Page 2 of 28
EUROFORUM
Within the ‘Euroforum’ framework, KU Leuven academics present policy papers on the Europe 2020 targets
(employment, social inclusion, climate, education and innovation) and the Eurozone’s design problems, in
interaction with policymakers from the European Commission and international experts.
Policy papers by Euroforum KU Leuven:
1.
Paul De Grauwe, ‘Design Failures in the Eurozone: Can They Be Fixed?’ (April 2013)
2.
Maarten Goos, Anna Salomons & Marieke Vandeweyer, ‘Job Polarization During the Great
Recession and Beyond’ (April 2013)
3.
Frank Vandenbroucke, Ron Diris & Gerlinde Verbist, ‘Excessive Social Imbalances and the
Performance of Welfare States in the EU’ (April 2013)
4.
Erik Schokkaert & Koen Decancq, ‘Beyond GDP: Measuring Social Progress in Europe’ (April
2013)
5.
Paul Schoukens, ‘From Soft Monitoring to Enforceable Action: A Quest for New Legal
Approaches in the EU Fight against Social Exclusion’ (April 2013)
Euroforum is part of the interdisciplinary think-tank Metaforum, which aims to strengthen the KU Leuven's
involvement in social debate by supporting multidisciplinary working groups in which researchers from different
disciplines combine their scientific expertise and discuss relevant social issues from different angles.
Metaforum KU Leuven
Interdisciplinary think-tank for social debate
Hollands College
Damiaanplein 9 bus 5009
3000 Leuven
[email protected]
www.kuleuven.be/metaforum
www.kuleuven.be/euroforum
Page 3 of 28
ABSTRACT
The job structure of labour markets is polarizing into high-paid and low-paid jobs at the expense of
middling jobs. This paper contributes to our understanding of this phenomenon both in the long run
and short run. In particular, we explain how recent innovation leads to economic growth but also job
polarization in the long run. Moreover, we explain why job polarization is stronger during recessions
and how it can lead to jobless recoveries in the short run. We provide some historical evidence by
comparing the period after 1980 to the Second Industrial Revolution from 1850 to 1910 and some
empirical evidence using data from 16 European countries over the period 1993-2012. In addition, we
provide. Finally, we propose five policy recommendations: no fiscal consolidation in the short run,
more investment in innovation and education, reducing skill-mismatch and tackling rising income
inequality.
Page 4 of 28
CONTENTS
Euroforum................................................................................................................................................................ 2
Abstract ................................................................................................................................................................... 3
1.
Introduction ..................................................................................................................................................... 5
2.
A simple framework for understanding the impact of recent technological progress .................................... 5
A.
Routinization in IR2 versus IR3 ................................................................................................................... 6
B.
A simple framework for understanding the impact of IR3 ......................................................................... 7
3.
Some evidence............................................................................................................................................... 10
A.
The relative decrease in routine employment ......................................................................................... 10
B.
Job polarization ........................................................................................................................................ 11
4.
Policy implications ......................................................................................................................................... 13
A.
No fiscal consolidation in the short run.................................................................................................... 13
B.
Innovation ................................................................................................................................................ 14
C.
Education .................................................................................................................................................. 15
D.
Matching skills and jobs............................................................................................................................ 16
E.
Tackling rising income inequality ............................................................................................................. 16
5.
Conclusions .................................................................................................................................................... 17
6.
References ..................................................................................................................................................... 18
7.
Figures and tables .......................................................................................................................................... 19
Page 5 of 28
1.
INTRODUCTION
There is a growing consensus in Europe that the Financial Crisis of 2007-2009 and the on-going Great
Recession are having devastating social consequences that will dampen economic growth for the
foreseeable future. Popular evidence are the grassroots movements of Occupy, 15-M, and many
others. Protesters point to the dependency of governments on international financial markets to
finance their debt and untimely fiscal austerity as the main causes for unnecessary welfare losses.
However, this paper argues that since the 1980s and especially during recessions there are equally
important changes occurring in the structure of employment.
Since the 1980s, innovation in the Third Industrial Revolution (IR3) has been automating routine
work, as was first pointed out by Autor, Levy and Murnane (2003). These Routine jobs consist of
routine manual tasks such as those done by machine operators but also many routine cognitive tasks
such as those done by office clerks. At the same time, automation has increased the demand for
non-routine jobs, at least in relative terms. These are non-routine abstract tasks performed by for
instance engineers but also non-routine manual tasks performed by for instance janitors.
Since routine jobs are concentrated in the middle of the job quality ladder measured in terms of
occupational wages, automation of routine tasks leads to job polarization. By job polarization we
mean a decrease over time in the employment share of middling routine manual (e.g. machine
operators) and cognitive (e.g. office clerks) jobs towards high-paid non-routine abstract jobs (e.g.
engineers) but also low-paid non-routine manual work (e.g. janitors). The pervasiveness of job
polarization has recently been documented for Europe (Goos and Manning, 2007; Goos, Manning
and Salomons 2009, 2011) and the US (Autor, Katz and Kearney 2006, 2008; Acemoglu and Autor
2011). Moreover, there is an increasing awareness that job polarization is being amplified during
recessions, including the on-going Great Recession, and that it is leading to jobless recoveries (e.g.
see Jaimovich and Siu 2012 for the US).
This paper is the first to explain the macroeconomic impact of IR3 and it does so in the simplest
possible way. It shows how IR3 leads to economic growth but also to job polarization in the long run.
Moreover, it shows why IR3 also leads to job polarization that is stronger during recessions and to
jobless recoveries in the short run. In light of these findings, the paper draws five policy conclusions:
no fiscal consolidation in the short run, increased investment in innovation and education, reducing
skill-mismatch and tackling rising income inequality.
This remainder of the paper is organized as follows. Section 2 provides a framework to understand
the labour market impact of recent technological progress. Section 3 shows some supportive
evidence for Europe. Section 4 draws important policy conclusions. Section 5 concludes.
2.
A SIMPLE FRAMEWORK FOR UNDERSTANDING THE IMPACT OF RECENT
TECHNOLOGICAL PROGRESS
In this section we present a simple and intuitive explanation of the macroeconomic impact of IR3. To
get a better understanding of the nature of IR3, the first subsection describes IR3 relative to the
Second Industrial Revolution (IR2), which witnessed inventions between 1870 and 1900 but with
lasting impacts possibly as late as the 1970s (see Gordon 2012). The second subsection then
Page 6 of 28
discusses how the specific nature of IR3 leads to economic growth but also job polarization in the
long run, and furthermore that job polarization is stronger during recessions and leads to jobless
recoveries in the short run.
