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Paper For Royal Statistical Society “Explanations of Rising Economic Inequality” Conference
Labour Market Inequality and Changes in the Relative Demand for Skills1
Stephen Machin*
January 2002
* Department of Economics, University College London and Centre for Economic Performance, London School of Economics
1. Introduction In the last twenty five years or so dramatic changes in labour market inequality have been
observed in some countries. Key to this has been the improving labour market position of
relatively skilled workers and the collapsing labour market for unskilled workers. In most
advanced countries skilled workers have improved their labour market position as
compared to their less skilled counterparts in at least one dimension of relative wages,
employment and unemployment. In some countries, most notably the US and UK, all of
these labour market outcomes moved in favour of the skilled. In these two countries there
1 This paper uses and builds on some of my existing work with my co-authors in this field (Eli Berman, John Bound, Thibaut Desjonqueres, John Van Reenen). I am grateful to them, to participants in the November 2001 RSS Conference on “Explanations for Rising Economic Inequality” and to participants in the September 2001 Conference on the Leverhulme Trust project “The Labour Market Consequences of Technological and Structural Change” (especially my discussant Steve Pischke) for a number of useful comments. I would also like to thank Francis Green for giving me access to some of the computer data use in the paper and John Schmitt for giving me the Economic Policy Institute Current Population Survey files (EPI ORG data).
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were very large rises in wage inequality that combined with employment shifts in favour
of the skilled to generate large rises in overall labour market inequality.
A lot of research attention has been motivated by these changes. A by now large
academic literature2 has documented shifts in labour market inequality and in changing
labour demand by skill type. A considerable amount of research effort has been expended
on testing hypotheses that specify particular factors as underpinning and driving the
observed changes. In this research a big emphasis has been placed upon the role played
by new technologies and how they have altered employers’ demand for skills. It has been
proposed that the kinds of new technologies diffusing into modern workplaces favour
skilled workers, so that they are biased towards skilled workers, and this skill-biased
technology change (sbtc) has driven the observed changes. One commonly cited example
of this has been the computerization of work that seemed to occur at the same time as
rising wage inequality and the increased relative demand for skills.
There are many empirical questions of interest to do with shifts in labour market
inequality. In this paper I consider some of these. I start, in Section 2, by describing
international comparisons of recent shifts in labour market inequality. I then move on to
the UK and US experiences in a bit more detail and present a simple relative supply and
demand framework to help interpret the observed facts. In Section 3 I review the
evidence and consider what we have learned on relative demand shifts and skill-biased
technology change. In Section 4 I present some new findings that compare and contrast
the 1980s and 1990s in the UK and US. In Section 5 I give some conclusions.
2 See the review of Katz and Autor (1999) and the references contained therein.
3
2. Changes in Labour Market Inequality
International Trends
Table 1 shows how wage inequality changed in ten countries from the late 1970s/early
1980s through to the mid 1990s. The Table reports the ratio of the 90th to the 10th
percentile of the male wage distribution for each of these countries at the two points in
time. The final column of the Table converts the numbers into annualised changes. The
numbers in the Table show a clear pattern. In some countries, most notably the UK and
US, the 90-10 ratio rose very sharply. In the US it rose by .073 points a year going from
3.18 in 1979 to 4.35 by 1995. In the UK it rose by .054 points a year, from 2.45 to 3.31.
However, in other countries much less change occurred, particularly in continental
Europe where the wage structure, at least according to this summary measure, did not
alter much. It is evident that rising wage inequality was specific to some countries.
On the other hand, employment shifts seem to have occurred most everywhere.
Table 2 shows changes in the employment share of non-production workers in twelve
countries in the 1970s and 1980s.3 The Table shows increases in the employment share of
non-production workers to occur in almost all cases. Interestingly, whilst the relative
wage of non-production workers vis-à-vis production workers fell slightly in the 1970s it
rose in most cases in the 1980s, despite the observed shifts in relative employment. Put
differently the 1980s saw simultaneously rising employment and wages for the non-
production workers.
Shifts in Relative Demand and Supply
More generally one can observe, in many advanced countries, that many more skilled
workers are now in employment than in the past, both in absolute numbers and relative to
3 The broad occupational grouping (non-production versus production workers) is used as it is the only measure of relative employment that is consistently defined over time for a large range of countries.
4
their less skilled counterparts. Table 3 focuses in more detail on the case of the UK and
US and now uses data on worker’s education to proxy skill. The upper panel of the Table
shows the employment and hours shares of workers with a degree in the UK, together
with the relative wage between graduates and non-graduates at five year intervals
between 1980 and 2000. The lower panel shows the same for the US.
The Table very clearly confirms that rapid increases in the shares of the relatively
skilled group of workers (graduates) have occurred in both countries. In the UK in 1980
5 percent of workers had a degree but this rose sharply through the 1980s and 1990s to
reach 17 percent by the year 2000. In the US there were many more graduates at the start
of the 1980s, at around 19 percent, but there were also sharp rises over time that reached
over 27 percent by 2000. In both countries, therefore, the relative supply of graduates
increased very sharply between 1980 and 2000. If one considers hours shares of
graduates, rather than employment shares, one sees a similar pattern with there being a
slightly bigger increase in the hours shares in both countries.
