The Economic Journal, 125 (May), 621–646. Doi: 10.1111/ecoj.12245 © 2015 Royal Economic Society. Published by John Wiley & Sons, 9600 Garsington
Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

UNTANGLING TRADE AND TECHNOLOGY: EVIDENCE
FROM LOCAL LABOUR MARKETS*
David H. Autor, David Dorn and Gordon H. Hanson
We juxtapose the effects of trade and technology on employment in US local labour markets
between 1980 and 2007. Labour markets whose initial industry composition exposes them to rising
Chinese import competition experience significant falls in employment, particularly in manufacturing and among non-college workers. Labour markets susceptible to computerisation due tospecialisation in routine task-intensive activities instead experience occupational polarisation withinmanufacturing and non-manufacturing but do not experience a net employment decline. Trade
impacts rise in the 2000s as imports accelerate, while the effect of technology appears to shift fromautomation of production activities in manufacturing towards computerisation of informationprocessing tasks in non-manufacturing.

Many economists view technology and trade as two of the paramount forces shaping
labour markets in the US and other advanced countries. New technologies augmenthuman and physical capital (Artuc et al., 2010) and enable firms to automate routine
tasks previously performed by middle-rank workers (Autor and Dorn, 2013), both ofwhich contribute to a rise in the relative demand for more-skilled labour (Katz andAutor, 1999). For its part, trade with low-wage countries depresses wages andemployment in the industries (Artuc et al., 2010), occupations (Ebenstein et al.,forthcoming) and regions (Autor et al., 2013a) that are exposed to import competition.
While literature on the labour-market consequences of technology and trade isextensive,1 existing work has not established the degree to which these two forces
represent distinct shocks or, rather, are varied facets of a common phenomenon.
There is an obvious temporal link between them, as rapid technical progress (e.g. thecomputer revolution) and growth in emerging economies (e.g. the rise of China) areroughly contemporaneous events. Have technology and trade had quantitatively
similar impacts on overall employment and is the timing of these impacts in factcoincident? The root of interest in these issues in large part stems from growing
income inequality and increasing employment polarisation in the US and other highincome countries.2 How do the magnitudes of employment changes in response totechnology and trade shocks compare for workers separated by age, education, sex andoccupational skill level? Differences in adjustment to shocks of varied origin are likely
to be evident at the sectoral level, with foreign competition affecting the tradable
manufacturing sector most acutely and technology shocks readily diffusing acrosssectors regardless of their tradability. Are the sectoral employment impacts of* Corresponding author: David. H. Autor, Department of Economics Massachusetts Institute ofTechnology 77 Massachusetts Avenue, E17-216 Cambridge, MA 02139, USA. Email: dautor@mit.edu.
Dorn acknowledges funding from the Spanish Ministry of Science and Innovation (grant ECO2010-16726
and fellowship JCI2011-09709). Autor and Hanson acknowledge funding from the National Science
Foundation (grant SES-1227334).
1
See Acemoglu and Autor (2010) and Harrison et al. (2010) for discussions of the literature.
2
See, for example Autor et al. (2006), Dustmann et al. (2009) and Goos et al. (2012).
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automation broader than those of globalisation or are there notable spillovers of trade
shocks into non-manufacturing? When it comes to addressing these questions, theliterature gives only incomplete answers. We know that technology and trade have been
disruptive but we do not know the extent to which these disruptions overlap and thus
whether economic analysis must treat them conjointly.
The aim of this study is to analyse the simultaneous impacts of technology and tradeon US employment levels and job composition, juxtaposing their effects across local
labour markets, over time, between sectors and occupations, and among workers ofdifferent education, age and sex categories. Our analysis reveals a surprising degree ofdivergence between the labour-market consequences of these two phenomena – bothacross industrial, occupational, geographic and demographic groups, and over time as
the trajectory of these forces has evolved.
The divergence that we document runs counter to perceptions that technology andtrade play mutually reinforcing roles in shaping labour-market developments in richcountries. Beyond their obvious synchroneity, one association between the twoemphasizes their interdependence. As falling trade costs permit firms to perform someproduction tasks offshore, the factors that remain at home become more productive(Grossman and Rossi-Hansberg, 2008). Reduced trade barriers may thus cause
simultaneous growth in productivity and trade.3 A second strand of reasoning thatlinks technology and trade recognises that many of the job tasks that are suitable for
automation are also suitable for offshoring (Blinder, 2009).4 Looking forward, it isreasonable to suppose that some of the low-skill work that cannot presently be
automated in rich countries could soon be headed for the developing world.
Critical inputs into our analysis are measures of local labour-market exposure totechnological change and to competition from international trade. As in our previous
work, we focus on changes in employment structure within 722 commuting zones (CZs)
that approximate local labour markets and that cover the entire continental US. On thetechnology front, we follow Autor and Dorn (2013; Autor–Dorn hereafter) who use
Census data on industry and occupation mix by CZ and data from the Dictionary of
Occupational Titles (US Department of Labor, Employment and Training Administration, 1977) on job tasks by occupation to measure the degree to which CZs were
historically specialised in routine, codifiable job activities that are well suited tocomputerisation. As documented by Autor–Dorn, variation in industry specialisation
across CZs observed in 1950 can account for the differential pace at which these markets
reacted to the precipitous decline in the price of computing power after 1980 by adopting
workplace computing and reducing employment in routine task-intensive occupations.

3

Offshoring links trade and technology in another manner, as well. When firms relocate production
stages within an industry abroad, the average factor intensity of the stages that remain at home changes(Feenstra and Hanson, 1999). Standard measures of TFP do not account for shifts in the composition ofactivities performed inside industries, such that trade-induced changes in the composition of production may
be confounded with TFP growth.
4
The reasoning here is that routine tasks that follow explicit codifiable procedures (as in Autor et al., 2003)
are well suited to automation because they can be computerised and well suited to offshoring because they canbe performed at a distance without substantial loss of quality. However, there are many tasks that are offshorable
but not routine (e.g. interpreting medical X-rays) and other tasks that are codifiable but not clearly offshorable(e.g. adding vast arrays of numbers for actuarial analysis, or, to borrow an example from popular culture, the jobthat Homer Simpson performs as Nuclear Safety Inspector at the Springfield Nuclear Power Plant).
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On the trade front, we follow Autor et al. (2013a; Autor–Dorn–Hanson hereafter) in
identifying trade shocks using cross-industry and cross-CZ variation in importcompetition stemming from China’s rapidly rising productivity and falling barriers
to trade. These forces have catapulted China’s US import presence – the share ofChinese imports in total US expenditure on goods – from less than 0.2 percentage
points in 1987 to 4.8 percentage points in 2007. To isolate the components of this risethat are driven by shifts in China’s competitive position rather than changes in US
product demand, we exploit the contemporaneous growth of Chinese exports byindustry to other high-income countries. This identification strategy posits that growthin Chinese imports within a given industry (e.g. apparel, footwear, furniture, luggage,toys) that occurs simultaneously in the US and other high-income countries is
primarily driven by the surge in Chinese productivity that has accompanied its
transition to a market economy (Brandt et al., 2012; Hsieh and Ossa, 2012) and by
reduced trade barriers resulting from China joining the World Trade Organisation(Pierce and Schott, 2012). We then project these industry-level import shocks to thelevel of local labour markets by interacting them with variation in CZ industry mix in
1980, prior to the rise of China. Since manufacturers within an industry tend to cluster
geographically, China’s rising penetration of specific industries results in sharp
disparities in the change in import exposure across local labour markets.5
While strong spatial variation in industry specialisation leaves commuting zones
differentially exposed to changes in trade and technology, designating CZs as local
labour markets only makes sense if labour is not highly mobile across these zones.
Otherwise, CZ-specific labour-market shocks may fully diffuse across space. Consistent
with partial labour mobility, Autor–Dorn and Autor–Dorn–Hanson find evidence ofsizeable impacts of adverse economic events on CZ employment but not on the size of
CZ working-age populations, suggesting that much labour-market adjustment happens
within commuting zones. These findings add to mounting evidence that the movementof labour across US cities and states in the aftermath of changes in regional labourdemand is slow and incomplete (Blanchard and Katz, 1992; Glaeser and Gyourko,
2005). It is this incompleteness that renders commuting zones an appropriate spatial
unit of analysis. Further relevant for our work, incomplete adjustment to labour-market
shocks appears to be most evident among less educated workers, who comprise a large
share of employment in the trade and technology-exposed manufacturing sector(Bound and Holzer, 2000; Malamud and Wozniak, 2012).
Using data on CZs from 1980 to 2007, we assess the effects of exposure to importcompetition and initial specialisation in routine tasks on overall employment,
unemployment and non-participation, on job polarisation in manufacturing andnon-manufacturing, and on the time path of adjustment overall and by sector. The
analysis produces three new sets of results on the causal effects of advancing
automation and rising low-wage country imports on local labour-market outcomes.
5
As a case in point, the CZ containing Providence, Rhode Island – a traditional manufacturing hub – saw
estimated increases in Chinese import exposure (i.e. competing Chinese manufactures that would potentiallybe produced in Providence if not imported) of $2,330 per worker between 1991 and 2000, and an additional$3,490 per worker between 2000 and 2007. In contrast, the CZ containing New Orleans, Louisiana – whichlacks industries that compete directly with China – saw comparatively small increases of $170 and $490 per
worker during these same intervals.

