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1 The Changing Nature of Labour Demand in the New Economy and Skill-Biased Technology Change 1 Stephen Machin * November 2001 January 2002 - Revised * Department of Economics, University College London and Centre for Economic Performance, London School of Economics Abstract In this paper I consider recent patterns of change in the labour market, placing particular emphasis on the way in which labour demand has shifted in favour of workers with more skills and education. The paper describes recent trends and critically evaluates the role played by skill-biased technological change. It also presents some recent evidence on the relation between relative demand shifts and computerization in the UK and US, showing that the links between simple headcount measures of computer use at work and shifts in skill demand were moderated in the 1990s as compared to the 1980s. JEL Classification: J0. Keywords: labour market inequality; relative demand shifts; skill-biased technology change; computer usage. 1 This paper draws upon and builds on some of my existing joint work funded under the Leverhulme project ‘The Labour Market Consequences of Technological and Structural Change’. I am very grateful to my co-authors in this work (Eli Berman, John Bound, Thibaut Desjonqueres, John Van Reenen). I would like to thank my discussant at the September 2001 Conference, Steve Pischke, for a number of useful comments that have been incorporated in this paper and Francis Green for giving me access to some of the computer data I use.

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1

The Changing Nature of Labour Demand in the New Economy

and Skill-Biased Technology Change1

Stephen Machin*

November 2001 January 2002 - Revised

* Department of Economics, University College London and Centre for Economic Performance, London School of Economics

Abstract In this paper I consider recent patterns of change in the labour market, placing particular emphasis on the way in which labour demand has shifted in favour of workers with more skills and education. The paper describes recent trends and critically evaluates the role played by skill-biased technological change. It also presents some recent evidence on the relation between relative demand shifts and computerization in the UK and US, showing that the links between simple headcount measures of computer use at work and shifts in skill demand were moderated in the 1990s as compared to the 1980s. JEL Classification: J0. Keywords: labour market inequality; relative demand shifts; skill-biased technology change; computer usage.

1 This paper draws upon and builds on some of my existing joint work funded under the Leverhulme project ‘The Labour Market Consequences of Technological and Structural Change’. I am very grateful to my co-authors in this work (Eli Berman, John Bound, Thibaut Desjonqueres, John Van Reenen). I would like to thank my discussant at the September 2001 Conference, Steve Pischke, for a number of useful comments that have been incorporated in this paper and Francis Green for giving me access to some of the computer data I use.

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1. Introduction There have been dramatic changes in the structure of employment in many countries in

recent years. A key aspect of this has been the upskilling of the workforce as employers

have shifted their demand requirements to employ more workers possessing higher

educational qualifications and with higher skill levels. A by now large academic

literature2 has documented the changing demand for skills in many countries and has

looked at the key factors underpinning the observed changes. A big emphasis has been

placed upon the role played by the kinds of new technologies used in modern workplaces

and how they have altered employers’ demand for skills. Some of this work uses

evocative phrases like ‘collapse in demand for the unskilled’, ‘the deteriorating position

of low skill workers’ and ‘rapidly rising wage gaps between the skilled and unskilled’, all

of which are in line with the notion that very large shifts have occurred.

In this paper I look at some of the evidence on changes in the demand for labour

in the new economy. I begin by reviewing the literature on the contribution made to

changing labour demand by new technologies, particularly those that are biased in favour

of relatively skilled workers. As will be made clear much of this work relies on fairly

coarse skill and technology definitions and much of the work concentrates on the 1980s.

So in the remainder of the paper I also present a small piece of new evidence looking at

the common nature of shifts in employer demand for skills in the same industries in

recent years in two countries, the United Kingdom and the United States.

2 See the review of Katz and Autor (1999) and the references contained therein.

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2. Changes in Labour Demand and Skill-Biased Technology Change

The Facts

Many more skilled workers are now in employment than in the past, both in absolute

numbers and relative to their less skilled counterparts. The upper panel of Table 1 shows

the employment, hours and wage bill shares of workers with a degree in the UK and US

between 1980 and 2000. It also shows (for full-timers) the relative wage of graduates

versus non-graduates. The Table very clearly confirms rapid increases in the shares of the

relatively skilled group of workers (graduates) that have occurred in both countries.

