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International Trade and Job Polarization:
Evidence at the Worker Level
Wolfgang Keller1 and Hale Utar∗2
1University of Colorado and NBER
2Bielefeld University
July 15, 2015
Job polarization is the shift of employment and earnings from mid-level wage jobs to both high- and low-
wage jobs. Using longitudinal employee-employer matched data covering all residents and workplaces
in Denmark, we study the effect of international trade on job polarization as trade barriers fell away
with China’s entry into the World Trade Organization. We show that trade can explain the U-shaped
pattern of employment changes that is characteristic for job polarization. For the large mid-level wage
group of machine operators, already adversely by automatization and the introduction of robots, import
competition leads to a loss of eight months of mid-wage employment, and increases of both low-wage
and high-wage employment of about four months and one month respectively over a period of eight
years. Trade leads to job polarization mainly by shifting workers from initially abundant manufacturing
jobs to both high- and low-paying services jobs. Trade leads to less job polarization for women than for
men even though women experience overall job polarization more than men, a finding that we relate
to the swiftness of the reduction in labor demand caused by import competition. Furthermore, trade
has increased gender inequality by shifting women to a lesser extent into high-wage jobs than men. We
discuss a number of reasons that might be behind these findings, as well as possible policy implications.
∗The study is sponsored by the Labor Market Dynamics and Growth Center (LMDG) at the Uni-versity of Aarhus. Support of the Dept. of Economics and Business, Aarhus University and StatisticsDenmark are acknowledged with appreciation. We thank Henning Bunzel for facilitating the accessto the confidential database of Statistics Denmark and for his support, David Autor for his insightfuldiscussion of the paper and Nick Bloom, Susanto Basu, Dave Donaldson for helpful comments andsuggestions.
1 Introduction
The recent transformation of China from a rather poor and largely closed economy to one
of the largest manufacturing producers over little more than two decades has been among
the most significant changes in the world economy. China’s share of world manufactures
exports rose from 2 % to 4 % over the years 1990 to 1999, only to accelerate pace
and account for 16 % of world exports in manufacturing goods by the year 2013.1 For
manufacturing producers in rich countries such as Denmark, the emergence of China is
felt as a major competitive shock. This is particularly true in the labor-intensive textiles
and clothing sector, where the traditional comparative advantage of low-wage countries
has been compounded by trade liberalization in form of import quota removals through
China’s entry into the World Trade Organization (WTO) in the year 2001.
In this paper we study the employment trajectories of Danish workers to determine
whether import competition from China has caused job polarization. Job polarization
is the shift of employment shares from mid-level wage jobs to both high- and low-wage
jobs. This phenomenon is among the most significant labor market developments in
recent decades because it has been observed in many rich countries.2 Figure 1 shows
that while job polarization has been felt strongly in the United States, developments in
Denmark have been even more dramatic. Mid-level employment share losses affect 60 %
of all occupations (40 % in the U.S.), and it occurred at a faster pace (seven more years
are shown for the U.S.).3 This means that Denmark may provide important new lessons
on job polarization. Our focus will be on the textiles and clothing industry, which faces
1Authors’ calculation using World Development Indicators dataset of the World Bank.2For the Unites States, see Autor, Katz, and Kearney (2006, 2008), Autor and Dorn (2013); United
Kingdom: Goos and Manning (2007); Germany: Spitz-Oener (2006), Dustmann, Ludsteck, and Schon-berg (2009); France: Harrigan, Reshef, and Toubal (2015) and across 16 European countries, see Goos,Manning, and Salomons (2014).
3The figure for Denmark is based on all employees in Denmark aggregated by 3-digit occupationlevel (International Standard Classification of Occupations) excluding agriculture. The figure for theUnited States is taken from Autor and Dorn (2013) and it covers all nonfarm employment in the US.Initial occupational mean wage is based on hourly wage data from 1991 for Denmark and for 1980 forthe U.S.
2
major import competition from China. In our analysis the effect of import competition
turns on whether a specific worker is employed by a firm manufacturing a product for
which China’s entry into the WTO removes binding import quotas. We study workers’
employment (and to a lesser extent earnings) trajectories over the years 2002 to 2009.
Covering the relatively high rates of export growth during China’s first eight years of
WTO membership, this period captures the short and medium term implications of
increased import competition. The end of our period of observation is determined by
data considerations.4
-.2
0.2
.4.6
0 20 40 60 80 100Wage Percentile (Ranked by Initial Occupational Mean Wage)
1980-2005 U.S.1991-2009 Denmark
100
x C
hang
e in
Em
ploy
men
t Sha
re
Smoothed Changes in Employment by Wage Percentile
Figure 1: Smoothed Changes in Employment in Denmark and in the United States
We employ administrative longitudinal data on the universe of persons aged 15 to 70
years old in Denmark to study the impact of import competition on worker’s movements
between jobs.5 The data set is unusually rich in providing information for each individ-
4Occupational categories were changed in the year 2009. The Great Recession starting in 2008 tookplace too late to be driving our results.
5Our data are not the administrative records themselves but an integrated database created byStatistics Denmark from administrative registers. They are owned by public authorities and supple-
3
ual worker on his or her employer, hours worked, unemployment history, and level of
education, among other variables. Information on the tasks workers perform is available
at a relatively detailed four-digit level (International Standard Classification of Occupa-
tions, ISCO). We can therefore distinguish workers not only by industry and education
but also by the tasks they perform.
Because we want to exploit a specific trade policy shock–the expiration of the Multi-fiber
Arrangement (MFA) quotas as China acceded the WTO–we focus on workers who ini-
tially were employed in Denmarks textiles and clothing sector.6 The original purpose of
the MFA in the year 1974 was to provide comprehensive protection against competition
from low-wage country exports of textiles and clothing through quantitative restrictions.
After years of multilateral negotiations, it was agreed in the year 1995 that the MFA
would gradually be lifted. China’s non-WTO status rendered it ineligible to benefit from
these trade liberalizations, which changed only once China had joined the WTO in the
year 2001. The subsequent dramatic surge of Chinese textiles and clothing exports and
the resulting increase in the competition is the plausibly exogenous source of shifts in
employment trajectories among Danish workers.7
An advantage of studying the incidence of trade in a single sector is that it eliminates
the influence of demand shocks, technology shocks, or secular trends that might be
correlated with imports in a multi-sector analysis. Our focus on workers with initial
employment in the textiles and clothing sector comes at the cost of a smaller sample
than we would have studying the entire economy. At the same time, we will see that not
only are the forces that drive job polarization in Denmark’s overall economy present in
mented with additional information drawn from various surveys (see Bunzel 2008, Timmermans 2010).Earlier work using this data set includes Groes, Kircher, and Manovskii (2015), Hummels, Jorgenson,Munch, and Xiang (2014).
6The increase in trade with China associated with the MFA quota removals has been employedrecently to study technology upgrading of European firms (Bloom, Draca, and van Reenen 2014), aswell as in Utar (2014, 2015) to study firms’ and workers’ adjustment. These studies do not focus onjob polarization.
7Utar (2014) shows that the quotas were binding, and that their removal led to increased importcompetition. Brambilla, Khandelwal, and Schott (2008) examine the effects of the quota lifting in theU.S.
4
the textiles and clothing industry, but movements outside of textiles and clothing (and
manufacturing) play a key role in trade’s effect on polarization.
We contribute to a growing literature on the importance of international trade in explain-
ing job polarization, much of which examines changes at the industry or region level.
Recent work on local labor markets has shown that international trade and offshoring
have not played a major role for job polarization in the U.S. (Autor and Dorn 2013), a
result that has been confirmed in other rich countries (Goos, Manning, and Salomons
2014, Michaels, Natraj, and van Reenen 2014). Job polarization is mainly explained
by information and communications technology investments whereby computers, robots
and automatization replace workers that perform easily programmable tasks (so-called
routine-biased technical change; Autor, Katz, Kearney 2006, Goos and Manning 2007).8
Our work advances the literature by shifting the focus from aggregate labor market
consequences to job changes at the worker level. By examining the difference in responses
between workers who are ex ante observationally similar except that one works for a firm
that will compete with Chinese imports while the other worker does not, we provide
causal evidence on the impact of trade on employment polarization. By shedding new
light both on the nature of frictions that workers face when they move between jobs
(such as loss of occupation-specific human capital), this paper contributes to a better
understanding of worker welfare and the overall welfare effect of international trade.
We provide one of the first accounts of job polarization using micro level data for any
country. The case of Denmark illustrates well the widely-shared experience in rich
countries from a world characterized by skill-biased technical change to one characterized
by job polarization. The paper shows that both worker’s education and gender were
important in shaping the emergence of job polarization in Denmark.
This paper is in a long tradition of work examining the role of international trade for
8An exception is Firpo, Fontin, and Lemieux (2011) who find offshoring to be important. Otherstudies of job polarization that do not specifically address the role of international trade include Spitz-Oener (2006) and Dustmann, Ludsteck, and Schonberg (2009).
5
labor market outcomes, including the increase in relative wage of educated workers in
many countries during the 1970s and 1980s (see Feenstra 2000). The focus in recent open
economy analysis has shifted to tasks instead of industries or education levels (Grossman
and Rossi-Hansberg 2008), which is line with evidence showing that worker outcomes
have become strongly related to occupation characteristics (Acemoglu and Autor 2010).
By affecting the relative labor demand for mid-level versus high- and low-wage jobs,
international trade could in principle be a cause of job polarization. This would require
that the factor content of imports from low-wage countries is intensive in tasks for which
mid-level wages are paid in rich countries. At the same time it can be challenging to
separate technology from trade explanations for job polarization, especially when trade
induces technical change (Bloom, Draca, and Van Reenen 2014, Utar 2014). We seek to
make progress on the causal role of trade by using a specific trade policy shock together
with worker-level information on shifts between occupations and sectors.
Our main finding is that international trade plays a statistically and economically signif-
icant role in explaining job polarization. Imports from China reduced the employment of
a machine operator, a typical mid-level wage textile worker, by about eight months over
the period 2002 to 2009. Trade with China also increased employment of machine oper-
ators in both high- and low-wage jobs by one month and four months respectively. Trade
can explain the U-shaped employment pattern in Figure 1. While this contrasts with
earlier work on the causes of job polarization, it is in line with recent work documenting
a powerful effect of the China trade for labor market outcomes in rich countries.9 In
particular, our finding reconciles worker-level evidence on the sizable effect of import
competition from China (Autor, Dorn, Hanson, and Song 2014, Utar 2015) with the
scant evidence for trade causing job polarization in studies using more aggregate data.10
Trade leads to job polarization mainly by pushing workers from initially abundant mid-
9See Autor, Dorn, and Hanson (2013), Bernard, Jensen, and Schott (2006), Pierce and Schott (2013),Utar (2014); also Utar and Torres-Ruiz (2013), and Hummels, Jorgenson, Munch, and Xiang (2014).
10Autor, Dorn, and Hanson (2014) present evidence that technology and trade both affect outcomesat the level of US local labor markets; their study does not focus on job polarization.
6
level jobs in manufacturing towards both high- and low-wage jobs in services. We eval-
uate the effect of trade alongside technology and offshoring explanations of job polar-
ization, finding that the trade effect is robust and hardly affected quantitatively by
other explanations. Trade affects most strongly manual labor, workers performing both
routine and non-routine tasks.
The paper also provides a number of interesting results on gender differences in the
experience of job polarization. We find that while women experience overall job polar-
ization more than men, job polarization caused by trade affects women less than men.
Key to understanding this, we believe, is that trade has affected the labor market in
a more concentrated way than technical change did. First, trade led to a reduction
of labor demand mostly in manufacturing, whereas technical change has changed labor
demand both in manufacturing and non-manufacturing. Second, while technical change
has gradually changed the workplace in industrialized countries since the 1980s, the im-
pact of trade was felt more suddenly around China’s entry into the WTO. The higher
costs of transitioning for women in the presence of a sudden drop in labor demand may
be due to differences in the availability of re-training possibilities, or because women
have on average a lower labor market attachment than men.
The next section discusses the facts on job polarization in Denmark during the 1990s
and 2000s. Section 3 introduces a number of possible causes of job polarization by
presenting a number of micro facts on individual worker transitions between occupations
in Denmark’s textiles and clothing sectors. Section 4 provides background on Denmark’s
textiles trade as China entered the WTO, and it describes the data that will be employed.
Regression results on the role of trade for job polarization are presented in section 5. In
this section we also consider technical change and offshoring as alternative explanations
of job polarization, and examine the extent to which trade has affected men and women
differently. Section 6 provides a concluding discussion. An number of additional results
are shown in the Supplementary Appendix.
7
2 Job Polarization in Denmark
This section describes the overall patterns of labor demand in Denmark that can be
distinguished over the years 1991 and 2009 to set the stage for our analysis. We then
examine demand shifts between broad sectors before focusing on Denmark’s textiles and
clothing workers in the years 2001 and 2009.
2.1 Major patterns of Denmark’s labor demand since the early
1990s
Over the last decades the nature of labor demand has changed in industrialized countries,
and Denmark is no exception to this. This is made strikingly clear by considering
the decade before and after the year 2000 separately. Figure 2 presents the smoothed
changes in employment shares ranked by mean initial occupational wage for all of Danish
workers outside agriculture, as in Figure 1, except that we distinguish the years 1991 to
2000 from the period 2001 to 2009. During the 1990s, we see that labor demand was
increasing with wages. To the extent that we can think of wage as a proxy for worker
skills, this pattern implies that skilled workers have tended to benefit while unskilled
workers have fallen behind during the 1990s. It has been shown that skill-biased technical
change was the most important driver of this pattern (Acemoglu 2002). While a lower
relative demand for less skilled workers could be important in raising inequality, the
policy implication from the 1990s is clear: if skills can shield a worker from declining
labor demand, investing in skills, particularly through education, is a plausible welfare-
enhancing policy.
The pattern for the years 2000-2009 in Figure 2 shows that Denmark’s workers now
live in a different world. Growth of jobs is increasingly concentrated at the tails of
the occupational wage distribution, while the middle of the distribution is hollowed
out. The employment growth pattern in Denmark is U-shaped in the 2000s, as in
8
many industrialized countries. We are interested in better understanding the reasons for
this, and in particular, the role played by international trade. That would also provide
relevant information for policy makers, at least with trade as an additional dimension
that should be considered in the formulation of labor market policies.11-.
4-.
20
.2.4
0 20 40 60 80 100Wage Percentile (Ranked by 1991 Occupational Mean Wage)
1991-2000 2000-2009
100*
Cha
nge
in E
mpl
oym
ent S
hare
Smoothed Changes in Employment by Wage Percentile
Figure 2: Smoothed Changes in Employment in Denmark by Occupational Wage Per-centile
2.2 Shifts between manufacturing and services, 2000-2009
Given this U-shaped pattern that is the hallmark of job polarization, we follow the
literature and distill the pattern into changes for three separate groups, called low-,
mid-level, and high-wage workers (Autor 2010, Goos, Manning, and Salomons 2014).
The groups are formed based on the median wage paid in an occupation in an initial
11While this paper does not focus on policy, we touch on some of the issues in the conclusions. Autor(2010) discusses economic policy issues in the light of job polarization in the U.S.
9
year.12 The high wage occupations comprise of managerial, professional, and technical
occupations. Mid-level wage occupations are clerks, craft and related trade workers,
and plant and machine operators and assemblers. Finally, low-wage occupations include
service workers, shop and market sales workers, as well as elementary occupations.13
These wage categories capture also broad differences in terms of skills across workers,
although each wage group encompasses workers in quite different occupations performing
rather diverse sets of tasks.
-.2
-.1
0.1
.2
Low-Wage Mid-Wage High-Wage
Changes in Occupational Employment Share, 2000-2009
Manufacturing Workers in 1999
Service Sector Workers in 1999
Figure 3: Changes in employment shares of low-, mid-, and high wage occupations byworkers’ initial industry, 2000-2009
Figure 3 analyzes employment growth for these three wage groups in Denmark’s man-
ufacturing and services sector during the years 2000 to 2009 (at the worker-level).14
12We rank major ISCO occupations using the median log hourly wage in 1991 across full-time workers.Table A-3 in the appendix present this ranking. Goos, Manning, Salomons (2014) use the 1993 wagesacross European countries (inclusive of Denmark) and arrive at a similar ranking as ours at the 2-digitISCO occupations.
13We follow the literature and focus on jobs outside of agriculture, however, the results are not affectedby this.