A.
ROUTINIZATION IN IR2 VERSUS IR3
From 1870 to 1900, IR2 introduced electricity; the combustion engine and the automobile; running
water, indoor plumbing and central heating; petroleum, chemicals, plastics and pharmaceuticals;
and the telephone and radio. To illustrate the impact of these changes, consider the introduction of
the automobile around 1870. Large gains from specialization were realized by introducing the
assembly line and routinizing labour input with the aid of machines. For example, rather than
assembling an entire car, a worker would be made responsible for the sole task of installing a hood
with the heavy lifting done by a machine. These assembly line jobs were paid the economy-wide
average wage because of the gains from specialization and because they required some but not
much special training such that they could be done by workers previously employed in agriculture or
other lower paying unskilled jobs. Moreover, with mass production of cars at low costs and the rapid
expansion of the car market, the relative employment of skilled white-collar services such as sales,
transport and communication, finance and business services rapidly increased.
More generally, IR2 increased the demand for middle-paid relative to low-paid jobs. Together with
the increased demand for skilled white-collar service jobs, IR2 resulted in a pattern of monotonic
skill upgrading and therefore an increase in living standards for many households. To illustrate this
further, in on-going research we use data on both employment and wages for Belgium between
1846 and 1910. Preliminary evidence suggests that for the economy as a whole, the employment
share of the three lowest-paid industries in 1846 (Agriculture; Textiles; Clothing and leather
industries) decreased from 64.9% in 1846 to 38.5% in 1910. At the same time, employment shares
for some manufacturing industries (Iron, steel and metal processing; Construction) paying about
average wages in 1846 increased from 4.3% in 1846 to 11.6% in 1910, although this increase was
fully offset by a decrease in the employment share of artisan trades workers who were also being
paid about (but somewhat above) the average wage. Moreover, the employment share for highly
paid workers in private service employment (Wholesale and retail; Transport and communications;
Finance, insurance and real estate; Business Services) increased from 4.3% in 1846 to 14.2% in 1910.
These numbers are in line with the recent findings in Katz and Margo (2013) who also document a
pattern of monotonic skill upgrading for the US economy between 1850 and 1900.
Starting in the 1980s, IR3 introduced the “computer revolution”. An important question is whether
the impact of IR3 is comparable to that of IR2. One key difference is that the nature of routinization
has changed between IR2 and IR3. Routinization in IR2 meant that the tasks workers were doing
became more routine and repetitive with the aid of machinery to avoid heavy lifting, stooping,
etcetera. However, routinization in IR3 means that our engineers (doing high-paid abstract nonroutine jobs by e.g. writing complicated software code) build machines that can increasingly
substitute for those routine jobs that embodied part of the gains from specialization for labour in
IR2. Autor (2013) argues that this change in the nature of routinization follows a clear economic
logic: novel tasks due to technological progress are often first assigned to labour because workers
are flexible and adaptive. But as these tasks become standardized and can be codified, machines
Page 7 of 28
obtain a comparative advantage over workers such that capital displaces labour in doing these
routine tasks.
To illustrate this, let’s return to our car manufacturing example. Today, car assembly workers doing
routine manual jobs are still in the middle of the wage distribution (e.g. they are typically paid less
than an engineer but more than a janitor) but their demand is decreasing rather than increasing
because robots can increasingly operate assembly lines autonomously. More generally, besides
substituting for labour in routine manual tasks (e.g. car assembly), many routine cognitive tasks (e.g.
clerical work) can also increasingly be performed by computers. But today’s computers cannot do
the non-routine manual work done in low-paid services (e.g. janitors) or the non-routine abstract
tasks done by highly-paid workers (e.g. engineers). Hence the recent automation of routine middling
jobs has resulted in job polarization rather than the pattern of monotonic skill upgrading that
resulted from IR2.
In sum, task specialization in IR3 leads to economic growth but also to job polarization in the long
run. Moreover, the next subsection explains why IR3 also leads to job polarization that is stronger
during recessions and to jobless recoveries in the short run.
B.
A SIMPLE FRAMEWORK FOR UNDERSTANDING THE IMPACT OF IR3 4
To understand the macroeconomic impact of IR3 better, consider an economy consisting of a
progressive sector producing type-1 goods and services, and a non-progressive sector producing
type-2 goods and services. The progressive sector uses Routine and Abstract tasks as inputs to
produce type-1 goods. Routine tasks are done by Routine workers (R) (the hood assembler in the
example above) or capital (T) and Abstract tasks can only be done by Abstract workers (A) (the
engineer who can write software to operate machinery in the example above). It is called the
“progressive” sector because over time machinery becomes more efficient in doing routine tasks,
captured by an increase in capital (T) over time (the robot lifting and attaching the hood to the car
autonomously in the example above). The non-progressive sector only uses Manual labour (M) (the
janitor in our example above) to produce type-2 goods that are not subject to any technological
progress. What would happen in this macro-economy over time?
Assume that technological progress in the progressive sector decreases production costs and
therefore the output price of type-1 goods. There is no technological progress in the non-progressive
sector such that production costs and hence the output price of type-2 goods do not change over
time. Given that the output price of type-1 relative to type-2 goods decreases, the demand for type1 goods relative to type-2 goods increases. However, Goos, Manning and Salomons (2013) find that
this increase in the demand for type-1 relative to type-2 goods is small (because the elasticity of
product demand is estimated to be less than unity at sufficiently aggregate levels of goods and
services), so we can ignore it in what follows. This also implies that the share of total income spent
on type-1 goods will be decreasing over time because the decrease in the price is stronger than the
increase output of type-1 goods relative to type-2 goods.
4
Although this section only uses a very basic economic intuition, non-economists may find it harder to read and can move
on to Section 3 immediately.