What is also interesting, and by now well known, is that, despite their increased

numbers, the wages of more skilled workers have not fallen relative to the less skilled.3 In

fact the opposite has occurred and wage gaps between graduates and non-graduates rose

in both countries (albeit at a faster rate in the US). There are differences in the rate of

increase in the two decades considered as the bulk of the rise in wage gaps between the

two education groups occurs in the 1980s. The Table confirms that relative wages and

employment moved in tandem when one observes that the wage bill share of graduates

rises by more than the employment (or hours) shares.4 This is true of both decades for

both countries but it is clear that it is employment share changes driving the bulk of the

shifts in the 1990s.

In a simple relative demand and supply framework simultaneously rising relative

wages and employment suggest that demand must have risen at a faster rate than supply.

3 The Table only looks at education based measures of skill. Much the same emerges if one looks at broad occupational categories. 4 If the elasticity of substitution between skilled and unskilled workers (here graduates and non-graduates) is unity the wage bill share corresponds exactly to the skill demand. If it is not unity then one needs to factor in the relative wage change. More precisely the demand for skills, D, is defined as D = (WsNs/WN) + (Φ-1)(Ws/WU) where W is wages (s subscript denotes skilled, u subscript denotes unskilled, no subscript denotes total), N is employment and Φ is the elasticity of substitution between skilled and unskilled workers.

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This has led many commentators (Nickell and Bell, 1995; Katz and Autor, 1999) to argue

that demand has shifted in favour of more skilled workers. Consider the Figure below:

In terms of this Figure think of a labour market with a supply of two sets of workers,

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 the relative demand and supply picture in the Figure one can rationalise the

rising demand for more skilled workers by 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 1 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.

An intuitive way of thinking about this 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 and demand. Here the more

S0

D0 D1

Skilled/Unskilled Employment

Skilled/Unskilled Wages

(Ns/Nu)0 (Ns/Nu)1

(Ws/Wu)1

(Ws/Wu)0

Figure 1

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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 and employers are prepared to pay workers with appropriate skills more

than less skilled workers, despite there being many more of them supplying their labour

(this argument is made in many places, for a recent exposition see Manning and

Manacorda, 1998).

Researchers have therefore tried to explore what lies behind the rise in wage

inequality by asking what might have caused relative demand to increase at a more rapid

rate than relative supply. Quite a large body of work has argued that the critical factor has

been the introduction of new technologies that are biased in favour of skilled workers. In

the next sub-section I review some of the evidence that has been brought forward to

support this hypothesis of skill-biased technology change.

Evidence on 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 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.

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What evidence has been brought to bear on the sbtc hypothesis? There is a range

of evidence that exists, much of which is supportive of the idea that sbtc has altered the

structure of labour markets across the world:

1). The extent of skill upgrading differs for workplaces, firms and industries.

For the sbtc story to hold one clearly requires that shifts in skill demand be

variable, and that the variability is systematically related to the adoption and introduction

of new technologies. Because of this one can think that an important component of skill

demand shifts is that which is going on within particular workplace, firms and industries

(each of whom is likely to differ in the extent of their use of new technologies). Some

indirect evidence on sbtc has therefore been considered by looking at how much of the

shifts in relative demand are within workplaces or firms or industries rather than between

them.

Consider the familiar decomposition of aggregate changes in wage bill (or

employment) shares of skilled workers, ∆S, for j (=1,…N) industries as follows:

∑∑−−

+=j

jjj

jj S∆PP∆S∆S

where the first term is the within-industry component of skill upgrading and the second

term is the between-industry component (P is a measure of the relative size of industry j,

namely the share of industry j’s wage bill or employment in the aggregate wage bill or

employment, and a bar denotes a time mean).