14Figure 3 is constructed using all employees of the manufacturing and service sectors respectively in1999, who were born between 1945 and 1984. The figure shows the change in employment shares acrosshigh-, mid-level, and low wage occupations of these workers between 2000 and 2009.
10
The manufacturing sector employed 440,000 workers in Denmark in 1999, while in the
same year 1,800,000 workers were employed in the service sector. The figure shows that
manufacturing is much more strongly characterized by job polarization than services. In
fact, employment growth in services is monotonically increasing with the wage, which is
the pattern of the 1990s. As we will show below, trade does not only help to explain the
U-shaped pattern in manufacturing but also the fact that employment growth in services
is monotonically increasing across wage groups. This result confirms other findings that
the growth of service jobs is to some extent the flip side of the hollowing out of mid-level
wage jobs in manufacturing (Autor and Dorn 2013, Utar 2015).
It is useful to keep in mind that our analysis tracks workers and their occupation, not
their industry. When Figure 3 shows that manufacturing workers experience reductions
in mid-level wage employment and increases in low-wage employment, in principle this
could be because workers shift within manufacturing from mid-level to low-level jobs
(within-industry shift), or because workers shift from mid-level occupations in manufac-
turing to low-wage occupations in other industries (between-industry shift). If employ-
ment shares were constant over time across industries, observing employment polariza-
tion in a particular industry such as textiles and clothing would imply that mid-level
workers such as assemblers move in roughly equal parts into high wage and low wage
jobs. While this is in principle possible, without between-industry shifts the potential
for job polarization would necessarily seem to be limited: it is difficult to imagine that
many machine operators and assemblers become professionals or managers while others
become laborers, all in the same industry.
Far from constant employment, however, the Danish textiles and clothing industry has
been undergoing massive changes in terms of size: in the year 1999, it employed 13,000
workers, down from 25,000 in 1991, while by the end of 2010 industry employment was
down to 4,500 workers.15 Part of this reduced demand for workers producing textiles
in Denmark after 1999 is due to the removal of import quotas for China (Utar 2014),
15In the year 1999, textiles and clothing accounted for 3 % of total manufacturing employment.
11
-.3
-.2
-.1
0.1
.2
Low-Wage Mid-Wage High-Wage
Changes in Occupational Employment Share, 2000-2009
T&C Workers in 1999
Manufacturing Workers in 1999
Figure 4: Changes in employment shares of low-, mid-, and high wage occupations byworkers’ initial industry, 2000-2009
although other factors, including a secular declining trend of the labor-intensive indus-
tries and technological factors, are surely present as well. Figure 4 shows the patterns
of job polarization among textiles and clothing and manufacturing workers as a whole.
We see that the job polarization pattern for textiles and clothing is stronger than for
manufacturing as a whole. Thus, job polarization in textiles and clothing accounts
over-proportionally for job polarization in manufacturing.16
In the following section we move from the aggregate to the micro level by providing
some initial evidence on job polarization in Denmark based on movements between jobs
by individual workers.
16It is important to distinguish the impact of trade on job polarization at the firm- versus worker-level. Figure 1 in the Supplementary Appendix show occupational employment share changes withinthe textile and clothing sector. In that figure, rather than following workers and their occupations, wefollow employment within the T&C sector. Along the lines of Utar (2014), occupational employmentshare changes exhibit skill upgrading within the sector, more so among the firms that were exposed toimports coming from China.
12
3 Facts from individual worker job transitions
In this section we present several facts about worker transitions between occupations
in Denmark during the sample period by looking at two specific two-digit occupations,
drivers and mobile plant operators (ISCO 83) versus machine operators and assemblers
(ISCO 82).
Fact 1: There is substantial variation in worker outcomes at broadly similar wage levels
but different occupations.
2001 2002 2003 2004 2005 2006 2007 2008 200920
30
40
50
60
70
80
90
100
Year
Perc
ent
Drivers (ISCO 83)Machine Operators and Assemblers (ISCO 82)
Figure 5: RBTC: Probability of Staying for Machine Operators versus Drivers
Because both drivers and machine operators are among mid-level wage categories (see
Table A-3), reduced employment in either of these occupations may help to explain
the overall finding of job polarization. Nevertheless workers in these two occupations
experienced differences in their job market experience over the sample period. Figure
5 shows the cumulative probability for a worker to stay in her initial occupation over
13
2001 2002 2003 2004 2005 2006 2007 2008 20090
1
2
3
4
5
6
7
8
9
10
Year
Perc
ent
Drivers (ISCO 83)Machine Operators and Assemblers (ISCO 82)
Figure 6: The Rise of Service Worker Employment: Machine Operators versus Drivers
the period 2001 to 2009. First, note that there is a considerable amount of movement
of these workers. By the year 2009, around two thirds of the workers do not work
anymore in the occupation that they worked in the year 2001. This is to some extent
the consequence of the relative ease of switching jobs (low hiring costs, low firing costs)
in the Danish labor market. Second, over these eight years there are marked differences
in the probability of staying in one’s initial occupation: in particular, drivers stay in
their initial occupation with a 15 % higher probability than do machine operators and
assemblers.
In the textile and clothing sector, operators would attend fibre-preparing-, spinning-, and
winding machines. They would also operate weaving, knitting, sewing, bleaching, and
dying machines. Given the relatively narrow and programmable nature of their tasks it is
plausible that the tasks of these operators and assemblers are prone to being automated
14
and done by robots. This is reflected in a relatively high ”routine task index” (RTI),
which is 0.64 for this occupation.17,18 In contrast, drivers in the textile and clothing
sector, who would drive cars, vans, trucks, as well as forklifts, perform generally less
routine tasks. A driver of a truck takes his load to different destinations, diggers enter
new terrain, or the personal driver of an executive makes new runs. The occupation
of drivers has a relatively low routine task intensity (RTI of -0.99). Our finding that
machine operators and assemblers are less likely to stay in their initial occupation than
drivers points to the importance of routine-biased technical change (RBTC).
Fact 2: Shrinking mid-level wage occupations are a source of growth of low-wage occu-
pations, especially in services.
Figure 6 shows the cumulative probabilities of machine operators and drivers to transi-
tion into personal and protective service occupations (ISCO 51). These service occupa-
tions include protective service workers as well as housekeepers, cooks, hairdressers, and
travel guides. Service occupations require generally relatively few skills and wages are
typically low. At the same time, service tasks are characterized by a relative low level of
routinization (RTI of -0.23). Figure 6 shows that the cumulative probability that a ma-
chine operator transitions into these service jobs is increasing over time, and by the year
2009 7.5 % of all workers that were machine operators in 2001 have moved into person
and protective service jobs. In contrast, drivers rarely move into these service worker
jobs, and the cumulative probability is hardly increasing over time. This is consistent
with recent evidence that the flip side of lower employment in high routine tasks due to
RBTC is increasing service employment (Autor and Dorn 2013). Figure 6 confirms this
result at the level of individual workers.
Fact 3: Import competition leads to worker transitions consistent with job polarization.
17See Autor, Levy, and Murnane (2003), Autor, and Dorn (2013). Autor (2013) discusses this tasksapproach more broadly.
18We thank Anna Salomons for sharing the RTI measure used in Goos, Manning, and Salomons(2014) with us.
15
2001 2002 2003 2004 2005 2006 2007 2008 200920
30
40
50
60
70
80
90
100
Year
Perc
ent
Machine Operators with Trade ShockMachine Operators without Trade ShockDrivers with Trade ShockDrivers without Trade Shock
Figure 7: Import Competition and Job Polarization
In Figure 7 we show cumulative transitions of drivers (ISCO 83) and machine operators
and assemblers (ISCO 82) out of their occupation depending on whether they were
exposed to increased competition from Chinese producers, or not. We see that both
drivers and machine operators are less likely to stay in their initial occupation in the
presence of Chinese import competition.19 Over the entire period of 2001-2009, machine
operators affected by Chinese import competition have a 29 % lower chance of staying in
their initial industry compared to machine operators not affected by import competition.
The corresponding figure for drivers is 22 %.
In the light of our finding that workers highly exposed to RBTC (such as machine
operators) have a 15 % higher chance to switch occupations than workers little exposed
19The transitions of machine operators over time result in a smoother picture mainly because thereare many more of them than drivers. In 2001 there were 2726 machine operators (ISCO 82) as opposedto only 101 drivers.
16
to RBTC (such as drivers), the difference in likelihood of staying associated with trade
of 22-29 % appears to be sizable. In section 5 below we will return to this question with
an approach that holds constant a range of worker and firm characteristics to estimate
the causal effect of trade.
Before we turn to this, the next section introduces our data and describes the policy
change that is employed.
4 Workers facing import competition: the data
4.1 Employee-employer matched data
The main database used in this study is the Integrated Database for Labor Market
Research, IDA, which is comprised of person, establishment, and job files.20 The person
files contain annual information on all persons of age 15-70 residing in Denmark with
a social security number. The establishment files contain annual information on all
establishments with at least one employee in the last week of November of each year.
The job files provide information on all jobs that are active in the last week of November
in each year. IDA data-sets are complemented with the domestic production data-set
(VARES) that covers all manufacturing firms with at least 10 employees, and the annual
longitudinal data-set that matches firms with their employees (FIDA). The data-sets are
from Statistics Denmark.
For each worker we have information on their annual salary, hours worked, industry code
of their primary employment, education level, demographic characteristics including age,
gender and immigration status, and occupation of primary employment. Of particular
interest to us here is the information on workers’ occupations. Occupational codes matter
in Denmark because they influence a worker’s wage due to a collective bargaining system.
20See Bunzel (2008) and Timmermans (2010) for more information.
17
Employers and labor unions pay close attention to occupational codes. This explains the
administrative nature of the data. As a consequence, the quality of occupational data
in Denmark is high compared to other countries.21 Information on workers’ occupation
closely follows the international ISCO-88 classification. For most workers, occupational
information is given at a detailed 4-digit level.
There were close to 13,000 workers in Denmark’s textile and clothing industry in the
year 1999. In our analysis we focus on workers who are in working age (17 to 67 years)
throughout our sample period. So there are around 11,000 workers in our sample (see
Table 1, Panel A for summary statistics). The textile and clothing industry is a typical
manufacturing industry in which plant and machinery operator tasks (ISCO occupation
code 8) play a large role; according to Figure A-1, they account for more than 40 % of
all workers. Nevertheless the textile and clothing industry employs workers performing
a diverse set of tasks. Technicians and associate professionals (ISCO 3), craft workers
(ISCO 7), but also clerks (ISCO 4) account each for around 10 % of the textile and
clothing workforce, whereas managers (ISCO 1) make up about 5 % (Figure A-1). Table
A-1 presents worker characteristics for several occupation groups separately. We see that
a majority of professionals (ISCO 2) in our sample is college educated (61 %) whereas,
for example, college educated workers only constitute 6 % among craft workers (ISCO
7).
Workers are on average 39 years old, indicating roughly the middle of the career span.
The share of female workers is 57 %, and 6 % are immigrants (Table 1). The share
of women is highest among clerks (78 %) and lowest among managers (21 %). Eleven
percent of workers are college educated, and 35 % of workers have formal vocational
training. In Denmark vocational education is provided by the technical high schools
(after 9 years of mandatory schooling) and involves several years of formalized training
including both schooling and apprenticeship. Roughly half of the workers have at most
high school education. These figures show that our sample includes workers from a range
21See, for example, Groes, Kircher, and Manovskii (2015) on this point.
18
of skill levels, important for our analysis of the effects of globalization on manufacturing
workers in advanced countries. Compared to the economy as a whole these workers
belong typically to the mid-level wage group. In Table 1 we report the breakdown of
workers into a three-way wage classification of occupations. As reported in Table 1, 62
% of our workers held mid-level wage jobs in the year 1999.
In the empirical analysis we also employ a number of additional variables to control for
both worker and firm characteristics. They include worker experience, measured by the
number of years each worker was in the labor force between 1980 and 1999, and the
worker’s unemployment history between 1980 and 1999 (see Table 1). Also, note that
80 % of workers are labor union members, and close to 90 % are members in Denmark’s
unemployment insurance program. The high degree of unionization is one indication
that Denmark’s labor market institutions are quite different from those in the United
States, for example, although it is worth noting that this does not make them necessarily
more rigid. In particular, Denmark ranked 6th among 148 countries in terms of its hiring
and firing practices, more de-regulated than the United States which came in 9th in the
same ranking (Global Competitiveness Report 2014). Relatively unrestricted hiring and
firing in Denmark is combined with active labor market policies and a generous social
safety net to yield a system that has been termed “flexicurity”. While the comparatively
limited wage flexibility in Denmark leads us to focus mostly on job polarization in terms
of employment, we will show some results on job polarization in terms of earnings as
well.
The Danish production database is used to identify firms with domestic production in
one or more of the goods that were subject to the MFA quotas for China. We identify
firms that in 1999 produce 8-digit Combined Nomenclature (CN) level goods that are
subject to the MFA quota removal for China, and using a firm identifier we map this
information to worker-level information. In the year 1999, about half of the workers
are exposed to increased import competition in the sense that they are employed at a
firm that will subsequently be affected by quota removals as a consequence of China’s
19
accession to the WTO. Panel (b) of Figure A-1 compares the distribution of occupations
in the exposed firms with that in the non-exposed firms as a sample balance check. We
see that plant and machine operators is the largest occupation in both sets of firms,
accounting for more than 40 % of the workforce in both exposed and non-exposed firms.
The two sets of firms have also a similar share of managers (ISCO 1). There are some
differences, for example exposed firms have a higher share of clerks (ISCO 4) than non-
exposed firms, while non-exposed firms have higher share of craft workers (ISCO 7).
Overall, however, we do not see major differences between the two sets of firms in panel
(b) of Figure A-1.
Given their importance in the textile and clothing industry, we examine the group of
“plant and machine operators” occupations in more detail. Table A-2 reports summary
statistics on workers initially employed at firms that were exposed to import competition,
versus workers that were not exposed to import competition separately. For example,
sewing machine operators in exposed firms in 1999 were 95.7 % women, while this fraction
was 94 % at non-exposed firms. We also see that 4.3 % of weaving and knitting machine
operators were college educated in non-exposed firms, while 4.9 % of these workers
were college educated in exposed firms. The weaving and knitting machine operators at
exposed firms had a labor market experience of about 16.5 years, compared to about
15.7 years in non-exposed firms. To the extent that longer labor market experience gives
valuable skills, and firms do not want to let go relatively skilled workers, this would bias
the analysis against finding an effect of trade on job polarization. The main finding
from comparing the initial workforce in exposed and non-exposed firms though is that
the differences are small and unlikely to explain our findings.
20
Table 1: Descriptive Statistics
Panel A. Characteristics of Workers with Primary Employment in T&C in 1999
Mean SD N
Age 39.174 10.473 10753
Female 0.571 0.495 10753
Immigrant 0.061 0.240 10753
College Educated 0.112 0.316 10753
Vocational School Educated 0.350 0.477 10753
At most High School 0.514 0.500 10753
Years of Experience in the Labor Market 14.500 5.864 10753
Unemployment History Index 127.241 182.741 10753
Log Hourly Wage 4.968 0.348 10753
High Wage Occupation 0.188 0.390 10753
Mid Wage Occupation 0.622 0.485 10753
Low Wage Occupation 0.112 0.315 10753
Union Membership 0.800 0.400 10753
Unemployment Insurance Membership 0.897 0.304 10753
Panel B. Main Outcome Variables
Cumulative Years of Employment in High- and Low Wage Jobs
net of Years of Employment in Mid Wage Jobs (2002-2009)
-0.143 4.938 10753
Cumulative Hours Worked in High- and Low Wage Jobs net of
Hours Worked in Mid Wage Jobs (2002-2009)
-0.062 6.573 10753
Cumulative Salary in High- and Low Wage Jobs net of Salary in
Mid Wage Jobs (2002-2009)
0.175 8.735 10753
Cumulative Years of Employment in High Wage Jobs (2002-2009) 1.379 2.505 10753
Cumulative Years of Employment in Mid Wage Jobs (2002-2009) 2.548 2.847 10753
Cumulative Years of Employment in Low Wage Jobs (2002-2009) 1.025 1.991 10753
Notes: Variables Female, Immigrant, Union Membership, UI Membership, High Wage, Mid Wage and Low
Wage Occupations, College Educated, Vocational School Educated and At most High School are worker-level
indicator variables. The Unemployment History Index is the cumulative sum of the percentage of working
time spent as unemployed within each year since 1980. Data Source: Statistics Denmark.
