
more employment years in other local labor markets (column(5), Panel B).
In summary, the low correlation between industry and local
labor market trade exposure allows us to focus on the former
shock, which is better observed in our data and contributes
more to the variation in career outcomes across manufacturing
workers, without confounding it with the latter dimension of exposure that was the focus of Autor, Dorn, and Hanson (2013a).

(2)Years w/
Earn > 00.535(0.505)

0.383(0.378)

0.270**(0.090)0.393**(0.140)

(3)
Earn/
Year

0.489(0.486)

0.396**(0.138)

0.118(6.688)1.583(2.160)0.029(0.411)Panel D: 2SLS (instr: Mex-OTH trade)6.836**(2.409)Panel C: 2SLS (instr: Low-OTH trade)4.684**(1.494)

Panel B: Reduced form OLS

6.864**(2.477)

Panel A: 2SLS main results(1)
Cumulative
Earnings

 Import penetration, using
China imp. adjusted forimported inputs

 Net imports from China
in worker-equivalent units Net import penetration,using China imports

 Import penetration,using China imports to U.S.
and other markets

Alternative Measure ofTrade Exposure(5)
Years w/
Earn > 0

(6)
Earn/
Year

0.493(0.442)

0.321**(0.113)

0.761*(0.360)0.204*(0.081)

0.790(0.497)

0.301**(0.112)

6.499**(2.386)

0.568(0.514)

0.368**(0.134)

Panel H: 2SLS (instr: Chn-OTH trade)5.658**(1.965)

Panel G: 2SLS (instr: Chn-OTH trade)4.170**(1.410)Panel F: 2SLS (instr: Chn-OTH trade)5.616**(2.029)Panel E: 2SLS (instr: Chn-OTH trade)(4)
Cumulative
Earnings

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Notes. Dependent variables: 100  cumulative earnings; 100  years with earnings (in multiples of initial annual wage); 100  earnings per year of employment (in multiples of
initial annual wage), 1992–2007. N = 508,129 in columns (1)–(2) and (4)–(5), N = 506,339 in columns (3) and (6). Panel A repeats results from Tables II and III. The mean (andstandard deviation) of trade exposure among manufacturing workers is 1.02 (3.11) in Panel B, 8.56 (14.94) in Panel C, 3.20 (5.54) in Panel D, 8.62 (15.22) in Panel E, 6.14 (13.96) inPanel F, 5.91 (13.87) in Panel G, and 5.77 (12.69) in Panel H. All models include the full vector of control variables from column (9) of Table II, except that start-of-period import
exposure levels are adjusted to reflect the alternative definitions of import exposure measures where feasible. Robust standard errors in parentheses are clustered on start-of-period
three-digit industry. p  .10, *p  .05, **p  .01.

 Import penetration,using imports
from Mexico/CAFTA Import penetration,using all low-income
country imports

 China–U.S. productivity
differential(gravity residual) Import penetration,using imports
from China

Alternative Measure ofTrade Exposure

TABLE XIALTERNATIVE MEASURES OF IMPORT EXPOSURE: 2SLS ESTIMATES1848
QUARTERLY JOURNAL OF ECONOMICSTRADE ADJUSTMENT

1849

2. Other Low-Income Countries. Changes in import penetration from China may overstate the change in trade exposure for

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

in equation (1) is based on the assumption that growth in imports
from China in high-income countries is due to supply shocks in
China or global changes in trade policy toward China, rather thanimport demand shocks in these economies. As an alternative to
instrumenting for observed U.S. imports from China with other
wealthy countries’ imports from China, we determine the supply
shock component of import growth from China using the gravity
model of trade. With data on bilateral imports by high-income
countries at the industry level over 1991–2007, we estimate agravity model in which the dependent variable is log industry
imports from China minus log industry imports from theUnited States and the regressors are dummy variables for theimporting country, dummy variables for the industry, and standard gravity model controls for trade costs. Taking the China–
U.S. difference in log imports removes import demand shocks inthe destination market that are common across sources of supply.
Changes over time in the residuals from this regression represent
the change in China’s comparative advantage in an industry relative to the United States. Following the procedure described in
the appendix to Autor, Dorn, and Hanson (2013a), we use theseresiduals to construct an alternative measure of import growth.
The gravity-based approach, shown in Panel B of Table XI,
allows us to estimate the impacts of trade exposure on cumulative
earnings under different identifying assumptions. Because wehave neutralized market-level import demand via the gravity estimation, the new identifying assumption is that changes in
China–U.S. comparative advantage are uncorrelated with U.S.
product demand shocks. Because the gravity-based measure captures the change in China–U.S. competitiveness across highincome markets, it subsumes supply shocks in both countries.
Hence, we now allow changes in productivity in either nation toaffect cumulative earnings for U.S. workers and no longer assume
that U.S. and Chinese supply shocks are uncorrelated. While thegravity-based measure broadens the impact coefficient to reflect
China–U.S. relative export growth, the much more rapid pace of
productivity growth in China over the sample period suggeststhat advances in the country’s industrial capabilities are likely
to be what drives changes in China–U.S. comparative advantage.