Table 2 reports evidence on this decomposition from various studies for the UK

and US at workplace and industry level. At both levels of aggregation it is clear that the

bulk of the observed (wage and employment) shifts towards the relatively skilled group5

5 Berman, Bound and Machin (1998) used the production/non-production distinction as it is the only comparison of ‘skill’ that is available for a reasonably large number of countries (their focus is very much on international comparisons). Indeed it should be noted that the definitions of ‘skill’ used in this literature are all rather coarse. But the measures used do seem to display similar trends. For example, the two most

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occur within, rather than between industries (the only exception concerns the Autor,

Katz, Krueger, 1998, results for the 1960s). That the bulk of the shifts are seen within

industries (i.e. some industries have faster rates of skill upgrading than others) is

essentially a prerequisite for skill-biased technological change to even be a starter as a

possible explanation of the observed shifts in skill demand.

2). Skill demand has risen faster in more technologically intensive workplace, firms and

industries

Table 2 makes it very clear that the bulk of the upgrading that has happened has

occurred since the start of the 1970s has been within, rather than between, industries.

Identification of which industries have had faster rates of upgrading, and their

characteristics, can therefore shed light on what may underpin the improving relative

labour market of the more skilled. Indeed if one investigates which have the biggest

increases in relative wages and/or employment of more skilled workers it is clearly those

where technological change has been more important. For example, industries that spend

more money on Research and Development (R&D), have produced and used more

commercially significant innovations and have more workers who use computers have

seen faster skill upgrading.

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

commonly used measures (forced upon researchers by data availability constraints) are to define non-production workers and workers with a college degree as relatively skilled and production workers and non-graduates as relatively unskilled. Trends in the wage and employment rates of non-production vis-à-vis production workers and graduates vis-à-vis non-graduates show very similar trends in the countries for which both measures are available.

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industry j in year t, is:

∆(Skilled wage bill share)jt = α + β∆log(Capitaljt) + δ∆log(Outputjt) + φTECHjt + εjt

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 3 summarises the US and UK results. 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.

3). Workers receive a wage payoff for using computers at work

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

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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 1989.6

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 does 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.

4). Faster changes in skill demand are concentrated in similar industries in different

countries

Adopting a wider international perspective can shed further light on the validity of

the sbtc hypothesis. If one considers more countries then there is evidence that one sees

that bigger improvements in the labour market position of more skilled workers appear to

be concentrated in similar kinds of industries in many countries. Table 4 shows cross-

country correlations of changes in non-production wage bill shares in the 1980-90 time

period (from Berman and Machin, 2000). There is indeed a cross-country

correspondence: 31 out of 36 pairwise comparisons are 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 may bring about, this suggests that

skill upgrading has a tendency to be clustered in the same sorts of industries across

6 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,

10

different countries. In this sense one might think that sbtc has been pervasive in changing

labour market outcomes across the developed world.

5). Skill upgrading is occurring in the more technology intensive countries in the developing world

As noted above, one also 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. Furthermore shifts in skill demand in

the developing world are correlated with the shifts seen in developed world and with

technology indicators from the developed world. Table 5 presents some evidence on this

(from Berman and Machin, 2000).

Table 5 reports correlations between industry changes in non-production wage bill

shares and US computer usage and OECD R&D intensity for those industries for

countries grouped by income levels. The pattern in the high income countries, given in

the upper panel of the Table, has already been noted above and shows a strong

correspondence between skill upgrading and the technology measures. But the pattern of

skill-biased technology transfer to the middle income countries is also strong. Indeed,

patterns of skill upgrading in middle income countries in the 1980s are well predicted by

the two indicators of recent skill-biased technological change in the OECD, indicating

skill-biased technology transfer into the middle income countries. There is weaker

evidence of skill-biased technology transfer altering the skill mix of employment in the

lower income countries where around half of the correlations with the technology

indicators are positive.

race, part-time job status, living a metropolitan area, gender, veteran status, whether married, whether a

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Difficulties With the SBTC Evidence

Whilst a sizable body of evidence has been amassed there are, of course,

difficulties with the sbtc evidence. Some of these include the following:

a) Other hypotheses may be consistent with the evidence.