21
4.2 Import competition through quota removals
The Multi-fibre Arrangement was introduced in 1974 to govern world trade in the textiles
and clothing. Under this agreement a large portion of textile and clothing exports from
low-wage countries to more developed countries was subject to quantitative restrictions
called quotas. The arrangement provided extensive protection for developed country
textile and clothing industry against competition from low-wage country products. The
liberalization of trade in the textile and clothing industry was not agreed until the
conclusion of the Uruguay Round in 1995, when the Agreement on Textiles and Clothing
(ATC) replaced the MFA, and provisions were made for phasing it out in four steps over
a period of 10 years. Quotas were to be eliminated equivalent to 16 percent of 1990
imports at the beginning of 1995 (Phase I), 17 percent at the beginning of 1998 (Phase
II), 18 percent at the beginning of 2002 (Phase III), and the remaining 49 percent at
the beginning of 2005 (Phase IV).
Quotas covered a wide range of both textile and clothing products ranging from bed
linens to synthetic filament yarns to shirts but at the same time coverage within each
broad product category varied, making it important to utilize MFA quotas at a detailed
product-level. For example, “shawls and scarves of silk or silk waste” was part of a
quota restriction for China while “shawls and scarves of wool and fine animal hair” was
not. “brasseries of all types of textile materials” was under quota but not “corselettes of
all types of textile materials”. As one of the smaller members of the EU, the extensive
coverage of quotas was largely exogenous to the Danish industrial structure. Coverage of
these quotas was determined throughout 1960s and 70s and the negotiations of the MFA
were held at the EU level (Spinanger 1999). Since 1993 the quotas were also managed
at the EU level.
China did not become eligible for quota removal until it joined the WTO at the end
of 2001. While there was a considerable amount of uncertainty as to whether China’s
negotiations for WTO membership would succeed, we choose 1999 as the year to de-
22
termine whether a firm had a product in its portfolio that would be subject to a quota
removal to limit any anticipation effects. In January 2002, its quotas on Phase I, II and
III goods were removed immediately. By being a WTO member, China was also allowed
to benefit from the scheduled last phase in January 2005.22 We utilize as our measure of
import competition the abolishment of MFA quotas for China in conjunction with her
accession to the WTO.23
Most of the quotas for China had more than 90 % filling rates. Figure 8 shows the
evolution in textile and clothing import shares of China compared to other developing
countries subject to MFA quotas. Clearly China is the country that stands out in this
figure. Using transaction-level import data, Utar (2014) confirms that the MFA quotas
were binding for China and both the 2002 and the 2005 quota lifting cause a substantial
surge of MFA goods from China in Denmark with associated decline in unit prices of
these goods.24 As a consequence, virtually all workers employed at firms subject to
the quota removals faced increased import competition from China. Employing a two-
stage least squares strategy in which exposure to Chinese quota removal instruments for
import competition generally leads to similar results (see Utar 2014). For this reason,
here we employ exposure to Chinese quota removal as our treatment variable.
22Due to a surge of Chinese imports in the first few months of 2005 at European Union (EU) portsin response to the final phase of the quota removal, the EU retained a few of the quota categories until2008. Since the sample period extends over 2008, those few quotas are also included in the currentanalysis.
23Here we focus on China’s accession to the WTO as a shock because while the ATC provided aschedule for gradual dismantling of MFA quotas already in 1995, removal of MFA quotas for Chinadepended on whether and when it would join the WTO. Furthermore, there is significant overlapbetween firms producing quota products subject to 2002 or 2005 phases, making it difficult to separatethe effects of different phases. Finally, due to a surge of Chinese imports in the first few months of 2005at the EU ports in response to the final phase of the quota removal, the EU retained a few of the quotaPhase IV categories until 2008. The selection of the retained categories were clearly endogenous to theEU-wide industrial structure as it was due to the pressure of the European industrialist.
24Khandelwal, Schott, and Wei (2013) shows that the quota removal for China led to an extraefficiency gain in China due to prior mismanagement of quotas by the Chinese government and thedecline in prices were a result of entry of more efficient Chinese producers into the export market.
23
1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 20090
0.05
0.1
0.15
0.2
0.25
0.3
ChinaArgentinaBrazilHong KongIndiaIndonesiaKoreaMacaoMalaysiaPakistanPeruPhillipinesSingaporeSri LankaThailand
Figure 8: Import shares of China and other developing countries subject to MFA quotasin Danish Textile and Clothing Imports 1999-2009
5 Empirical results
To examine the role of trade for job polarization, we consider the following regression:
JP empi = β0 + β1Tradei + ZW
i + ZFi + εi (1)
The dependent variable JP empi is a measure job polarization at the worker level (subscript
i) over the period from 2002 to 2009. On the right hand side we have the measure of
trade (Tradei), as well as measures of worker (ZWi ) and firm (ZF
i ) characteristics. The
sample consists of all workers who are at the working age throughout 2002-2009 and
had their primary employment in textiles and clothing in the year 1999. Equation (1) is
a cross-sectional, worker-level regression relating job polarization during China’s post-
24
WTO membership period of 2002-2009 to worker-level characteristics in the pre-WTO
year of 1999. In particular, job polarization is defined as
JP empi =
T=2009∑t=2002
{Emphit + Emplit − Empmit }. (2)
In this expression, Empxit is a dummy variable that takes the value of one if worker i in
year t (t = 2002, ..., 2009) has held a job in high-, mid-level, or low-wage occupations,
denoted by x = h,m, l; our classification into these three brackets follows Table A-3.25
The dependent variable JP empi is the number of years that worker i has held primary
employment in a high-wage or low-wage occupation between 2002 and 2009, minus the
number of years that worker i has worked primarily in mid-level wage occupations. By
summing over high- and low-wage employment after netting out mid-level employment,
JP empi conveniently summarizes worker i’s polarization experience. Below we will also
show results that treat the three wage groups separately. The variable Tradei takes the
value of one if in the year 1999 worker i is employed in a firm that domestically manu-
factures a product that with China’s entry into the WTO is subject to the abolishment
of the MFA quotas for China, and zero otherwise.
The vector ZWi includes the following controls for worker i in the year 1999: age, gender,
immigration status, education level, and the logarithm of i’s average hourly wage.26
Further, ZWi includes initial occupation and education controls: indicators for worker
i’s occupation in a high-, mid-level, or low-wage occupation in year 1999, and indicator
variables for at least some college education, vocational education, and at most a (non-
technical) high school degree. We also include information on worker i’s labor market
experience before 1999. First, this is the cumulative sum of the percentage of working
time a worker spent as unemployed within each year since 1980, and the number of
25We also use the European ranking of occupations as provided by Goos, Manning and Salomons(2004). It employs the two-digit International Standard Classification of Occupations 1988 (ISCO-88),and the ranking of occupations as high/mid-level/low is based on the 1993 mean European wage. Ourresults are robust alternatively using European-wide ranking.
26We take the average wage for years 1999 and 2000 to smooth out temporary effects.
25
years of labor market experience before 1999. Finally, ZWi includes indicator variables
for worker i’s union and unemployment insurance membership status in year 1999.
The vector of firm controls ZFi holds constant differences across workers’ workplaces
in the year 1999 that might induce a spurious correlation. It includes the size of the
firm, measured by the number of full-time equivalent employees, firm quality, proxied
by the logarithm of the average hourly wage paid in this firm, as well as a measure of
the strength of the firm-worker bond, which is the percentage of workers that are not
employed in the same firm from year 1998 to year 1999.
In addition to the measure of job polarization based on years of employment, we will
employ an alternative measure that captures changes in the hours worked of each worker.
It is defined as
JP hrsi =
∑T=2009t=2002 {Hourshit +Hourslit −Hoursmit }
Hoursit0(3)
whereHoursxit is defined as the total number of hours worked by worker i in year t in high-
/mid-level/low-wage wage primary occupations. JP hrsi captures the cumulative hours
worked in high-wage jobs plus the hours worked in low- wage jobs, minus hours worked
in mid-level wage jobs throughout 2002-2009 for worker i; the measure is normalized by
Hoursit0 , which is worker i’s hours worked in his or her primary employment in initial
year t0.27 We also present results for an earnings polarization measure, defined as
JPwagei =
∑T=2009t=2002 {Earningshit + Earningslit − Earningsmit }
Earningsit0(4)
where Earningsxit is total labor earnings of worker i from his/her high-, mid-level, or
low-wage primary occupation, x = h,m, l. In the denominator of equation (4) are worker
i’s initial earnings in his or her primary job.
27To reduce measurement errors, both JPhrsi and the variable JPwage
i , defined below, employ theaverage of the respective values for years 1999 and 2000.
26
5.1 Can trade explain the U-shaped job polarization pattern?
The estimate of β1 in equation (1) captures the impact of lifting the import quotas
for China on Danish workers’ movements away from mid-level wage jobs towards high-
and low wage jobs. By focusing on the textiles and clothing sector we rule out that
the results are affected by the secular decline of labor intensive industries in advanced
countries, or the corresponding secular increase in non-manufacturing as these forces
are in effect in both the control and the treatment group.28 In addition to industry we
control for firm characteristics that could have an important influence on the results.
Perhaps most importantly, the worker controls ensure that we compare workers within
fairly narrow cells, making the workers that are exposed to import competition virtually
identical to those that are not exposed to import competition from China. To see which
set of variables matters we include them step by step.
Table 2 presents the results from estimating equation (1) for the dependent variable
JP emp. When only Tradei is included on the right hand side, β1 is estimated at about
1.2, see column 1. The estimate indicates that workers exposed to import competition
from China have more than one calendar year of additional employment in high- and
low- wage jobs net of employment in mid-level wage jobs over the eight years from 2002
to 2009. The shift of employment from mid-level to low and high-wage jobs appears
to be stronger for women than for men (column 2). Non-immigrants and relatively
young workers exhibit the employment polarization more strongly than other workers.
Including these demographic controls has only a small effect on the Tradei estimate.
If workers exposed to import competition from China held primarily mid-level jobs,
our dependent variable would be relatively high for these workers simply because they
disproportionately lose their jobs. To account for this we include indicator variables for
worker i’s occupation in the year 1999 by wage categories (high-, mid-level, and low;
28Figure A-2 shows that 52 % of all workers in the sample who were working in 2009 moved to theService Sector by the end of 2009. Figure A-3 shows this figure separately for exposed and non-exposedworkers. The figure is 60 % and 44 % respectively for exposed and non-exposed workers.
27
see column 3). We find that the indicators for high-wage and low-wage occupations in
1999 come in positive while the mid-level occupation indicator is negative; these results
are plausible as they reflect the influence of the non-switchers on JP empi . Controlling for
initial occupations, we see that the Tradei coefficient is now around 0.8.
We see further that college educated workers are more likely to be experiencing employ-
ment polarization while workers with vocational education less, although these coeffi-
cients are only weakly significant. Employment polarization is increasing in worker’s
wage in 1999 and decreasing with labor market experience (columns 5 and 6). Union
members and workers covered by unemployment insurance experience employment po-
larization less strongly than other workers, presumably because they switch out less of
mid-level employment than other workers (column 7). Overall, beyond the occupation of
a worker initially, controlling for additional worker characteristics has only minor effects
on the Tradei coefficient.
We also add variables at the firm level on the right hand side of equation (1), see
column 8. Firm heterogeneity might affect workers’ accumulation of human capital and
may lead divergent labor market trajectories among workers. We find that polarization
is less likely for workers employed in firms that pay relatively high wages. This may be
both because workers prefer to not leave those firms and because such firms may have
prepared themselves better than other firms for the onset of import competition from
China. Furthermore, there is more polarization for workers at firms that typically have
high separation rates, presumably because the firm-worker bond is less tight than in
other firms. With all worker and firm controls included, the trade effect is estimated at
around 0.8 with a robust t-statistic of about 10 (column 8). This means that on average
exposed workers experience a gross churn of an additional 0.8 years of employment
during 2002-2009, or 0.1 employment years for each year of the sample period.
It is interesting to note that the coefficient estimate for female workers increases as
other worker characteristics are controlled for. In column (7), the coefficient estimate
28
Tab
le2:
Job
Pol
ariz
atio
nan
dT
rade
-Y
ears
ofE
mplo
ym
ent
Dep
.V
ar.
:C
um
ula
tive
Yea
rsof
Em
plo
ym
ent
inH
igh
-an
dL
owW
age
Job
sm
inu
sY
ears
ofE
mp
loym
ent
inM
idW
age
Job
s(2
002-
2009
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)T
rad
e1.1
67*
**
1.09
9***
0.77
8***
0.78
2***
0.79
1***
0.83
7***
0.85
3***
0.81
6***
(0.0
95)
(0.0
96)
(0.0
88)
(0.0
87)
(0.0
87)
(0.0
88)
(0.0
88)
(0.0
89)
Fem
ale
0.34
6***
0.66
8***
0.62
9***
0.78
9***
0.75
8***
0.82
0***
0.70
6***
(0.0
97)
(0.0
87)
(0.0
87)
(0.0
95)
(0.0
95)
(0.0
96)
(0.0
97)
Imm
igra
nt
-0.8
42**
*-0
.199
-0.3
26-0
.286
-0.6
24**
*-0
.505
**-0
.606
***
(0.1
83)
(0.1
69)
(0.1
72)
(0.1
73)
(0.1
81)
(0.1
82)
(0.1
81)
Age
-0.0
32**
*-0
.024
***
-0.0
20**
*-0
.023
***
-0.0
02-0
.003
-0.0
06(0
.004
)(0
.004
)(0
.004
)(0
.004
)(0
.005
)(0
.005
)(0
.005
)H
igh
-Wage
Occ
3.17
9***
2.85
6***
2.65
0***
2.71
9***
2.85
6***
2.87
3***
(0.1
72)
(0.1
78)
(0.1
88)
(0.1
89)
(0.1
88)
(0.1
88)
Mid
-Wag
eO
cc-2
.102
***
-2.0
88**
*-2
.134
***
-2.0
25**
*-1
.757
***
-1.6
59**
*(0
.154
)(0
.153
)(0
.154
)(0
.156
)(0
.161
)(0
.162
)L
ow-W
age
Occ
1.24
0***
1.29
0***
1.28
8***
1.34
8***
1.56
0***
1.66
3***
(0.1
94)
(0.1
94)
(0.1
93)
(0.1
95)
(0.1
97)
(0.1
99)
Col
lege
0.62
1*0.
566
0.53
60.
580*
0.57
9*(0
.294
)(0
.293
)(0
.292
)(0
.292
)(0
.291
)V
oca
tion
al
-0.6
51*
-0.6
45*
-0.5
69*
-0.5
25-0
.589
*(0
.272
)(0
.271
)(0
.271
)(0
.271
)(0
.270
)at
most
Hig
hS
chool
-0.4
66-0
.421
-0.3
62-0
.372
-0.4
43(0
.270
)(0
.269
)(0
.268
)(0
.268
)(0
.267
)L
og
Hou
rly
Wag
e0.
632*
**0.
823*
**0.
908*
**1.
283*
**(0
.140
)(0
.144
)(0
.144
)(0
.154
)E
xp
erie
nce
-0.0
62**
*-0
.047
***
-0.0
41**
*(0
.011
)(0
.011
)(0
.011
)U
nem
plo
ym
ent
His
tory
0.00
0*0.
001*
*0.
000*
(0.0
00)
(0.0
00)
(0.0
00)
Un
ion
Mem
ber
ship
-0.3
54**
-0.3
11*
(0.1
21)
(0.1
21)
UI
Mem
ber
ship
-0.6
20**
*-0
.621
***
(0.1
59)
(0.1
59)
Fir
mW
age
-1.8
68**
*(0
.289
)F
irm
Siz
e0.
000
(0.0
00)
Sep
ara
tion
Rate
0.01
5***
(0.0
02)
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvati
on
sis
10753.
Aco
nst
ant
isin
clu
ded
bu
tn
ot
rep
ort
ed.
All
contr
ol
vari
ab
les
are
bel
on
gto
the
init
ial
sam
ple
per
iod
and
par
tof
ZW i
orZ
F i.
Rob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
at
the
10
%,
5%
an
d1%
leve
lsre
spec
tive
ly.
Dat
aS
ourc
e:S
tati
stic
sD
enm
ark
.