What the Table also shows is that, despite their increased numbers, the wages of
more skilled workers (graduates) have not fallen relative to the less skilled (non-
graduates).4 The final column of the Table actually shows the opposite to have occurred.
The numbers given in this column are the relative wages of graduates versus non-
graduates for full-time workers (after standardizing for age and gender in a least squares
regression). In both countries the relative wage for graduates rose between 1980 and
2000. The increase is very sharp in the US, going from 1.36 to 1.66, whilst the UK
increase is less marked but shows a rise from 1.48 to 1.64. These numbers square up well
Looking at education shares (as I do a little later for the UK and US) does, where both relative proxies for skill exist, reveal similar patterns. 4 Much the same pattern emerges if one looks at broad occupational categories.
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with the wage inequality numbers reported earlier and show that part of that rise can be
attributed to the rising wage gaps between the more and less educated.
Considering the decade changes at the bottom of the Table also reveals some
interesting differences. The relative wage gaps opened out by more in both countries in
the 1980s. In the UK .08 of the .12 change between 1980 and 2000 took place in the
1980s and in the US .19 of the .30 change occurred between 1980 and 1990. In fact the
1980s appear to the decade of faster wage inequality increases in both the UK and US. In
the 1990s there seems to be less change in the wage structure. To re-iterate this
observation, Figure 1 shows the 90-10 hourly wage ratio for the UK and US from the
mid-1970s onwards. The pattern shows the 1980s widening of the wage structure to be
greater than was seen in previous periods, or since. From what we know from elsewhere
(e.g. Gosling, Machin and Meghir, 2000, for the UK, or Card and DiNardo, 2001 for the
US) the 1960s and 1970s saw small shifts in the inequality of wages. The 1975-80
changes in the Figure bear this out. In fact the UK actually saw a compression that
reduced wage inequality (related to the incomes policies introduced during that decade).
From the 1980s onwards the wage structures of both countries widened, but at a faster
rate in the 1980s than in the 1990s.5 There appear to be cross-country differences in the
timing of when the 1990s slowdown began with the US 90-10 being flat for most of the
decade, but the flattening out occurred in the UK from the mid-1990s.
A Simple Framework
How might one try and rationalize simultaneously rising relative wages and employment
for the skilled? One straightforward way is to use a simple relative demand and supply
framework to organize one’s thoughts around this question. Consider Figure 2 below:
5 In the UK the 90-10 went from 2.90 in 1980 to 3.55 by 1990 and then to 3.86 by 2000. In the US the 901-0 went from 3.63 (1980) to 4.39 (1990) to 4.53 (2000).
6
Figure 2 shows a labour market with two skill types, skilled and unskilled (or with
high and low educational qualifications), where employers demand a certain number of
each. Equilibrium in such a model is given by the intersection of the relative demand and
supply curves given by D0 and S0 in the Figure, with a relative wage of (Ws/Wu)0 and
relative employment of (Ns/Nu)0.
In terms of the UK and US experience of the 1980s and 1990s what seems to have
happened is that the ratio of skilled to unskilled wages has gone up at the same time as
the ratio of skilled to unskilled employment. It becomes clear that, to get such an
outcome, there has to have been an outward shift in the relative demand curve. Suppose
the demand curve shifts out to D1 (and supply is held fixed for convenience, although in
practice as the data of Table 3 shows supply has increased). One then ends up with
simultaneously higher relative wages and employment for the skilled at (Ws/Wu)1 and
(Ns/Nu)1.
A plausible, and more general, way of thinking about this kind of relative demand
shift in favour of the skilled is in terms of an economic model where the wages and
employment of skilled and unskilled workers are the outcomes of a race between supply
S0
D0 D1
Skilled/Unskilled Employment
Skilled/Unskilled Wages
(Ns/Nu)0 (Ns/Nu)1
(Ws/Wu)1
(Ws/Wu)0
Figure 2
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and demand. Here the more general implication is that both demand and supply curves
are shifting and the question is which curve has moved the most. To have generated
simultaneously higher wages and employment for the skilled it seems that relative
demand must have increased by more than relative supply. Put alternatively, over the
period of rising wage inequality, demand has won the race so that employers are prepared
to pay workers with appropriate skills more than less skilled workers, despite there being
many more of them supplying their labour.6
3. Evidence on Relative Demand Shifts
Skill-Biased Technology Change
The skill-biased technology change (sbtc) hypothesis is founded upon the notion that
employers’ demand for more skilled workers has been shaped by the kinds of new
technologies that are permeating into modern workplaces. The critical idea is that these
new technologies lead to higher productivity, but that only some workers possess the
necessary skills to use them. As such employers are prepared to increase the wages of the
skilled workforce who are complements with the new technology. But at the same time
less skilled workers do not possess enough skills to operate the new technologies and
their wages are lowered or they lose their jobs. As such the relative wages and
employment of the more skilled rise. There is a range of evidence that has been proposed
to test this hypothesis. Some is rather indirect, and some relates the observed changes to
direct measures of technology. I next therefore consider some of this work.