Of course it is well known that there has been a vigorous debate on whether

technology or trade matters most for rises in labour market inequality. The trade view is

predicated on the notion that there has been a widespread opening up of markets to

increased international competition and it is this that has damaged the labour market

prospects of low skill workers.

In its simplest form the argument goes as follows. Suppose there are two countries

that, to start with, do not trade with each other. Both have skilled and unskilled

workforces who respectively manufacture skill intensive and unskill intensive products.

One country, the high wage or developed country, has a comparative advantage in

making skill intensive products with skilled labour, the other country, the low wage or

developing country, has a comparative advantage in making unskill intensive products

with unskilled labour. Suppose these countries begin to trade with one another. Then (in

the standard Heckscher-Ohlin model of international trade) the developed country will

begin to import unskill intensive products from the low wage country as they are cheaper

to produce there. This will then lower the wages of unskilled workers in the developed

country. It will also be likely to reduce the employment of less skilled workers. As such

one sees the pattern of relative demand shifts discussed above. The relative wages and

employment of skilled workers rise because of the opening up to trade with the

developing country.

union member and broad occupation . Computer usage rises from 25 to 37 percent between 1984 and 1989.

12

This argument has lots of intuitive appeal. However, it has proven rather hard to

back it up with empirical evidence. This is for several reasons:

i) Whilst rising very fast in recent years (from very low initial levels) trade flows

with low wage developing countries do not seem big enough to be able to explain

the very large changes in labour market inequality seen in a number of developed

countries. Indeed, most trade is still done between developed countries

themselves.

ii) The industries that have seen the biggest increases in trade with the developing

world do not actually appear to be the ones that have seen bigger labour market

shifts in favour of more skilled workers.7

iii) As noted above, one sees skill upgrading (higher relative wages and employment

for more skilled groups of workers) going on in the developing world as well as

the developed world. This runs counter to the Heckscher-Ohlin model which

predicts that skill upgrading should increase in developed economies but that the

less skilled should do better in the developing world as demand for the products

they manufacture rises.

iv) Skill upgrading appears to be happening in industries that do not trade across

international borders (see the evidence in Desjonqueres, Machin and Van Reenen,

1999). Again, this runs counter to the Heckscher-Ohlin model. If one includes a

traded sector and a non-traded sector in the model the prediction would be that the

unskilled workers displaced from the traded sector by the opening up to trade

should find jobs (or lower the wages of less skilled workers or both) in the non-

traded sector. In reality one does not see this. In non-traded sectors (e.g. non-

7 Machin and Van Reenen (1998) enter various import competition measures into the kind of cost share equation described above and, unlike the technology measures, cannot pick up any correlation with these measures of trade.

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manufacturing industries like retail trade) one has also seen skill upgrading

happening, and at similar rates to those in more traded sectors (like most

manufacturing industries).

All this means that the literature has found it hard to present evidence in line with

the notion that increased trade is the principal factor behind increased inequalities for the

skilled and less skilled.8 Of course, this does not mean that trade will not necessarily have

an impact on labour markets in future. It seems implausible to believe that globalisation

is unlikely to have serious ramifications for labour. Rather the bulk of the recent rises in

labour market inequality seen in the last couple of decades do not seem attributable to

rising competition with low wage countries.

b) A lot of sbtc evidence is based only on manufacturing and the 1980s.

A second worry with the sbtc evidence is tends to be rather limited in that it

covers short time periods and, perhaps more worrying is often confined to manufacturing

because of lack of good technology data outside of manufacturing, or because this is

forced on researchers who want to look at the same industries across countries. I present a

bit of evidence on this in the next Section but some work is careful to try and use as much

data as possible to look at longer time periods and at the whole economy. Autor, Katz and

Krueger (1998) do this most comprehensively for the US, using Census data back to 1960

and as much as possible not focusing on manufacturing alone. They seem to show

important shifts in the skill structure of employment that are related to technology and

that have occurred economy wide.

c) Issues of reverse causation

Much of the more direct evidence in this research area is based upon industry-

level regressions of changes in wage bill shares, or employment shares, of relatively

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skilled workers on observable technology indicators. If, rather than technology inducing

shifts in employer demand, it is really that technological changes occur because of

employers employment strategies (e.g. employers do more R&D because they hire more

scientists) then one may be looking at the relationship the wrong way round. Related to

this, and as noted in the debate on wage returns to computer usage considered above, it

may be that the technology indicators are just proxying other unobservables that are

driving shifts in employment structure. When these kinds of arguments are made it does

make it rather difficult to give clear conclusions, but the varieties of different evidence

accumulated would suggest that such biases would have to be large to roll over the

observed correlations between skill upgrading and technology.9

d) Skilled demand shifts have been happening for years and it is longer run supply

changes that matter more.