Page 8 of 28
Assuming that the nominal wage is a constant in both sectors, this decrease in the price of type-1
goods increases real wages. This is how innovation leads to increasing living standards and therefore
economic growth. To see this, think about what happens to the aggregate price index using the
following approximation:
(1)
Pt = α t P1t + (1 - α t )P2
with Pt the aggregate price index; α t the share of income spent on type-1 goods, which is decreasing
as we argued above; P1t the price index of type-1 goods which will be decreasing; and P2 the price
index of type-2 goods which we assume to be a constant and such that P1t > P2 . What equation (1)
shows is that the aggregate price index Pt falls because of a decrease over time in P1t and α t
following innovation in the progressive sector. Assuming for simplicity that Q 2t = M t with Q 2t
output in the non-progressive sector, we have that:
(2)
P2 = W
where W is the constant economy-wide nominal wage.5 Dividing both sides by the aggregate price
index Pt gives the real wage in the macro-economy:
(3)
W / Pt = P2 / Pt
which shows that the real average wage will be growing over time as Pt falls due to innovation. This
is how IR3 leads to increasing living standards and therefore economic growth.
To predict job polarization, we need to be more specific about the production technologies in the
progressive sector. Firstly, assume that Routine task input is given by Tt R t with Tt loosely defined as
capital that is increasing over time due to recent innovation. To understand this assumption better,
initially set T0 = 1 such that R 0 routine tasks used in production are done by R 0 workers. Next
assume that T1 = 2 such that producing the same amount of R 0 routine tasks only requires R 0 / 2
workers. This is how we can think of recent innovation automating routine tasks, thereby
substituting for routine workers. Referring to our example of the hood assembler in car
manufacturing in the previous section, the increase in capital stock would be capturing the
introduction of a robotic arm lifting and installing the hood thereby making labour obsolete.6
Secondly, assume for simplicity a fixed proportions technology in the progressive sector such that we
5
This is only an approximation assuming away the importance of price mark-ups due to imperfect competition in the nonprogressive sector. Adding a constant price mark-up to the right-hand-side of equation (2) would not change any of the
results.
6
Also note that we assume increasing returns to scale in the production of the Routine task input. This is not implausible:
for example, many communication and information technologies used in routine clerical tasks exhibit network
externalities (e.g. the more Windows is used as an operating platform, the greater the productivity gains in clerical task
inputs). Another example is the recent introduction of robotics for small and medium sized companies. The UR5 (build by
the Danish company Universal Robots) is a robotic arm that mainly performs “pick and place” tasks but that, in contrast
to conventional industrial robots mainly used in larger companies, does not require any complex programming (the arm
only has to be manually moved once, after which the robot remembers this motion) and comes at a relatively low cost.
In the US, a similar robot named Baxter has been developed with the aim of increasing competitiveness in
manufacturing. Given that these robots have been specifically designed to work with each other and with workers, it is
not unrealistic to assume that Routine task input production exhibits increasing returns to scale.
Page 9 of 28
must have that Tt R t = A t = Q1t 7 with Q1t output of type-1 goods. We can now easily illustrate how
recent innovation leads to job polarization because both R t / A t and R t / M t will decrease
following an increase in Tt .
Start by considering Routine relative to Abstract employment, R t / A t . Firstly, there is a direct
displacement of Routine workers by capital in producing one unit of Routine task input. In our simple
framework, this is captured by an increase in Tt in Tt R t = A t = 1 such that R t and therefore R t / A t
must be decreasing. Secondly, an increase in real wages increases the demand for type-1 goods and
therefore the demand for both Routine and Abstract workers. But because the capital stock also
increases in the supply of Routine tasks, Routine employment increases by less relative to Abstract
employment leading to a further decrease in R t / A t . In our simple framework, this is captured by
an increase in the production of type-1 goods Q1t = A t = Tt R t requiring an increase in A t that is
larger than R t for raising Tt . Next turn to what happens to Routine relative the Manual
employment, R t / M t . Firstly, the fact that routinization leads to the direct substitution of capital for
Routine but not Manual workers explains part of the decrease in R t / M t . Secondly, an increase in
real wages increases the demand for both type-1 as well as type-2 goods leading to increases in both
R t and M t . However, because the capital stock increases in the supply of Routine tasks, the
increase in Routine employment will be smaller thereby decreasing R t / M t . This is how recent
technological progress not only leads to economic growth but also to job polarization in the long
run.8
We can also use our framework to show that IR3 also leads to job polarization that is stronger in
recessions and to jobless recoveries in the short run. The intuition for this is as follows. Think of a
depressed economy leading to downward pressure on all nominal prices. If nominal wages are
downward rigid (as we assume in our framework that takes nominal wages as given), one way firms
in the progressive sector can lower their output price is by increasing Tt by even more during
recessions. One should again think of this increase in Tt in very general terms in that it captures all
cost savings in the short run that affect Routine employment more strongly.9 To see this in our
simple framework, assume that both Q1t = A t = R t Tt and Q 2 t = M t decrease by the same amount
(which will be approximately true if product demand is inelastic even though relative output prices
are changing). This leads to a decrease in A t and M t but a stronger decrease in R t if Tt also
increases. Consequently, job polarization is stronger during recessions. Moreover, as the economy
recovers, only part of the Routine jobs lost in the recession will return if the cost savings in Routine
task input are partially irreversible, leading to a jobless recovery.
7
8
9
A fixed proportions technology assumes that inputs are perfect complements. That is, output can only increase if both
inputs are increased in the same proportion which is what this captures. Assuming a fixed proportions technology greatly
simplifies the intuition but is not important for the results.
We assumed a unique nominal wage paid to all workers which does not capture the notion of low-paid, middling and
high-paid jobs and the process of job polarization as we discussed it in the introduction. However, introducing a nominal
wage that is lowest for Manual workers, middling for Routine workers and highest for Abstract workers does not
qualitatively change any of the results.
For example, Eurostat data show that since 2008 most European job losses were in middling occupations, and from
Eurofound’s Employment Restructuring Monitor there is ample anecdotal evidence that many of these jobs were lost
because of internal restructuring and relocation due to cost savings.
Page 10 of 28
3.
SOME EVIDENCE
In this section we use data for the 16 European countries also examined in Goos, Manning and
Salomons (2011) between the years 1993 and 2012. These countries are Austria, Belgium, Denmark,
Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Norway, Portugal, Spain,
Sweden and United Kingdom. For these countries we first provide evidence of how the job structure
is becoming less routine because of a decrease in the relative employment in routine intensive
occupations that is even stronger in the on-going recession. We then show that this is leading to a
pattern of job polarization in Europe that is being amplified by its current recession.
A.