6 This argument has been made in a number of places (for a recent exposition see Manning and Manacorda, 1998).
8
Indirect Evidence on sbtc
The sbtc hypothesis requires that technology introduction and diffusion drives shifts in
skill demand. Because of this one should see variations in skill demand shifts occurring
where employers have more to gain from new technology and consequently there should
be systematic differences in the extent of relative demand shifts within particular
workplaces, firms and industries (each of whom is likely to differ in the extent of their
use of new technologies). A first indirect test of relevance to the sbtc argument therefore
comes from a decomposition of aggregate changes in skill demand (usually measured by
wage bill or employment shares of skilled workers), say ∆S, for j (=1,…N) industries as
follows:
∑+∑==
−
=
− N
1jjj
N
1jjj S∆PP∆S∆S
The decomposition breaks the overall shift in skill demand into two components. The
first is the within-industry component of skill upgrading (weighted by P, the relative size
of industry j, and where a bar is a time mean). The second measures between-industry
shifts, namely how much bigger or smaller an industry is becoming over time (weighted
by the time averaged skill demand).
A number of studies have used this kind of decomposition and have
systematically found that the bulk of the aggregate changes have occurred within, rather
than between industries or workplaces. These studies cover different countries, levels of
aggregation, time period and skill measures (see Machin, 2001, for a Table that pulls
together the various studies). An example of this is given in Table 2 which was discussed
earlier. There one sees then bulk of industry shifts in non-production employment shares
occurring within, rather than between, industries. That the bulk of the shifts are seen
within industries (i.e. some industries have faster rates of skill upgrading than others) is
9
essentially a prerequisite for skill-biased technological change to even be a starter as a
possible explanation of the observed shifts in skill demand.
A second piece of indirect evidence on sbtc comes from considering whether one
can identify common patterns of cross-country change. In particular, if one sees faster
skill demand shifts occurring in the same sorts of industries in different countries one
may view this as informing the sbtc hypothesis (to the extent that similar industries in
different countries utilize similar technologies). Berman, Bound and Machin (1998)
looked at country by country pairwise correlations of industry skill demand shifts for the
same industries in different countries (using data from the United Nations Industrial
Statistics database for the countries in Table 2). They found that most industrial demand
shifts covary positively across countries. In fact they report 31 out of 36 pairwise
comparisons they calculate to be positive and a sizable number of the correlations are
statistically significant (13 of them). Bearing in mind the possible attenuation that
looking at correlations of changes (rather than levels) may bring about, this suggests a
tendency of similarly sized skill demand shifts to be clustered in the same sorts of
industries across different countries. One can read this as indirect evidence that sbtc has
been pervasive in changing labour market outcomes across the developed world.
A third piece of indirect evidence comes from extending the cross-country
evidence to the developing world. In fact when one does so it is possible to find evidence
of demand shifts in favour of the more skilled going on in much poorer countries. Some
work has noted that one sees skill upgrading happening in the more technologically
advanced industries of some developing countries (Feliciano, 2001, Hanson and
Harrison, 1999, Robbins, 1995). This is entirely consistent with sbtc altering relative
wage and employment outcomes globally.
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Furthermore shifts in skill demand in the developing world appear to be correlated
with the shifts seen in the developed world. Berman and Machin (2000) extend the
analysis of Berman, Bound and Machin (1998) to look at industry skill demand shifts in
28 high, middle and low income countries.7 They present evidence that patterns of
industrial skill upgrading in some developing countries are similar in some respects to
those seen in the higher income countries. They present pairwise correlation coefficients
testing whether one sees common industry patterns and uncover patterns of similarity,
certainly for the middle income countries (the evidence is more mixed for the low income
countries).
More Direct Evidence on sbtc
The evidence of the previous sub-section is in line with sbtc being important, but
largely relies on empirical patterns that do not relate shifts in skill demand to observable
technology indicators. But identification of the technology related characteristics of
which industries have had faster rates of upgrading can be considered so as to shed more
light on the sbtc hypothesis. One (frequently used) way in which researchers have
formally tested this is to estimate cost share equations that relate changes in the skilled
wage bill/employment share in a given industry to observable measures of technology. A
typical specification (Berman, Bound and Griliches, 1994; Machin and Van Reenen,
1998), measured for industry j in year t, is:
∆(Skilled wage bill share)jt = α + β∆log(Capitaljt) + δ∆log(Outputjt) + φTECHjt + εjt
7 The high income countries are the 12 in Table 2. The middle income countries are Colombia, Cyprus, Czechoslovakia, Greece, Guatemala, Hungary, Ireland, Malta, Portugal, South Korea, Spain and Turkey and the low income countries are Bangladesh, Egypt, Ethiopia, India, Nigeria and Tanzania. The sample of countries considered is entirely driven by data availability on industry skill demand shifts over time from the United Nations Industrial Statistics database.