This argument is a more subtle and probably more significant one, resting on the

notion that there has been a trend increase in demand for skills and movements in relative

wages around that trend shift are influenced by relative supply changes. For example, it is

well known that education supply rose very fast in the 1970s, at a slower rate in the

1980s, and then likely speeded up again in the 1990s in the UK and US. The wage gap

between educated and less educated workers fell in the 70s, rose sharply in the 80s and

probably rose but at a much slower rate in the 90s. This is entirely consistent with

constant demand shifts and the wage gaps being shifted by longer run, decade length

shifts in relative supply.

8 Although see an interesting paper, with a different angle, by Haskel and Slaughter (2001) who prefer to emphasise the potential for trade to affect sector bias, thereby looking more at between-sector shifts. 9 There is some evidence on this possibility but it is largely an unsatisfactory literature in that it either relies on ad hoc exclusion restrictions that enable researchers to treat technology as endogenous, or removes person specific fixed effects from wage equations based on longitudinal data to control for unobserved heterogeneity. This latter approach may be likely to remove too much of the variance in wages, some of which one is interested in for possible wage-computer use correlations. See the special issue of the Economics of Innovation and New Technology (1998, Volume 5) for several papers in this vein.

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This argument clearly does warrant attention. Card and Lemieux (2001), for

example, present a cohort based analysis showing relative supply changes to be

important. However, at the time of writing, the importance of this in ruling out the sbtc

route is unclear and more work based on more recent data seems to be required.

Moreover, rising residual wage inequality (a feature of the recent inequality changes)

seems hard to square up with the patterns of supply changes. It does look as if the 1980s

was the period of rapid wage inequality changes in the UK and US, but it is evident that

one needs to delve deeper into wage and employment trends by skill at the micro-level,

and to develop plausible tests of whether demand shifts are indeed constant over time and

space to consider this in more detail.

3. A Bit of More Recent Evidence on Changes in the Skill Structure of Labour

Demand

In this section of the paper I consider some more recent evidence on the nature of

demand shifts for skilled and less skilled workers. The purpose of this is to try to see

whether technology driven shifts in employer demand have continued to affect

contemporary labour markets in similar ways to that described in earlier work.

Earlier Table 1 of the paper reported evidence on changes in the education

structure of US and UK labour markets between 1980 and 2000. Both show persistent

educational upgrading and, at the same time, rising relative wages for graduates leading

to increased wage bill shares for that group. There are, however, clear decade differences

in the rate of change of these labour market outcomes, with the faster changes in wage

structure occurring in the 80s.

In this Section of the paper I therefore consider some more recent evidence on

possible connections between skill upgrading and technological change. For the US I am

16

able to look at changes in the relationship between industry shifts in skill demand and

changes in technology across the two decades. For the UK I can 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 level across the whole economy for both countries are those

measuring computer usage in the workplace. These have obvious drawbacks (see some of

the discussion above and some more 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 of 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 in this paper.