THE RELATIVE DECREASE IN ROUTINE EMPLOYMENT
Figure 1 uses the annual micro data from the European Union Labour Force Survey (ELFS) pooled
across countries for each year between 1993-2010 together with the occupational ONET task data
used in Goos, Manning and Salomons (2011).10 For each occupation Goos, Manning and Salomons
(2011) report three standardized task measures: 1) Abstract, which requires non-routine cognitive
tasks (e.g. high for engineers); 2) Routine, requiring both manual and cognitive routine tasks (e.g.
high for machine operators and office clerks); 3) Manual, which uses non-routine manual skills (e.g.
high for janitors). For each of these tasks, an average across occupations weighted by total hours
worked in each occupation is calculated each year and an index is constructed by normalizing this
average to 1993=100. This way, an increase (decrease) in a task index captures that occupations that
use this task intensively are becoming relatively more (less) important. In line with the ALM
hypothesis, it is clear from Figure 1 that employment, in hours worked, in occupations that require
doing Routine tasks have become relatively less important over time.
Figure 2 repeats this exercise but uses quarterly employment data from Eurostat’s online database
between periods 2006Q1-2012Q3. One drawback of using Eurostat’s online database is that
information on occupational employment is only available at the 1-digit level rather than hours
worked for 3-digit occupations as we have in the ELFS used in Figure 1. However, the advantage of
using Eurostat’s online database is that it allows us to examine data after 2010 (which is the most
recent year of data in the ELFS) and up to 2012Q3. The inclusion of these more recent quarters is
particularly important to examine job changes during the on-going recession. Figure 2 shows that
between 2006Q1 and 2007Q4 not much was happening to the task indices in this short time period
preceding the current recession but that from 2008Q1 onwards relative employment in Abstract and
especially Manual tasks has increased at the expense of Routine tasks (as a robustness check, note
that this conclusion also follows from comparing years 2006-2007 to 2008-2010 in Figure 1). What
this means is that the recession is biased against Routine workers. One reason for this could be that
10
Because data for Germany in the EULFS have small sample sizes and are therefore unreliable, we have
replaced them by the much larger administrative SIAB from which we use full-time employment.
Page 11 of 28
firms that make intensive use of Routine tasks can more easily make cost savings changes as we
discussed above.11
The secular long-run impact of IR3 on relative employment can also be shown from the ELFS micro
data by regressing hours worked in an occupation (for the 21 occupations also examined in Goos,
Manning and Salomons 2011) in a country (for the 16 countries mentioned above) and in a year (for
years from 1993 to 2010) onto an occupation specific Routine Task Index (RTI) interacted with a
linear time trend and a vector of occupation-country and country-year fixed effects. The first column
of Table 1 presents the coefficient on the RTI specific time trend where the RTI measure is taken
from Goos, Manning and Salomons (2011) and which is a standardized measure of the Routine
divided by the sum of Abstract and Manual task measures used above. The point estimate is
negative and significant indicating that an occupation that is one standard deviation more intense in
RTI has grown 1.62% less fast annually between 1993 and 2010. Column (2) of Table 1 uses a related
standardized RTI measure constructed from DOT rather than ONET and used in ALM. The estimated
coefficient and standard error are very similar. Finally, column (3) of Table 1 uses the interaction of
the five key task measures used in ALM with a linear time trend in one single regression to test the
ALM hypothesis more rigorously. The point estimates are positive and significant for “Quantitative
reasoning” (Abstract) tasks and “Eye-hand-food coordination” (Manual) tasks and negative and
significant for “Direction, control and planning”, “Set limits, tolerances or standards”, and “Finger
Dexterity” (Routine) tasks.
Table 2 repeats the analysis in column (1) of Table 1 but now further adds to the RTI specific time
trend the change in the unemployment rate (column (1)) or GDP growth rate (column (2)) to see
whether the relative displacement of Routine employment is amplified during recessions. The point
estimates and standard errors suggest that an increase in the unemployment rate or a slowdown in
GDP growth lead to stronger shift in occupational employment away from Routine jobs. This is in line
with our findings in Figures 1 and 2 and the idea that the ALM hypothesis is even stronger during
recessions.
B.
JOB POLARIZATION
To see job polarization in the long run, Table 3 uses the ELFS microdata to construct employment
shares, in hours worked, for each of 21 occupations from 1993 to 2010 for all 16 countries pooled
together. The occupations in Table 3 have been ranked by their wage in 1993 as in Goos, Manning
and Salomons (2011) going from highest-paid to lowest-paid job.12 Moreover, the occupations in
Table 3 have also been grouped into the 8 highest-paid, 9 middling, and 4 lowest-paid occupations
which corresponds by and large to differences in RTI intensity by occupation rank (with the 9
middling occupations having higher RTI). We do this to intuitively relate the process of job
11
Another reason could be that employment in both Routine and Abstract intensive occupations is producing goods and
services that are more tradable and that trade volumes have disproportionately fallen relative to the decrease in
aggregate demand. However, we do not aim to examine the importance of cost savings changes relative to e.g.
the impact of a disproportionate fall in the demand for traded relative to non-traded goods and services.
12
Goos, Manning and Salomons (2011) construct time series for country specific occupational wages from ECHP, EU-SILC
and various country specific micro data. For simplicity, Table 3 uses the average ranking of an occupation across
countries in 1993 because the ranking of wages is relatively stable across countries at any given time and over time.
Goos, Manning and Salomons (2011) estimate a structural model of labor demand which uses country-time specific
occupational wages to come to similar conclusions.
Page 12 of 28
polarization to the RTI task content of an occupation.13 Column (1) gives the employment shares in
1993 and column (2) reports their percentage point changes between 1993 and 2010. For the group
of highest-paid occupations, intense in Abstract tasks, the employment share increased by 9.92
percentage points. Relative employment in the group of lowest-paid occupations, intense in Manual
tasks, increased by 1.89 percentage points whereas the employment share of the group of middling
occupations, intense in Routine tasks, decreased by 11.81 percentage points. This is in line with the
pattern of job polarization found in Goos, Manning and Salomons (2011) who report a similar table
for the period 1993-2006.
Figure 3 plots the employment shares, in hours worked, of high-paid, middling and low-paid
occupations (after normalizing the employment share for each group to 1993=100) over time (the
employment shares underlying Figure 3 are the same as those underlying Figure 1). It is clear from
Figure 3 that job polarization is a secular phenomenon given the gradual decrease in middling
relative to low-paid and especially high-paid jobs. However, Figure 3 also suggests that job
polarization has been even stronger after 2008. To see this more clearly, Figure 4 uses quarterly data
from Eurostat’s online database for the period 2006Q1 to 2012Q3 (the employment shares
underlying Figure 4 are the same as those underlying Figure 2). From Figure 4 it is clear that the
Great Recession has amplified the longer-run process of job polarization.