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where the cost share equation can be generated from a translog cost function with two
labour inputs (skilled and unskilled) and assuming capital to be a quasi-fixed factor. The
focus in these equations then becomes whether the coefficient φ on the technology
indicator TECH is estimated to be positive.
Table 4 summarises the US and UK estimates of φ. It is clear that for a range of
time periods, different levels of aggregation and different technology measures that there
exists a positive association between industry shifts in skilled wage bill or employment
shares and observable technology measures. Put differently it appears to be the
technologically more advanced industries where one has seen faster increases in the
relative demand for skilled workers. This has been taken in some quarters as evidence in
line with the hypothesis that skill-biased technology changes lie behind the demand shifts
favouring relatively skilled workers.
A more controversial area of research in this field asserts that individuals receive
a wage payoff for working with computers. If true this would, of course, be very much in
line with the sbtc hypothesis as it would imply computer users are rewarded for higher
productivity linked to their use of computers. The most well known paper here is
Krueger’s (1993) study of US Current Population Survey data where he augments
standard human capital earnings functions with a computer usage dummy. Even after
controlling for a range of human capital and job related characteristics he reports a
sizable wage premium for computer users. In his most detailed specification Krueger
reports a 15 percent wage premium in 1984 and this goes up, despite a coincident rise in
the number of computer users, to 18 percent by 1989.8
8 These are calculated as [exp(.140) – 1]X100 and [exp(.162)-1]X100 where .140 and .162 are the coefficients on the computer use dummy in a semi-log wage equation controlling for education, experience, race, part-time job status, living a metropolitan area, gender, veteran status, whether married, whether a union member and broad occupation. Computer usage rises from 25 to 37 percent between 1984 and 1989.
12
There are clearly some concerns with this, relating to possible reverse causation
and omitted variable bias. Indeed DiNardo and Pischke (1997) adopt a similar approach
replacing the computer use variable with a pencil use variable and uncover a wage
premium linked to pencil use. This seems suggestive of the idea that the computer use
variable may be proxying other unobserved characteristics of people not measured in the
survey data (and therefore not controlled in the regression equation). Nonetheless the
computer premia in Krueger’s analysis are sizable and one would require a large
unobserved heterogeneity (or endogeneity) bias to eliminate them. As such they have also
been cited in some quarters as evidence in line with the sbtc hypothesis.
A final more direct set of evidence comes again from the developing world. In the
study of Berman and Machin (2000) already cited above they correlate changes in skill
demand in middle and low income countries with industry technology measures from the
advanced world.9 Certainly in middle income countries there is a significant positive link
between faster industry skill upgrading and the advanced world industry technology
measures. Thus, to the extent that new technologies are likely to diffuse across
international borders in similar industries, one can think of this as being in line with
technology altering the skill mix of wages and employment at a faster rate in favour of
skilled workers in more technologically advanced industries in many countries in the
world.
4. Recent Evidence on Relative Demand Shifts
In this Section of the paper I consider some more recent evidence on relative demand
shifts and technological change. I focus on the labour markets of the United Kingdom
and the United States. For the US I am able to look at changes in the relationship between
13
industry shifts in skill demand and changes in technology across the 1980s and 1990s.
For the UK I can only look at changes seen in the 1990s. I begin by discussing the data
used to measure technological change and then move to the US and UK empirical
analyses in turn.
Changes in Technology Across Industries
The only data on technology measures that exists for similar definitions at a
reasonably disaggregated industry-level across the whole economy for both countries are
those measuring computer usage in the workplace. These have obvious limitations (as
some of the discussion above has already made clear and some more does below) but I
use them in what follows.
Data on computer usage at work is available for several years in the US in various
supplements of the monthly Current Population Survey. The first of these is in October
1984, then there are further supplements of the same structure in October 1989 and 1993,
and the most recent is October 1997.10 I use all these to look at correlations between skill
upgrading and changes in computer usage at work. Data for the UK is more sparse. There
is data in the British Social Attitudes Surveys of 1985 (for a very small sample) and in
1987 and 1990. There is also data in a more recent survey, the 1997 Skills Survey, and it
is these latter data I use here.
The first thing to notice is that there exists a very strong correspondence between
industry computer usage across the two countries. In other words, it is very much the
same industries that have more employees working with computers. Despite industry
definition differences across countries (which force me to aggregate industries), coupled
with small cell sizes in the UK data (which force further aggregation) I have put together
9 Specifically they consider US computer usage and OECD R&D intensity matched up by industry to the industries of developing countries.
14
series on computer usage for the same 31 industries in the two countries in 1992/3 and in
1997. Figure 3 plots these US and UK computer usage variables against one another.
There is a strong correlation between industrial computer usage across the countries
(correlation coefficients = .88 for 1997 and .86 for 1992/3).