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

series on computer usage for the same 31 industries in the two countries in 1992/3 and in

1997. Figure 2 plots the US and UK computer usage variables, showing a strong

17

correlation between industrial computer usage across the countries (correlation

coefficients = .88 for 1997 and .86 for 1992/3).11

The implications of changing computer use can also be studied with the graphs in

Figure 2. It is fairly self evident that they show a similar structure across years, with

essentially an upward move to the right between 1992/3 and 1997 occurring. The neutral

nature of this shift, maintaining a similar cross-country computer structure, is borne out if

one considers the cross-time correlations in computer usage within countries. These

reveal strong persistence through time in both countries:

US: correlation coefficient between 1993 and 1997 computer usage = .98

UK: correlation coefficient between 1992 and 1997 computer usage = .96

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 3 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.12 It becomes

clear that in the earlier time periods, computer usage grew fastest at the top end of the

computer use spectrum. By the 1993 to 1997 comparison, however, this is no longer true

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. 11 The same is true if one plots the CPS data against the BSAS data for the same UK and US industries in 1989. 12 The quadratic functional form works well to illustrate the changing shape of the cross-time correlations.

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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

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 6 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 half

the workforce 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 1.

Table 7 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. The same is true of the first two

sub-periods, 1984-89 and 1989-93. However, the relationship disappears by the time we

get to the final column specification looking at the 1993-97 sub-period. It appears, as one

19

might have suspected from the pattern in Figure 3, 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 wage structure, but it may cast doubt on simple

headcount measures of computer use as picking up biased technology change in the

1990s.

UK Industry Skill Upgrading and Computer Use at Work

The UK situation in the 1990s is considered in Tables 8 and 9. The upper panel of

Table 8 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

20

I carry out the industry-level empirical analysis between 1994 and 1997 for a consistent

set of industries.

In Table 9 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

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.

21

4. Conclusions

In this paper I have considered existing evidence on the links between skill-biased

technology change and shifts in the employment and wage structure of modern labour

markets. As in many areas of empirical work in economics one can quibble with the large

body of evidence that has been amassed on the role played by technology in influencing

the skill structure of labour demand. There are clearly some worries linked to

measurement of the key variables, especially technology, and some data driven and

modeling drawbacks in the literature. Nonetheless it seems rather hard in my view to

totally debunk the sbtc hypothesis. The array of evidence that has been accumulated

seems to be in line with the notion that technological changes that have occurred in recent

years seem to be linked strongly to the relative demand shifts and movements in labour

market inequality seen in the labour markets of many countries.

Some more recent evidence on the question highlights some other interesting

observations. It now seems very 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. One can certainly uncover empirical associations showing that proxies for skill-

biased technology change 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 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

22

Murnane’s, 2001, detailed discussion on the skill content of technical change). However I

do present some evidence that the latter may be important 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.

23

References Autor, David, Lawrence F. Katz and Alan Krueger (1998) “Computing Inequality: Have

Computers Changed the Labor Market?”, Quarterly Journal of Economics, 113, 1169-1214.

Autor, David, Frank Levy and Richard Murnane (2001) “The Skill Content of Recent

Technological Change: An Empirical Exploration”, National Bureau of Economic Research Working Paper 8337.

Berman, Eli, John Bound and Zvi Griliches (1994)"Changes in the demand for skilled labor

within U.S. manufacturing industries: Evidence from the Annual Survey of Manufacturing", Quarterly Journal of Economics, 109, 367-98.

Berman, Eli, John Bound and Stephen Machin (1998) "Implications of Skill-Biased

Technological Change: International Evidence”, Quarterly Journal of Economics, 113, 1245-1280.

Berman, Eli and Stephen Machin (2000) “Skill-Biased Technology Transfer Around the World”,

Oxford Review of Economic Policy, 16(3), 12-22. Card, David and Thomas Lemieux (2001) “Can Falling Supply Explain the Rising Return to

College for Younger Men? A Cohort-Based Analysis", Quarterly Journal of Economics, 116, 705-46.

Desjonqueres, T., Machin, S. and Van Reenen, J. (1999) “Another Nail in the Coffin? Or Can the

Trade Based Explanation of Changing Skill Structures be Resurrected?”, Scandinavian Journal of Economics, 101, 533-54.