Figure 5 shows what has happened to the level of total employment over time in our group of 16
countries by plotting data from Eurostat’s online database for quarters 2006Q1 to 2012Q3 and
normalized such that 2006Q1=100. The figure shows that before the current recession total
employment was increasing up to 2008Q3, but that during this recession total employment sharply
fell between 2008Q3 and 2010Q1 and remained relatively stable thereafter. Figure 6 shows breaks
down these changes in total employment into changes in employment levels for the group of highpaid, middling, and low-paid occupations separately with each series normalized to 2006Q1=100.
Note that there is an important difference between Figures 4 and 6 in that Figure 4 captures changes
in employment shares whereas Figure 6 informs about changes in the level of employment by
occupation group. What Figure 6 suggests is that IR3 leads to employment growth in all occupations
in the long run as is reflected in the period 2006Q1-2008Q3, but that the current recession is
characterized by positive but lower employment growth for high-paid and low-paid occupations and
a decrease in the absolute number of middling jobs. And part of this loss in middling jobs could be
permanent, as our framework suggests that not all will return when the economy recovers.
13
In this, our methodology differs from related work in Fernandez-Macias and Hurley (2008) and Hurley, Storrie and
Jungblut (2011) who group occupations into country-specific income quintiles rather than their task content. In this
paper we are interested in the ALM hypothesis and how it leads to non-monotonic task upgrading in relative
occupational employment. So the unit of observation is the task content of an occupation as we think this has relevance
in light of technological progress and what can be offshored. To the contrary, Fernandez-Macias and Hurley (2008) and
Hurley, Storrie and Jungblut (2011) use employment by income quintile as a unit of observation to compare relative
employment changes for initially equally sized groups with different wages. This is an interesting exercise if one is
interested in changes in relative employment by income group rather than task content. For example, using the data
from Fernandez-Macias and Hurley (2008) we find pervasive job polarization across our sample of 16 countries at the
task level (just as in Table 3) but their methodology is informative about how country-specific institutional differences
that shape the income density interact with pervasive technological progress. Similar to our findings here, Hurley, Storrie
and Jungblut (2011) also find that job polarization has been stronger during the Great Recession using income quintiles
rather than task content to group occupations.
Page 13 of 28
4.
POLICY IMPLICATIONS
Over the past 10 years and even more so since the start of the Great Recession, the phenomenon of
job polarization has made policy makers consider its policy implications. In light of the framework
and empirical analysis presented above, this section draws five conclusions for European policy
makers. Firstly, there is a growing consensus that the multipliers of current fiscal consolidation are
larger than previously assumed in macroeconomic simulations. In line with our framework, the jobs
multiplier of current fiscal consolidation is also larger such that, together with the risk of a jobless
recovery, fiscal consolidation significantly increases unemployment in both the short run and the
long run. Therefore, fiscal consolidation should be postponed until the economy recovers. Secondly,
the simple framework above predicts that innovation raises the average living standard in the long
run. However, Total Factor Productivity (TFP) growth – a measure of innovation obtained from
growth accounting – since IR3 has been low in Europe and even negative in some European
countries since the mid-1990s. Because much innovation involves some sort of government
intervention, spurring TFP is straightforward and necessary. Thirdly, government spending on
education is currently falling in the majority of European countries. However, more rather than less
education is needed to spur innovation, to decrease the risk of youth unemployment and worsening
working conditions for the young in today’s recession, and to insulate workers from the
consequences of job polarization. Given that much schooling in Europe is publically provided,
education policies should be expansionary and not contractionary. Fourthly, our framework
introduced the notion of tasks (Abstract, Routine and Manual tasks) which are conceptually different
from the skills that workers supply to jobs. This difference between “task demand” and “skill supply”
is important to understand the observed skill-mismatch to changing task demands due to IR3. To
reduce this skill-mismatch, policies are needed to make informed schooling choices and to stimulate
more on-the-job training. Finally, job polarization and the current recession lead to increased
income inequality. Tackling rising inequality is important to sustain long-run economic growth.
A.
NO FISCAL CONSOLIDATION IN THE SHORT RUN
Figure 7 uses online Eurostat data to show the rise in the unemployment rate for the EU27 since
2008. In the final quarter of 2012, unemployment stood at a record high of 10.9%. Figure 8 provides
an image of differences in unemployment rates across EU27 countries in the final quarter of 2012. A
darker shade of four possible reds (based on 4 equal data point quantiles) implies a higher
unemployment rate. The highest unemployment rates are observed in Greece (26.3%) and Spain
(26.2%) and the lowest in Austria (4.3%) and Germany (5.3%). In sum, unemployment rates have
risen in most European countries, but this increase differs greatly across countries. This rise in
unemployment and its enormous societal costs require macroeconomic stabilization. But despite
automatic stabilizers that are increasing government spending, primary budget deficits as a
percentage of GDP have decreased due to discretionary fiscal consolidation.
To assess the impact of today’s fiscal consolidation, a recent IMF (2012) study estimates that the
GDP multiplier of fiscal consolidation is 1.5 on average, suggesting that a 1%-of-GDP fiscal
consolidation leads to a 1.5% decrease in GDP. This number is larger than the multiplier of 0.5
previously assumed on average in macroeconomic simulations by the Economist Intelligence Unit,
European Commission, IMF and OECD. Similarly, according to the IMF (2012) the current jobs
Page 14 of 28
multiplier of fiscal consolidation is also larger, suggesting that a 1%-of-GDP fiscal consolidation leads
to a 0.7 percentage point increase in the unemployment rate rather than a 0.2 increase as was
previously assumed on average.