What is also clear is that computer usage is at high levels in some industries,
which, certainly by 1997, are near saturation point and cannot rise much on the basis of
the percent using computers variable. This presents some concerns if, as we do below,
one wishes to relate skill upgrading to changes in industry computer usage, in the aim of
picking up shifts in skill demand related to increased computer usage.
This concept of reaching saturation point can be further considered using the US
data which goes back further in time, and which can be considered at a more disaggregate
level. Using data on computer use in the four available years (1984, 1989, 1993 and
1997) Figure 4 plots computer use in a given year against computer use in the previous
time period for 220 US industries. It then fits a quadratic through the points.11 It becomes
clear that in the earlier time periods, computer usage grew fastest at the top end of the
computer use spectrum. By the time one gets to the 1993 to 1997 comparison, however,
this is no longer true and computer use appears to have reached its saturation point in
already high computer using industries. In fact one can also see this by considering
regressions of the change in computer usage on the initial level of computer use for the
three time periods (regressions weighted by industry employment, standard errors in
parentheses, constant not reported):
Change in computer use, 1984-89 = (.005).039 Computer use, 1984
Change in computer use, 1989-93 = (.004).013 Computer use, 1989
10 There are two more up to date CPS supplements in the US (December 1998 and August 2000) but they do not ask about computer use at work and are very much more focused on internet usage at home.
15
Change in computer use, 1993-97 = (.003).011- Computer use, 1993
So the computer use pattern moves from faster computerization in already high computer
industries in the first two periods, to the opposite pattern between 1993 and 1997. This
tilt in the computer use growth profile will prove important to bear in mind when we look
at correlations between skill upgrading and computer use. I turn to this next.
US Industry Skill Upgrading and Computer Use at Work
Table 5 reports some descriptive statistics on the data I use to consider the
connections between skill upgrading and increased computerization in the US. The Table
shows the percent using computers at work doubling from around 1 in 4 in 1984 to 1 in 2
by 1997. The graduate shares of employment and the wage bill rise sharply as well, as
pointed out earlier for the decade differences considered in Table 3.
Table 6 reports estimated coefficients from regressions of changes in the graduate
wage bill share on increases in computer usage for the full 1984 to 1997 time period and
then for the three sub-periods of change for which I have data (1984-89, 1989-93 and
1993-97). The overall period regression shows a strong association between changes in
graduate wage bill shares and increased computer usage. However, when one looks at the
sub-period regressions, the coefficient on computer usage falls over time and the
relationship disappears by the time we get to the final column specification looking at the
1993-97 sub-period. It appears, as one might have suspected from the pattern in Figure 4,
that some technologically advanced industries have reached saturation point in terms of
computer diffusion and as such links between skill upgrading and increased
computerization, at least measured in head count terms, have gone away. Of course, this
does not mean that skill-biased technology change no longer exerts an influence on the
11 The quadratic functional form works well to illustrate the changing shape of the cross-time correlations.
16
wage structure, but it may cast doubt on simple headcount measures of computer use as
picking up the labour market effects of technology change in the 1990s.
UK Industry Skill Upgrading and Computer Use at Work
The UK situation in the 1990s is considered in Tables 7 and 8. The upper panel of
Table 7 shows some descriptive statistics on computer usage in 1992 and 1997 from the
1997 Skills Survey. Because the data come from a cross-section two 1992 numbers are
reported, those for all people and for people in the same job as in 1997. The latter should
more accurately pick up trends between 1992 and 1997. The first row of the Table clearly
confirms the increased computerization of jobs carrying on through the 1990s (this is also
emphasized in Green et al, 2001).
The Skills Survey also contains data on the importance of computers and the
Table reports the percent of computer users broken down into sub-groups indicating
whether computers are ‘essential’, ‘very important’, ‘fairly important’ or ‘not very
important’. This breakdown shows a rise over time in the first three categories, showing
computers to be more important by 1997, and a fall in the ‘not very important group’ by
1997. Such a distinction is useful in the earlier discussion about computer saturation of
the job market in some industries.
The lower panel of the Table reports numbers on the graduate share of
employment and the wage bill from the Labour Force Survey. Three years are reported
because of an industry definition change that occurs between 1992 and 1997. This means
I carry out the industry-level empirical analysis between 1994 and 1997 for a consistent
set of industries.
In Table 8 I therefore report a set of industry-level regressions of changes in
graduate wage bill shares in the UK in the 1990s on changes in the percent of people
using a computer at work (in column (1)) and then on changes in the importance of
17
computers (to varying degrees) amongst computer users in the remaining columns. The
first column shows no relation between 1990s skill upgrading and the increased use of
computers in the 1990s. This mirrors the insignificant US finding over the same period
and supports the notion that simple computer usage measures may not be particularly
good measures of technology change in the 1990s when computer use levels have
reached such high levels in technologically advanced industries.