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624

THE ECONOMIC JOURNAL[MAYFirst, technology and trade have distinct effects on labour-market aggregates.
Whereas import competition leads to sharp declines in local manufacturing employment and corresponding growth in local unemployment and non-employment,
exposure to routine task specialisation has largely neutral overall employment effects.
Workers with less than a college education are those most affected by trade but show
only small employment declines from technological change. Negative gross manufacturing employment effects were evident in Autor–Dorn–Hanson with regards toincreased import competition but were not examined in Autor–Dorn with regard totechnical change. Our contribution here is to place the overall manufacturing
employment consequences from technology and from trade side by side, which reveals
the larger aggregate employment effects of globalisation when compared to routinisation.
Second, technology and trade affect employment in broad occupational categories
and sectors in quantitatively different magnitudes and in qualitatively different
directions. CZs more specialised in routine occupations have employment losses inroutine task-intensive occupations, but these losses are largely offset by local
employment growth in abstract and manual-task-intensive occupations, thus leading
to the pattern of occupational polarisation that has been the focus of Autor–Dorn. A
novel result is that this polarisation emerges both in the manufacturing and nonmanufacturing sectors, primarily due to the loss of routine production jobs inmanufacturing and routine clerical jobs in non-manufacturing. We contrast thesepatterns with new results on the impact of trade exposure on occupational
composition: while trade-exposed CZs also experience large employment declines inroutine task-intensive occupations in manufacturing, these CZs suffer further employment losses, rather than gains, in manual and particularly in abstract task-oriented jobs,
which together yield the strongly negative overall employment effect of greater importcompetition. The novel results of this article for aggregate occupation-sector cells
highlight a critical difference between the impacts of technology and trade shocks:
While technology affects the labour market at the occupation level by shifting
occupational composition within sectors, trade competition has a broad sectoral
impact and depresses employment across all occupation groups in manufacturing, witha notable negative employment effect for higher skilled managerial, professional andtechnical jobs.
Third, and perhaps most strikingly, the timing of the sectoral impacts of technology
and from trade strongly diverge. With the rapid growth of US imports from China, theeffect of trade competition on manufacturing has increased over time. Conversely, theeffect of technological change on employment composition inside of manufacturing
has decelerated, with the largest impacts detected in the 1980s and the smallest impacts
found in the 2000s. Outside of manufacturing, however, the impact of automation
accelerates during the three decades of our sample, suggesting that computerisation of
information processing in knowledge-intensive industries continues to intensify.
Neither Autor–Dorn nor Autor–Dorn–Hanson considered temporal variation in
the magnitude of sectoral and occupational labour-market shocks. Our new results
bring to light two under-appreciated features of the US labour market. The
impacts of technology and trade appear to have little overlap either across space oracross time, which substantially simplifies the task of identifying their independent© 2015 Royal Economic Society.

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contributions to changes in labour market outcomes.6 Further, routinisation affects
sectors asynchronously, meaning that its gross local labour-market consequences willvary by decade depending on regions’ initial patterns of industrial specialisation.
Our article builds on two broad and active literatures. The first explores the impact
of technical change and trade on skill demands7 while the second studies how theseforces shape labour-market outcomes at the sub-national (i.e. local labour-market)
level.8 This article contributes to these bodies of work along two dimensions. First, ourempirical approach exploits robust measures of exposure to technology and trade andconsiders their distinct impacts. This is in contrast to existing research that tends tofocus on either technology or trade as candidate explanatory variables but rarely places
the two on equivalent empirical footing.9
An additional contribution of the article is to examine a rich set of adjustment
margins that help to compare and contrast the magnitude, scope, and timing oftechnology and trade shocks. Existing studies tend to focus on a just a few of thesemargins at a time, which creates an incomplete panorama of labour-market
adjustment. The margins we examine include employment to population, unemployment and non-participation, as well as shifts in employment across occupational
categories that differ in their intensity of abstract, routine and manual task input.
Further, we consider these outcomes separately by demographic groups comprisinggender, education and age and sector. It is by disaggregating technology and trade
impacts by sector and occupation that we uncover the differential timing of theseshocks. In combination, we believe these analyses provide valuable evidence on how
the distinctive impacts of trade and technology on rich country (or, more specifically,US) labour markets can be characterised and interpreted.

1. Measurement
1.1. Local Labour Markets
Our analysis requires a time-consistent definition of regional economies in the US. We
approximate local labour markets using the construct of commuting zones developedby Tolbert and Sizer (1996), who analysed county-level commuting data from the 1990
Census data to create 741 clusters of counties that are characterised by strong
commuting ties within CZs, and weak commuting ties across CZs. Our analysis includes
the 722 CZs that cover the mainland US (both metropolitan and rural areas).
Commuting zones are particularly apt for our analysis of local labour markets because
they cover both urban and rural areas, are based primarily on economic geography

6
The negligible geographic correlation between trade and technology shocks is documented by Autoret al. (2013b). The differing temporal roles played by these shocks is a key finding of this paper.
7
See, for example, Doms et al. (1997), Beaudry et al. (2010), Firpo et al. (2012) and other literature citedin Autor and Dorn (2013).
8
See, for example, Borjas and Ramey (1995), Michaels (2008), McLaren and Hakobyan (2010), Topalova(2010), Kovak (2013) and other literature cited Autor et al. (2013a).
9
A number of papers consider the roles of both computerisation and potential offshoring simultaneously(Firpo et al., 2012; Goos et al., 2012; Oldenski, 2012; Autor and Dorn, 2013; Michaels et al., 2014). We are notaware of any comparable effort to simultaneously consider the effects of computerisation and competition
from international trade in goods on local labour-market outcomes.

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rather than incidental factors such as minimum population and can be consistently
constructed using census public use micro areas (PUMAs) for the full period we
examine.10
1.2. Exposure to Computerisation
Following an extensive literature, we conceive of recent automation as taking the formof a decline in the cost of computerising routine tasks, such as bookkeeping, clerical
work and repetitive production and monitoring activities, thereby potentially displacing the workers performing these tasks.
To measure the degree to which CZs were historically specialised in routine,
codifiable job activities that were intrinsically well suited to computerisation, we
proceed in two steps. Using data from US Department of Labor, Employment andTraining Administration (1977), we create a summary measure of the routine taskintensity RTI of each occupation, calculated as:
R
M
A
RTIk ¼ lnðTk;1980Þ  lnðTk;1980Þ  lnðTk;1980
Þ;(1)where TkR , TkM and TkA are, respectively, the routine, manual and abstract task inputs in
11each occupation k in 1980. This measure is rising in the importance of routine tasks
in each occupation and declining in the importance of manual and abstract tasks.
To measure cross-market variation in employment in routine-intensive occupations,
we apply a simple binary approach to distinguish ‘routine’ and ‘non-routine’occupations. We classify as routine occupations those that fall in the top-third of theemployment-weighted distribution of the RTI measure in 1980. We then assign to each
commuting zone j a routine employment share measure (RSHjt ) equal to the fractionof CZ employment at the start of a decade that falls in routine task-intensive
occupations:
2
3
!1
K
K
X
X


RSHjt ¼ 4 Ljkt  1 RTIk [ RTI P 66 5
Ljkt
:
(2)
k¼1

k¼1

Here, Ljkt is the employment in occupation k in CZ j at time t, and 1() is the indicatorfunction, which takes the value of one if the occupation is routine-intensive by ourdefinition. By construction, the mean of this measure is 0.33 in 1980 and the
population weighted 75/25 percentile range is 6 percentage points.
To isolate the long-run, quasi-fixed component of the routine occupation share thatis determined prior to the onset of the era of rapid computerisation, we exploit
historical cross-CZ differences in industry specialisation as instruments for theobserved level in each decade. Our instrumental variables approach is as follows: let10

