This paper uses longitudinal data for the UK to investigate the observed correlation between computer use at work and labor market earnings. Our findings suggest that there are no returns to computer use at work. This is evidence against the productivity interpretation of these returns and supports the view that the premium can be attributed to unobserved characteristics.technological change; earnings

Mary A. Silles

English

Research Papers in Economics

CAM 
 
Centre for Applied  
Microeconometrics 
 
Institute of Economics 
University of Copenhagen 
 
http://www.econ.ku.dk/CAM/ 
 
 
 
 
 
 
 
 
 
 
 
 
Computer Use and Earnings 
 
Mary A. Silles 
 
2005-05 
 
 
 
 
The activities of CAM are financed by a grant from 
The Danish National Research Foundation   1
COMPUTER USE AND EARNINGS 
MARY A. SILLES 
 
This paper uses longitudinal data for the UK to investigate the observed correlation 
between computer use at work and labor market earnings.  Our findings suggest that there 
are no returns to computer use at work. This is evidence against the productivity 
interpretation of these returns and supports the view that the premium can be attributed to 
unobserved characteristics. 
 
1. Introduction 
Various studies have shown that individuals who use computers at work earn more than 
otherwise similar workers who do not use computers.  Using the US Current Population 
Survey (CPS) for 1984 and 1989, Krueger (1993) estimated a wage premium of 10 to 15 
percent.  Autor et al. (1997), using the same data, showed that the rate of return to 
computer use rose to 20 percent in 1993.  Using data from the Skills Survey for Great 
Britain, Borghans and ter Weel (2001) also estimated a computer premium of 17 to 21 
percent.  While many authors have interpreted these findings as evidence that computers 
have had a direct influence on earnings, some analysts have been cautious to draw 
inferences about a causal relation.  
  Concerns about a causal relationship between computer use and earnings originated 
from the observation that computer users appear to have characteristics, such as higher 
ability or more education, which are associated with higher wages.  Most notably, 
DiNardo and Pischke (1997) cast doubt on the literal interpretation of the computer use 
wage differential as reflecting the returns to computer skills
1.  These authors conjecture 
that other individual unmeasured differences that happen to be correlated with computer 
use at work were the primary cause of the computer wage differential. Another argument 
against the literal interpretation is that workers who use a computer at work would have 
                                                 
1 DiNardo and Pischke (1997) estimated the rate of return to computer use was at 11 percent in 1979, rising 
to 16 percent in 1985/86 and 17 percent in 1991/92 using data for German workers.   2
had higher wages even in the absence of the technology i.e., higher wages, which reflect 
a higher opportunity cost of time, cause workers to use computers. 
  In this paper, we examine the observed correlation between computer use at work and 
wages.   We use data from the National Child Development Study of Great Britain which 
contains information about an entire cohort of children born in one week in 1958.  
Comprehensive information has been collected from birth through until the present with 
earnings data available for individuals in 1991 and 2000.  A variety of estimation 
methods are used to investigate the increase in earnings associated with computer use at 
work.  The results support the view that the returns to computer use are capturing 
unobserved characteristics. 
  The remainder of this paper is organised as follows.  The next section describes the 
data sets used and offers some descriptive statistics.  Section III presents and discusses 
the results. In section IV we summarise and provide some concluding remarks.   
 
2.   Data 
The National Child Development Study (NCDS) is a continuing longitudinal study which 
is seeking to follow the lives of all those living in Great Britain who were born between 
3
rd and 9
th March, 1958.  Since then there have been six successive waves of the survey, 
with the last wave in 2000. Information has been collected regarding earnings and 
computer use at work from the latest two sweeps in 1991 when the cohort members were 
33 years of age and 2000 when they were 42. The early sweeps give information on early 
test scores, family background and schooling.  Later sweeps contain updated information 
on educational attainment, extensive labor market information and other socio-economic 
data.  The variables used in our analysis are defined in the Appendix.  
  There are two benefits to using this data over other studies. The data set consists of 
individuals who are all the same age. Therefore, we can ignore any complications related 
to age or cohort effects as everyone in the survey has experienced the same aggregate 
labor market conditions and been exposed to the same technological advances.  These 
individuals would have acquired computer skills at school and would have been present 
in the labor market in the early 1980s when computers technology was first introduced to 
the general workplace.   3
  The computer variable used in the present paper is simply defined as a dummy 
variable identifying whether or not an individual uses a computer at work.  Over the 
course of our data, the percentage of workers who use a computer increased from 56 
percent in 1991 to 76 percent in 2000.   
  Our sample is based on individuals who participated in both the fifth and sixth sweeps 
of the survey for whom there is relevant information on wages, computer use and all our 
control variables.  Our sample is restricted to men who were in full-time employment 
with all self-employed and part-time workers being omitted.  This leaves us with a 
sample size of 1,370.  
     