However, once broken down by importance of the computer to the job I do find
industry skill upgrading to be associated with the increased importance of computers. As
one moves across the Table increasingly stringent measures of the importance of
computers are considered. In column (2) I regress changes in graduate wage bill shares
on changes in the percent of people using computers that are stated to be at least fairly
important to their job. In column (3) the cutoff is at very important for the job and in
column (4) is at essential for the job. The strongest positive (and statistically significant)
association is between changes in graduate wage bill shares and changes in the percent
using computers for whom the computer is essential to their job. It seems that relative
demand is still shifting in favour of skilled workers in industries where computers are
becoming more important, even in the 1990s.
5. Conclusions
In this paper I have considered shifts in the employment and wage structure of
modern labour markets. There have been important changes in the way in which workers
of different skills are rewarded in contemporary labour markets as compared to the past.
Indeed, there is now a higher price to skills than there used to be as employers
increasingly are demanding more workers with the necessary skills and aptitudes to use
the sorts of new technologies that have been diffusing into workplaces. Indeed it seems
18
rather hard to deny that technological changes that have occurred are closely linked to the
relative demand shifts and movements in labour market inequality seen in the recent past.
Some more recent evidence highlights some other interesting observations about
the way in which such relationships may be changing. It seems by now clear that the
1980s was the decade of the recent past where wage gaps between the skilled and less
skilled widened out by most in the UK and US. And one can uncover empirical
associations showing that proxies for skill-biased technology change (like computer
usage or R&D intensity) were associated with the rapid rise in wage inequality seen in
that period. However, once one moves to the 1990s, with slower rising inequality, it
seems that the basic computer use at work variable that was strongly connected to the
1980s changes is not correlated with industry relative demand shifts of the 1990s. This is
most likely because the diffusion of computers at work has become so widespread that a
simple headcount measure no longer adequately measures technological advances as
computers have become a standard work aid, especially in technologically advanced
industries. Whether this means computers are no longer skill-biased, or whether one
requires more refined data on the nature of skill-bias is not entirely clear (though see
Autor, Levy and Murnane’s, 2001, detailed discussion on the skill content of technical
change). However, the final piece of (tentative) evidence presented in this paper is in line
with the latter in that, at least in the UK, industry patterns of increased skill demand are
linked to the increased use of computers that are deemed essential for the job.
19
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in Mexico," Industrial and Labor Relations Review, 52, 271-288. Katz, Lawrence and David Autor (1999) “Changes in the Wage Structure and Earnings
Inequality”, in O. Ashenfelter and D. Card (eds.) Handbook of Labor Economics, North Holland
Krueger, Alan (1993) “How Computers Have Changed the Wage Structure: Evidence
from Microdata, 1984-1989", Quarterly Journal of Economics, 108, 33-60.
20
Machin, Stephen (1996) “Changes in the relative demand for skills in the UK labor market,” in Acquiring Skills: Market Failures, Their Symptoms and Policy Responses, Alison Booth and Dennis Snower (eds.), Cambridge: Cambridge University Press.
Machin, Stephen (2001) ‘The Changing Nature of Labour Demand in the New Economy
and Skill-Biased Technology Change’, end of project report paper for Leverhulme Trust project “The Labour Market Consequences of Technological and Structural Change”.
Machin, Stephen, and John Van Reenen (1998) "Technology and changes in skill
structure: Evidence from Seven OECD Countries," Quarterly Journal of Economics, 113, 1215-1244.
Manning, Alan and Marco Manacorda (1998) "Just Can't Get Enough: More on Skill-
Biassed Change and Labour Market Performance”, Center for Labor Economics Working Paper #7, Berkeley.
Nickell, Stephen and Brian Bell (1995) “The Collapse in Demand for the Unskilled and
Unemployment across the OECD”, Oxford Review of Economic Policy, 11, 40-62.
Robbins, Donald J. (1995) "Trade, Trade Liberalization and Inequality in Latin America
and East Asia- Synthesis of Seven Country Studies." Harvard mimeo.
21
Figure 1: 90-10 Hourly Wage Ratios in the UK and US, 1975-2000
UK New Earnings Survey
90-1
0 ho
urly
wag
e ra
tio
Year1975 1980 1985 1990 1995 2000
2.5
3
3.5
4
4.5
UK Current Population Survey
90-1
0 ho
urly
wag
e ra
tio
Year1975 1980 1985 1990 1995 2000
2.5
3
3.5
4
4.5
22
Figure 3: Cross-Country Correlations of Industry Computer Usage, UK and US, 1992/3 and 1997
1997
UK
1997
com
pute
r use
US 1997 computer use0 .2 .4 .6 .8 1
0
.2
.4
.6
.8
1
Agricult
Mining,
ConstrucFood, Dr
Textiles
Printing
Chemical
Rubber &Concrete
Metal In
MachinerElectricTransporUtilitie
Wholesal
Retail T
Rail, Bu
Air Tran
Other TrPost & T
Banking
Real EstBusiness Public AEducatio
Health &
MembershEntertai
Personal
Private
1992/3
UK
1992
com
pute
r use
US 1993 computer use0 .2 .4 .6 .8 1
0
.2
.4
.6
.8
1
Agricult
Mining,
Construc
Food, Dr
Textiles
Printing
Chemical
Rubber &Concrete
Metal In
Machiner ElectricTranspor
UtilitieWholesal
Retail T
Rail, Bu
Air Tran
Other Tr
Post & T
Banking
Real Est
Business
Public A
Educatio
Health &
MembershEntertai
Personal
Private
Notes: Sources: UK – Skills Survey; US - Current Population Survey.