Our analysis draws on public use microdata from Ruggles et al. (2004). If a PUMA overlaps with several
counties, our procedure is to match PUMAs to counties assuming that all residents of a PUMA have equal
probability of living in a given county. The aggregation of counties to CZs then allows computing theprobability that a resident of a given PUMA falls into a specific CZ.
11Tasks are measured on a 0 to 10 scale. For the 5% of microdata observations with the lowest manual task
score, we use the manual score of the fifth percentile. A corresponding adjustment is made for abstractscores.
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Eij;1950 equal the employment share of industry i 2 1,. . .,I in CZ j in 1950 and let
Ri;j;1950 equal the routine occupation share among workers in industry i in 1950 in all
US states except the state that includes CZ j.12 The product of these two measuresprovides a predicted value for the routine employment share in each CZ, which
depends only on the local industry mix in 1950 and the occupational structure ofindustries nationally in 1950:
gj ¼
RSH

I
X

Ei;j;1950  Ri;j;1950 :

(3)

i¼1

Because the instrument is determined three decades prior to the onset of rapid
computerisation in the 1980s, we expect it to be correlated with the long-run
component of the routine occupation share but uncorrelated with contemporaneous
innovations to this share.13
1.3. Exposure to International Trade
Following Autor–Dorn–Hanson, we examine changes in exposure to international
trade for US CZs associated with the growth in US imports from China. The focus on
China is a natural one: rising trade with China is responsible for much of the
expansion in US imports from low-income countries since the early 1990s. China’s
export surge is a consequence of its transition to a market-oriented economy, which
has involved rural-to-urban migration of over 150 million workers, Chinese industries
gaining access to long banned foreign technologies, capital goods and intermediate
inputs (Hsieh and Klenow, 2009), and multinational enterprises being permitted tooperate in the country (Naughton, 2007).14 Compounding the positive effects ofinternal reforms on China’s trade is the country’s accession to the WTO, which gives it
most-favoured nation status among the 157 WTO members (Pierce and Schott, 2012).
How can examining trade exposure in Commuting Zones be justified in terms oftrade theory? Because trade shocks play out in general equilibrium, one needs

12
We exclude own state employment from the construction of our instrument for local labour-market
conditions to remove any mechanical correlation between the instrument and the endogenous variable.
Throughout the analysis, we implicitly consider CZs to be part of the state that contains the largest share of
their population.
13
Appendix Table 3 of Autor and Dorn (2013) presents first-stage estimates for this instrumental variables
g and RSH is sizeable and highly significant, with t-ratios ofmodel. The predictive relationship between RSH
six or above in each decade. The first-stage coefficient is close to unity in 1950 and takes smaller values insuccessive periods, obtaining a coefficient of 0.27 in 2000. The decrease in magnitude is to be expected sinceinitial conditions become less determinative over time.
14
While China overwhelmingly dominates low-income country exports to the US, trade with middleincome nations, such as Mexico, may also matter for US labour-market outcomes. The North American Free
Trade Agreement (1994), for instance, lowered US barriers to imports to a country in which US firms alreadyhad extensive supply networks. Finding exogenous sources of variation in Mexico’s export growth, however,is tricky. Whereas China has had dramatic productivity growth in manufacturing – making internal supply
shocks an important source of its export growth – Mexico has not (Hsieh and Klenow, 2012). The expansion
of US trade with Mexico is thus primarily driven by changes in US bilateral trade policy which could be
influenced by economic conditions in US industries. Arguably, such simultaneity concerns are less an issue
with regards to US trade with China because of China’s phenomenal productivity surge, which has been due
in large part to how far inside the global technology frontier the country remained at the end of the Maoist
era. See McLaren and Hakobyan (2010) on the effects of NAFTA on US local labour markets.

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empirically to map many industry-specific shocks into a small number of aggregate
outcomes. For national labour markets at annual frequencies, one is left with few
observations and many confounding factors. By taking regional economies as the unitof analysis, we circumvent the degrees-of-freedom problem endemic to estimating thelabour-market consequences of trade. This approach is valid for identifying the labourmarket consequences of trade insofar as:(i) CZs differ in their pattern of industry specialisation (e.g. due to initialdifferences in comparative advantage at the regional level); and(ii) frictions in labour markets allow regional differences in wages, unemployment
and labour-force non-participation to persist over the medium run. Autor–
Dorn–Hanson find strong evidence that greater exposure to trade with China
affects local labour-market outcomes across CZs.
Following the empirical specification derived by Autor–Dorn–Hanson, our main
measure of local labour-market exposure to import competition is the change in
Chinese import exposure per worker in a region, where imports are apportioned toeach region according to its share of national industry employment:
DIPWjtChinaUS ¼

X Lijt DM ChinaUS
it

j

Luit

Ljt

:

(4)

In this expression, DMitChinaUS is the observed change in US imports from China inindustry i between the start and end of period t, Ljt is total start of period employment
in region j and Lijt =Luit is region j’s share in national employment of industry i.
In (4), the difference in DIPWjtChinaUS across local labour markets stems entirely fromvariation in local industry employment structure at the start of period t. This variationarises from two sources: differential concentration of employment in manufacturing
versus non-manufacturing activities and specialisation in import-intensive industries
within local manufacturing. Differences in manufacturing employment shares are not
the primary source of variation, however: in a bivariate regression, the start-of-period
manufacturing employment share explains less than 25% of the variation in
DIPWjtChinaUS . In our main specifications, we control for the start-of-period manufacturing share within CZs so as to focus on variation in exposure to Chinese imports
stemming from differences in industry mix within local manufacturing sectors.
A concern for our subsequent estimation is that realised US imports from China in(4) may be correlated with industry import demand shocks. In this case, OLS estimatesof the relationship between increased imports from China and changes in US
manufacturing employment may understate the true impact, as both US employment
and imports may be positively correlated with unobserved shocks to US productdemand. To identify the causal effect of rising Chinese import exposure on US
manufacturing employment and other local labour-market outcomes, we employ an
instrumental variables strategy that accounts for the potential endogeneity of US trade
exposure. We exploit the fact that during our sample period, much of the growth inChinese imports stems from the rising competitiveness of Chinese manufacturers (asupply shock from the US producer perspective) and China’s lowering of tradebarriers, dismantling of the constraints associated with central planning and accession© 2015 Royal Economic Society.

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to the WTO. This approach requires that import demand shocks in high-income
countries are not the primary cause of China’s export surge.
To identify the supply-driven component of Chinese imports, we instrument for
growth in Chinese imports to the US using the contemporaneous composition andgrowth of Chinese imports in eight other developed countries.15 Specifically, we
instrument the measured import exposure variable DIPWjtChinaUS with a non-US
exposure variable DIPWjtChinaother that is constructed using data on contemporaneous
industry-level growth of Chinese exports to other high-income markets:
DIPWjtChinaother ¼

X Lijt1 DM Chinaotherit:Luit1
Ljt1
j

(5)

This expression for non-US exposure to Chinese imports differs from the expressionin (4) in two respects. First, in place of realised US imports by industry (DMjtChinaUS ),it uses realised imports from China to other high-income markets (DMjtChinaother ).
Second, in place of start-of-period employment levels by industry and region, thisexpression uses employment levels from the prior decade. We use 10-year-lagged
employment levels because, to the degree that contemporaneous employment byregion is affected by anticipated China trade, the use of lagged employment toapportion predicted Chinese imports to regions will mitigate this simultaneity bias.16
Our instrumental variable strategy requires that the common component of import
growth in the US and in other high-income countries derives from factors specific to
China, associated with its rapidly evolving productivity and trade costs. Any correlationin product demand shocks across high-income countries would represent a threat toour strategy, possibly contaminating both our OLS and IV estimates.17 To checkrobustness against correlated demand shocks, Autor–Dorn–Hanson develop analternative estimation strategy based on the gravity model of trade, which utilises theinferred change in China’s comparative advantage and market access relative to the
US. To implement the strategy, they regress China exports relative to US exports to acommon destination market on fixed effects for each importing country and for eachindustry. The time difference in residuals from this regression captures the percentage
growth in imports from China due to changes in China’s productivity and foreign trade
costs vis-a -vis the US. By using China–US relative exports, the gravity approach15