1850

QUARTERLY JOURNAL OF ECONOMICS

3. Other Destination Markets. Growth in China’s exports affects U.S. industry output not just through intensifying competition in the U.S. consumer market but also in foreign markets inwhich U.S. firms compete with China. Following this logic, we
expand the definition of import penetration in equation (1) to include all destination markets to which U.S. industries export
goods. Results using this measure of trade exposure are shownin Panel E of Table XI.
4. Net Imports. China’s growth causes an increased supply of
goods to the U.S. market but may also increase demand for U.S.
exports. To account for U.S. exports to China, we also measuretrade exposure using net imports rather than gross imports,
which allows U.S. exports to China to offset some of the loss in
production from greater import penetration. Because U.S.
manufacturing imports from China are six times larger thanDownloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

U.S. workers if China competes with other low-wage countries in
the U.S. market. One worry is that imports from China simply
displace other countries’ exports to the United States. To address
this concern, we add to imports from China imports from all otherlow-income countries. Given that China accounts for over 90% of
recent growth in U.S. imports from low-wage economies, thismodification is unlikely to materially affect the trade penetration
measure. Results using this measure of trade exposure are shownin Panel C of Table XI.
To provide further comparisons with alternative low-wagecountry sources of U.S. imports, we replace import penetrationfrom China with import penetration from Mexico and Central
America. Results for this outcome are in Panel D of Table XI.
Over the sample period, Mexico had minimal industrial productivity growth (Hanson 2012), making U.S. demand shocks a relatively important driver for growth in U.S. imports from Mexico,especially when compared with China. We would therefore expect
our instrumentation strategy to perform poorly for Mexico,
because absent strong export supply shocks its shipments tohigh-income countries would be particularly subject to idiosyncratic demand shocks in these markets. Shortly we confirm thatgrowth in import penetration from Mexico and Central America
has effects that are economically small and statisticallyinsignificant.

TRADE ADJUSTMENT

1851

5. Factor Content of Trade. If the labor content of production
varies across China’s exports, measuring trade in dollar terms
may not accurately capture the impact of import growth on U.S.
workers (Borjas, Freeman, and Katz 1997; Burstein and Vogel2011). To account for sectoral differences in labor intensity, wemeasure net imports in worker equivalent units, using direct andindirect labor usage in the production of industry outputs, basedon the 1992 U.S. input-output table. Our measure calculates industry exposure to trade by imputing labor services embodied in
net imports using net imports times employment per dollar ofE~gross shipments in U.S. industries ( j0 ), where E~ j0 is based on
Yj0

the direct plus indirect employment of labor used to manufacture
goods in an industry. Results using this exposure measure areshown in Panel G of Table XI.

6. Intermediate Inputs. Growth in exports by China represents
not just greater competition for U.S. producers but also greater
supply of inputs that U.S. industries require, which may positively
affect U.S. productivity (Goldberg et al. 2010). To account for input
supply effects, we adjust total industry imports for imports of intermediate inputs by netting out the latter, which we estimate

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

U.S. manufacturing exports to China, this change has a relatively
modest effect on the trade exposure measure. Yet this approach
does add the challenge of instrumenting for U.S. net imports from
China, requiring that we find an exogenous source of variation in
U.S. exports to the country. We use two instruments for U.S.
net imports from China. One, given by equation (2), is otherhigh-income-country imports from China, as in our core specification; the second is the analogous expression to equation (2) for
other high-income-country exports to China. This instrumentation strategy assumes that growth in high-income-country exports to China is driven by shocks to China’s economy and thathigh-income-country exports respond similarly to these shocks.
The latter condition may hold imperfectly if high-income countries vary widely in their export strengths, an issue of possible
concern for the United States given its unusually broad resourcebase. Results using this measure of trade penetration are shownin Panel F of Table XI.