23
Figure 4: Computer Usage By Industry in the US Over Time
Predicted Quadratic
US
com
pute
r use
199
7
US computer use 19840 .5 1
0
.5
1
Predicted Quadratic
US
com
pute
r use
199
3US computer use 1989
0 .5 1
0
.5
1
Predicted Quadratic
US
com
pute
r use
198
9
US computer use 19930 .5 1
0
.5
1
Notes: Sources: US - Current Population Survey (CPS industries with 10 or more observations). The circles show the plots to be weighted by CPS cell sizes (larger circles are industries with larger cell sizes). The predicted lines are from a regression [computer use(t)] = a + b[computer use(t-1)] + c[computer use(t-1)]2 + u. The regression is weighted by the CPS industry cell size.
24
Table 1: International Changes in Wage Inequality
Late 1970s/Early 1980s
Mid 1990s Annualised Change
Australia 2.74a 2.94g .013 Austria 2.27b 2.40f .010 Canada 3.46c 3.77f .024 Finland 2.44b 2.53f .006 Germany 2.38d 2.25e -.013 Italy 2.29a 2.64e .022 Japan 2.59a 2.77f .001 Sweden 2.11b 2.20e .007 UK 2.45a 3.31f .054 US 3.18a 4.35f .073
Notes: The source for these numbers is the July 1996 OECD Employment Outlook. The reported numbers are ratios of the 90th to 10th percentile of the male wage distribution (except for Austria which is the 80-10 male wage ratio). The actual years for each number are given by the letter superscripts: a – 1979; b –1980; c – 1981; d – 1983; e – 1993; f – 1994; g – 1995.
25
Table 2: Patterns of International Skill Upgrading in the 1970s and 1980s
1970-80 1980-90
Country Change in % non
production (annualized)
% within Change in wage ratio
(%)
Change in % non
production (annualized)
% within Change in wage ratio
(%)
Note
US 0.20 81 -2 0.30 73 7 Norway 0.34 81 -3 . . . 1970,80,n/a
Luxembourg 0.57 90 6 0.30 144 12 Sweden 0.26 70 3 0.12 60 -3 Australia 0.40 89 -17 0.36 92 2 1970,80,87
Japan . . . 0.06 123 3 n/a,81,90
Denmark 0.44 86 -11 0.41 87 7 1973,80,89
Finland 0.42 83 -11 0.64 79 -2 W.Germany 0.48 93 5 . . . 1970,79,n/a
Austria 0.46 89 7 0.16 68 7 1970,81,90
UK 0.41 91 -3 0.29 93 14 Belgium 0.45 74 6 0.16 96 -5 1973,80,85
Average 0.40 84.3 -1.8 0.28 91.5 4.2
Notes: From Berman, Bound and Machin (1998). The percent within column is based on comparing changes over time in the same 28 industries in each country (except for Belgium [24], W. Germany [22], Japan [27], Luxembourg [9 in 1970-80, 6 in 1980-90] and Norway [26]).
26
Table 3: Aggregate Trends in Graduate/Non-Graduate Employment,
Hours and Relative Wages, UK and US 1980-2000
UK Labour Force Survey/General Household Survey % Graduate
Share of Employment
% Graduate Share of Hours
Relative Weekly Wage
(Full-Timers) 1980 5.0 5.1 1.48 1985 9.8 10.5 1.50 1990 10.2 11.0 1.60 1995 14.0 15.4 1.60 2000 17.2 18.8 1.64 1980-2000 12.2 13.7 .12 1980-1990 5.2 5.9 .08 1990-2000 7.0 7.8 .04
US Current Population Survey % Graduate
Share of Employment
% Graduate Share of Hours
Relative Hourly Wage
(Full-Timers) 1980 19.3 20.4 1.36 1985 22.0 23.6 1.47 1990 23.8 25.6 1.55 1995 25.5 28.1 1.61 2000 27.5 29.5 1.66 1980-2000 8.2 9.1 .30 1980-1990 4.5 5.2 .19 1990-2000 3.7 3.9 .11
Notes: Sample is all people age 18-64 in work and earning, except for relative wages which are defined for full-time workers. The relative wage ratios are derived from coefficient estimates on a graduate dummy variable in semi-log earnings equations controlling for age, age squared and gender (they are the exponent of the coefficient on the graduate dummy). The UK employment and hours shares are from the LFS. The relative wage gaps are from the GHS for 1980, 1985 and 1990 and the LFS in 1995 and 2000 (relative wages from regressions for the overlap year, 1995, were very similar in GHS and LFS). They are weekly wages due to changes to the hours question in the GHS in the 1980s that mean a consistent hourly wage cannot be defined through time. The CPS data is the Economic Policy Institute CPS ORG labor extracts data. I thank John Schmitt for making them available to me.