The eight other high-income countries are those that have comparable trade data covering the fullsample period: Australia, Denmark, Finland, Germany, Japan, New Zealand, Spain and Switzerland. Our
identification strategy is related to that used by Bloom et al. (2009), who consider the relationship between
imports from China and innovation in Europe.
16
A subtle point regarding our instrumentation strategy is that there is a larger time gap between theemployment values used to construct the instrument and those used to construct the regressor with regards
to routinisation (instrument data going back to 1950) than with regards to trade exposure (1980). We view it
as unlikely that this difference in time gap can account for the larger impacts that we estimate for trade
exposure on employment levels than for routinisation on employment levels. As we report below, for theroutinisation variable OLS and 2SLS coefficient estimates end up being very similar. Thus, narrowing thetime gap between the instrument and the regressor for routinisation to bring it more in line with the time
gap for trade exposure would be unlikely to change the results – our instrument already captures much of theconditional variation in routinisation.
17
Positive correlation in product demand shocks across high-income countries would make the impact oftrade exposure on labour-market outcomes appear smaller than it truly is.
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differences out import demand conditions in the purchasing country, thus isolating
supply and trade-cost-driven changes in China’s exports.
The gravity-based approach helpfully addresses a second threat to identification, as
well. It allows for the possibility that US – rather than Chinese – productivity shocks
may be driving growth in imports from China. Suppose, for example that low
productivity growth in the US textile industry induced shipments of its textile products
to fall both in the domestic US and in foreign European markets. Each market may
then import more from China, with this across-venue increase in Chinese imports
being driven by changes in US supply. Because the gravity model residuals summarise
the change in China’s comparative advantage relative to the US, the measureeffectively subsumes changes in US productivity. The gravity approach thus broadens
the interpretation of the estimated coefficient from capturing the impact of supply
shocks in China to capturing the impact of China-US relative supply shocks. Despite
this change in interpretation, China’s much more rapid productivity growth makes itlikely that its supply shocks, rather than those specific to the US, are the primary driversof the country’s export surge.18 Reassuringly, Autor–Dorn–Hanson show that thegravity-based estimation strategy yields coefficient estimates quite similar to the IV
approach that we employ in this article.

2. Results
We examine the local labour-market consequences of exposure to routine task
specialisation and import competition from China in three stages, beginning withchanges in labour-market aggregates (overall employment, unemployment, labour-force
participation), then considering differences in employment effects by demographic
group (sex, education, age), occupation (abstract, routine and manual task-intensivejobs) and sector (manufacturing, non-manufacturing), finally evaluating how outcomesat the sector and occupation level vary by decade from the 1980s to the 2000s.
As prelude to the analysis, we note that the divergent employment impacts oftechnology and trade on CZs discussed in the following subsections have a spatial
analogue. Autor et al. (2013b) document that there is weak overlap in the geographic
exposure of CZs to trade and technology shocks. The CZs with the highest employment
shares in routine task-intensive occupations constitute a mixture of manufacturingintensive locations (in particular, locations around the Great Lakes and in theSoutheast) and human-capital-intensive large cities, including New York, Chicago,
Dallas and Los Angeles. Routine task intensity has dual sources: blue-collar production
occupations associated with capital-intensive manufacturing, represented in the firstgroup of CZs; and white-collar office, clerical and administrative-support occupations
associated with banking, insurance, finance and other information-intensive sectors,
represented in the second group.
Trade-exposed CZs, by contrast, are the subset of manufacturing-intensive regions
specialised in labour-intensive manufacturing, such as furniture, toys, apparel,
18
Brandt et al. (2012) estimate that over 1998 to 2007, China had average annual TFP growth inmanufacturing of 8.0%, compared to Bureau Labour Statistics’ estimate (http://www.bls.gov/mfp/) of 3.9%
for the US.

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footwear and leather goods. Because CZs with high routine-task intensity include a
broad collection of manufacturing and service centres whereas CZs with high trade
exposure constitute a narrow set of specialised industry clusters, the potential
intersection of these two sets of regions is limited. Moreover, the geography of trade
exposure is relatively concentrated. A substantial fraction of the most trade-exposed
CZs are located in a handful of states, including Tennessee, Missouri, Arkansas,
Mississippi, Alabama, Georgia, North Carolina and Indiana, whereas routine taskintensive CZs are more dispersed throughout the US. A simple population-weighted
correlation between technology exposure in (2) and trade exposure in (4) finds that
there is almost no relationship between the two: the correlation is 0.02 for the 1990
to 2000 period and 0.01 for the 2000 to 2007 period.19 The sets of heavily tradeexposed CZs and of heavily technology-exposed CZs are thus largely disjoint. Thisfeature of the data facilitates the identification of separate effects of trade andtechnology on local labour markets.
2.1. Comparing the Impacts of Trade and Technology on Employment, Unemployment and Nonparticipation
We now turn to the main estimates on the impacts of trade and technology on local
labour markets. We focus initially on employment, unemployment and labour-force
participation using an estimating equation of the form:
DYjkt ¼ ct þ b1 DIPWjtChinaUS þ b2 RSHjt þ X 0jt b2 þ dk þ ejkt :(6)Here, the dependent variable DYjkt is the decadal change in the employment-topopulation ratio, unemployment-to-population ratio or non-participation rate among
working age adults ages 16 to 64 in CZ i in US Census division k during decade t.20 The
main variables of interest are the contemporaneous change in import exposure per
worker DIPWit and the start of decade routine employment share RSHjt , bothmeasured at the CZ level. Also included are time-period effects ct , a vector of eight
Census division indicators dk that allow for differential employment trends acrossregions, and a vector of control variables X jt measuring start-of-period demographics
and labour-market structure in each CZ. Most estimates stack two sets of firstdifferences, 1990–2000 and 2000–7, though we later explore estimates separately by
decade while adding results for technology exposure in the 1980–90, a period in which
exposure to Chinese imports was small. All regressions are weighted by CZ shares ofnational population, and standard errors are clustered by state to allow for overtime
and within-state error correlations. Following our strategy outlined above, (6) isestimated using two-stage least squares, with the import exposure variable instrumented by contemporaneous changes in Chinese imports to other non-US high-income
19

The unweighted correlations are 0.21 and 0.31 in 1990 and 2000 respectively. The difference between
the weighted and unweighted correlations almost certainly reflects the fact that rural areas are typicallyneither manufacturing intensive nor concentrated in information-intensive or production-intensive
occupations, both of which have high routine task content. Without weighting, these sparsely populatedrural areas increase the correlation substantially.
20
For the period 2000 to 2007, we rescale the dependent variable to represent a decadal change bymultiplying it by the factor 10/7.
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Table 1
Effect of Exposure to Chinese Import Competition and Routine-biased Technological Change on
Employment Status among Working Age Population, 1990–2007: 2SLS Estimates
Dependent variable: 10-year equivalent changes in share of working age population in indicated employment
status (in percentage points)(1)
Panel (a): outcome: share employed
Share of employed in routine occupations

0.05(0.22)(D imports from China to US)/Worker
Panel (b): outcome: share unemployed
Share of employed in routine occupations(D imports from China to US)/worker(3)0.70**(0.16)

0.21(0.25)
0.83**(0.22)

0.21**(0.06)

0.01(0.07)
0.19**(0.05)

0.49**(0.15)

0.21(0.19)
0.65**(0.19)

0.01(0.06)(D imports from China to US)/worker
Panel (c): outcome: share not in labour force
Share of employed in routine occupations

(2)

0.06
(0.17)

Notes. N = 1,444 (722 commuting zones 9 2 time periods). All regressions control for the start of period levelsof share of employment in manufacturing, share of population that is college educated, share of populationthat is foreign born, employment rate among females and Census division dummies. Robust standard errors
in parentheses are clustered on state. Models are weighted by start of period commuting zone share ofnational population. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.10.

countries in (5) and the routine-share measure instrumented by CZs’ historicalindustry structures in (3).21
The first panel of Table 1 presents estimates of the impact of technology andtrade exposure on the employment-to-population ratio. While Autor–Dorn documented substantially faster adoption of computer technology in routine-intensive
CZs, the novel results in column 1 do not detect a robust relationship between
technology exposure and changes in the employment-to-population rate. The point
estimate of 0.05 on the routine-share measure is statistically insignificant and small
in magnitude. It implies a reduction in the employment-to-population rate of twotenths of a percentage point per decade in the 75th percentile CZ relative to the25th percentile CZ.22 Consider next the impact of exposure to import competition,in column 2, which replicates regression results in Autor–Dorn–Hanson. The highly
significant coefficient of 0.70 on the import exposure variable in the second row
indicates that a $1,000 rise in a CZ’s import exposure per worker (in 2007 dollars)
over a 10-year period reduces the CZ’s employment-to-population rate by seventenths of a percentage point. This economically large impact is well within therange of variation seen in our sample. Between 1990 and 2007, the cross-CZ
21
The F statistics of the first stages in Table 1 are 92.0 and 61.6 for the models in columns 1 and 2, and84.3 and 60.0 for the model in column 3. All instruments are statistically significant at p > 0.001.
22
The cross-CZ interquartile range of the start-of-period routine share variable is 4.0 percentage points in1990 and 3.3 percentage points in 2000.