29
for the Female indicator is 0.8 and significant at the 1 percent level, that is women have
an additional of 0.8 years of employment away from mid-level jobs toward the tails of
the distribution. The major change occurs when we distinguish movers from stayers by
including the initial occupation controls (compare columns 2 and 3). We will return to
this issue in the discussion below. Once firm-level controls are included, the coefficient
decreases a bit to 0.7, this indicates that women were disproportionately employed in
firms that are more prone to job polarization, with lower wages and higher separation
rates.
Table 3 presents analogous results for our cumulative hours worked measure, JP hrsi .
While the findings are generally similar to those with years of employment, the tendency
towards job polarization is somewhat stronger for hours than for years of employment
(the Tradei coefficient β1 is estimated at 0.87 with hours (column 8), compared to 0.82
with years of employment). This indicates that reducing mid-level worker hours, even
if they continue to be employed, is another channel through which import competition
generates job polarization. But the magnitude differences implies that this channel is
relatively unimportant in Denmark.29
We find also a significant effect from import competition on cumulative earnings polar-
ization, see Table 4. The estimate of 0.96 (column 8) implies that the average exposed
worker sees a shift in earnings away from mid-level and towards high- and low-wage earn-
ings that is roughly equal to the worker’s annual earnings before the fall of the import
quotas for China. The coefficient estimate implies an annualized shift of roughly 12 % of
one year’s earnings over the period of 2002 to 2009. This is quite similar to the average
annual effect measured in hours worked, 11 % of initial annul hours worked as well as
the effect measured in years worked, 10 % of a year. Import competition has a sizable
impact on (cumulative) earnings polarization because workers move across occupations.
29Utar (2015) notes that the main channel through which the import shock is felt at the initialemployer is a reduction in tenure at the initial employer, rather than reduction in hours worked or thehourly wage.
30
Tab
le3:
Job
Pol
ariz
atio
nan
dT
rade-
Hou
rsW
orke
d
Dep
.V
ar.
:C
um
ula
tive
Hou
rsW
ork
edin
Hig
h-
and
Low
Wag
eJob
sm
inu
sH
ours
Wor
ked
inM
idW
age
Job
s(2
002-
2009
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)T
rad
e1.1
80*
**
1.09
0***
0.77
6***
0.78
1***
0.78
9***
0.86
1***
0.88
2***
0.86
7***
(0.1
26)
(0.1
28)
(0.1
21)
(0.1
20)
(0.1
20)
(0.1
22)
(0.1
22)
(0.1
24)
Dem
ogra
ph
icC
ontr
ols
yes
yes
yes
yes
yes
yes
yes
Init
ial
Occ
up
atio
ns
yes
yes
yes
yes
yes
yes
Ed
uca
tion
Contr
ols
yes
yes
yes
yes
yes
Init
ial
Wag
eye
sye
sye
sye
sL
ab
orM
arke
tH
isto
ryye
sye
sye
sU
nio
nan
dU
IC
ontr
ols
yes
yes
Init
ial
Fir
mC
ontr
ols
yes
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvat
ion
sis
10753.
Aco
nst
ant
isin
clu
ded
bu
tn
ot
rep
ort
ed.
Dem
ogra
ph
icco
ntr
ols
are
age,
gen
der
du
mm
y,an
dim
mig
rati
onst
atu
sd
um
my.
Ed
uca
tion
contr
ols
are
du
mm
yva
riab
les
ind
icati
ng
wh
eth
eran
ind
ivid
ual
has
at
most
hig
hsc
hool
deg
ree,
voca
tion
al
trai
nin
g(a
fter
hig
hsc
hool
)or
coll
ege
an
dab
ove
deg
ree
in1999.
Lab
or
Mark
etH
isto
ryva
riab
les
are
un
emp
loym
ent
his
tory
an
dex
per
ien
ceva
riab
les.
Un
emp
loym
ent
his
tory
isth
enu
mb
erof
year
sb
etw
een
1980-1
999
that
the
ind
ivid
ual
spen
tas
an
un
emp
loye
dp
erso
n.
Exp
erie
nce
isth
enum
ber
of
years
per
son
was
inth
ela
bor
mar
ket
bet
wee
n19
80-1
999.
Init
ial
wage
isth
elo
gari
thm
of
the
aver
age
hou
rly
wage
of
an
ind
ivid
ual
(fro
mh
is/h
erp
rim
ary
occ
up
atio
nin
T&
C)
in19
99an
d20
00.
Init
ial
Occ
up
ati
on
sare
ind
icato
rva
riab
les
wh
eth
eran
ind
ivid
ual
was
emp
loyed
in1999
hav
ing
ah
igh
-wage,
mid
-wag
eor
low
-wag
eocc
up
atio
n(o
uts
ide
cate
gory
isu
nsp
ecifi
edocc
up
ati
on
s).
Un
ion
an
dU
IC
ontr
ols
are
Un
ion
mem
ber
ship
an
dU
Im
emb
ersh
ipva
riab
les.
Un
ion
mem
ber
ship
isa
du
mm
yva
riab
lein
dic
ati
ng
ifth
ein
div
idual
isa
mem
ber
of
ala
bor
un
ion
.U
Im
emb
ersh
ipis
ad
um
my
vari
ab
lein
dic
atin
gif
the
ind
ivid
ual
isa
mem
ber
ofth
e(v
olu
nta
ry)
Un
emp
loym
ent
Insu
ran
ceF
un
d.
Init
ial
work
pla
ceco
ntr
ols
are
the
logari
thm
of
the
aver
age
hou
rly
wag
ein
the
wor
kp
lace
in19
99,
the
nu
mb
erof
full-t
ime
equ
ivale
nt
nu
mb
erof
emp
loye
esin
1999,
an
dth
ese
para
tion
rate
in1999
(per
centa
ge
of
emp
loye
esth
atle
ftth
ew
orkp
lace
sin
ceth
ep
revio
us
year
(1998))
.R
ob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
atth
e10
%,
5%an
d1%
level
sre
spec
tive
ly.
Data
Sou
rce:
Sta
tist
ics
Den
mark
.
31
Tab
le4:
Job
Pol
ariz
atio
nan
dT
rade
-E
arnin
gs
Dep
.V
ar.
:C
um
ula
tive
Pri
mary
Ear
nin
gsin
Hig
h-
and
Low
Wag
eJob
sm
inu
sP
rim
ary
Ear
nin
gsin
Mid
Wag
eJob
s(2
002-
2009
)(1
)(2
)(3
)(4
)(5
)(6
)(7
)(8
)T
rad
e1.3
06*
**
1.20
0***
0.89
1***
0.89
6***
0.87
7***
0.94
8***
0.98
1***
0.95
5***
(0.1
68)
(0.1
71)
(0.1
63)
(0.1
62)
(0.1
64)
(0.1
69)
(0.1
69)
(0.1
71)
Dem
ogra
ph
icC
ontr
ols
yes
yes
yes
yes
yes
yes
yes
Init
ial
Occ
up
atio
ns
yes
yes
yes
yes
yes
yes
Ed
uca
tion
Contr
ols
yes
yes
yes
yes
yes
Init
ial
Wag
eye
sye
sye
sye
sL
ab
orM
arke
tH
isto
ryye
sye
sye
sU
nio
nan
dU
IC
ontr
ols
yes
yes
Init
ial
Fir
mC
ontr
ols
yes
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvat
ion
sis
10753.
Aco
nst
ant
isin
clu
ded
bu
tn
ot
rep
ort
ed.
Dem
ogra
ph
icco
ntr
ols
are
age,
gen
der
du
mm
y,an
dim
mig
rati
onst
atu
sd
um
my.
Ed
uca
tion
contr
ols
are
du
mm
yva
riab
les
ind
icati
ng
wh
eth
eran
ind
ivid
ual
has
at
most
hig
hsc
hool
deg
ree,
voca
tion
al
trai
nin
g(a
fter
hig
hsc
hool
)or
coll
ege
an
dab
ove
deg
ree
in1999.
Lab
or
Mark
etH
isto
ryva
riab
les
are
un
emp
loym
ent
his
tory
an
dex
per
ien
ceva
riab
les.
Un
emp
loym
ent
his
tory
isth
enu
mb
erof
year
sb
etw
een
1980-1
999
that
the
ind
ivid
ual
spen
tas
an
un
emp
loye
dp
erso
n.
Exp
erie
nce
isth
enum
ber
of
years
per
son
was
inth
ela
bor
mar
ket
bet
wee
n19
80-1
999.
Init
ial
wage
isth
elo
gari
thm
of
the
aver
age
hou
rly
wage
of
an
ind
ivid
ual
(fro
mh
is/h
erp
rim
ary
occ
up
atio
nin
T&
C)
in19
99an
d20
00.
Init
ial
Occ
up
ati
on
sare
ind
icato
rva
riab
les
wh
eth
eran
ind
ivid
ual
was
emp
loyed
in1999
hav
ing
ah
igh
-wage,
mid
-wag
eor
low
-wag
eocc
up
atio
n(o
uts
ide
cate
gory
isu
nsp
ecifi
edocc
up
ati
on
s).
Un
ion
an
dU
IC
ontr
ols
are
Un
ion
mem
ber
ship
an
dU
Im
emb
ersh
ipva
riab
les.
Un
ion
mem
ber
ship
isa
du
mm
yva
riab
lein
dic
ati
ng
ifth
ein
div
idual
isa
mem
ber
of
ala
bor
un
ion
.U
Im
emb
ersh
ipis
ad
um
my
vari
ab
lein
dic
atin
gif
the
ind
ivid
ual
isa
mem
ber
ofth
e(v
olu
nta
ry)
Un
emp
loym
ent
Insu
ran
ceF
un
d.
Init
ial
work
pla
ceco
ntr
ols
are
the
logari
thm
of
the
aver
age
hou
rly
wag
ein
the
wor
kp
lace
in19
99,
the
nu
mb
erof
full-t
ime
equ
ivale
nt
nu
mb
erof
emp
loye
esin
1999,
an
dth
ese
para
tion
rate
in1999
(per
centa
ge
of
emp
loye
esth
atle
ftth
ew
orkp
lace
sin
ceth
ep
revio
us
year
(1998))
.R
ob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
atth
e10
%,
5%an
d1%
level
sre
spec
tive
ly.
Data
Sou
rce:
Sta
tist
ics
Den
mark
.
32
5.2 Trade and workers’ shift across occupations with different
wage levels
While the previous section has shown that trade can explain the U-shaped pattern of
job polarization, we have so far employed a summary measure of polarization, additions
to the tails minus reductions in the middle of the wage distribution. In order to see what
the relative contribution of tails versus middle of the distribution is, and whether there is
indeed evidence that the hollowing out of the middle is the flip side of gains in the tails,
we now turn to analyzing the high-, mid-level, and low-wage occupations separately.
The regression specification is analogous to equation (1), except that the dependent
variables are summing the years of employment for each occupation category x = h,m, l
separately. For example, JP h,empi is the number of years worker i was employed in high-
wage occupations during the years 2002 to 2009, and analogously JPm,empi and JP l,emp
i
for mid-level and low-wage employment.
Table 5 presents these results. We see that workers exposed to import competition from
China spend about 0.2 years more on average in high-wage jobs, compared to otherwise
similar workers that are not exposed to import competition from China. For mid-level
employment, the coefficient on Tradei is about −0.4, while for low-level employment the
coefficient is again about 0.2 (columns 2 and 3, respectively). Trade has hollowed out the
middle of the wage distribution, and it has added jobs both at the top and the bottom of
the distribution. Furthermore, the sum of the Tradei coefficients across the occupation
categories is approximately equal to zero, and it is likely that the fatter tails and the
hollowed out middle are the flip sides of the same phenomenon. Additional analysis
shows that hours worked losses in mid-level wage jobs are only to 2/3 compensated by
gains in high-wage and low-wage hours, and high-wage hour gains are more important
than low-wage gains. Results for earnings polarization are similar (results are available
33
Tab
le5:
Job
Pol
ariz
atio
nan
dIm
por
tC
omp
etit
ion
-Y
ears
ofE
mplo
ym
ent
inH
igh,
Mid
,an
dL
owW
age
Job
s
JP
h,emp
iJP
m,emp
iJP
l,em
pi
JP
h,emp
iJP
m,emp
iJP
l,em
pi
(1)
(2)
(3)
(4)
(5)
(6)
Tra
de
0.22
9***
-0.3
94**
*0.
193*
**0.
216*
**-0
.382
***
0.17
8***
(0.0
40)
(0.0
52)
(0.0
38)
(0.0
40)
(0.0
52)
(0.0
38)
Fem
ale
0.19
9***
-0.3
35**
*0.
171*
**0.
032
-0.3
71**
*0.
292*
**(0
.045
)(0
.057
)(0
.041
)(0
.046
)(0
.060
)(0
.043
)2-
Dig
itIS
CO
Du
mm
ies
no
no
no
yes
yes
yes
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvat
ion
sis
10753.
Alt
hou
gh
on
lyco
effici
ent
esti
mate
sof
‘Fem
ale
’co
ntr
ol
isre
port
ed,
all
regre
ssio
ns
incl
ud
ea
con
stan
tan
dth
efu
ll-s
etof
contr
ols
,Z
W ian
dZ
F i.
Inco
lum
ns
(4)-
(6)
init
ial
occ
upati
on
sare
contr
oll
edfo
ru
sin
g2-d
igit
ISC
Oocc
up
atio
nd
um
mie
s.R
obu
stst
and
ard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
at
the
10
%,
5%
an
d1%
level
sre
spec
tive
ly.
Dat
aSou
rce:
Sta
tist
ics
Den
mar
k.
34
upon request).30
We have also confirmed the robustness of the results by employing a logarithmic trans-
formation of the dependent variable, as well as the revenue share of MFA goods instead
of the Tradei indicator variable. See Tables A-4 and A-5 in the appendix.31
5.2.1 Worker’s susceptibility to job polarization
Occupation While the set of regressors includes indicator variables for each worker’s
initial occupation in the broad wage distribution (high, mid-level, or low-wage occupa-
tion), there are differences across occupations within broader occupation groups in how
susceptible workers are to job polarization; we have seen evidence of that in the case of
machine operators versus drivers in section 2 above. If this variation would be corre-
lated with exposure to import competition our results might be spurious, even though
Figure A-1 shows that the distribution of broad occupational categories for workers at
trade exposed and non-exposed firms was quite similar. We address these concerns by
adding indicator variables for worker i’s employment in each of the 22 two-digit occupa-
tional codes. The results for high-, mid-level, and low-wage employment job polarization
regressions turn out to be not strongly affected by the inclusion of the two-digit occupa-
tional dummies. The trade coefficients for high-, mid-level, and low-wage employment
remain roughly at 0.2, −0.4, and 0.2, respectively (columns 4, 5, and 6 of Table 5). From
this it does not appear to be the case that the trade effect on job polarization is driven
30Our findings here are in line with Utar’s (2015) analysis of worker’s adjustment costs. She showsthat workers who move down the ladder of occupations to ’low-end’ service sector jobs face significantadjustment costs in the form of difficulties in keeping these jobs. These workers suffer from frequentunemployment disruptions while their primary attachment to the labor market continued to be these’low-end’ jobs.
31 In Table A-4 we provide the results from transforming the dependent variables into logarithmicscale (after adding an arbitrary amount to make the minimum value one) and the results are robust.We also employ the manufacturing revenue share of goods that were subject to the removal of quotasfor China in 1999 in worker i’s initial employer as an alternative trade exposure variable. See Utar
(2014, 2015) on these modifications. The revenue share trade variable, T radei , takes into account thedegree in which the initial firms were exposed to the competition. We find that our results are robust(see Table A-5).
35
by differences in the susceptibility of occupations for polarization.
In addition, we have examined a number of major occupations in more detail by creating
interaction variables, such as Tradei ×Managersi, where Managersi is equal to one if
worker i worked as a manager in year 1999, and zero otherwise. Other occupations for
which we construct interaction variables are professionals, associate professionals and
technicians, clerks, and machine operators. These interaction variables are introduced
together with the Tradei variable.
We find that plant and machine operators (ISCO 8) constitute the group that is most
strongly behind the result that trade causes job polarization (Table A-6, Panel F). We
also present the analysis separately only among machine operators (ISCO 82) (see Table
A-7) and find that trade leads to a loss of eight months of mid-wage employment, and
increases of low-wage and high-wage employment of about four months and one month
respectively over a period of eight years. Outside of machine operator occupations, trade
does in fact not cause a significant hollowing out of employment in mid-level wage jobs.