3.    The wage returns to computer use 
Our general approach is to augment a standard earning function with a variable for 
whether or not an individual uses a computer at work.  The n individuals are indexed by i 
and the time periods by t. The underlying econometric model is of the following form: 
 
ln it i t it it it yC X α λδ β µ =++ + +     i=1,2,..n; t=1,2  (1) 
 
where y refers to real gross hourly wages
2, " is an individual specific effect, 8, * and $ 
are parameters to be estimated, C is a dummy variable equal to 1 if the individual uses a 
computer at work and zero otherwise, X is a vector of control variables, and µ is the error 
term.  
  Following Krueger (1993) and others in this literature, Table I presents the results of 
fitting equation (1) by OLS.   The first specification is a human capital model where the 
control variables include educational qualifications.  The second specifications adds an 
elaborate number of covariates for test scores at age 7, occupational classification, firm 
size, union affiliation, marriage and children.  Even after including the wider set of 
covariates, the estimated impact of computer use is sizeable and statistically significant 
with workers who use a computer at work earning approximately 18 percent more in 
1991 and 17 percent more in 2000 than those who do not use a computer at work.  These 
estimates are in line with those of Krueger (1993) for the US and Bell (1996) for the UK. 
                                                 
2 Our earnings information is deflated at 2000 prices.   4
TABLE I. FIRST OLS ESTIMATES  
                                    1991                2000           
                               (1)           (2)             (1)            (2)         
 
Computer use             0.278        0.184        0.312        0.168 
                       (0.021)***        (0.022)***        (0.025)***        (0.026)*** 
R-squared       0.30            0.38            0.31            0.42 
Robust standard errors in parentheses                                            
* significant at 10%; ** significant at 5%; *** significant at 1%         
 
 
  The fall in the returns to computer use as more controls is added to the equation is 
what we would expect if the returns to computer use were positively correlated with the 
previously omitted variables.  Of course, the estimated returns to computer use may be 
biased due to other unobserved variables which have been omitted from the specification. 
In previous studies researchers have attempted to reduce the extent of these biases by 
including otherwise unobserved skills widely believed to be linked with the skill 
components of technological change. For instance, Bell (1996), DiNardo and Pishke 
(1997) and Dickerson and Green (2002) included other job attributes such as use of tools, 
diagrams and other skills.   
  There are at least two problems with this approach.  First, conditional on ability and 
specific skills, many other unobserved attributes may affect wages.  Any of these might 
account for the observed correlation between computer use and earnings.  Second, one 
must expect that test scores and other measures of skill to be quite noisy indicators of 
ability, which may result in incorrect inferences. 
 
TABLE II.  RANDOM AND FIXED EFFECTS ESTIMATES 
                                  Random          Fixed  
          E f f e c t s        E f f e c t s  
 
Computer use             0.319             0.315 
                       (0.018)***                 (0.028)*** 
R - s q u a r e d          0 . 1 5       0 . 1 7  
Robust standard errors in parentheses                                            
*significant at 10%; ** significant at 5%; *** significant at 1%         
   5
 
  Anger and Schwarz (2002) and Oosterbeek (1997) have used fixed effects models to 
eliminate the effects of the unobservable individual characteristics on the assumption that 
the rate of return to computer use remains constant across time. Table II presents random 
and fixed effects estimates of the computer use variable.  The estimators indicate that 
computer use increases earnings by approximately 32 percent, which supports the literal 
interpretation that computer technology increases productivity.  However, the higher 
returns to computer use are curious given that the returns to computer use are generally 
expected to fall upon the elimination of positively correlated individual effects.  
  The difference in the expected values raises the possibility that the two sets of 
estimators differ because the parameters are not stable over time. This does not seem 
plausible, as the OLS estimators for 1991 and 2000 are of similar magnitudes. However, 
individuals adopted computers at work at different rates and the rate of adoption may be 
correlated with unobserved productivity characteristics which also affected earnings.  To 
investigate this hypothesis we exploit a simple idea.  Our data allows us to split computer 
users into two groups: Those who were using computers at work in 1991, and those who 
adopted computers at work between 1991 and 2000.  Approximately 20 percent of our 
sample went from being non-computer users in 1991 to using one in 2000.  Using this 
information we can test whether there was a rate of return to future computer use at work 
in 1991 earnings.  The model can be represented as follows: 
 