1852

QUARTERLY JOURNAL OF ECONOMICS

VI. Conclusion
China’s spectacular export growth in recent decades provides
a rare opportunity to examine how workers adjust to trade
shocks. Changes in trade flows typically have myriad causes
and are jointly determined with other outcomes of interest. In
the case of China, its economically rudimentary state at thetime the country began to open to foreign trade and investment
meant that its subsequent export growth would be driven by theconvergence of its economy toward the global technology frontierrather than by idiosyncratic shocks in its trading partners. We
exploit this feature of recent Chinese history to examine how U.S.
workers adjust to a surge in imports in their initial industries of
employment. Data from the SSA give us a unique longitudinal
perspective over an extended period of time to observe how workers respond to greater import competition.
Workers who in 1991 (prior to China’s rapid growth) were
employed in industries that were subsequently exposed to greater
import competition from China experienced lower cumulative

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

based on the 1992 U.S. input-output table. Results using this measure of trade exposure are in Panel H of Table XI.
Table XI documents that each of these alternative measuresof trade exposure has a negative impact on cumulative earnings
of workers over the 16-year sample period, consistent with themain results in Table III. The estimated effects are statisticallysignificant in all specifications except Panel D, where import penetration is measured for Mexico and Central America and not
China. Impact coefficients for net imports in Panel F arearound one third smaller than for the baseline specification in
Panel A, possibly reflecting difficulties in using other highincome countries to instrument for U.S. exports to China or positive effects of growth in U.S. exports to China on worker adjustment to changes in trade exposure. Results for cumulative years
with positive earnings and cumulative earnings per year worked
also reflect the pattern of our main specification in Table III,
which documented strong negative effects of exposure to
Chinese imports on earnings per year and more modest effects
on employment years. The estimates in Table XI underscore therobustness of our results to alternative specifications of rising
import competition from China.

TRADE ADJUSTMENT

1853

Data Appendix
Social Security Data
Our main source of data is the Annual Employee-Employer
File (EE), an extract from the Master Earnings File (MEF) of the
SSA that provides longitudinal earnings histories for a randomly
selected 1% of workers in the United States. These data provide
annual earnings, an employer identification number (EIN), and aStandard Industrial Classification (SIC) code for each job that aworker held. Our analysis draws on information covering the

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

earnings, weakly lower cumulative employment, lower earnings
per year worked, and greater reliance on SSDI over the 1992–2007
period. Exposure to trade induces workers to move between employers and industries but generates little spatial mobility.
Workers initially employed in industries with larger increases
in import competition were more likely to leave their initial employer, their initial two-digit industry, and manufacturing overall.
There is considerable heterogeneity across workers in adjustment to import competition, which distinguishes our work fromprevious analyses that examine the labor market consequences ofmass layoffs or intensifying import competition. Reductions incumulative earnings are concentrated among workers with low
initial wages, workers with low tenure at their initial firm, andworkers with weak attachment to the labor force. Trade competition also affects the careers of high-wage workers, who rapidly
separate from their initial employers and move to other firms,
often outside manufacturing. Our results are robust to including
a large set of worker, firm, and industry controls; to using alternative measures of trade exposure; and to falsification tests,
which verify that future increases in trade exposure do not predict past changes in worker outcomes by industry.
We focus on import growth from China while recognizingthat China actively participates in global production networks.
Goods exported by China use inputs produced in other developing
economies and in high-income countries. Still, China’s enormous
size and its rapid rate of technology convergence means that its
own growth has been a major impulse for the expansion of global
production networks in recent decades. Our findings do notpreclude a role for other countries in the recent growth in U.S.
imports of labor-intensive manufactures.