DiNardo, John and Steve Pischke (1997) “The Returns to Computer Use Revisited: Have Pencils

Changed the Wage Structure Too?” Quarterly Journal of Economics, 112, 291-303. Feliciano, Zadia (2001) "Workers and Trade Liberalization: The Impact of Trade Reforms in

Mexico on Wages and Employment," Industrial and Labor Relations Review, 55, 95-115. Green, Francis, Alan Felstead and Duncan Gallie (2001) "Computers and the changing skill-

intensity of jobs." Applied Economics, forthcoming. Hanson, Gordon H. and Ann Harrison (1999) "Trade, Technology, and Wage Inequality in

Mexico," Industrial and Labor Relations Review, 52, 271-288. Haskel, Jonathan and Matthew Slaughter (2001) “Trade, Technology and UK Wage Inequality”,

Economic Journal, 111, 163-87. 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.

24

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, 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.

25

Figure 2: Computer Usage By Industry in the US and UK, 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.

26

Figure 3: 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

3

US computer use 19890 .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.

27

Table 1: 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 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 for the overlap year, 1995, were very similar). 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.

28

Table 2: Within/Between Decompositions of Skill Demand Changes Study Unit of Analysis Time

period Skill Demand Measure Annualised

Change (Percentage Points)

Percent Within

College employment share

.300 87 1990-96

College wage bill share

.587 82

College employment share

.469 79 1980-90

College wage bill share

.878 70

College employment share

.586 79 1970-80

College wage bill share

.662 84

College employment share

.324 27

Autor, Katz and Krueger (1998)

140 US industries

1960-70

College wage bill share

.511 45

Non production employment share

.552 70 450 US manufacturing industries

1979-87

Non production wage bill share

.774 60

360000 US manufacturing plants

1977-87 Non production employment share

.367 82

Non production employment share

.387 82 100 UK manufacturing industries

1979-90

Non production wage bill share

.669 83

Berman, Bound and Machin (1998)

402 British workplaces

1984-90 Non production employment share

.41 83

Managers employment share

.14 86 Machin (1996) 402 British workplaces

1984-90

Senior technical and professionals employment share

.19 95

29

Table 3: 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

30

Table 4: Cross-Country Correlations Changes in Nonproduction Wage Bill Shares in Developed Countries: 1980-90

U S

S w e d e n

A u s t r a l i a

J a p a n

D e n m a r k

F i n l a n d

A u s t r i a

U K

Sweden

.15

Australia

.35

.16

Japan

.09

.14

.08

Denmark

.66*

.06

.11

.14

Finland

.70*

.12

.37*

.33

.52*

Austria

.27

-.44*

.14

-.11

.31

.29

UK

.64*

.06

.38*

.01

.53*

.39*

.47*

Belgium

.45*

-.19

-.28

-.12

.41

.45*

.51*

.47*

Notes: Calculations based on the 28 industry data used in Berman, Bound and Machin (1998). A star denotes statistical significance at the 5 percent level or better.

31

Table 5: Correlations of Country-Specific Industry Skill Upgrading With Technology Variables

Correlations of 1980s Upgrading

With US Computer Usage

Correlations of 1980s Upgrading With OECD R&D Intensity (1980-90)

Correlations of 1970s Upgrading

With US Computer Usage

Correlations of 1970s Upgrading With

OECD R&D Intensity (1973-80)

High Income Group

Countries 10 10 12 12

Positive 10 8 10 10

Significant Positive 5 4 6 4

Significant Negative 0 0 1 1

Middle Income Group

Countries 12 12 8 8

Positives 8 9 5 4

Significant Positives 3 2 3 1

Significant Negatives 0 0 1 2

Low Income Group

Countries 6 6 5 5

Positives 3 3 4 2

Significant Positives 1 1 0 0

Significant Negatives 1 0 0 1

Notes: Taken from Berman and Machin (2000). Groups of countries are as follows. High income: Australia, Austria, Belgium, Denmark, Finland, Germany, Japan, Sweden, UK, US (80s), plus Norway, Germany (70s). Middle income: Colombia, Cyprus, Czechoslovakia, Greece, Guatemala, Hungary, Ireland, Malta, Portugal, South Korea, Spain, Turkey (80s). Low income: Bangladesh, Egypt, Ethiopia, India, Nigeria, Tanzania (80s).

32

Table 6: 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 7: 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.

33

Table 8: 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 1277 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

34

Table 9: 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.