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633

interquartile range of the increase in imports per worker averaged approximately$1,100 per decade.23
Including both the technology and trade measures in the regression simultaneously
has little impact on the results (column 3). The point estimate on each measure rises
in absolute magnitude (specifically, the routine-share measure increases from 0.05 to0.21 and the trade measure increases from 0.70 to 0.83) while statisticalsignificance is unaffected. Notably, the fact that both measures become slightly morenegative when the other is included implies that the conditional correlation between
the (instrumented) technology and trade variables is negative – areas with high trade
exposure have somewhat lower exposure to routine-task displacement and vice versa.
The next two panels of Table 1 present complementary estimates for changes inunemployment and non-participation. As with the employment-to-population rate,
both the unemployment and non-participation variables are constructed by dividing
the count of workers in the relevant status (unemployed, not in the labour force) by CZ
working-age population ages 16–64. A comparison of the point estimates for thesethree margins of adjustment thus provides an implicit decomposition of thedisemployment effects of trade or technology into unemployment and non-participation components. In the case of the routinisation variable, the estimates suggest thatany adverse employment effect, if present, accrues to non-participation rather thanunemployment (all point estimates are, however, statistically insignificant). Trade
exposure, by contrast, significantly increases both unemployment and non-participation, with the non-participation effect in panel (c) of column 1 accounting for threequarters (0.65/0.83) of the trade-induced decline in employment in panel (a) ofcolumn 1.
To evaluate the importance of the instrumentation strategy for our results, Table A1
in the Appendix reports OLS estimates for the regressions shown in column 3 ofTable 1. For the routinisation variable, OLS coefficients differ little from those in
2SLS specifications, being slightly more negative in the employment regression (0.17
in column 1 of Table A1 versus 0.21 in column 3, panel (a) of Table 1), slightly lesspositive in the non-participation regression (0.14 in column 3 of Table A1 versus 0.21 incolumn 3, panel (c) of Table 1), and also effectively zero in the unemployment
regression (0.03 in column 2 of Table A1 versus 0.01 in column 3, panel (b) of Table1). The similarity in OLS and 2SLS routinisation impacts arises in part from strong
persistence in local labour markets’ routine employment shares, such that the 1950
industry employment composition used to construct the instrument in (3) captures
much of the conditional variation in the routine share in (2). The OLS estimates forthe trade exposure measure in Table A1 have the same signs but smaller magnitudes
than the corresponding 2SLS estimates in Table 1: (0.12 in column 1 of Table A1
versus 0.83 in column 3, panel (a) of Table 1), the unemployment regression (0.05 in
23

During the first decade of the sample, imports per worker rose by $1,320 in the 75th percentile CZ and$623 in the 25th percentile CZ, yielding an interquartile range of approximately $700. Between 2000 and
2007, imports per worker rose even more rapidly, with decadal-equivalent gains of $3,114 at the 75th
percentile, $1,599 at the 25th percentile and an interquartile range of $1,515. Averaging over both decades
yields a mean interquartile range of approximately $1,100. Notably, there is no evidence of CZ-level mean
reversion in import exposure across decades, so the interquartile range of the exposure variable for the fullperiod is near to the sum of the interquartile ranges for the 1990s and 2000s.
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column 2 of Table A1 versus 0.19 in column 3, panel (b) of Table 1), and thenon-participation regression (0.07 in column 3 of Table A1 versus 0.65 in column 3,
panel (c) of Table 1). Larger magnitudes for 2SLS coefficients are consistent with OLS
regressions being contaminated by unobserved US product demand shocks, which
induce positive covariation between industry employment and imports, thereby
leading OLS estimates to understate the true impact of trade exposure on employmentoutcomes.24
Our first main empirical result is thus that technology and trade do not havecomparable impacts on aggregate employment, unemployment and non-participation.
Greater trade exposure results in significant overall losses of employment in local
labour markets, whereas greater exposure to routinisation does not. Before considering why these effects may differ, we first drill down on the possible heterogeneity of
impacts across demographic groups.
2.2. Differences in Employment Effects by Demographic GroupWe explore estimates comparable to those above performed separately for threedemographic breakdowns: males versus females; non-college versus college-educatedadults; and younger (ages 16 to 39) versus older adults (ages 40 to 64).25 Table 2
presents estimates.
Focusing first on the routine share variable, we find that in contrast to theinsignificant relationship between routinisation and aggregate employment, unemployment and non-participation, CZs that were initially specialised in routine-intensive
occupations saw significant falls in the employment-to-population rate of females; theimplied effect is economically meaningful. The point estimate of 0.49 in column 2 ofpanel (a) implies that comparing a CZ at the 75th percentile and 25th percentile of
exposure to task-replacing technical change, the more exposed CZ would see a relative
decline in the female employment-to-population rate of 1.8 percentage points per
decade. The effects of exposure to routinisation also appear larger for older versus
younger workers, though this difference is less precisely estimated. Any negative effects
of technology exposure on employment are largely absorbed by a corresponding
increase in non-participation, seen in panel (c), rather than by an increase in
unemployment, seen in panel (b).
Turning next to trade exposure, a striking but not altogether unsurprising result isthat the disemployment impact of trade shocks seen in panel (a) appears to be
substantially more severe for non-college workers in column 3 than for college workers
in column 4. A $1,000 increase in per-worker import exposure is estimated to reduce
the non-college employment rate by 1.21 percentage points and the college
employment rate by 0.53 percentage points. More surprising, perhaps, is that theeffects of trade shocks on employment are otherwise uniformly large and significant
for both males and females and for both younger and older workers. Moreover, for all

24
As discussed in Autor et al. (2013a), the instrument in (5) may further help correct for measurement
error in trade exposure that attenuates OLS estimates.
25
We define non-college workers as those with a high school degree or lower educational attainment, andcollege workers as those with at least one year of college education.

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Table 2
Effect of Exposure to Chinese Import Competition and Routine-biased Technological Change on
Employment Status among Working Age Population, 1990–2007: 2SLS Estimates
Dependent variable: 10-year equivalent changes in share of working age population in indicated employment
status (in percentage points)
Outcomes measured among
Males

FemalesNon-college

CollegeAge < 40

Age ≥ 40

(1)

(2)

(3)

(4)

(5)

(6)

Panel (a): outcome: share employed
Share of employed in routine
0.10
occupations(0.33)(D imports from China to0.71**
US)/worker(0.23)

0.49*(0.20)
0.93**(0.22)

0.34(0.32)
1.21**(0.31)

0.29***(0.16)
0.53**(0.14)

0.10(0.27)
0.82**(0.20)

0.42***(0.23)
0.89**(0.24)

Panel (b): outcome: share unemployed
Share of employed in routine
0.04
occupations(0.08)(D imports from China to
0.17**
US)/worker(0.05)

0.03(0.06)
0.20**(0.06)

0.02(0.10)
0.25**(0.09)

0.05(0.05)
0.08*(0.04)

0.03(0.09)
0.22**(0.06)

0.03(0.05)
0.14*(0.06)

Panel (c): outcome: share not in labour force
Share of employed in routine
0.05
occupations(0.27)(D imports from China to
0.54*US)/worker(0.22)

0.46**(0.15)
0.73**(0.18)

0.32(0.24)
0.96**(0.26)

0.33*(0.13)
0.44**(0.13)

0.13(0.19)
0.60**(0.17)

0.39*(0.20)
0.75**(0.22)

Notes. N = 1,444 (722 commuting zones 9 2 time periods). All regressions control for the start of period levelsof share of employment in manufacturing, share of population that is college educated, share of populationthat is foreign born, employment rate among females and Census division dummies. Robust standard errors
in parentheses are clustered on state. Models are weighted by start of period commuting zone share ofnational population. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.10.