1854

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

years 1972–2007. For workers who have multiple jobs in a given
year, we aggregate earnings across all jobs and retain the EIN of
the employer that accounted for the largest share of the worker’s
earnings. Earnings data are inflated to 2007 using the Personal
Consumption Expenditure Index, and annual uncapped earnings
are Winsorized at the 99th percentile of each year’s wage distribution to mitigate the impact of outliers on the empirical analysis. We augment the EE data with individual-level information on
birth year, sex, race, and immigrant status (U.S. or foreign born)from the SSA’s NUMIDENT file, which records information fromSocial Security card application forms. We code race as nonwhite
if the race indicator is missing in the data, which is the case forabout 3.5% of all observations. We also add information on annual
uncapped self-employment earnings from the MEF (available
since 1992), and on Social Security benefit entitlement and benefit amounts from the Master Beneficiary Record (MBR). The
Social Security program that is most relevant for our study ofworking-age individuals is Disability Insurance.
For about 97% of all employees in 1991, we are able to match
the EIN of the employer to firm data that provides information on
industry and firm size, measured by total employment and payroll in the complete MEF data. The industry classification isbased on firms’ registration with the Internal Revenue Service(IRS). Coders at the SSA transform the write-in information fromthe IRS form to a four-digit SIC code, or a three-digit or two-digit
SIC code if the description of firm activity is not sufficiently detailed to permit a more precise classification. The IRS switchedfrom a paper-based application for obtaining an EIN to an online
application procedure in 2000, and the SIC was replaced by
the North American Industry Classification System (NAICS)
starting in 1997. For a large portion of new firms that havebeen incorporated as of these years, we are no longer able to observe industry codes, and the fraction of workers with missing
industry information thus grows from 3–4% throughout the
1990s to 20% by 2007.
We are able to observe the county of residence for at least
95% of all employees in each year between 1993 and 2007.
Information on residential location is unavailable prior to 1993
and is never observed when an individual is not employed in agiven calendar year. The data also contain county codes for firms,
but these correspond to the location where a firm is registered,
not necessarily to an employee’s place of work. Since our analysis

TRADE ADJUSTMENT

1855

43. More specifically, the imputation uses the following sequential procedure:(i) use the worker’s first observed residential location (usually in 1993) as a proxy for1991 location if (a) the worker continues to be employed at the 1991 firm, or else if (b)the worker’s 1991 firm employs other individuals in the worker’s first observed
location during any of the years 1993–1997, or else if (c) the worker’s 1991 firm isregistered in the location where the worker’s first residence is observed; (ii) otherwise assume that the worker’s 1991 location was: (a) the location where, during theyears 1993–1997, we observe most worker-years for other employees of the worker’s
1991 firm, or else (b) the location where the 1991 firm was registered, or else (c) the
location where we observe most worker-years for the worker’s initial industry
during 1993–1997. Step (i.a) of the imputation assigns a 1991 residential location
to 68% of workers in our main sample, with steps (i.b)–(i.c) and (ii.a)–(ii.c) contributing another 18% and 14%, respectively.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

measures trade exposure in 1991, we impute a worker’s residential location in 1991 for the subsection of the analysis that compares the impacts of trade exposure at the industry and local
labor market levels. Most workers in our sample did not change
employers between 1991 and 1993, and we thus largely rely on
the first observed location in 1993 as a proxy for location in1991.43 Following Dorn (2009) and Autor and Dorn (2013), we
aggregate geographic information to the level of 722 CZs thatcover the entire mainland of the United States. CZs are clusters
of countries that are linked by strong within-cluster commuting
ties, and they approximate the concept of local labor markets.
Since our 1% sample of Social Security data is too small to provide
precise measurement of detailed industry composition at the CZ
level, we instead draw on tabulations of employment by industry
by county from the 1990 County Business Pattern to computelocal industry structure.
Our main sample comprises workers who were born between
1943 and 1970. We use this sample to study outcomes during theperiod 1992–2007 when these workers were between 22 and 64
years old. The sample is restricted to workers who were earningat least $8,193 a year in each of the four years 1988 to 1991, prior
to the outcome period. The value of $8,193 (in 2007 dollars) corresponds to the earnings of a worker who was employed during
1,600 annual hours at the federal minimum wage of 1989. The
sample size of this main sample is 508,129. We also show additional results for an extended sample that includes workers witha weaker labor force attachment. This alternative sample comprises the 880,465 workers who had positive earnings (and a valid