Managers, associate professionals and technicians, and clerks hardly contribute to our
finding that trade causes job polarization (Panels A, C, and D). Interestingly, profes-
sionals significantly gain mid-level employment through trade, which is the opposite of
the experience of machine operators (Panel B). If job switching triggered by trade lets
professionals to disproportionately stay within the same sector as their tasks are rela-
tively demanded compared to other occupations (see Utar 2014, 2015 as well as Figure
1 in the Supplementary Appendix), this combined with the fact that manufacturing
sector is relatively rich in mid-wage jobs compared to service sectors may explain the
difference. This provides initial evidence for significant differences in the impact of trade
across different occupations. We will return to this in more detail when discussing the
relationship between trade and task characteristics in contributing to job polarization
in section 5.4 below.
36
Education We are also interested in how workers with different levels of education
fare in the face of import competition from China. We employ another set of interaction
variables of the form Tradei×Educi, where Educi takes on a value of one if the highest
education level of worker i in 1999 is college, vocational school, and at most high school,
respectively, and zero otherwise. Including the trade-education interaction together
with the Tradei variable, we find that college-educated workers do not contribute to
job polarization caused by trade (Table A-8, Panel A), in fact college-educated workers
at firms exposed to import competition tend to gain, not lose, in terms of mid-level
employment. In contrast, workers with at most high school education bear the brunt
of the job polarization caused by trade (Panel C). This is in line with the general
pattern of job polarization observed in Denmark between 2000-2009 (see Figure 4 in the
Supplementary Appendix).
5.2.2 Job polarization through shifts within versus between industries
In this section we discuss the impact of trade for high-, mid-level, and low-wage employ-
ment across sectors. The results are shown in Table 6. In the upper left corner, recall
that the coefficient on Tradei of 0.229 means that workers employed in 1999 in textiles
and clothing firms exposed to import competition from China have had between 2002
and 2009 0.229 years of employment more in high-wage occupations than workers that
were not employed by exposed textiles and clothing firms. The question addressed in
this section is whether the job polarization impact of trade is associated with worker
movements between broad sectors.
As columns 2 and 3 show, the gain in high-wage employment that trade has caused is en-
tirely due to workers moving into sectors outside of manufacturing (coefficient of 0.283).
There is no significant high-wage employment gain through trade within manufacturing
(coefficient of -0.054).32 On the other hand, the employment loss in mid-level jobs is
32By construction, the coefficient in column 1 is the sum of the coefficients in columns 2 and 3, andanalogously for the other columns of Table 6.
37
entirely due to reductions in mid-level manufacturing jobs (columns 5 and 6). Indeed
trade causes increase in mid-wage jobs in the service sector (as it pushes workers toward
the service sector), yet the net effect is negative. Columns 8 and 9 show that the increase
in low-wage employment is split between manufacturing jobs and non-manufacturing.
Overall, the pattern can be summarized by noting that the mid-wage employment losses
are due to decline in manufacturing jobs while employment gains at the tails require to
leave manufacturing, especially if a worker succeeds in moving to high-wage employment.
This finding shows that frictions to the movement of workers across sectors implies that
international trade can increase welfare for some workers (those with sufficiently low
cost of moving) while at the same time it decreases welfare of other workers (those with
high cost of moving), irrespective of any other effects of trade on welfare.
Because non-manufacturing includes a broad set of diverse activities, in the following we
isolate the services sector, see Panel B of Table 6.33 The results indicate that the em-
ployment changes caused by trade into non-manufacturing are almost entirely accounted
for by shifts into the services sector.34 Specifically, columns 1-3 of Panel B show that 100
% of all high-wage employment gain is due to service sector jobs and columns 8 through
9 shows that 60 % of all low-wage employment gain is due to ’low-paid’ service jobs.
Earlier work has emphasized the service sector in the context of job polarization (Autor
and Dorn 2013). We extend this evidence by showing on the basis of micro evidence
that this pattern is particularly true for job polarization caused by trade.
33The other non-manufacturing sectors are agriculture, fishing, energy and construction.34See also Utar (2015) on this.
38
Tab
le6:
Job
Pol
ariz
atio
nan
dJob
Shif
tsW
ithin
vers
us
Outs
ide
Man
ufa
cturi
ng
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Pan
elA
.
Dep
.V
ar
JP
h,emp
iJP
h,emp
iJP
h,emp
iJP
m,emp
iJP
m,emp
iJP
m,emp
iJP
l,em
pi
JP
l,em
pi
JP
l,em
pi
Wit
hin
Ou
tsid
eW
ith
inO
uts
ide
Wit
hin
Ou
tsid
eM
anu
f.M
anu
f.M
anu
f.M
anu
f.M
anu
f.M
anu
f.T
rad
e0.2
29***
-0.0
54
0.28
3***
-0.3
94**
*-0
.551
***
0.15
7***
0.19
3***
0.07
4**
0.11
9***
(0.0
40)
(0.0
34)
(0.0
30)
(0.0
52)
(0.0
48)
(0.0
29)
(0.0
38)
(0.0
23)
(0.0
32)
Fem
ale
0.1
99***
0.0
490.
150*
**-0
.335
***
-0.3
39**
*0.
004
0.17
1***
-0.1
20**
*0.
291*
**(0
.045
)(0
.035
)(0
.034
)(0
.057
)(0
.053
)(0
.034
)(0
.041
)(0
.025
)(0
.033
)
Pan
elB
.
Dep
.V
ar
JP
h,emp
iJP
h,emp
iJP
h,emp
iJP
m,emp
iJP
m,emp
iJP
m,emp
iJP
l,em
pi
JP
l,em
pi
JP
l,em
pi
Wit
hin
Wit
hin
Wit
hin
Wit
hin
Wit
hin
Wit
hin
Manu
f.S
ervic
eM
anu
f.S
ervic
eM
anu
f.S
ervic
eT
rad
e0.2
29***
-0.0
54
0.27
6***
-0.3
94**
*-0
.551
***
0.15
6***
0.19
3***
0.07
4**
0.11
6***
(0.0
40)
(0.0
34)
(0.0
30)
(0.0
52)
(0.0
48)
(0.0
27)
(0.0
38)
(0.0
23)
(0.0
31)
Fem
ale
0.1
99***
0.0
490.
158*
**-0
.335
***
-0.3
39**
*0.
128*
**0.
171*
**-0
.120
***
0.33
2***
(0.0
45)
(0.0
35)
(0.0
34)
(0.0
57)
(0.0
53)
(0.0
31)
(0.0
41)
(0.0
25)
(0.0
32)
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvat
ions
is10753.
Aco
nst
ant
isin
clu
ded
bu
tn
ot
rep
ort
ed.
Alt
hou
gh
on
lyco
effici
ent
esti
mate
sof
‘Fem
ale
’co
ntr
olis
rep
orte
d,
all
regr
essi
ons
incl
ud
ea
con
stant
an
dth
efu
ll-s
etof
contr
ols
,Z
W ian
dZ
F i.
Rob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗
and
∗∗∗
ind
icat
esi
gnifi
can
ceat
the
10%
,5%
an
d1%
leve
lsre
spec
tivel
y.D
ata
Sou
rce:
Sta
tist
ics
Den
mark
.
39
5.3 Technology and offshoring explanations for job polarization
In existing analyses of the causes of job polarization technology explanations play a
central role. We therefore expect that routine-biased technical change (RBTC) helps to
explain job polarization in our sample. The question is only to what extent, and whether
trade mimics RBTC, in which case after controlling for RBTC the Tradei variable will
lose its significance. We are also interested in the extent to which the import competition
effect is correlated with offshoring. China is a major offshoring destination, especially
after it entered the WTO, and an increase of textiles and clothing goods from China
could be related to offshoring decisions of Danish firms. Alternatively, if trade turns out
to be unrelated to offshoring, it might be more plausible to think of autonomous Chinese
production as the result of technology diffusion to Chinese-owned firms.
We employ measures of RBTC and offshoring that are well established in the literature.
One, the potential for occupational changes due to RBTC is captured by the routine
task intensity (RTI; the variable is denoted as RTIi) introduced in section 2. Two,
the likelihood of polarization due to offshoring, denoted Offshoringi, is captured by a
measure of Goos, Manning, and Salomons (2014).35 Both RBTC and offshoring variables
are defined at the two-digit level of occupations.36 Table 7 presents these results. All
regressions include the full set of worker and firm controls of Table 2.
We see that workers who were exposed to the import shock have a significantly higher
likelihood of moving to jobs that are located at either end of the wage distribution, while
differences in offshorability help to explain job polarization as well (column 1). The table
gives standardized beta coefficients in square brackets, which indicate that trade has a
larger effect on job polarization than offshoring. Given the central role of computers for
technical change in manufacturing recently, we add next a measure of the intensity of the
35We have also considered Blinder and Krueger’s (2013) offshoring measure, finding broadly similarresults.
36Note that RBTC and offshoring measures are taken from Goos, Manning, and Salomons (2014) andthey are at the 2-digit ISCO level.
40
worker’s interaction with computers (ICTi, column 2). Note that this does not simply
measure whether the worker is prone to be replaced by a computer. Rather, the measure
picks up that there are certain aspects of the job that can be codified, explaining the
presence of the computer, but the worker’s task might involve to monitor and evaluate
output from the computer, which is likely a non-routine task. We find no significant
effect for this ICT variable.
However, when we add the routine task index (RTI), not only is there more job polar-
ization for routine tasks, as expected, but interacting with computers now enters with
a negative sign (column 3). This suggests that the routine task index helps to separate
routine from non-routine tasks in our ICT variable. The positive and significant coeffi-
cient on RTI in column 3 confirms earlier results that RBTC is a factor explaining job
polarization in Denmark. We estimate a coefficient for Tradei that is hardly affected by
the inclusion of the offshoring and task variables. In terms of beta coefficients the impact
of trade appears to be substantial, larger than that of technology factors captured by
the RTI variable.
Next, we investigate the relationship between import competition from China and off-
shoring. While offshoring affects primarily intermediate inputs trade and the China
quota removal leads to an inflow of final goods into Denmark (Utar 2014), it is neverthe-
less possible that the two are related. In particular, it could be that certain tasks that
workers used to perform in Denmark are offshored to China, where they are combined
with other tasks before the final textiles and clothing good is exported from China to
Denmark.
We employ an interaction variable between import competition from China and off-
shoring, Tradei × Offshoringi, to see whether there is evidence for a complementary
relationship between offshoring and imports from China. The inclusion of the interac-
tion variable turns the linear offshoring effect to zero, while the size of the linear Tradei
effect shrinks to around 0.3 (column 4). Offshoring and imports from China appear to
41
Tab
le7:
Job
Pol
ariz
atio
n,
Off
shor
ing,
Tec
hnol
ogy,
and
Tra
de
JP
emp
iJP
emp
iJP
emp
iJP
emp
iJP
emp
iJP
emp
iJP
emp
i(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Tra
de
0.8
39**
*0.
842*
**0.
833*
**0.
341*
*0.
712*
**0.
848*
**0.
769*
**(0
.098
)(0
.098
)(0
.098
)(0
.131
)(0
.100
)(0
.114
)(0
.104
)[0
.083
][0
.084
][0
.083
][0
.034
][0
.071
][0
.084
][0
.076
]O
ffsh
orin
g0.1
45**
0.13
8**
0.13
5**
0.00
20.
116*
0.13
5**
(0.0
44)
(0.0
47)
(0.0
47)
(0.0
53)
(0.0
47)
(0.0
47)
[0.0
46]
[0.0
43]
[0.0
43]
[0.0
01]
[0.0
37]
[0.0
43]
Inte
ract
ing
wit
hC
omp
ute
rs(I
CT
)-0
.029
-0.2
26**
-0.2
13*
0.07
3-0
.224
*(0
.069
)(0
.088
)(0
.088
)(0
.098
)(0
.088
)[-
0.00
6][-
0.04
4][-
0.04
1][0
.014
][-
0.04
4]R
outi
ne
Task
Inte
nsi
ty(R
TI)
0.34
8**
0.36
9***
0.38
1***
0.35
9**
(0.1
06)
(0.1
07)
(0.1
07)
(0.1
16)
[0.0
60]
[0.0
64]
[0.0
66]
[0.0
62]
Tra
de*
Off
shori
ng
0.33
5***
(0.0
59)
[0.
087]
Tra
de*
ICT
-0.6
54**
*(0
.100
)[
-0.0
90]
Tra
de*
RT
I-0
.026
(0.1
15)
[-0
.004
]4-
Dig
itIS
CO
Du
mm
ies
no
no
no
no
no
no
yes
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvati
on
sis
8702.
Aco
nst
ant
an
dth
efu
llse
tof
contr
ols
,Z
W ian
dZ
F i,
are
incl
ud
edin
all
regre
ssio
ns
bu
tnot
rep
orte
d.
“Off
shor
ing”
isth
eoff
shor
abil
ity
ind
exof
the
corr
esp
on
din
gtw
od
igit
init
ial
ISC
O-8
8occ
up
ati
on
cod
eof
aw
ork
er.
Itis
con
stru
cted
by
Goos,
Man
nin
gan
dS
alom
ons
(201
4).
“IC
T”
isth
ein
dex
vari
ab
lein
dic
ati
ng
the
deg
ree
inw
hic
hth
ein
itia
l(4
-dig
it)
occ
up
ati
on
of
aw
ork
erin
tera
cts
wit
hco
mp
ute
rs.
Itis
anO
*NE
T(v
ersi
on14
)va
riab
lean
dva
ries
acr
oss
4-d
igit
occ
up
ati
on
s.“R
TI”
isth
ero
uti
ne
inte
nsi
tyin
dex
of
the
corr
esp
on
din
gtw
od
igit
init
ial
ISC
O-8
8occ
up
atio
nco
de
ofa
wor
ker
.T
he
RT
Iin
dex
foll
ows
Au
tor,
Lev
yan
dM
urn
an
e(2
003)
an
dA
uto
ran
dD
orn
(2012).
Off
shori
ng
ind
exis
not
avai
lab
lefo
rce
rtai
nIS
CO
2-d
igit
cate
gori
es(s
eeth
eS
up
ple
men
tary
Ap
pen
dix
)an
dIC
Tin
dex
isn
ot
avail
ab
leacr
oss
cert
ain
4-d
igit
ISC
Os
as
they
are
pro
vid
edby
O*N
ET
.F
inal
ly7
%of
wor
kers
hav
eu
ndet
erm
ined
init
ial
occ
up
ati
on
s.H
ence
run
nin
gth
ees
tim
ati
on
sin
aco
mm
on
sam
ple
dec
rease
sth
enu
mb
erof
obse
rvat
ion
sto
8702
.C
ontr
ol
vari
ab
les
are
des
crib
edin
the
note
sof
Tab
le3.
Sta
nd
ard
ized
coeffi
cien
tsare
rep
ort
edin
squ
are
bra
cket
s.R
obu
stst
and
ard
erro
rsar
ere
por
ted
inp
aren
thes
es.
∗ ,∗∗
an
d∗∗
∗in
dic
ate
sign
ifica
nce
at
the
10
%,
5%
an
d1
%le
vels
resp
ecti
vely
.D
ata
Sou
rce:
Sta
tist
ics
Den
mar
k.
42
be complements, most likely because task offshoring leads to new final goods that are
being shipped from China to compete for customers in Denmark. We also consider an
analogous interaction variable between Tradei and ICTi,. Trade hardly leads to job
polarization through workers whose tasks involve interacting with computers, see col-
umn 5. When instead we include an interaction between Tradei and the broader RTIi
variable, it does not enter significantly (column 6). It appears that the ICTi variable
captures quite well the task characteristics that shield a worker from job polarization.37
Finally, column 7 introduces four-digit occupational indicator variables. By construc-
tion, the occupational dummies fully account for the variation in RTI–our RTI variable
varies at the two-digit ISCO level–, and the trade effect is with 0.77 similar in size to
before.
5.4 Unpacking the trade effect
It is important to go further because as noted above, it has been challenging at times to
distinguish trade from technology causes of job polarization. In this section we look at
the intersection of exposure to import competition and specific tasks from the O*NET
data base at the level of individual workers.38 Our approach will be to interact the 0/1
variable Tradei with a measure of the importance of a particular task in that worker i’s
four-digit occupation class. The regression of cumulative worker employment in 2002 to
2009 then includes three terms, in addition to all worker and firm characteristics from
before. These three variables are the linear trade and task measures, denoted by Tradei
and ONETi, together with their interaction Tradei × ONETi. In Table 8 we show
results for six specific O*NET measures; additional results can be found in Tables 2 to
37As we will see below, workers interacting with computers are less likely to lose mid-level wage jobs,and also less likely shift to low-wage jobs than the average worker (Table 8).