That the jobs multiplier of fiscal consolidation is larger today is partially due to the fact that the GDP
multiplier of fiscal consolidation is larger. This in turn is due to the well-known reasons that there is
currently less crowding-in of investment and household consumption from reduced government
borrowing (in terms of reducing long-term interest rates and expected taxation) and from reduced
government spending (in terms of freeing-up scarce inputs). However, our framework provides
another reason why the jobs multiplier of fiscal consolidation is larger. As job polarization amplifies
and the level of routine employment is likely to fall when fiscal consolidation reduces the demand
for goods and services, more middling workers are displaced and the rise in unemployment is
stronger. Moreover, our framework also implied the risk of a jobless recovery. If this is the case,
fiscal consolidation not only raises unemployment in the short run but also in the long run. In sum,
today’s fiscal consolidation is having devastating consequences for the unemployed and the societal
benefits of postponing it until the economy recovers are large.
B.
INNOVATION
Our framework showed how innovation in progressive sectors leads to an increase in the average
real wage in the economy (in our specific set-up this was because output prices of progressive goods
are falling for a given average nominal wage) and therefore an increase in the average living
standard in the long run. However, innovation has been faltering in Europe since the mid-1990s
relative to the US, especially in some European countries.
Figure 9 uses online EU-KLEMS data to plot a time series of TFP estimates obtained from growth
accounting for six large European countries (Spain, France, Germany, Italy and the UK) and the US,
all normalized to 100 in 1995. Before 1995, TFP growth (captured by the slope of the lines in Figure
9) in Europe kept pace with the US. However, after 1995 TFP growth in the US was higher than in any
European country (although not by much in comparison to Germany and France) and TFP growth
was even negative in Spain and Italy. Despite the many difficulties related to using growth
accounting, the general conclusion from Figure 9 that Europe is less innovative is in line with the
consensus in the literature.
To understand this better, Figure 10 uses the same online EU-KLEMS database to compare sectors
that saw strong TFP growth in the US between 2000 and 2007 to their productivity growth in Europe.
For example, TFP in manufacturing grew by 28.9% in the US and by 22% in Europe between 2000
and 2007 which gives a percentage point difference of -6.9 as indicated by the first bar in Figure 10.
But the figure also shows rising productivity gaps for all other sectors that saw particularly strong
growth in the US recently: Wholesale and retail trade (-2.8); Transport, Storage and Communication
(-10.1); and Other Community, Social and Personal Services (-10.7). In sum, innovation in Europe is
increasingly falling behind in most sectors relative to the US benchmark. In terms of our framework,
this means that many more sectors in Europe must be labelled as being “non-progressive” which
implies a slower increase in average living standards. Because TFP growth at least partially originates
from some sort of government intervention, spurring innovation in all sectors of the economy is
straightforward and necessary.
Page 15 of 28
C.
EDUCATION
Education matters a great deal for innovation and therefore for raising living standards as explained
above. Education also protects the young from unemployment and worsening working conditions in
the current recession. Figure 11 uses online Eurostat data to show how education reduces the risk of
youth unemployment. The figure shows unemployment rates for the EU27 as a whole by age
(horizontal axis) and education (the two lines) in the fourth quarter of 2012. The unemployment rate
for the young lower-educated (ISCED 0-2 which means less than upper-secondary education) is
30.0% whereas the unemployment rates for the young with at least upper-secondary education are
20.9% (ISCED 3-4) and 18.4% (ISCED 5-6). Moreover, working conditions for young lower-educated
are most likely to have worsened considerably compared to young higher-educated workers because
of the recession. For example, Katz and Murphy (1992) have shown that the recession of the late
1970s and early 1980s decreased the wages of young lower-educated workers much more compared
to young higher-educated workers because young lower-educated workers are more likely to be on
fixed term contracts, less likely to receive union wages or other protection. And although the nature
of the 1970s-1980s recession is very different from today’s, its impact on youth unemployment is
similar. In sum, education significantly reduces the risk of unemployment and worsening working
conditions for the young in today’s labour market.
Education also insulates workers against the consequences of job polarization. Firstly, education
increases job mobility as pointed out by Autor and Dorn (2009). Secondly, Autor and Handel (2009)
show that there is a wage premium to being more educated than average for any given job,
including middling and low-paid jobs. Importantly, what this implies is that education policies should
aim at increasing educational attainment rates at all levels of schooling. For example, a worker in a
low-paid job can benefit as much from having an upper-secondary degree as a worker in a high-paid
job would benefit from obtaining a PhD.
Given that education matters to protect the young from the consequences of the current recession
and job polarization, education spending should be a policy priority. Yet although educational
attainment rates in Europe are increasing they remain relatively low, especially in those countries
where unemployment rates and working conditions for the young are currently worst. Figure 12
uses online Eurostat data to plot the fraction of the EU27 working-age population with at least
upper-secondary education from 2000 to 2012. Although increasing over time, this attainment rate
remains lower than in the US because European schooling systems are generally less forgiving and
inclusive at low levels of education – also see Goldin and Katz (2008). Figure 13 shows the same
educational attainment rate across countries in 2008 (based on 4 equal data point quantiles with a
darker green indicating more education). It shows that educational attainment is particularly lowless than 60%- in Greece, Italy, Portugal and Spain. Moreover, a recent study by the European
Commission (2013) shows that cuts in education budgets are currently being made especially in
those same countries. Together with their high unemployment rates (Figure 8) especially for those
with less than upper-secondary education (Figure 11), this calls for more and not less investment in
increasing upper-secondary educational attainment rates especially in those countries.
Page 16 of 28
D.
MATCHING SKILLS AND JOBS
The framework that we presented above uses the recently developed “task approach” to labour
markets (for a recent overview of this literature, see Autor 2013). The reason for using tasks (such as
Abstract, Routine and Manual tasks) as our unit of observation is that it allows us to capture drivers
of labour market changes such as IR3. This concept of changing “task” demands in production is
different from the more traditional notion of “skills” (such as education or accumulated on-the-job
training) supplied by workers to job tasks. Consequently, the way in which the labour market assigns
worker skills to job tasks is important in understanding the phenomenon of skill-mismatch.
Figure 14 uses online Eurostat data to look at skill-mismatch for school leavers (those with at least
upper-secondary education and who left school at most 10 years before the survey year) in 2000.