groups, the bulk of the reduction in employment to population is accounted for by
reductions in labour-force participation rather than increases in unemployment –though the non-participation effect is larger for older relative to younger workers, asseen in the comparison between column 6 and column 5.
Why do we not observe a stronger effect on the fraction of adults who are
unemployed? One potential reason is that our outcome variables are measured at low
frequency (10 and 7 years respectively, for the first and second periods) and thus
capture medium-run effects. If, as seems likely, technology or trade-induced job
displacement leads initially to unemployment followed in the longer term with reemployment or labour-force exit, these dynamics are likely to be less visible using lowfrequency outcome measures.
The estimates in Table 2 further underscore our first result that trade andtechnology are not a unified, monolithic force acting on the local labour market. The
negative employment impacts of routinisation are concentrated among females and tosome extent among older workers, with smaller and inconsistently signed effects forother demographic groups. By contrast, trade shocks appear to reduce employment
among all groups of workers that we considered, with a disproportionately large effectamong non-college workers. Some of the results from our next two analyses foroccupational and sectoral impacts offer help to interpret these demographic patterns.
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2.3. Effects of Trade and Technology on Occupations and Tasks
We have so far focused on employment status as our sole outcome measure. We now
deepen this analysis by asking how trade and technology shocks alter the distributionof job tasks that workers supply, which we proxy using employment by occupation. The
following analysis explores employment in three broad occupational categories thatdiffer in their primary job task content. The first category includes managerial,
professional and technical occupations, which are relatively specialised in abstract
problem-solving and organisational tasks and employ comparatively highly educatedand highly paid workers. The second broad job category includes production, clericaland administrative support and sales occupations. These occupations are routine-task
intensive and hence potentially subject to increasing substitution of computer capitalfor labour. The third category encompasses mechanics, craft and repair occupations,agricultural occupations and service occupations. These occupations employ primarily
non-college labour and are intensive in manual job tasks that demand physicalflexibility and adaptability, which have proven challenging to automate.26
To explore how technology and trade affect employment in these three task
categories, we estimate a variant of (6) where the dependent variable is the change inthe fraction of the working-age population employed in each occupational group.
Table 3 presents estimates.27 The first column, which pools all demographic groups,
finds substantial differences between the effects of technology and trade on
occupations. The estimated effect of routinisation on employment is negative,
significant and large for only one occupational category: routine task-intensive
occupations in panel (b). The point estimate of 0.36 implies a substantial 1.8
percentage point per decade differential decline in the share of working-age adults
employed in this broad occupational category in the 75th percentile CZ relative to the25th percentile CZ. The point estimates also suggest that employment in abstract andmanual-task-intensive occupations experiences offsetting gains, though these effects
are not statistically significant. In combination, the pattern of results is consistent withthe well-known finding that computerisation is associated with occupational polarisation – that is, gains in the share of employment in relatively high-education, abstracttask-intensive occupations and relatively low-education, manual-task-intensive occupations relative to the employment in middle-skill, routine task-intensive jobs.
By contrast, increases in trade exposure reduce overall employment in column 1
across all three broad task categories, with the largest impact found in employment inroutine task-intensive occupations in panel (b) (0.48 percentage points for a $1,000
rise in trade exposure), the second largest effect in manual-task-intensive occupations
in panel (c) (0.22), and the smallest effect in abstract-task-intensive occupations inpanel (a) (0.14, which is not significant).28

26

The analysis in Autor and Dorn (2013) offers summary information on task content by occupation that
documents the logic of this categorisation. See especially Table 2 of their paper.
27
Note that non-employment (unemployment and non-participation) constitutes a fourth outcome
category. The impact of trade or technology on this category is simply the negative of its effect on
employment in the three occupational groups considered in Table 3 (see panel (a) of Table 2).
28
Note that these three coefficients sum to 0.84, which is identical (up to rounding) to the negative
estimated effect of trade on the employment to population rate in column 3 of Table 1.
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Table 3
Effect of Exposure to Chinese Import Competition and Routine-biased Technological Change on
Employment by Occupation Group among Working Age Population, 1990–2007: 2SLS
Estimates
Dependent variable: 10-year equivalent changes in share of working age population employed in indicatedoccupation group (in percentage points)
Outcomes measured among
All

Males

FemalesNon-college

CollegeAge < 40

Age ≥ 40

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Panel (a): outcome: share employed in managerial/professional/technical occupations
Primary task: abstract
Share of employed in
0.15
0.35*
0.05
0.05
0.09
routine occupations(0.12)(0.16)(0.09)(0.05)(0.11)(D imports from China 0.14
0.05
0.22*
0.17**
0.16
to US)/worker(0.09)(0.11)(0.10)(0.04)(0.11)

0.32*(0.15)
0.08(0.10)

0.11(0.11)
0.24*(0.09)

Panel (b): outcome: share employed in production/clerical/retail sales occupations
Primary task: routine
Share of employed in0.36** 0.32** 0.44**
0.37*
0.32**
routine occupations(0.12)(0.12)(0.13)(0.18)(0.09)(D imports from China 0.48** 0.37** 0.61**
0.63**
0.32**
to US)/worker(0.08)(0.08)(0.11)(0.11)(0.07)

0.37**(0.14)
0.46**(0.08)

0.43**(0.11)
0.52**(0.11)

Panel (c): outcome: share employed in craft/mechanics/agricultural/service occupations
Primary task: manual
Share of employed in
0.01
0.07
0.00
0.09
0.06***
routine occupations(0.06)(0.11)(0.05)(0.14)(0.04)(D imports from China 0.22** 0.29** 0.10
0.41*
0.05(0.08)(0.11)(0.07)(0.20)(0.05)
to US)/worker

0.05(0.09)
0.29**(0.09)

0.12***(0.07)
0.14***(0.08)

Notes. N = 1,444 (722 commuting zones 9 2 time periods). All regressions control for the start of period levelsof share of employment in manufacturing, share of population that is college educated, share of populationthat is foreign born, employment rate among females and Census division dummies. Robust standard errors
in parentheses are clustered on state. Models are weighted by start of period commuting zone share ofnational population. * p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.10.

Together, these estimates offer two novel insights that constitute the second major
finding of our study: exposure to technology and to trade have in common that theirlargest negative effects are on the middle category of routine task-intensive occupations; and exposure to trade and to technology differs in that trade has negative
employment effects throughout the task distribution, whereas technology does not.
The qualitatively distinct impacts of routinisation on employment by occupation andthe qualitatively similar occupational impacts of import competition are responsible
for the divergent effects of these two forces on overall employment – that is, neutral
gross technology impacts and strongly negative gross trade impacts.
To reveal possible heterogeneity in these occupational impacts according to worker
characteristics, we next examine how the varying exposure of occupational groups totechnology and trade shocks depends on workers’ sex, education and age. Following
the format of Table 2, columns 2 to 7 of Table 3 present estimates of the impacts oftechnology and trade on job tasks by demographic subgroup: males and females,© 2015 Royal Economic Society.

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college and non-college adults and younger and older adults. Across all demographic
groups, technology exposure significantly and quite uniformly reduces employment inroutine task-intensive occupations according to panel (b). While most estimates for the
impact on abstract and manual-task intensive occupations in panels (a) and (c) are
positive but insignificant, these results can account for some of the heterogeneous
employment effects documented in Table 2. In particular, while losses in routine
employment among men are offset by corresponding gains in occupations with
abstract tasks, such offsetting gains are absent for women, thus generating a negative
overall impact of technology exposure on female employment. The results for different
age and education groups also reveal differential patterns of labour reallocation
following declines in routine task-intensive jobs: among young and among collegeeducated workers, all offsetting employment gains occur in abstract task-intensive jobs,
while any offsetting gain among older and less educated workers is in the manual taskintensive jobs that include many low-wage occupations.
Trade shocks uniformly have the greatest (negative) impact on employment inroutine task-intensive occupations across all demographic groups in panel (b), with thelargest impacts found for females in column 3 and non-college adults in column 4.
Trade shocks also substantially reduce employment in manual-task-intensive occupations in panel (c) among males (column 2), non-college workers (column 4) andyounger workers (column 6), and reduce employment in abstract-task-intensive
occupations in panel (c) among females (column 3), non-college adults (column 4)
and older adults (column 7).
These results shed light on our earlier finding that non-college adults suffer
disproportionate employment losses from trade shocks. While one might havespeculated that this is because they are concentrated in production occupations, theTable 3 results suggest otherwise. Though non-college employment falls most inroutine task-intensive occupations – which, logically, include many production
positions – it also drops significantly in manual and abstract-task-intensive occupations.
In fact, net employment losses in these two job categories are essentially equal to theloss in the routine task-intensive categories. Thus, non-college adults in all occupationgroups appear exposed to greater importer competition from China.
The Table 3 findings are also helpful for reconciling alternative views of offshoringthat have emerged in the trade literature. Older approaches to offshoring (Feenstra
and Hanson, 1999) emphasise variation in factor intensity across manufacturing stages
to explain the fraction of production moved offshore whereas newer approaches tooffshoring (Grossman and Rossi-Hansberg, 2008) focus on the inherent offshorabilityof tasks, abstracting away from factor intensity. Our results suggest there is a role forboth channels: factor intensity matters (as shown by non-college workers being the skill
group most impacted by trade) but so does the nature of the task (as shown by routine
occupations being most affected by exposure to import competition).
2.4. Sectoral Impacts
Our final set of empirical exercises considers the sectoral dimension of technology andtrade shocks, which leads naturally into an examination of their timing. We expect theeffects of international trade on the domestic labour market to be most concentrated© 2015 Royal Economic Society.