38We use O*NET June 2009 version 14. See the data appendix section in the Supplementary Appendixfor further details.
43
8 of the Supplementary Appendix.39
The six O*NET tasks are representative of routine manual tasks (Panel A), non-routine
manual tasks (Panel B), routine cognitive tasks (Panel C), abstract tasks (Panel D),
information and communication technology-intensive tasks (Panel E), and tasks that
are intensive in face-to-face communication (Panel F). Tasks along these lines have been
emphasized in influential studies of job polarization, and based on this work one would
expect that routine tasks contribute to job polarization (Panes A and C) while non-
routine tasks do not (Panels B and D) (Autor, Levy, Murnane 2003, Autor and Dorn
2013). Tasks that involve intensively interacting with computers would also tend to
be non-routine (the computer itself performs the routine part of the tasks), while face-
to-face communication intensive tasks tend to prevent offshoring and thus reduce job
polarization (Blinder and Krueger 2013, Firpo, Fortin, and Lemieux 2011). We present
results both for the measure that sums the absolute value of employment changes in
high-, mid-level, and low-wage jobs (JP empi ), and for each of these three wage groups
separately.
Starting with the former, we see that workers have been affected quite differently by
trade depending on the specific tasks they perform. On average trade has caused an
employment churn of about 0.8 years in our JP empi measure. The first column of Table 8
shows that the marginal effect of Tradei ranges from essentially zero for routine cognitive
tasks (0.524+(-0.587)= -0.063, see Panel C) to a whopping 1.4 years in the case of non-
routine manual tasks such as those requiring gross body coordination (Panel B). At the
same time, while there is variation in the size of trade’s impact on job polarization, trade
has contributed to job polarization through workers performing most kinds of tasks. We
also see that trade’s effect is particularly high for manual tasks.
In contrast, as expected there are tasks (and associated technologies) that contribute to
39In order to keep the analysis transparent we utilize individual O*NET measures and check therobustness of the results by employing similar O*NET measures classified under the same broad group,rather than creating new composite indices; on this point, see Autor (2013).
44
job polarization (a positive effect on JP empi ) while other tasks are associated with lower
levels of job polarization (a negative effect on JP empi ). The marginal effect of ONETi
ranges from 0.31 (-0.30+0.61=0.31, Panel B for non-routine manual tasks) to -0.42 (for
ICT intensive tasks Panel E). While routine manual tasks are prone to polarization,
we do not find evidence that this is the case for routine cognitive tasks.40 In line with
expectations is the result that tasks that involve interacting with computers and face-
to-face tend to be less associated with job polarization (Panels E and F).
Looking at the three individual wage groups, we see that across different tasks trade
has caused between almost 0.8 and virtually 0 years of employment less in the mid-
level wage occupations (Panels B and E, column 3). While on average trade leads to a
higher employment of 0.2 years for high-wage occupations, workers performing routine
cognitive tasks do not experience that (column 2, Panel C). In contrast, trade has a
particularly strong effect of shifting workers performing manual tasks into low-wage
occupations (column 4). At the same time, workers that perform ICT-intensive tasks
or require face-to-face communication are less likely shifted through trade into low-wage
jobs (column 4, Panels E and F).
Overall, there is evidence that technology factors and trade overlap to some extent in
causing job polarization. Manual tasks, in particular, whether routine or non-routine,
are prone to job polarization both through technology factors and trade. The fact that
Tradei and ONETi variables are often both significant in Table 8 provides evidence
that technology and trade are sufficiently distinct that we can separately identify their
effect on labor demand. It is also useful to contrast our trade and technology measures
with each other. The O*NET measures highlight important aspects of the worker’s
tasks. At the same time, while the variable Repetitive Motions suggests that these tasks
could be replaced by a robot (e.g. an automated machine-driven process controlled by
a computer), it is not direct evidence of robotization, and it is hard to delineate the
40This is confirmed by results for additional O*NET categories, see Tables 2 and 4 in the Supple-mentary Appendix.
45
factors that the variable Repetitive Motions picks up. Our comparison of workers who
suddenly are or are not exposed to new import competition from China makes for a
comparatively easy interpretation of the coefficient on Tradei. Strong identification of
the impact of trade is also suggested by the fact that the Tradei × ONETi interaction
effect is estimated quite precisely in all six panels of Table 8 (column 1).41
We now examine gender differences in the way that trade has caused job polarization in
Denmark.
41Also note that in many cases it is the trade-task interaction that yields what one would generallyexpect as the task measure’s marginal effect on job polarization based on earlier research.
46
Table 8: Tasks, Trade, and Polarization
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. Routine Manual TasksTasks with Repetitive Motions
Trade 0.570*** 0.270*** -0.229*** 0.071(0.117) (0.066) (0.062) (0.043)
Repetitive Motions -0.224* -0.312*** 0.050 0.137***(0.089) (0.041) (0.053) (0.034)
Trade*Repetitive Motions 0.397*** -0.022 -0.274*** 0.145***(0.100) (0.052) (0.055) (0.038)
Panel B. Non-Routine Manual TasksTasks with Gross Body Coordination (GBC)
Trade 0.792*** 0.196*** -0.399*** 0.197***(0.095) (0.043) (0.055) (0.040)
GBC -0.304*** -0.494*** 0.050 0.240***(0.091) (0.047) (0.052) (0.030)
Trade*GBC 0.610*** 0.073 -0.374*** 0.163***(0.114) (0.058) (0.064) (0.040)
Panel C. Routine Cognitive TasksEvaluating Information to Determine Compliance with Standards
Trade 0.524*** 0.123* -0.281*** 0.120**(0.107) (0.054) (0.060) (0.043)
Evaluating 0.289*** 0.019 -0.130** 0.139***(0.081) (0.038) (0.048) (0.031)
Trade*Evaluating -0.587*** -0.152** 0.263*** -0.171***(0.113) (0.056) (0.064) (0.047)
Panel D. Abstract TasksTasks that involves thinking creatively (Thinking)
Trade 0.633*** 0.253*** -0.314*** 0.066(0.113) (0.063) (0.061) (0.045)
Thinking 0.200* 0.123** -0.070 0.008(0.081) (0.038) (0.048) (0.034)
Trade*Thinking -0.280** 0.038 0.137* -0.181***(0.098) (0.054) (0.053) (0.040)
Panel E. ICTTasks that involves interacting with computers (ICT)
Trade 0.669*** 0.179*** -0.330*** 0.160***(0.096) (0.048) (0.054) (0.038)
ICT 0.200* 0.359*** 0.009 -0.150***(0.080) (0.038) (0.047) (0.029)
Trade*ICT -0.641*** -0.014 0.387*** -0.239***(0.097) (0.048) (0.055) (0.037)
Panel F. Face to Face Communication Intensive TasksTasks that involve establishing interpersonal relationships
Trade 0.526*** 0.230*** -0.221*** 0.076(0.113) (0.062) (0.061) (0.041)
Interpersonal 0.201** 0.337*** -0.006 -0.141***(0.072) (0.033) (0.043) (0.026)
Trade*Interpersonal -0.420*** -0.007 0.264*** -0.150***(0.085) (0.043) (0.048) (0.033)
Notes: The number of observations is 8819, 9505, 9887, 8876, 9776, and 8816 in Panels A throughF respectively. All regressions include a constant and the full-set of controls, ZW
i and ZFi . Tasks
measures are from O*NET and vary by workers’ initial 4-digit occupations (ISCO). The availability oftask measures across occupations determine the number of observations. Robust standard errors arereported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.Data Source: Statistics Denmark.
47
5.5 Technology, Trade, Man, Woman
We have shown that women tend to experience job polarization more strongly than men
in Denmark. Controlling for worker and firm characteristics, women experience an ad-
ditional gross churn of 0.7 years of employment away from mid-level and into high- and
low-wage jobs (column 8 of Table 2). This finding is in line with the aggregate pattern
in Denmark during the sample period (see Figure 2 in the Supplementary Appendix)
and it also parallels results for the U.S. (Autor 2010, Figure 4). While job churn can
entail substantial job switching costs, the fact that reductions in mid-level employment
for women are accompanied by substantial gains in high-wage occupations may be con-
sidered evidence that women have more successfully adapted to shifts in demand that
have eroded mid-level wage job opportunities than men (Autor 2010).
At the same time to date we know very little on gender patterns of job polarization at
the level of individual workers. In this section we begin by presenting such evidence for
Denmark. First, is there reason to be more encouraged by the pattern for women than
for men, as is the case in the U.S.? Second, we examine the role of trade for these gender-
specific patterns of polarization. In particular, does import competition allow women
to move up from mid-level to high-level jobs, or does import competition increase the
chance that women fall down the job ladder to low-wage jobs? If women move both
up and down the job ladder, are both shifts of equal importance quantitatively? The
following section provides initial answers on these questions.
48
Table 9: Trade, Man, Woman
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A.Trade 1.145*** 0.364*** -0.546*** 0.236***
(0.134) (0.065) (0.078) (0.053)Trade*Female -0.575** -0.236** 0.265* -0.074
(0.176) (0.082) (0.104) (0.074)Panel B.
Trade 1.112*** 0.317*** -0.549*** 0.245***(0.135) (0.064) (0.079) (0.053)
Trade*Female -0.584*** -0.176* 0.290** -0.117(0.177) (0.081) (0.104) (0.074)
2-digit ISCO Dummies yes yes yes yesPanel C.
Trade 1.110*** 0.283*** -0.562*** 0.266***(0.140) (0.065) (0.082) (0.055)
Trade*Female -0.659*** -0.171* 0.371*** -0.118(0.181) (0.082) (0.106) (0.075)
4-digit ISCO Dummies yes yes yes yes
Notes: In all regressions the number of observations is 10753. All regressions include a constant andthe full-set of controls, ZW
i and ZFi . Robust standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗
indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
To get a better feel of job polarization for women in Denmark, we begin by reporting
results for a number of major occupations: managers, professionals, associate profes-
sionals and technicians, clerks, craft workers, and machine operators. We find that
women that were technicians and clerks in year 1999 experience job polarization much
less than men in these occupations, while women who were machine operators in 1999
experience job polarization to a far greater extent than male machine operators (Table
A-11). Female machine operators in the textiles and clothing industry would typically
be operating sewing machines. We also see that there is no difference between 1999
female and male managers in the extent to which they are employed in high-wage jobs
during the subsequent years 2002 to 2009.
In terms of education, we see that women with college education are less prone to job
polarization than men, while in contrast women with at most high school education
are more prone to job polarization than men (Table A-12, column 1, Panels A and C).
49
College-educated women are less likely than college-educated men to shift into high-wage
occupations, while high school educated women are just as likely to move into high-wage
jobs as men (column 2, Panels A and C). On the other hand, college educated women are
less likely than college-educated men to move into low-wage jobs (column 4, Panel A).
The fact that women with at most a high school diploma are substantially more prone
to job polarization and they move more into low-wage jobs compared to men is in line
with the general pattern observed in Denmark between 2000-2009 (see Supplementary
Appendix, Figure 4).
Next we examine employment polarization through trade in which women are separated
from men (see Table 9). While job polarization overall has affected women more than
men, the extent to which it is driven by trade is only about half as large for women as
it is for men (column 1, Panel A). One might be concerned that this does not control
for differences in the propensity with which men and women work in different occupa-
tions. To address this issue, we control in Panel B for two-digit and in Panel C for
four-digit occupation fixed effects. The result remains largely unchanged: trade causes
substantially less job polarization for women than for men.
Before we look into the reasons for this it is important to ask whether trade-driven job
polarization has had an impact on gender inequality. Columns 2, 3, and 4 of Table 9
show the impact of trade on cumulative employment in high-, mid-level, and low-wage
occupations separately. The key finding here is that trade has shifted women less than
men into high-wage jobs while at the same time trade has pushed women to the same
extent as men into low-wage jobs.42 Trade is not a woman’s best friend because it raises
gender inequality.
An important question is why we estimate that trade affects women and men differently
in leading to job polarization even when we control for detailed occupation characteristics
(at the four-digit ISCO level). In the following we consider movements within and
42This is true even if we look only at the point estimates and ignore the standard errors.
50
between industries along the lines of our earlier analysis in Table 6. In Table 10 we
report employment changes for the three wage groups (high, mid-level, low) separately
for manufacturing and non-manufacturing. The question is whether there are differences
in how women and men behave in response to a trade shock across wage groups and
sectors.
We see that trade does not lower mid-level wage employment for women as much as for
men within manufacturing (column 4). At the same time, trade does not shift women
as much as men to high-wage jobs outside of manufacturing (column 3). This suggests
that women stay more in the jobs that are hollowed out (mid-level manufacturing) and
move less to high-paying jobs outside of manufacturing than men. Women tend to be
more stayers, not movers, compared to men. This finding is limited, however, to job
polarization caused by trade, as women in general are found experience job polarization
more (they are more prone to leaving mid-wage jobs). While foregoing high-wage jobs
outside of manufacturing relative to men (column 3), trade exposed womens’ tendency
to stay in mid-level manufacturing jobs might benefit women because they do not shift
as much as men into low-wage jobs within manufacturing as men (column 6).
If trade exposed women are less likely to be separated by their competition exposed firms
compared to trade exposed men, then this may explain why women are relatively less
prone to polarization caused by trade. To better understand this we focus on mid-wage
level jobs within the manufacturing sector (see Table 11). In column (1) of Panel A is
the cumulative employment in mid-wage occupations within the manufacturing sector.
We then decompose the asymmetric effect of trade on women into the effect within and
outside the initial firm in columns 2 and 3 respectively. We repeat the analysis with
cumulative hours worked since women may be relatively likely to accept reduced hours
of work at the initial firm instead of leaving for another job (Utar 2015).
First, comparing the Tradei coefficients of columns 1 and 2, in Panel A, Table 11 shows
that the decrease in mid-level employment caused by trade is entirely due to employment
51
Tab
le10
:T
rade,
Man
,W
oman
-Wit
hin
and
Outs
ide
Man
ufa
cturi
ng
Dep
.V
ar
JP
emp
iJP
h,emp
iJP
m,emp
iJP
l,em
pi
Wit
hin
Ou
tsid
eW
ith
inO
uts
ide
Wit
hin
Ou
tsid
eM
anu
f.M
anu
f.M
anu
f.M
anu
f.M
anu
f.M
anu
f.(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Tra
de
1.1
45*
**
-0.0
030.
367*
**-0
.661
***
0.11
5**
0.12
7***
0.10
9**
(0.1
34)
(0.0
56)
(0.0
48)
(0.0
74)
(0.0
44)
(0.0
38)
(0.0
39)
Tra
de*
Fem
ale
-0.5
75**
-0.0
90-0
.146
*0.
192*
0.07
3-0
.092
*0.
018
(0.1
76)
(0.0
68)
(0.0
63)
(0.0
96)
(0.0
59)
(0.0
45)
(0.0
62)
Fem
ale
0.9
66*
**
0.09
00.
216*
**-0
.426
***
-0.0
29-0
.078
*0.
283*
**(0
.125)
(0.0
46)
(0.0
41)
(0.0
71)
(0.0
41)
(0.0
31)
(0.0
43)
Not
es:
Inal
lco
lum
ns
the
nu
mb
erof
obse
rvat
ion
sis
10753.
Aco
nst
ant
isin
clu
ded
bu
tn
ot
rep
ort
ed.
All
regre
ssio
ns
incl
ud
ea
con
stant
an
dth
efu
ll-s
etof
contr
ols,
ZW i
andZ
F i.
Rob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
at
the
10
%,
5%
an
d1%
leve
lsre
spec
tive
ly.
Dat
aS
ourc
e:S
tati
stic
sD
enm
ark
.
52
losses at the initial firm, not due to lower employment in other manufacturing firms.
Given the importance of employment losses at the initial firm, does this differ between
men and women? Table 11 shows that import shock does not have any significant
asymmetric effect on women compared to men at the initial firm. That is, both women
and men are affected by the shock to the same extent. This is reassuring because we
have controlled for a substantial number of worker and firm characteristics (see Table
2). Panel A shows that 64 % of the asymmetric effect of trade on women (0.123/0.192)
is due to mid-wage jobs within manufacturing but outside the initial firm. In terms
of hours worked, 71 % of the effect is due mid-wage manufacturing jobs outside the
initial firm. Thus, the difference in the extent that trade causes job polarization for men
and women is largely due to gender differences in the propensity of moving to different
sectors.