1856

QUARTERLY JOURNAL OF ECONOMICS

industry code) during at least one year in each of the three-year
periods 1987–1989 and 1990–1992.
Matching Trade Data to Industries

Massachusetts Institute of Technology and National
Bureau of Economic Research
University of Zurich and Centre for Economic Policy
Research
University of California, San Diego, and National
Bureau of Economic Research
U.S. Social Security Administration

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Data on international trade for 1991 to 2007 are from the UN
Comtrade Database (http://comtrade.un.org/db/default.aspx),
which gives bilateral imports for six-digit HS products. To concord these data to four-digit SIC industries, we proceed as follows.
First, we take the crosswalk in Pierce and Schott (2009), whichassigns 10-digit HS products to 4-digit SIC industries (at whichlevel each HS product maps into a single SIC industry) and aggregate up to the level of 6-digit HS products and 4-digit SIC
industries (at which level some HS products map into multiple
SIC industries). To perform the aggregation, we use data on U.S.
import values at the 10-digit HS level, averaged over 1995 to
2005. The crosswalk assigns HS codes to all but a small numberof SIC industries. We slightly aggregate the four-digit SIC industries so that each of the resulting 397 manufacturing industries
matches to at least one trade code, and none are immune to tradecompetition by construction. We also aggregate the trade data to
three-digit and two-digit SIC industries to construct measures ofimport exposure for firms whose industry is not identified at the
four-digit level in the SSA data. Details on our industry classification are available on request. Second, we combine the HS-SIC
crosswalk with six-digit HS Comtrade data on imports for theUnited States (for which Comtrade has six-digit HS trade datafrom 1991 to 2007) and for all other high-income countries thathave data covering the sample period (Australia, Denmark,Finland, Germany, Japan, New Zealand, Spain, andSwitzerland) and then aggregate up to SIC industries. All
import amounts are inflated to 2007 US dollars using the
Personal Consumption Expenditure deflator.