27
Table 4: Regression Correlations of Skill Demand Changes and Technology Measures
Study Unit of Analysis Time period
Skill Demand Measure
Technology Measure
Coefficient (Standard Error)
Controls
1990-96
.289 (.081)
1980-90
.147 (.046)
1970-80
.127 (.031)
140 US industries
1960-70
Industry computer use (1984-93)
.071 (.025)
None
123 US industries
1960-90
College wage bill share
Computer investment per FTE
.130 (.027) Change in log(capital/labour), decade dummies
Autor, Katz and Krueger (1998)
450 US manufacturing industries
1959-89
Non production wage bill share
Computer investment / investment
.027 (.007) Change in log(capital/output), Change in log(output)
Computer investment / investment
.028 (.006) Berman, Bound and Griliches (1994)
143 US manufacturing industries
1979-87
Non production wage bill share
R&D / Sales .097 (.021)
Change in log(plant/output), Change in log(equipment/output), Change in log(output)
16 UK manufacturing industries
1982-89
R&D/Sales .065 (.026)
16 UK manufacturing industries
1980-85
Non production wage bill share
Innovation Count From 1970s
.092 (.053)
Change in log(capital), Change in log(real sales), 1 digit industry dummies
Machin (1996)
398 British workplaces
1984-90
Managers , senior technical and professional employment share
Micro computers introduced
.044 (.022) Dummy for employment decline, 1 digit industry dummies
Machin and Van Reenen (1998)
15 UK manufacturing industries
1973-89
Non production wage bill share
R&D/Value Added
.026 (.009) Change in log(capital), Change in log(output), year dummies
28
Table 5: Changes in Computer Usage and the Wage Structure: US 1984-97
Descriptive Statistics 1984 1989 1993 1997 % Using Computer at Work 25.1 37.4 46.6 50.6 Sample Size 61667 62748 59852 56247 % Graduate Share of Employment 21.6 23.4 24.8 26.2 % Graduate Share of Wage Bill 32.2 35.8 38.5 41.5 Sample size 168208 167526 166665 147033 Notes:
1. All people with a job aged 18-64. 2. Computer numbers based on October Current Population Survey supplement in relevant year.
Responses to question ‘Does….directly use a computer at work?’. 3. Wage data from all outgoing rotation groups in each year (from the EPI ORG files). 4. Weighted using CPS person weights.
Table 6: Industry Level Regressions of Changes in Graduate Wage Bill Shares
on Changes in Computer Usage in the United States 1984-97
Annualised Change in Graduate Wage Bill Share (1) (2) (3) (4) 1984-97 1984-89 1989-93 1993-97
Changes in % Using Computer at Work .069 (.025)
.102 (.031)
.075 (.050)
.021 (.050)
Sample size 660 220 220 220
Notes:
1. Dependent variable is annualized change in graduate wage bill share. 2. All regressions weighted by average of industry wage bill across the relevant time periods. 3. Year dummies included in column (1). 4. Standard errors in parentheses.
29
Table 7: Changes in Computer Usage and Changes in
Wage Structure in Britain in the 1990s Skills Survey Data 1997 1997 if same job
as 1992 1992 Change 1992-1997
(if same job) % Using Computer at Work
68.2 71.7 54.4 17.3
Of Which: Essential 30.3 28.6 15.7 12.9 Very Important 14.7 16.5 10.6 5.9 Fairly Important 12.7 14.1 12.9 1.2 Not Very Important
11.5 12.5 15.1 -2.6
Sample size 2467 1270 1270 1270 Labour Force Survey 1997 1994 1992 Annualised
Change (Percent log points)
A: 1992-1997 B: 1994-1997
% Graduate Share of Employment
14.8 13.4 12.1 A: 4.0 B: 3.3
% Graduate Share of Wage Bill
24.7 23.7 21.2 A: 3.0 B: 1.4
Notes:
1. Many thanks to Francis Green for providing me with the Skills Survey data used in the upper panel of the Table
30
Table 8: Industry Level Regressions of Changes in Graduate Wage Bill Shares
on Changes in Computer Usage in Britain in the 1990s Annualised Change in Graduate Wage Bill Share,
1994-97 (1) (2) (3) (4) Changes in % Using Computer at Work -.045
(.080)
Changes in % Using Computer at Work For Whom Fairly Important, Very Important or Essential
.086 (.057)
Changes in % Using Computer at Work For Whom Very Important or Essential
.106 (.068)
Changes in % Using Computer at Work For Whom Essential
.138 (.044)
Sample size 53 53 53 53 Notes:
1. Dependent variable is annualized change in graduate wage bill share. 2. All regressions weighted by average of industry wage bill across the relevant time periods. 3. Standard errors in parentheses.