What might explain these different responses in the face of a trade shock? It is important
to keep in mind that women do not in general switch jobs and sectors less than men,
because we have seen above that women are more likely than men to gain both high-
and low-wage employment in the services sector (Table 6). The question is why women
are more attached to mid-level manufacturing jobs than men in the presence of a trade
shock? We believe that this could be related to both the suddenness of the trade shock, as
well as its massive size. The shock to labor demand through trade in the Danish textiles
and clothing industry lead to a drastic resizing of the industry. When the industry is
undergoing massive changes, the labor market environment might be similar to that in
times of mass layoffs during recessions (Jacobson, Lalonde, and Sullivan 1993). In the
more recent 2008 Great Recession, aggregate data for the U.S. suggests that women’s
employment rates have recovered less well than those of men (Pew Research Center
2011). It is important to better understand similarities and differences between the
labor market impact of trade on the one, and of recessions on the other hand. What we
have documented is that a sudden and large-scale reduction in labor demand through
trade is relatively more challenging for women than for men, compared to a gradual
53
shift of labor demand across sectors as, for example, the introduction of information
technology and computers since the early 1980s.43
Table 11: Women and Mid-Wage Occupations
Panel A.Dep. Var Cumulative employment in mid-wage occupations within manufacturing 2002-2009
Within Manuf. Within the Initial Firm Outside the Initial Firm(1) (2) (3)
Trade -0.661*** -0.668*** 0.007(0.074) (0.058) (0.059)
Trade*Female 0.192* 0.069 0.123(0.096) (0.076) (0.074)
Female -0.426*** -0.052 -0.375***(0.071) (0.061) (0.052)
Panel B.Dep. Var Cumulative hours worked in mid-wage occupations within manufacturing 2002-2009
Overall Within Manuf. Within the Initial Firm Outside the Initial Firm(1) (2) (3)
Trade -0.768*** -0.688*** -0.080(0.085) (0.061) (0.071)
Trade*Female 0.205 0.059 0.146(0.116) (0.082) (0.096)
Female -0.390*** -0.038 -0.352***(0.091) (0.066) (0.075)
Notes: In all columns the number of observations is 10753. A constant is included but not reported. All regressionsinclude a constant and the full-set of controls, ZW
i and ZFi . Robust standard errors are reported in parentheses.
∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively. Data Source: Statistics Denmark.
6 Conclusions
Using administrative employee-employer matched data from Statistics Denmark covering
all residents of Denmark, we document that Denmark, like many other rich countries,
experienced job polarization in recent years which coincided with a dramatic increase
in Chinese exports to these countries. We document that employment share changes
of workers in the manufacturing sector exhibit a U-shaped pattern away from mid-
43We have also examined how gender affected the impact of technology factors on job polarizationby employing the interaction of Femalei and O NETi, see Table A-13. Overall, these results showthat the gender difference plays an important role in our finding that trade and technology factors aredistinct causes of job polarization in Denmark.
54
level and toward high- and low-wage jobs while workers in the service sector experience
employment changes that are increasing with wages and skills between 2000 and 2009.
This is important for at least two reasons. The trade effect is relatively concentrated in
manufacturing, whereas technical change affects also, and increasingly, sectors outside
of manufacturing. Second, while technology has gradually changed the workplace in
industrialized countries since the early 1980s, the intensification of import competition
was felt more suddenly, especially in the early 2000s.
To investigate the causal link between Chinese imports and job polarization, we exploit
the exogenous abolishment of trade quotas for China associated with her entry into the
WTO, and utilize heterogeneity in workers’ exposure to this trade shock within the same
narrow industry. This allows us to distinguish the effects of trade from technical change,
whose effect on job polarization is well established. We have shown that trade can
explain the U-shaped changes in employment that are the hallmark of job polarization.
Trade leads to job polarization mainly by pushing workers from initially abundant mid-
level jobs in manufacturing towards both high- and low-wage jobs in services. We have
seen that trade affects most strongly manual labor, workers performing both routine and
non-routine tasks.
We find that the shift from manufacturing to services is crucial for this trade effect.
Worker level evidence sharpens the focus because it lets us observe, in the data, indi-
vidual worker transitions that are the link between sectors. It may be that worker-level
information is one of the reasons why we have found more evidence that trade causes
job polarization than other studies before us.
We also find that while women experience overall job polarization more than men, women
contribute less than men to the part of job polarization that is related to trade. One
reason for the former may be that even within fine occupation classes women might
tend to perform more routine tasks than men, and it appears that women adjust well
in terms job changes in response to gradual changes. In contrast, in the face of the
55
sudden reduction in labor demand caused by trade, women do not adjust as well as
men. While it is clear that a sudden shock does not give as much opportunity to plan
and prepare, for example re-training, what explains the gender difference? One reason
might be that because women tend to be more strongly involved with child care and
other joint household activities than men, women have higher short-term adjustment
costs. It could also be that the on average lower labor market attachment of women
compared to men makes it harder for women to move when the change is sudden as
opposed to gradual.
The mid-level wage jobs that job polarization feeds on are primarily manufacturing jobs.
Can one make a case that this matters? A shrinking manufacturing sector in industrial-
ized countries might be quite different from other instances of structural change; it may
have broader implications, by affecting industry clustering, city formation, or innova-
tion.44 Mid-level jobs might have important positive spillovers. To the extent that trade
causes job polarization, it determines the size of these spillovers, and trade should be
considered in the formulation of policies that are designed to internalize these spillovers.
What are the implications of job polarization on inequality? While our results show
that trade tends to increase gender inequality because it shifts men to a greater extent
into high-wage jobs than women, this may be all but a small part of the impact of trade
on inequality. As international economic integration increases, if imports from poor
countries continue to hollow out the middle of the wage distribution in rich countries,
the implied increase in inequality might affect social cohesion and broader political
outcomes in rich countries, and beyond.
44On clustering and city formation, see Moretti (2012). The importance of near-by manufacturingfor innovation is also an important question (Keller, Yeaple, and Zolas 2015).
56
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Appendix A
60
0.1
.2.3
.4D
ensi
ty
ISCO-1
Man
ager
s
ISCO-2
Pro
fess
ionals
ISCO-3
Tec
hnici
ans a
nd A
ssoc
iate
Profe
ssion
als
ISCO-4
Cler
ks
ISCO-5
Ser
vice
Wor
kers
ISCO-6
Skil
led A
gr a
nd F
isher
y Wor
kers
ISCO-7
Cra
ft W
orke
rs
ISCO-8
Plan
t and
Mac
hine
Opera
tors
ISCO-9
Elem
enta
ry O
ccup
ation
s
Unkno
wn
OneDigOc
(a) All Workers
0.1
.2.3
.4
ISCO-1
Man
ager
s
ISCO-2
Pro
fess
ionals
ISCO-3
Tec
hnici
ans a
nd A
ssoc
iate
Profe
ssion
als
ISCO-4
Cler
ks
ISCO-5
Ser
vice
Wor
kers
ISCO-6
Skil
led A
gr a
nd F
isher
y Wor
kers
ISCO-7
Cra
ft W
orke
rs
ISCO-8
Plan
t and
Mac
hine
Opera
tors
ISCO-9
Elem
enta
ry O
ccup
ation
s
Unkno
wn
ISCO-1
Man
ager
s
ISCO-2
Pro
fess
ionals
ISCO-3
Tec
hnici
ans a
nd A
ssoc
iate
Profe
ssion
als
ISCO-4
Cler
ks
ISCO-5
Ser
vice
Wor
kers
ISCO-6
Skil
led A
gr a
nd F
isher
y Wor
kers
ISCO-7
Cra
ft W
orke
rs
ISCO-8
Plan
t and
Mac
hine
Opera
tors
ISCO-9
Elem
enta
ry O
ccup
ation
s
Unkno
wn
Workers employed in exposed firms Workers employed in non-exposed firms
Den
sity
OneDigOcGraphs by AffW
(b) By Import Competition
Figure A-1: Histogram of Workers across Major Occupations in 1999
61
Table A-1: Workers’ Characteristics in 1999 by Selective Occupation Groups
Mean SD NISCO 1: ManagersAge 44.912 8.302 555Female 0.211 0.408 555Immigrant 0.020 0.140 555College Educated 0.256 0.437 555Vocational School Educated 0.436 0.496 555At most High School 0.297 0.457 555ISCO 2: ProfessionalsAge 39.461 9.634 152Female 0.362 0.482 152Immigrant 0.013 0.114 152College Educated 0.612 0.489 152Vocational School Educated 0.178 0.383 152At most High School 0.184 0.389 152ISCO 3: Technicians and Associate ProfessionalsAge 37.756 9.097 1338Female 0.652 0.477 1338Immigrant 0.028 0.164 1338College Educated 0.371 0.483 1338Vocational School Educated 0.365 0.482 1338At most High School 0.226 0.418 1338ISCO 4: ClerksAge 36.423 9.569 1093Female 0.780 0.414 1093Immigrant 0.019 0.137 1093College Educated 0.151 0.358 1093Vocational School Educated 0.531 0.499 1093At most High School 0.317 0.466 1093ISCO 7: Craft WorkersAge 40.702 10.381 916Female 0.377 0.485 916Immigrant 0.040 0.197 916College Educated 0.061 0.240 916Vocational School Educated 0.487 0.500 916At most High School 0.425 0.495 916ISCO 8: Plant and Machine OperatorsAge 40.617 10.181 4679Female 0.594 0.491 4679Immigrant 0.090 0.287 4679College Educated 0.032 0.175 4679Vocational School Educated 0.276 0.447 4679At most High School 0.665 0.472 4679ISCO 9: Elementary OccupationsAge 38.046 11.247 923Female 0.510 0.500 923Immigrant 0.052 0.222 923College Educated 0.027 0.162 923Vocational School Educated 0.325 0.469 923At most High School 0.627 0.484 923
Notes: Data Source: Statistics Denmark.
62
Table A-2: Workers’ Characteristics in 1999 by Selective Occupations and Import Exposure
Mean SD NWeaving and Knitting Machine Operators employed in non-exposed firmsAge 40.566 10.479 304Female 0.247 0.432 304Immigrant 0.089 0.285 304College Educated 0.043 0.203 304Years of Experience in the Labor Market 15.740 5.439 304Unemployment History Index 1019.102 1509.095 304Weaving and Knitting Machine Operators employed in exposed firmsAge 41.966 9.459 205Female 0.288 0.454 205Immigrant 0.112 0.316 205College Educated 0.049 0.216 205Years of Experience in the Labor Market 16.493 4.813 205Unemployment History Index 1122.215 1557.671 205Sewing Machine Operators employed in non-exposed firmsAge 42.849 9.368 537Female 0.946 0.226 537Immigrant 0.143 0.351 537College Educated 0.020 0.142 537Years of Experience in the Labor Market 15.294 5.579 537Unemployment History Index 2573.940 2511.583 537Sewing Machine Operators employed in exposed firmsAge 43.257 10.026 915Female 0.957 0.202 915Immigrant 0.072 0.259 915College Educated 0.026 0.160 915Years of Experience in the Labor Market 16.216 5.255 915Unemployment History Index 1429.028 1695.268 915Post-Processing Textile Machine Operators employed in non-exposed firmsAge 40.478 9.894 502Female 0.329 0.470 502Immigrant 0.084 0.277 502College Educated 0.064 0.245 502Years of Experience in the Labor Market 14.964 5.535 502Unemployment History Index 1374.861 1736.771 502Post-Processing Textile Machine Operators employed in exposed firmsAge 39.030 9.667 133Female 0.323 0.470 133Immigrant 0.045 0.208 133College Educated 0.030 0.171 133Years of Experience in the Labor Market 15.609 5.145 133Unemployment History Index 1464.586 2002.921 133Cutting, Rope Making, Netting, Leather Textile Machine Operators in non-exposed firmsAge 39.888 9.311 251Female 0.199 0.400 251Immigrant 0.008 0.089 251College Educated 0.020 0.140 251Years of Experience in the Labor Market 16.275 4.791 251Unemployment History Index 1713.175 2117.432 251Cutting, Rope Making, Netting, Leather Text. Machine Operators in exposed firmsAge 40.466 10.201 221Female 0.674 0.470 221Immigrant 0.081 0.274 221College Educated 0.032 0.176 221Years of Experience in the Labor Market 15.330 5.396 221Unemployment History Index 1618.543 2068.907 221
Notes: Data Source: Statistics Denmark.
63
43%52%
Agr, Fishing, Mining ConstructionManufacturing OthersService
The T&C Workers' Industry Affiliation in 2009
Figure A-2: Industry Affiliation of the 1999 T&C Workers in 2009 (conditional onworking)
34%
60%50%
44%
Exposed Non-Exposed
Agr, Fishing, Mining ConstructionManufacturing OthersService
Graphs by ImportCompetition
The T&C Workers' Industry Affiliation in 2009
Figure A-3: Industry Affiliation of the 1999 T&C Workers in 2009 by exposure to importcompetition (conditional on working)
64
Tab
leA
-3:
Ran
kin
gof
Ma
jor
Occ
upat
ions
inD
enm
ark
Occ
up
ati
on
Med
ian
Log
Mea
nL
ogE
mp
loym
ent
Cor
resp
ond
ing
Hou
rly
Wag
eH
ourl
yW
age
Sh
are
in19
91M
ajo
rin
1991
in19
91IS
CO
Hig
h-W
age
Leg
isla
tors
,S
enio
rO
ffici
als,
and
Man
ager
s5.
430
5.46
00.
042
1P
rofe
ssio
nal
s5.
269
5.32
00.
128
2T
ech
nic
ian
san
dA
ssoci
ate
Pro
fess
ion
als
5.07
85.
101
0.18
63
Mid
-Wage
Cra
ftan
dR
elate
dT
rade
Wor
ker
s5.
032
4.94
50.
149
7P
lant
an
dM
ach
ine
Op
erato
rsan
dA
ssem
ble
rs5.
000
5.01
30.
065
8C
lerk
s4.
907
4.89
80.
170
4L
ow
-Wage
Ele
men
tary
Occ
upat
ion
sin
Sal
es,
Ser
vic
es,
4.84
24.
873
0.10
69
(exce
pt
92)
Min
ing,
Con
stru
ctio
n,
Manufa
ctu
rin
g,an
dT
ran
spor
tS
ervic
eW
ork
ers
an
dS
hop
Sale
sW
orke
rs4.
842
4.79
90.
154
5
Dat
aar
efr
om19
91.
Val
ues
are
exp
ress
edin
2000
Dan
ish
Kro
ner
.A
llhou
rly
wages
are
calc
ula
ted
am
on
gw
ork
ers
wit
hfu
ll-t
ime
job
sem
plo
yed
conti
nu
ousl
yw
ith
atle
ast
one
year
tenu
re.
Em
plo
ym
ent
share
sare
calc
ula
ted
usi
ng
the
nu
mb
erof
emp
loye
esan
dex
clu
din
garm
yan
dagri
cult
ure
/fi
sher
yocc
up
atio
ns.
Dat
aS
ourc
e:S
tati
stic
sD
enm
ark.
65
Appendix B
Table A-4: Job Polarization and Import Shock
Dep. Var. ln(9 + JP empi ) ln(1 + JP h,emp
i ) ln(1 + JPm,empi ) ln(1 + JP l,emp
i )(1) (2) (3) (4)
Trade 0.132*** 0.084*** -0.109*** 0.073***(0.015) (0.012) (0.015) (0.013)[0.079] [0.053] [-0.064] [0.054]
Notes: In all regressions the number of observations is 10753. All regressions include a constant and the full-set ofcontrols, ZW
i and ZFi . Robust standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at
the 10 %, 5% and 1% levels respectively. Standardized coefficients are reported in square brackets. Data Source:Statistics Denmark.
Table A-5: Job Polarization and Import Shock–Alternative Trade Exposure
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
T rade 3.220*** 0.904*** -1.582*** 0.734***(0.316) (0.146) (0.181) (0.138)[0.092] [0.051] [-0.079] [0.052]
Notes: T rade is the revenue share of the quotas at worker i’s initial employer in 1999. In all regressionsthe number of observations is 10753. All regressions include a constant and the full-set of controls, ZW
i
and ZFi . Robust standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at the
10 %, 5% and 1% levels respectively. Standardized coefficients are reported in square brackets. DataSource: Statistics Denmark.