The figure shows substantial skill-mismatch to jobs, measured as the fraction of school leavers that
do not have the average degree in an occupation. The lowest skill-mismatch is observed in the
Netherlands, but the number still suggest that 29% of Dutch school leavers have skills that are less
well-matched than average to do their jobs. Skill-mismatch is highest in Italy, with 47% of school
leavers not supplying the skills most adequate to the tasks required in their occupation. Eurostat
numbers not captured by Figure 14 further suggest that skill-mismatch is larger for school leavers
from humanities compared to engineering and that skill-mismatch decreases somewhat but remains
high as school leavers get older. These findings are line with our framework predicting that
engineering skills are more easily matched to Abstract tasks such as writing software code, and that
tasks demands are changing due to IR3 in ways that are different from changes in skill supplies. As
the skill-mismatch documented in Figure 14 is the partially the result of incomplete information
about the abilities of school leavers and the characteristics of jobs offered by employers. In sum, a
lot more should be done to avoid ill-informed schooling choices and to stimulate young people to
choose fields of study for which skill-mismatch is lower. A good example of this are the STEM
programs in the US stimulating children’s interest in Science, Technology, Engineering and Math.
As skill-mismatch persists even as workers get older, changing task demands require continuing onthe-job training. Figure 15 uses online Eurostat data to show the incidence of on-the-job training in
EU27 countries for those aged 25-64 between 2004 and 2012. Less than 10% of all workers are
participating in on-the-job training and the incidence seems to be falling, not increasing. Moreover,
Figure 16 maps the same numbers by country (based on 4 equal data point quantiles with a darker
green shade indicating more training) in 2011 and suggests that investment in on-the-job training is
relatively low in Eastern and Southern Europe. In sum, investing in policies that stimulate on-the-job
training and life-long learning are essential for reducing skill-mismatch to task demands.
E.
TACKLING RISING INCOME INEQUALITY
Job polarization can lead to increases in wage inequality. The intuition for this is that an increase in
the relative employment in high-paid and low-paid jobs at the expense of middling jobs must, for
given wages as we had in our framework, also increase wage inequality (see the references in
Acemoglu and Autor 2011 for a very large literature). There is, however, another and probably more
important way in which inequality is rising that has received much less attention: in terms of income
inequality rather than wage inequality. Income inequality differs from wage inequality in that
income captures all earnings and not just income from labour. For example, income also contains
Page 17 of 28
dividends, capital gains and other non-wage earnings from production. If IR3 is capital-biased, it will
increase the income from capital investment. Consequently, income inequality will increase if capital
owners are concentrated in the upper-tail of the income distribution.
Evidence that job polarization and the capital-biased nature of IR3 are increasing income inequality
has started to emerge. For example, in almost all advanced nations top income inequality has risen
over the past decades. Moreover, the recent steady decrease of the labour share in GDP also
suggests capital-biased changes in income and rising income inequality. If income inequality
becomes too high, policies are needed to tackle rising income inequality.
5.
CONCLUSIONS
In Europe and other advanced nations, recent innovation leads to economic growth but also a
polarizing job structure in the long run. To get a better understanding of this, this paper has
discussed a simple conceptual framework. Our framework also explains why job polarization is
stronger during recessions and why innovation can lead to the recent phenomenon of jobless
recoveries in the short run. Adequate policy responses must start from a correct understanding of
these changes. Policies should therefore be aimed at postponing fiscal consolidation as much as
possible until the economy is recovering, more investment in innovation and education, reducing
skill-mismatch by guiding schooling choices and increased investment in on-the-job training, and
tackling rising income inequality.
Page 18 of 28
6.
REFERENCES
Acemoglu, Daron and David Autor. 2011. "Skills, Tasks and Technologies: Implications for Employment and
Earnings", Handbook of Labor Economics, Orley Ashenfelter and David E. Card (eds.), Amsterdam:
Elsevier, Volume 4B: 1043-1171.
Autor, David H. 2013. "The Task Approach to Labor Markets: An Overview", NBER Working Paper No. 18711,
January 2013.
Autor, David H., and David Dorn. 2012. "Inequality and Specialization: The Growth of Low-Skill Service Jobs in
the United States", forthcoming American Economic Review.
Autor, David H., and Michael Handel. 2012. “Putting Tasks to the Test: Human Capital, Job Tasks and Wages”,
forthcoming Journal of Labor Economics.
Autor, David H., Katz, Lawrence F. and Melissa S. Kearney. 2006. "The Polarization of the US Labor Market",
American Economic Review Papers and Proceedings, 96: 189-194.
Autor, David H., Katz, Lawrence F. and Melissa S. Kearney. 2008. "Trends in U.S. Wage Inequality: Revising the
Revisionists", Review of Economics and Statistics, 90: 300-23.
Autor, David H., Levy, Frank, and Richard J. Murnane. 2003. "The Skill-Content of Recent Technological Change:
An Empirical Investigation", Quarterly Journal of Economics, 118: 1279-1333.
European Commission. 2013. “Funding of Education in Europe 2000-2012: The Impact of the Economic Crisis”,
Eurydice Report, February 2013.
Fernandez-Macias, E. and John Hurley. 2008. “More and Better Jobs: Patterns of Employment Expansion in
Europe”, ERM Report 2008, European Foundation for the Improvement of Living and Working Conditions.
Goldin, Claudia and Lawrence F. Katz. 2008. “The Race Between Education and Technology”, Cambridge,
Mass.: Harvard University Press.
Goos, Maarten and Alan Manning. 2003. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain",
CEP Discussion Paper No. 0604, London School of Economics.
Goos, Maarten and Alan Manning. 2007. "Lousy and Lovely Jobs: The Rising Polarization of Work in Britain",
Review of Economics and Statistics, 89: 118-133.
Goos, Maarten, Alan Manning and Anna Salomons. 2009. "The Polarization of the European Labor Market",
American Economic Review Papers and Proceedings, 99:58-63.
Goos, Maarten, Alan Manning and Anna Salomons. 2011. "Explaining Job Polarization: The Roles of
Technology, Globalization and Institutions", mimeo University of Leuven, November 2011.
Gordon, Robert J. 2012. “Is US Economic Growth Over? Faltering Innovation Confronts the Six Headwinds”,
CEPR Policy Insight No. 63, September 2012.
Hurley, John and Donald Storrie with Jean-Marie Jungblut. 2011. “Shifts in the Job Structure in Europe During
the Great Recession”, EET Jobs Monitor, European Foundations for the Improvement of Living and
Working Conditions.
Jaimovich, Nir and Henry E. Siu. 2012. “The Trend Is the Cycle: Job Polarization and Jobless Recoveries”, NBER
Working Paper No. 18334, August 2012.
Katz, Lawrence F. and Robert A. Margo. 2013. "Technical Change and the Relative Demand for Skilled Labor:
The United States in Historical Perspective", NBER Working Paper No. 18752, February 2013.