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in the manufacturing sector, where competition from imports is most intense. Should
we expect the same for technology? On the one hand, earlier literature finds
substantial impacts of the adoption of computer capital on skilled labour demand in
manufacturing and offers some evidence that this relationship started a decade earlier
in manufacturing than non-manufacturing (Berman et al., 1994; Autor et al., 1998).
Conversely, computerisation is now ubiquitous in the workplace and serves as the
backbone of most information-intensive activities. Thus, we might expect any
employment effects to be as large or larger outside of manufacturing.
We explore these relationships in Table 4, by estimating a variant of (6) for the effectof technology and trade exposure on the share of working-age population employed insix sector-occupation cells: manufacturing and non-manufacturing sectors crossed with
abstract, routine and manual-task-intensive occupations. As in prior Tables, ouroutcome variables are measured as 10-year equivalent changes in the percentage ofworking-age population employed in each cell, with non-employment constituting a
residual category. Thus, the sum of the trade or technology effect on the fraction ofworking-age adults employed in these six sector-occupation cells will equal its effect on
the employment to population ratio. One difference between these estimates and theearlier specifications is that we construct separate CZ-level routine-share variables forthe manufacturing and non-manufacturing sectors.29 Further, within manufacturing
we divide routine task-intensive jobs into two groups, production occupations andclerical and sales occupations. The impacts of automation in manufacturing production are likely to be concentrated on the former and our subsequent analysis will
confirm this intuition.
Beginning with the results for trade in the second row, we find that consistent withexpectations, trade shocks have disproportionate effects on employment in manufacturing. In column 1 of panel (a), a $1,000 per worker increase in trade exposure
reduces manufacturing employment by 0.50 percentage points. While the negative
impact of trade exposure on manufacturing employment is not surprising, thebreakdown of this result by occupation groups provides a striking insight: only half ofthe trade-induced decline in manufacturing employment (0.240/0.504) accrues to
production occupations, while a similar reduction stems from reductions in ‘whitecollar’ jobs in managerial, professional, technical and clerical occupations (columns 2
and 3 of panel (a)), which like manufacturing-production jobs employed about 5% ofworking-age adults in 1990. These results suggest that adverse employment effects ofChinese trade competition have not been concentrated solely on US production
workers but have affected the manufacturing sector more broadly, with notable
employment losses in both production and non-production activities.
The effect of trade shocks is not limited to manufacturing. Consistent with the resultsin Autor–Dorn–Hanson, we estimate a smaller but non-trivial contemporaneous
reduction in non-manufacturing employment. While the point estimate of 0.20 incolumn 1 of panel (b) is not statistically significant, this reflects countervailing
effects across occupational categories within non-manufacturing. Employment in
29
Introducing this additional degree of freedom is likely to be important because the cross-CZ correlation
between the manufacturing and non-manufacturing routine share variables is surprisingly low: 0.18 in 1990
and 0.13 in 2000 (weighted by CZ population).

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0.016(0.081)
0.504**(0.077)

All
(1)
0.022
(0.021)
0.196**(0.065)

Abstract
(2)

0.00
0.05

0.000
(0.017)
0.050**(0.014)

Routine(3)

Cleric/retail

0.15
0.27

0.029(0.040)0.240**(0.047)

Routine(4)

Production

0.11
0.02

0.023
(0.018)
0.019(0.020)Manual(5)

All otheroccupations

0.29
0.22

0.063
(0.177)
0.203(0.189)All(1)

All occupations

0.64
0.07

0.141
(0.086)0.064(0.107)

Abstract(2)

Managerial/
professional/
technical

0.59
0.10

0.131***(0.072)
0.092***(0.050)Routine(3)

Cleric/retail/production(b) Non-manufacturing sector

0.24
0.19

0.053
(0.055)
0.176*(0.085)Manual(4)

All otheroccupations

Notes. N = 1,444 (722 commuting zones 9 2 time periods). All regressions control for the start of period levels of share of employment in manufacturing, share of
population that is college educated and foreign born, female employment rate and Census division dummies. Robust standard errors in parentheses are clusteredon state. Models are weighted by start of period commuting zone share of national population. Predicted effects in panel (b) are computed as the product of theregression coefficients from panel (a) with the P75–P25 differential of the corresponding variable (averaged over the 1990–2000 and 2000–2007 periods).
* p ≤ 0.05, ** p ≤ 0.01, *** p ≤ 0.10.

Predicted effects, 75th versus 25th percentile of exposure
Routine employed share
0.08
0.11
Imports from China
0.56
0.22

Regression results
Share of sectorial employed
in routine occupations(D imports from China toUS)/worker

Primary task

All occupations

Managerial/
professional/
technical(a) Manufacturing sector

Dependent variable: 10-year equivalent changes in share of working age pop employed in indicated sector-occupation cell (in percentage points)

Table 4
Effect of Exposure to Chinese Import Competition and Routine-biased Technological Change on Employment in Manufacturing and Nonmanufacturing, 1990–2007: 2SLS Estimates

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641

manual-task-intensive occupations falls by a significant 0.18 percentage points incolumn 4 and in routine task-intensive occupations and by a marginally significant 0.09
percentage points in column 3, while rising slightly by 0.06 percentage points in abstracttask-intensive occupations in column 2. This pattern is likely to reflect local demand
spillovers from manufacturing to non-manufacturing. As manufacturing employment
contracts, demand from both businesses and consumers for locally produced services
such as construction, entertainment, food away from home and retail trade is likely to fall.
The consequence is reduced employment in various routine-task and manual-task
activities outside the sector, as shown in the last two columns of the Table.
Results for the impacts of exposure to technology are presented in the first row ofTable 4. Local labour markets with a routine task-intensive manufacturing sector
experience a slight shift of employment from routine to abstract and manual
occupations, as seen by comparing column 4 to columns 2 and 5 in panel (a), thoughnone of these effects nor the overall effect of employment in manufacturing isstatistically significant. By contrast, routinisation more clearly predicts employment
polarisation in non-manufacturing, with reduced employment in routine task-intensive
occupations in column 3 of panel (b) and offsetting gains in both abstract and manualtask-intensive occupations in columns 2 and 4. While neither of the latter two point
estimates is statistically significant, it is noteworthy that the net effect of routinisation
on employment in non-manufacturing appears to be weakly positive.
The final two rows of Table 4 illustrate the magnitudes of these effects by computing
the interquartile range of effect sizes for both the trade and technology measures
averaged over the two decades of our sample. These computations suggest thatvariation in trade exposure explains more of the decline in routine production
employment in the manufacturing sector than does variation in technology exposure(column 4 of panel (a)) while variation in technology exposure predicts a larger
decline in routine task-intensive employment outside manufacturing (column 3 ofpanel (b)). In both sectors, technology exposure is associated with expansions inabstract and manual task-intensive occupations that roughly offset employment losses
in routine occupations.
Given dramatic advances in computer-aided manufacturing in recent decades as wellas the high levels of manufacturing investment in computer capital, it may seem
surprising that we do not find a stronger negative effect of technology exposure on
production jobs in manufacturing, or manufacturing employment overall. These
results pose a puzzle, whose resolution helps draw a sharp distinction between thetemporal pattern of technology and trade shocks by sector and leads to the final main
result in the study: it may be that negative employment effects of technology were
evident in a period before our sample begins. To investigate this possibility, we extend
the sample backward by a decade to the 1980s. While we can measure technology
exposure for the 1980s, a corresponding analysis for exposure to Chinese tradecompetition it is not practical because large-scale trade with China only commenced in
the 1990s.30 Table 5 presents these results.
30
Furthermore, harmonised trade data, needed to match bilateral trade flows to US industry codes, is only
available for the 1990s and later. Autor–Dorn–Hanson show that the local labour markets with differential
exposure to China after 1990 did not have differential trends in manufacturing employment in the 1980s.