TRADE ADJUSTMENT

1857

Supplementary Material
An Online Appendix for this article can be found at QJE
online (qje.oxfordjournal.org).
References
Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Addison, John T., Douglas A. Fox, and Christopher J. Ruhm, ‘‘Trade andDisplacement in Manufacturing,’’ Monthly Labor Review, 118 (1995), 58–67.
Amiti, Mary, and Donald Davis, ‘‘Trade, Firms, and Wages: Theory andEvidence,’’ Review of Economic Studies, 79 (2012), 1–36.
Amiti, Mary, and Caroline Freund, ‘‘An Anatomy of China’s Export Growth,’’ in
China’s Growing Role in World Trade, Robert Feenstra, and Shang-Jin Wei,
eds. (Chicago: National Bureau of Economic Research, 2010).
Artuç, Erhan, Shubham Chaudhuri, and John McLaren, ‘‘Trade Shocks andLabor Adjustment: A Structural Empirical Approach,’’ American Economic
Review, 100 (2010), 1008–1045.
Autor, David H., and David Dorn, ‘‘The Growth of Low Skill Service Jobs and thePolarization of the U.S. Labor Market,’’ American Economic Review, 103(2013), 1593–1597.
Autor, David H., David Dorn, and Gordon H. Hanson, ‘‘The China Syndrome:
Local Labor Market Effects of Import Competition in the United States,’’American Economic Review, 103 (2013a), 2121–2168.
———, ‘‘The Geography of Trade and Technology Shocks in the United States,’’American Economic Review Papers and Proceedings, 103 (2013b), 220–225.
———, ‘‘Untangling Trade and Technology: Evidence from Local Labor Markets,’’
NBER Working Paper No. 18938, 2013c.
Autor, David H., and Mark Duggan, ‘‘The Rise in the Disability Rolls and the
Decline in Unemployment,’’ Quarterly Journal of Economics, 118 (2003),
157–206.
———, ‘‘The Growth in the Social Security Disability Rolls: A Fiscal Crisis
Unfolding,’’ Journal of Economic Perspectives, 20 (2006), 71–96.
Autor, David H., Lawrence F. Katz, and Alan B. Krueger, ‘‘Computing Inequality:
Have Computers Changed the Labor Market?’’ Quarterly Journal of
Economics, 113 (1998), 1169–1213.
Bartelsman, Eric J., Randy A. Becker, and Wayne B. Gray, ‘‘The NBER
Manufacturing Industry Database,’’ Mimeo, NBER, 2000.
Bernard, Andrew B., J. Bradford Jensen, and Peter K. Schott, ‘‘Survival of theBest Fit: Exposure to Low-Wage Countries and the (Uneven) Growth of U.S.
Manufacturing Plants,’’ Journal of International Economics, 68 (2006),
219–237.
Black, Dan, Kermit Daniel, and Seth Sanders, ‘‘The Impact of Economic
Conditions on Participation in Disability Programs: Evidence from the Coal
Boom and Bust,’’ American Economic Review, 92 (2002), 27–50.
Blanchard, Olivier Jean, and Lawrence F. Katz, ‘‘Regional Evolutions,’’ Brookings
Papers on Economic Activity, 1 (1992), 1–75.
Bloom, Nicholas, Mirko Draca, and John Van Reenen, ‘‘Trade Induced Technical
Change? The Impact of Chinese Imports on Diffusion, Innovation, andProductivity,’’ Mimeo, Stanford University, 2011.
Borjas, George J., Richard B. Freeman, and Lawrence F. Katz, ‘‘How Much Do
Immigration and Trade Affect Labor Market Outcomes?,’’ Brookings Papers
on Economic Activity, 1 (1997), 1–90.
Bound, John, and Harry J. Holzer, ‘‘Demand Shifts, Population Adjustments, andLabor Market Outcomes during the 1980s,’’ Journal of Labor Economics, 18(2000), 20–54.
Brandt, Loren, Johannes Van Biesebroeck, and Yifan Zhang, ‘‘Creative
Accounting or Creative Destruction? Firm-Level Productivity Growth inChinese Manufacturing,’’ Journal of Development Economics, 97 (2012),
339–351.
Burstein, Ariel, and Jonathan Vogel, ‘‘Factor Prices and International Trade: A
Unifying Perspective,’’ NBER Working Paper No. 16904 (2011).