Katz, Lawrence F. and Kevin M. Murphy. 1992. "Changes in Relative Wages, 1963-1987: Supply and Demand
Factors," Quarterly Journal of Economics, 107: 35-78.
World Economic Outlook. 2012. “Coping with High Debt and Sluggish Growth”, International Monetary Fund,
October 2012.
Page 19 of 28
7.
FIGURES AND TABLES
Figure 1. Task Indices Weighted by Occupational Hours Worked Yearly, 1993-2010, 1993=100
110
108
106
104
102
100
98
96
94
92
90
Abstract
Routine
Manual
Figure 2. Quarterly Task Indices Weighted by Occupational Employment Quarterly, 2006Q12012Q3, 2006Q1=100
103
102
101
100
99
98
97
Abstract
Routine
Manual
Page 20 of 28
Figure 3. Shares of Hours Worked by Occupation Group Yearly, 1993-2010, 1993=100
135
125
115
105
95
85
75
65
high-paid
middling
low-paid
Figure 4. Employment Shares by Occupation Group Quarterly, 2006Q1-2012Q3, 2006Q1=100
109
107
105
103
101
99
97
95
93
91
high-paid
middling
low-paid
Page 21 of 28
Figure 5. Total Employment Quarterly, 2006Q1-2012Q3, 2006Q1=100
106
105
104
103
102
101
100
Figure 6. Total Employment by Occupation Group Quarterly, 2006Q1-2012Q3, 2006Q1=100
110
108
106
104
102
100
98
96
94
92
90
high-paid
middling
low-paid
Page 22 of 28
Figure 7. Unemployment Rate in EU27, 2005Q1-2012Q4
% of labor force unemployed
12,0
11,0
10,0
9,0
8,0
7,0
6,0
Figure 8. Unemployment Rates by Country, 2012Q4
Page 23 of 28
Figure 9. Total Factor Productivity, 1980-2007
115
TFP, 1995=100
110
105
100
95
90
85
80
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006
ESP
FRA
GER
ITA
UK
USA
Figure 10. Differences in TFP Growth Rates between Europe and the US, 2000-2007
-8,00
-10,00
-12,00
OTHER COMMUNITY,
SOCIAL AND PERSONAL
SERVICES
-6,00
TRANSPORT AND
STORAGE AND
COMMUNICATION
-4,00
WHOLESALE AND
RETAIL TRADE
-2,00
MANUFACTURING
Percentage Point Differences in TFP
Growth Rates
0,00
Page 24 of 28
unemployment rate (in %)
Figure 11. Unemployment Rate by Age and Education in EU27, 2012Q4
30
25
20
15
10
5
0
15-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64
ISCED 0-2
ISCED 3-4
ISCED 5-6
Figure 12. Upper-Secondary or Tertiary Education in EU27, 2000-2012
% of working-age population
72,0
70,0
68,0
66,0
64,0
62,0
60,0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
Page 25 of 28
Figure 13. Upper-Secondary or Tertiary Education by Country, 2008
Figure 14. Job Mismatches among School Leavers, 2000
% of jobs with mismatch
50
45
40
35
30
25
Page 26 of 28
Figure 15. Participation in On-The-Job-Training in EU27, 2004-2012
% aged 25-64 particpating
9,6
9,4
9,2
9,0
8,8
8,6
8,4
8,2
8,0
2004
2005
2006
2007
2008
2009
Figure 16. Participation in On-The-Job-Training by Country, 2011
2010
2011
2012
Page 27 of 28
Table 1. Task Trends in Occupational Employment in the Long Run
Dependent variable: Log (Hours Worked)
(1)
Routine Task Index (GMS-ONET)
(2)
(3)
-1.62
(0.05)
Routine Task Index (ALM-DOT)
-1.46
(0.05)
Quantitative reasoning (MATH)
1.97
(0.07)
-0.79
(0.04)
-0.38
(0.03)
-1.71
(0.09)
0.57
(0.07)
Direction, control and planning of activities (DCP)
Set limits, tolerances or standards (STS)
Finger dexterity (FINGER)
Eye-hand-foot coordination (EHF)
Notes: The coefficients and standard errors (in brackets) are multiplied by 100. Given all point estimates are in
absolute value at least twice their standard error, all point estimates are statistically significant at the 5% level.
Table 2. Task Trends in Occupational Employment in the Long and Short Run
Dependent variable: Log (Hours Worked)
Routine Task Index (GMS-ONET)
Routine Task Index * Δ unemployment rate
Routine Task Index * Δ GDP
(1)
-1.6022
(0.0634)
(2)
-1.6115
(0.0654)
-0.0003
(0.0001)
0.0001
(0.0001)
Notes: The coefficients and standard errors (in brackets) are multiplied by 100. Given all point estimates are in
absolute value at least twice their standard error, all point estimates are statistically significant at the 5% level
(the coefficient and standard error on the interaction with changes in GDP have been rounded in the table).
Page 28 of 28
Table 3. Employment Shares by Occupation, 1993-2010
High-paid occupations
Corporate Managers
Engineering science professionals
Life science and health professionals
Other professionals
Managers of Small Enterprises
Physical and engineering associate professionals
Other associate professionals
Life science and health associate professionals
Middling occupations
Drivers and mobile plant operators
Stationary plant and related operators
Metal, machinery and related trades workers
Precision, handicraft and related trades workers
Office clerks
Customer services clerks
Extraction and building trades workers
Machine operators and assemblers
Other craft and related trades workers
Low paid-occupations
Personal and protective services workers
Other labourer’s
Models, salespersons and demonstrators
Sales and services elementary occupations
Employment Share,
1993
30.98
4.49
2.99
1.98
3.40
4.12
4.28
7.32
2.40
47.15
4.97
1.62
8.92
1.48
10.69
2.00
7.37
6.19
3.92
21.87
6.59
4.38
6.53
4.37
Change in
Employment
Share, 1993-2010
9.92
2.75
1.50
0.37
0.87
2.24
0.19
1.41
0.60
-11.81
-0.08
-0.42
-3.58
-0.90
-2.62
0.46
-0.01
-2.84
-1.83
1.89
2.98
-1.35
-1.25
1.51
Notes: The occupations in Table 3 have been ranked by their wage in 1993 as in Goos, Manning and Salomons (2011)
going from highest-paid to lowest-paid job.