66
Table A-6: Polarization and Trade by Initial Occupation
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. Managers (ISCO: 1)
Trade 0.830*** 0.240*** -0.404*** 0.186***(0.093) (0.040) (0.055) (0.040)
Trade*Managers -0.972** -0.429 0.393** -0.149(0.321) (0.241) (0.140) (0.081)
Panel B. Professionals (ISCO: 2)
Trade 0.810*** 0.228*** -0.398*** 0.184***(0.090) (0.040) (0.053) (0.038)
Trade*Profs -2.349** -0.830 1.088*** -0.431(0.724) (0.496) (0.322) (0.233)
Panel C. Associate Professionals and Technicians (ISCO: 3)
Trade 0.864*** 0.191*** -0.438*** 0.235***(0.096) (0.039) (0.057) (0.042)
Trade*Technicians -0.706** 0.203 0.449*** -0.460***(0.259) (0.169) (0.126) (0.077)
Panel D. Clerks (ISCO: 4)
Trade 0.879*** 0.258*** -0.442*** 0.179***(0.093) (0.041) (0.054) (0.041)
Trade*Clerks -0.977** -0.396** 0.570** -0.010(0.313) (0.150) (0.184) (0.087)
Panel E. Craft Workers (ISCO: 7)
Trade 0.803*** 0.214*** -0.402*** 0.187***(0.093) (0.042) (0.054) (0.040)
Trade*Craftsmen -0.328 0.029 0.241 -0.116(0.321) (0.130) (0.204) (0.114)
Panel F. Plant and Machine Operators and Assemblers (ISCO: 8)
Trade 0.360** 0.252*** -0.080 0.028(0.120) (0.064) (0.065) (0.046)
Trade*Operators 0.937*** -0.081 -0.681*** 0.337***(0.175) (0.073) (0.104) (0.077)
Notes: In all regressions the number of observations is 10753. All regressions include a constant andthe full-set of controls, ZW
i and ZFi as well as the 2-digit ISCO (initial occupation) dummies. Robust
standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and1% levels respectively.
67
Table A-7: Trade induced Job Polarization among Machine Operators
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)Among Machine Operators (ISCO: 82)
Trade 1.079*** 0.097* -0.659*** 0.323***(0.148) (0.039) (0.095) (0.072)
Notes: Estimation sample consist of workers who were machine operators (ISCO=82) in the textile andclothing sector in 1999. In all regressions the number of observations is 3773. All regressions include aconstant and the full-set of controls, ZW
i and ZFi . Robust standard errors are reported in parentheses.
∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.
Table A-8: Polarization and Trade by Education
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. College Educated
Trade 0.931*** 0.253*** -0.464*** 0.214***(0.095) (0.040) (0.056) (0.042)
Trade*College -0.998*** -0.212 0.609*** -0.178*(0.270) (0.163) (0.140) (0.078)
Panel B. Vocational School
Trade 0.852*** 0.214*** -0.388*** 0.250***(0.108) (0.048) (0.063) (0.048)
Trade*Vocational -0.103 0.040 -0.016 -0.159*(0.180) (0.082) (0.106) (0.075)
Panel C. at most High School Diploma
Trade 0.572*** 0.201** -0.279*** 0.091(0.126) (0.064) (0.071) (0.049)
Trade*HS 0.480** 0.054 -0.226* 0.201**(0.170) (0.077) (0.100) (0.073)
Notes: In all regressions the number of observations is 10753. All regressions include a constant andthe full-set of controls, ZW
i and ZFi . Robust standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗
indicate significance at the 10 %, 5% and 1% levels respectively.
68
Tab
leA
-9:
Job
Pol
ariz
atio
n,
Off
shor
ing,
Tec
hnol
ogy,
and
Tra
de
-H
ours
Wor
ked
JP
hrs
iJP
hrs
iJP
hrs
iJP
hrs
iJP
hrs
iJP
hrs
iJP
hrs
i(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Tra
de
0.9
12*
**
0.92
2***
0.91
0***
0.51
4**
0.77
4***
0.96
2***
0.86
4***
(0.1
32)
(0.1
32)
(0.1
32)
(0.1
77)
(0.1
35)
(0.1
54)
(0.1
38)
Off
shor
ing
0.1
68*
*0.
142*
0.13
9*0.
032
0.11
80.
138*
(0.0
58)
(0.0
64)
(0.0
64)
(0.0
72)
(0.0
64)
(0.0
63)
ICT
-0.1
04-0
.339
**-0
.329
*-0
.003
-0.3
34**
(0.0
97)
(0.1
28)
(0.1
28)
(0.1
39)
(0.1
29)
RT
I0.
417*
*0.
434*
*0.
454*
*0.
455*
*(0
.156
)(0
.157
)(0
.156
)(0
.166
)T
rad
e*O
ffsh
ori
ng
0.27
0***
(0.0
76)
Tra
de*
ICT
-0.7
37**
*(0
.134
)T
rad
e*R
TI
-0.0
88(0
.161
)4-
Dig
itIS
CO
Du
mm
ies
no
no
no
no
no
no
yes
Not
es:
Inal
lco
lum
ns
the
num
ber
ofob
serv
atio
ns
is8702.
Aco
nst
ant
an
dth
efu
llse
tof
contr
ols
,Z
W ian
dZ
F i,
are
incl
ud
edin
all
regre
ssio
ns
bu
tn
otre
por
ted
.“O
ffsh
orin
g”is
the
offsh
orab
ilit
yin
dex
of
the
corr
esp
on
din
gtw
od
igit
init
ial
ISC
O-8
8occ
up
ati
on
cod
eof
aw
ork
er.
Itis
con
stru
cted
by
Goos
,M
ann
ing
and
Sal
omon
s(2
014)
.“I
CT
”is
the
ind
exva
riab
lein
dic
ati
ng
the
deg
ree
inw
hic
hth
ein
itia
l(4
-dig
it)
occ
up
ati
on
of
aw
ork
erin
tera
cts
wit
hco
mp
ute
rs.
Itis
anO
*NE
T(v
ersi
on
14)
vari
ab
lean
dva
ries
acr
oss
4-d
igit
occ
up
ati
on
s.“R
TI”
isth
ero
uti
ne
inte
nsi
tyin
dex
of
the
corr
esp
ond
ing
two
dig
itin
itia
lIS
CO
-88
occ
up
ati
on
cod
eof
aw
ork
er.
Th
eR
TI
ind
exfo
llow
sA
uto
r,L
evy
an
dM
urn
an
e(2
003)
an
dA
uto
ran
dD
orn
(201
2).
Con
trol
vari
able
sar
ed
escr
ibed
inth
en
ote
sof
Tab
le3.
Rob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
atth
e10
%,
5%an
d1%
leve
lsre
spec
tive
ly.
Data
Sou
rce:
Sta
tist
ics
Den
mark
.
69
Tab
leA
-10:
Job
Pol
ariz
atio
n,
Off
shor
ing,
Tec
hnol
ogy,
and
Tra
de
-E
arnin
gs
JP
inc
iJP
inc
iJP
inc
iJP
inc
iJP
inc
iJP
inc
iJP
inc
i(1
)(2
)(3
)(4
)(5
)(6
)(7
)
Tra
de
0.9
92*
**
1.00
0***
0.98
8***
0.76
4**
0.85
1***
1.05
5***
0.95
1***
(0.1
77)
(0.1
75)
(0.1
75)
(0.2
43)
(0.1
77)
(0.2
07)
(0.1
80)
Off
shor
ing
0.1
86*
0.16
40.
161
0.10
10.
140
0.16
0(0
.077)
(0.0
87)
(0.0
87)
(0.0
97)
(0.0
86)
(0.0
86)
ICT
-0.0
88-0
.336
-0.3
300.
002
-0.3
29(0
.132
)(0
.174
)(0
.174
)(0
.187
)(0
.175
)R
TI
0.43
8*0.
447*
0.47
5*0.
487*
(0.2
17)
(0.2
17)
(0.2
17)
(0.2
29)
Tra
de*
Off
shori
ng
0.15
3(0
.095
)T
rad
e*IC
T-0
.738
***
(0.1
76)
Tra
de*
RT
I-0
.114
(0.2
11)
4-D
igit
ISC
OD
um
mie
sn
on
on
on
on
on
oye
s
Not
es:
Inal
lco
lum
ns
the
num
ber
ofob
serv
atio
ns
is8702.
Aco
nst
ant
an
dth
efu
llse
tof
contr
ols
,Z
W ian
dZ
F i,
are
incl
ud
edin
all
regre
ssio
ns
bu
tn
otre
por
ted
.“O
ffsh
orin
g”is
the
offsh
orab
ilit
yin
dex
of
the
corr
esp
on
din
gtw
od
igit
init
ial
ISC
O-8
8occ
up
ati
on
cod
eof
aw
ork
er.
Itis
con
stru
cted
by
Goos
,M
ann
ing
and
Sal
omon
s(2
014)
.“I
CT
”is
the
ind
exva
riab
lein
dic
ati
ng
the
deg
ree
inw
hic
hth
ein
itia
l(4
-dig
it)
occ
up
ati
on
of
aw
ork
erin
tera
cts
wit
hco
mp
ute
rs.
Itis
anO
*NE
T(v
ersi
on
14)
vari
ab
lean
dva
ries
acr
oss
4-d
igit
occ
up
ati
on
s.“R
TI”
isth
ero
uti
ne
inte
nsi
tyin
dex
of
the
corr
esp
ond
ing
two
dig
itin
itia
lIS
CO
-88
occ
up
ati
on
cod
eof
aw
ork
er.
Th
eR
TI
ind
exfo
llow
sA
uto
r,L
evy
an
dM
urn
an
e(2
003)
an
dA
uto
ran
dD
orn
(201
2).
Con
trol
vari
able
sar
ed
escr
ibed
inth
en
ote
sof
Tab
le3.
Rob
ust
stan
dard
erro
rsare
rep
ort
edin
pare
nth
eses
.∗ ,
∗∗an
d∗∗
∗in
dic
ate
sign
ifica
nce
atth
e10
%,
5%an
d1%
leve
lsre
spec
tive
ly.
Data
Sou
rce:
Sta
tist
ics
Den
mark
.
70
Table A-11: Polarization and Women by Initial Occupation
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. Managers
Trade 0.778*** 0.216*** -0.383*** 0.178***(0.090) (0.040) (0.052) (0.038)
Managers*Female -0.575 -0.082 0.304 -0.189(0.385) (0.292) (0.164) (0.126)
Panel B. Professionals
Trade 0.778*** 0.216*** -0.383*** 0.179***(0.090) (0.040) (0.052) (0.038)
Profs*Female -1.392 0.456 0.805* -1.043***(0.742) (0.458) (0.360) (0.207)
Panel C. Associate Professionals and Technicians
Trade 0.775*** 0.216*** -0.382*** 0.178***(0.089) (0.040) (0.052) (0.038)
Technicians*Female -2.174*** -0.640*** 1.104*** -0.430***(0.261) (0.175) (0.125) (0.075)
Panel D. Clerks
Trade 0.734*** 0.209*** -0.357*** 0.168***(0.090) (0.040) (0.052) (0.038)
Clerks*Female -2.769*** -0.472* 1.659*** -0.638***(0.360) (0.184) (0.208) (0.109)
Panel E. Craft Workers
Trade 0.775*** 0.216*** -0.381*** 0.178***(0.090) (0.040) (0.052) (0.038)
Craft*Female 0.635 0.202 -0.490* -0.057(0.368) (0.150) (0.234) (0.138)
Panel F. Machine Operators
Trade 0.684*** 0.194*** -0.334*** 0.156***(0.090) (0.040) (0.052) (0.038)
Operators*Female 2.073*** 0.494*** -1.089*** 0.490***(0.184) (0.078) (0.111) (0.078)
Notes: In all regressions the number of observations is 10753. All regressions include a constant andthe full-set of controls, ZW
i and ZFi as well as the 2-digit ISCO (initial occupation) dummies. Robust
standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and1% levels respectively.
71
Table A-12: Polarization and Women by Education
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. College Educated
Trade 0.816*** 0.229*** -0.394*** 0.194***(0.089) (0.040) (0.052) (0.038)
College*Female -1.613*** -0.375* 0.786*** -0.452***(0.269) (0.165) (0.140) (0.078)
Panel B. Vocational School
Trade 0.810*** 0.229*** -0.391*** 0.190***(0.089) (0.040) (0.052) (0.038)
Vocational*Female -0.371* 0.023 0.190 -0.204**(0.180) (0.082) (0.106) (0.073)
Panel C. at most High School Diploma
Trade 0.802*** 0.228*** -0.387*** 0.187***(0.089) (0.040) (0.052) (0.038)
at most High School*Female 0.844*** 0.056 -0.392*** 0.397***(0.172) (0.078) (0.102) (0.071)
Notes: In all regressions the number of observations is 10753. All regressions include a constant andthe full-set of controls, ZW
i and ZFi . Robust standard errors are reported in parentheses. ∗, ∗∗ and ∗∗∗
indicate significance at the 10 %, 5% and 1% levels respectively.
72
Table A-13: Tasks, Trade, Man, Woman
Dep. Var. JP empi JP h,emp
i JPm,empi JP l,emp
i(1) (2) (3) (4)
Panel A. Routine Manual TasksTasks with Repetitive Motions
Trade 0.752*** 0.246*** -0.368*** 0.138**(0.099) (0.044) (0.057) (0.043)
Repetitive Motions -0.550*** -0.405*** 0.169** 0.024(0.095) (0.049) (0.055) (0.032)
Female*Repetitive Motions 0.884*** 0.139* -0.431*** 0.313***(0.104) (0.055) (0.059) (0.038)
Panel B. Non-Routine Manual TasksTasks with Gross Body Coordination (GBC)
Trade 0.800*** 0.195*** -0.405*** 0.200***(0.094) (0.041) (0.055) (0.041)
GBC -0.854*** -0.611*** 0.355*** 0.112***(0.103) (0.059) (0.055) (0.034)
Female*GBC 1.343*** 0.241*** -0.773*** 0.329***(0.119) (0.065) (0.064) (0.043)
Panel C. Routine Cognitive TasksEvaluating Information to Determine Compliance with Standards
Trade 0.807*** 0.199*** -0.409*** 0.199***(0.093) (0.042) (0.054) (0.040)
Evaluating 0.009 -0.105* 0.013 0.127***(0.083) (0.041) (0.049) (0.030)
Female*Evaluating -0.004 0.104 -0.033 -0.141**(0.116) (0.058) (0.066) (0.046)
Panel D. Abstract TasksTasks that involves thinking creatively (Thinking)
Trade 0.796*** 0.226*** -0.396*** 0.174***(0.098) (0.044) (0.057) (0.043)
Thinking 0.377*** 0.265*** -0.089 0.023(0.096) (0.047) (0.056) (0.034)
Female*Thinking -0.488*** -0.187*** 0.135* -0.165***(0.104) (0.056) (0.058) (0.040)
Panel E. ICTTasks that involves interacting with computers (ICT)
Trade 0.754*** 0.171*** -0.385*** 0.198***(0.093) (0.041) (0.054) (0.040)
ICT 0.744*** 0.512*** -0.270*** -0.038(0.092) (0.048) (0.052) (0.032)
Female*ICT -1.308*** -0.242*** 0.715*** -0.351***(0.102) (0.052) (0.057) (0.038)
Panel F. Face to Face Communication Intensive TasksTasks that involve establishing interpersonal relationships
Trade 0.678*** 0.216*** -0.329*** 0.132**(0.099) (0.044) (0.058) (0.042)
Interpersonal 0.534*** 0.420*** -0.147*** -0.033(0.075) (0.039) (0.043) (0.025)
Female*Interpersonal -0.961*** -0.151*** 0.486*** -0.324***(0.087) (0.045) (0.049) (0.033)
Notes: The number of observations is 8819, 9505, 9887, 8876, 9776, and 8816 in Panels A throughF respectively. All regressions include a constant and the full-set of controls, ZW
i and ZFi . Tasks
measures are from O*NET and vary by workers’ initial 4-digit occupations (ISCO). The availability oftask measures across occupations determine the number of observations. Robust standard errors arereported in parentheses. ∗, ∗∗ and ∗∗∗ indicate significance at the 10 %, 5% and 1% levels respectively.Data Source: Statistics Denmark. 73