1858

QUARTERLY JOURNAL OF ECONOMICS

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Chan, Sewin, and Ann Huff Stevens, ‘‘Job Loss and Employment Patterns of Older
Workers,’’ Journal of Labor Economics, 19 (2001), 484–521.
Chiquiar, Daniel, ‘‘Globalization, Regional Wage Differentials, and the StolperSamuelson Theorem: Evidence from Mexico,’’ Journal of International
Economics, 74 (2008), 70–93.
Coşar, A. Kerem, ‘‘Adjusting to Trade Liberalization: Reallocation and Labor
Market Policies,’’ Mimeo, University of Chicago, 2011.
Coşar, A. Kerem, Nezih Guner, and James Tybout, ‘‘Firm Dynamics, Job
Turnover, and Wage Distributions in an Open Economy,’’ Mimeo,
University of Chicago, 2011.
Couch, Kenneth A., and Dana W. Placzek, ‘‘Earnings Losses of Displaced Workers
Revisited,’’ American Economic Review, 100 (2010), 572–589.
di Giovanni, Julian, Andrei Levchenko, and Jing Zhang, ‘‘The Global Welfare
Impact of China: Trade Integration and Technological Change,’’ Mimeo,
University of Michigan, 2011.
Dix-Carneiro, Rafael, ‘‘Trade Liberalization and Labor Market Dynamics,’’
Econometrica, 82 (2014), 825–885.
Doms, Mark, Timothy Dunne, and Kenneth R. Troske, ‘‘Workers, Wages, andTechnology,’’ Quarterly Journal of Economics, 112 (1997), 253–290.
Dorn, David, ‘‘Essays on Inequality, Spatial Interaction, and the Demand for
Skills,’’ Dissertation University of St. Gallen No. 3613 (2009).
Ebenstein, Avraham, Ann Harrison, Margaret McMillan, and Shannon Phillips,‘‘Estimating the Impact of Trade and Offshoring on American Workers Using
the Current Population Surveys,’’ Mimeo, UC Berkeley, 2011.
Farber, Henry S., ‘‘What Do We Know about Job Loss in the United States?
Evidence from the Displaced Workers Survey, 1981–2004,’’ Economic
Perspectives, 2nd Quarter (2005), 13–28.
Feenstra, Robert C., Advanced International Trade (Princeton, NJ: Princeton
University Press, 2004).
Feenstra, Robert C., and Gordon H. Hanson, ‘‘The Impact of Outsourcing andHigh-Technology Capital on Wages: Estimates for the U.S., 1979–1990,’’
Quarterly Journal of Economics, 114 (1999), 907–940.
———, ‘‘Ownership and Control in Outsourcing to China: Estimating theProperty-Rights Theory of the Firm,’’ Quarterly Journal of Economics, 120(2005), 729–761.
Gittleman, Maury, and Brooks Pierce, ‘‘Inter-Industry Wage Differentials, Job
Content and Unobserved Ability,’’ Industrial and Labor Relations Review,
64 (2011), 356–374.
Glaeser, Edward L., and Joseph Gyourko, ‘‘Urban Decline and Durable Housing,’’
Journal of Political Economy, 113 (2005), 345–375.
Goldberg, Pinelopi K., Amit K. Khandelwal, Nina Pavcnik, and Petia Topalova,‘‘Imported Intermediate Inputs and Domestic Product Growth: Evidence
from India,’’ Quarterly Journal of Economics, 125 (2010), 1727–1767.
Hanson, Gordon, ‘‘The Rise of Middle Kingdoms: Emerging Economies in Global
Trade,’’ Journal of Economic Perspectives, 26 (2012), 41–64.
Helpman, Elhanan, Oleg Itskhoki, and Stephen Redding, ‘‘Inequality and
Unemployment in a Global Economy,’’ Econometrica, 78 (2010), 1239–1283.
Helpman,Elhanan,Oleg
Itskhoki,Marc-Andreas
Muendler,andStephen Redding, ‘‘Trade and Inequality: From Theory to Estimation,’’
NBER Working Paper No. 17991, 2012.
Hsieh, Chang-Tai, and Peter J. Klenow, ‘‘Misallocation and Manufacturing TFP
in China and India,’’ Quarterly Journal of Economics, 124 (2009), 1403–1448.
Hsieh, Chang-Tai, and Ralph Ossa, ‘‘A Global View of Productivity Growth inChina,’’ NBER Working Paper No. 16778, 2011.
Huang, Hanwei, Jiandong Ju, and Vivian Yue, ‘‘Structural Adjustments in
Production and International Trade: Theory and Evidence from China,’’
Mimeo, Tsinghua University, 2011.
Hummels, David, Rasmus Jorgensen, Jakob Munch, and Chong Xiang, ‘‘The
Wage Effects of Offshoring: Evidence from Danish Matched Worker-Firm
Data,’’ American Economic Review, 104 (2014), 1597–1629.
Jacobson, Louis S., Robert LaLonde, and Daniel G. Sullivan, ‘‘Earnings Losses ofDisplaced Workers,’’ American Economic Review, 83 (1993), 685–709.