© 2015 Royal Economic Society.

© 2015 Royal Economic Society.

0.024(0.018)
0.164***(0.086)

0.026(0.029)
0.254*(0.104)1990–2000
Share of sectorial employed inroutine occupations(D imports from China
to US)/worker

2000–7
Share of sectorial employed inroutine occupations(D imports from China
to US)/worker
0.038**(0.014)
0.060**(0.018)

0.027**(0.010)0.046(0.044)

0.036**(0.010)
n/a

(2)

0.017
(0.038)
0.129**(0.040)0.068*(0.031)
0.095(0.115)

0.094**(0.028)n/a(3)RoutineProduction

0.026***(0.015)0.024(0.019)0.021***(0.012)0.016(0.036)

0.019(0.014)n/a(4)ManualAll otheroccupations

0.100(0.067)0.135*(0.058)0.065(0.061)
0.104(0.134)0.258**(0.027)n/a(1)

Abstract

Managerial/
professional/
technical

0.282**(0.057)
0.008(0.087)

0.183**(0.041)
0.206*(0.098)

0.077**(0.021)n/a(2)RoutineProduction/
cleric/retail

0.057(0.098)0.150***(0.090)0.108**(0.034)
0.295***(0.155)0.068**(0.020)n/a(3)ManualAll otheroccupations(b) Non-manufacturing sector

THE ECONOMIC JOURNAL

Notes. N = 722 commuting zones. All regressions control for start of period share of employment in manufacturing and Census division dummies. Robust standard
errors in parentheses are clustered on state. Models are weighted by start of period commuting zone share of national population. * p ≤ 0.05, ** p ≤ 0.01,*** p ≤ 0.10.

0.003(0.011)n/a(1)RoutineAbstract

1980–90
Share of sectorial employed inroutine occupations(D imports from China
to US)/worker

Primary task

Cleric/RetailManagerial/
professional/
technical(a) Manufacturing sector

Dependent variable: 10-year equivalent changes in share of working age population employed in sector-occupation cell (in percentage points)

Table 5
Effect of Exposure to Chinese Import Competition and Routinisation on Employment by Sector and Occupation Group, 1980–2007: 2SLS Estimates

642
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2015]UNTANGLING TRADE AND TECHNOLOGY643

Consistent with our conjecture, we find strong evidence that routinisation led
to significant employment polarisation in manufacturing in the 1980s, characterisedby a strong decline in routine occupation employment for production workers
and little change in abstract and manual employment. The impact of thetechnology exposure measure on routine task-intensive production employment incolumn 3 becomes weaker in each of the subsequent decades, seen by comparing the coefficients for the 1980s (0.094), to the 1990s (0.068) and to the 2000s(0.017), by which point the impact is weakly positive. For routine task-intensive
manufacturing employment in clerical and sales occupations, the effect ofroutinisation is negative, significant and stable in magnitude across all three
decades.31
Strikingly, the declining secular effect of routinisation on job polarisation in
manufacturing is matched by an accelerating impact of technology on routine-task
employment in non-manufacturing. The significant point estimate for the routine
share of 0.08 for the decade of the 1980s (row 1 of column 2 in panel (b)) more thandoubles to 0.18 in the 1990s (row 3) and almost quadruples to 0.28 by the 2000s(row 5). Net, these results suggest that the primary impact of technological change on
employment has shifted from automation of routine production tasks in manufacturing to computerisation of routine information-processing tasks, which are concentrated in services.
These findings stand in sharp contrast to the direct impacts of trade exposure on
employment. The coefficient estimates in Table 5 indicate that the negative effectof import competition in manufacturing increases slightly in absolute value fromthe 1990s to the 2000s for routine and abstract task-intensive occupations (columns
1 to 3) and for abstract task-intensive occupations (column 2), though not for
manual task-intensive jobs (column 4). Compounding these changes, the magnitudeof the trade shock itself, as defined in (4), doubles between the first and second
decades of our sample due to the very rapid rise in Chinese import penetration in
the US market following China’s accession to the WTO in 2001.32 Thus, thenegative impact of trade exposure on manufacturing employment has intensified
strongly over time.
Overall, the Table 5 estimates suggest that computerisation did have substantial
impacts on job task composition in manufacturing but that this impact was felt withgreatest force in the 1980s and 1990s and had little further effect in the 2000s. This
result encapsulates the third major finding of our study: whereas the negative
employment effects on manufacturing from import competition have intensified overtime, the corresponding effects from routinisation have weakened. By contrast, theimpact of technology exposure on routine task-intensive jobs outside of manufacturing
has intensified, suggesting that the labour-market effect of technology is shifting from
replacement of production work to automation of information processing tasks in theservice sector.

31
The mean value of the routine share variable declines slowly over the sample period, from 39.1% to35.7% in the manufacturing sector, and from 30.8% to 26.0% in the non-manufacturing sector.
32
The CZ average for the 10-year equivalent growth of employment per worker is $1,140 in the 1990s, and$2,627 in the 2000s.

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3. Conclusions
There is a wide agreement among economists that technological change andexpanding international trade have led to changing skill demands and growing
inequality and rising polarisation of labour-market outcomes in the US and in otherrich countries. While this article confirms that both forces have shaped employment
patterns in US local labour markets in the last three decades, its main contribution is tohighlight important differences in the impact of technology and trade on labour
markets. The effects of trade and technology can be observed separately because local
labour-market exposure to technological change, as measured by specialisation inroutine task-intensive production and clerical occupations, is largely uncorrelated withlocal labour-market exposure to trade competition from China.
Local labour markets with greater exposure to trade competition experience
differentially large declines in manufacturing employment, with corresponding
growth in unemployment and non-employment. The employment decline is not
limited to production jobs but instead affects all major occupation groups, including
a notable decline in managerial, professional and technical jobs. Employment losses
are particularly large among workers without college education, for whom we also
observe employment declines outside the manufacturing sector which may stem fromlocal demand spillovers. While trade exposure reduces overall employment and shifts
the distribution of employment between sectors, exposure to technological change
has substantially different impacts, characterised by neutral effects on overall
employment but substantial shifts in occupational composition within sectors. In
particular, we find that susceptibility to technological change predicts declining
employment in routine task-intensive production and clerical occupations both in the
manufacturing and non-manufacturing sectors. For most demographic groups, thesedeclines in routine employment are largely offset by increasing employment inabstract or manual task-intensive occupations which tend to comprise the highest andlowest paid jobs in the economy. One exception is among women, for whom thereduction in routine-occupation employment translates to an overall decline in
employment.
Concurrent with the rapid growth of US imports from China, the effect of tradecompetition on the manufacturing sector has become stronger over time, while theeffect of technological change on employment composition in the manufacturingsector has subsided. Conversely, the impact of technology on the non-manufacturingsector is growing as technological change seems to be shifting from automation of
production in manufacturing to computerisation of information processing inknowledge-intensive industries.

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645

UNTANGLING TRADE AND TECHNOLOGYAppendix A. Additional Results
Table A1
Effect of Exposure to Chinese Import Competition and Routine-biased Technological Change on
Employment Status among Working Age Population, 1990–2007: OLS Estimates
Dependent variable: 10-year equivalent changes in share of working age population in indicated employment
status (in percentage points)
Share of working age population
Employed

Share of employed in routine occupations(D Imports from China to US)/worker

Unemployed

Not in labour force

(1)

(2)

(3)

0.17***(0.09)
0.12(0.08)

0.03(0.02)
0.05*(0.02)

0.14***(0.07)
0.07(0.08)

Notes. N = 1,444 (722 commuting zones 9 2 time periods). All regressions control for the start of period levelsof share of employment in manufacturing, share of population that is college educated, share of populationthat is foreign born, employment rate among females and Census division dummies. Robust standard errors
in parentheses are clustered on state. Models are weighted by start of period commuting zone share ofnational population. * p ≤ 0.05, *** p ≤ 0.01, *** p ≤ 0.10.

MIT and NBER
University of Zurich and CEPR
UCSD and NBER

Additional Supporting Information may be found in the online version of this article:
Data S1.

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