TRADE ADJUSTMENT

1859

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016

Katz, Lawrence F., and Lawrence H. Summers, ‘‘Industry Rents: Evidence andImplications,’’ Brookings Papers on Economic Activity: Microeconomics(1989), 209–290.
Khandelwal, Amit, Peter Schott, and Shang-Jin Wei, ‘‘Trade Liberalization and
Embedded Institutional Reform: Evidence from Chinese Exporters,’’
American Economic Review, 103 (2013), 2169–2195.
Kletzer, Lori G., ‘‘Trade and Job Loss in US. Manufacturing, 1979–1994,’’ in The
Impact of International Trade on Wages, Robert Feenstra, ed. (Cambridge,
MA: NBER, 2000).
Koopman, Robert, Zhi Wang, and Shang-Jin Wei, ‘‘Estimating Domestic Content
in Exports when Processing Trade Is Pervasive,’’ Journal of Development
Economics, 99 (2012), 178–189.
Kovak, Brian, ‘‘Regional Effects of Trade Reform: What Is the Correct Measure of
Liberalization?,’’ American Economic Review, 103 (2013), 1960–1976.
McLaren, John, and Shushanik Hakobyan, ‘‘Looking for Local Labor Market
Effects of NAFTA,’’ NBER Working Paper No. 16535, 2010.
Menezes-Filho, Naercio A., and Marc-Andreas Muendler, ‘‘Labor Reallocation in
Response to Trade Reform,’’ Mimeo, UC San Diego, 2011.
Molloy, Raven, Christopher L. Smith, and Abigail Wozniak, ‘‘Internal Migrationin the United States,’’ Journal of Economic Perspectives, 25 (2011), 173–196.
Naughton, Barry, ‘‘China’s Emergence and Prospects as a Trading Nation,’’Brookings Papers on Economic Activity, 2 (1996), 273–344.
———, The Chinese Economy: Transitions and Growth (Cambridge, MA: MIT
Press, 2007).
Neal, Derek, ‘‘Industry-Specific Human Capital: Evidence from Displaced
Workers,’’ Journal of Labor Economics, 13 (1995), 653–677.
Notowidigdo, Matthew J., ‘‘The Incidence of Local Labor Demand Shocks,’’
Mimeo, University of Chicago, 2013.
Oreopoulos, Philip, Till von Wachter, and Andrew Heisz, ‘‘The Short- and LongTerm Career Effects of Graduating in a Recession,’’ American Economic
Journal: Applied Economics, 4 (2012), 1–29.
Parent, Daniel, ‘‘Industry-Specific Capital and the Wage Profile: Evidence fromthe National Longitudinal Survey of Youth and the Panel Study of Income
Dynamics,’’ Journal of Labor Economics, 18 (2000), 306–323.
Pierce, Justin R., and Peter Schott, ‘‘A Concordance between Ten Digit
Harmonized System Codes and SIC/NAICS Product Classes andIndustries,’’ NBER Working Paper No. 15548, 2009.
———, ‘‘The Surprisingly Swift Decline of U.S. Manufacturing Employment,’’
NBER Working Paper No. 18655, 2012.
Rogerson, William, and Robert Shimer, ‘‘Search in Macroeconomic Models of theLabor Market,’’ in Handbook of Labor Economics, Vol. 4A, Orley Ashenfelter,and David Card, eds. (Amsterdam: Elsevier, 2011).
Romalis, John, ‘‘Factor Proportions and the Structure of Commodity Trade,’’American Economic Review, 94 (2004), 67–97.
Schott, Peter, ‘‘Across-Product versus Within-Product Specializationin International Trade,’’ Quarterly Journal of Economics, 119 (2004),
647–678.
Sullivan, Daniel, and Till von Wachter, ‘‘Job Displacement and Mortality: An
Analysis Using Administrative Data,’’ Quarterly Journal of Economics, 124(2009), 1265–1306.
Topalova, Petia, ‘‘Factor Immobility and Regional Impacts of Trade
Liberalization: Evidence on Poverty from India,’’ American Economic
Journal: Applied Economics, 2 (2010), 1–41.
Topel, Robert H., ‘‘Local Labor Markets,’’ Journal of Political Economy, 94 (1986),
111–143.
———, ‘‘Specific Capital and Unemployment: Measuring the Costs andConsequences of Job Loss,’’ Carnegie-Rochester Conference Series on Public
Policy, 33 (1990), 181–214.
von Wachter, Till, Jae Song, and Joyce Manchester, ‘‘Long-Term Earnings Losses
due to Mass Layoffs during the 1982 Recession: An Analysis Using U.S.
Administrative Data from 1974 to 2004,’’ (Mimeo, Columbia University,2009).

1860

QUARTERLY JOURNAL OF ECONOMICS

Walker, W. Reed, ‘‘The Transitional Costs of Sectoral Reallocation: Evidence
From the Clean Air Act and the Workforce,’’ Quarterly Journal of
Economics, 128 (2012), 1787–1835.
Wozniak, Abigail K., ‘‘Are College Graduates More Responsive to Distant Labor
Market Opportunities?,’’ Journal of Human Resources, 45 (2010), 944–970.

Downloaded from http://qje.oxfordjournals.org/ at University of California, San Diego on January 6, 2016