( ) 11 2 2 1 ln it t it it it t it it yC C C X λ δγ βµ = ++ − ++     ( 2 )  
 
where C1 represents computer use in 1991, and C2-C1 represents computer adoption 
between 1991 and 2000. 
  Table III shows the estimates of this equation and includes all our control variables 
mentioned above in the second earnings specification.  The estimate on the computer use 
1991 variable is large and statistically significant.  The computer use post-1991 variable 
is statistically significant in the 1991 sample as well as in the 2000 sample.  Since future 
computer use cannot have a causal effect on earlier earnings, this suggests that the 
computer use variable is capturing unobserved ability which affects earnings.  This would   6
imply that computer wage differentials merely reflect a wage premium due to unobserved 
worker characteristics rather than to productivity improvements attributable to computers.  
Arguing along these lines, then the estimate of the computer use 1991 variable may too 
be due to other abilities and not the result of computer use.   
 
TABLE III.  SECOND OLS AND VALUE ADDED ESTIMTES 
 
                                     Simple           Simple    Value  Added  
          199  1     2000     2000 
 
Computer  use  1991              0.247       0.246     0.113 
                       (0.025)***             (0.030)***         (0.028)*** 
Computer  use  post-1991    0.104     0.081     0.034 
                       (0.026)***             (0.029)***          (0.027) 
R-squared         0.39    0.44    0.58 
Robust standard errors in parentheses                                            
* significant at 10%; ** significant at 5%; *** significant at 1%         
 
   
  We further investigate these results using what has been called the value-added model.  
Researchers have adopted this approach in estimating production functions to mitigate 
potential bias arising from omitted data on historical inputs or endowments. The ‘value-
added’ specification differs from the ‘contemporaneous’ specification only in the 
inclusion of a lagged output measure as an explanatory variable. The lagged variable is 
taken to be a sufficient statistic for all historical inputs and heritable endowments, or 
‘fixed-effect’ specifications, which attempt to difference out unobservables over time.  
Evidence based on the value-added specification is generally regarded as more 
convincing than that based on a contemporaneous specification (Hanushek, 2003; and 
Krueger, 2000). In our model, this specification adds previous earnings to the list of regressors 
in our “contemporaneous” specification i.e., equation (2).  The addition is taken as a proxy for 
unobserved individual effects such as unmeasured worker ability which may also affect earnings.  
This model is of the following form: 
 
() 22 1 1 2 2 1 2 12 2 2 ln ln ii t i t i t i i i yC C Cy X λ δγ χ β µ =+ + − + + +    (3) 
   7
 
  The final column of Table III shows the results of this specification.  The impact of 
computer use in 1991 on earnings in 2000 is estimated at 11 percent with no return 
apparent to the adoption of computers at work after 1991.  These results concur with the 
previous model.  Although the value-added model is regarded as being superior to that of 
contemporaneous specifications, Todd and Wolphin (2003) point out that the value-added 
model imposes strong assumptions on the underlying production technology.  Most 
notably, the inclusion of a lagged output variable as a conditioning variable may make 
the model highly susceptible to endogeneity bias when data on some of the relevant 
inputs are missing.  Potentially missing regressors are a less important consideration in 
this study given that the NCDS has provides one of the most comprehensive lists of 
control variables to any study in this area.  
 
4. Conclusion 
This paper presents results about the returns to computer use in the UK. The cross-
sectional estimates are similar to the findings reported by Krueger (1993) for the US and 
Bell (1996) for the UK: Workers who use a computer on-the-job earn higher wages than 
otherwise similar workers who do not use a computer. This holds over a range of 
different specifications of the wage equation and is also corroborated by the results from 
our fixed effect models. We present evidence against the productivity interpretation that 
the return to computer use is caused by technological change. The inclusion of future 
computer use at work in earnings regressions was shown to be statistically significant 
indicating that computer users would have earned higher wages in the absence of 
computer technology.  Furthermore, value added models showed that there was no return 
to the adoption computer use at work over the last decade.  A straightforward implication 
of these results is that the return to computer use can be attributed to unobserved 
characteristics. 
 
 
   8
Appendix 
 
This Appendix describes the control variables used in our regressions.  The wages 
reported in the NCDS is somewhat cumbersome.  Respondents were asked their usual 
weekly hours, their net and gross pay, and their pay interval. We first calculate the 
number of hours in the pay interval by examining the usual weekly hours, and then 
calculate the hourly pay rates by taking the pay reported and dividing by the number of 
hours in the pay interval.  We focus on gross pay only and wages are deflated at 2000 
prices. 
  Our measure of education is highest educational qualifications attained which have 
been grouped into six categories: no qualifications (the omitted category), NVQ level 1, 
NVQ level 2, NVQ level 3, NVQ level 4 and NVQ level 5. 
  Reading and mathematics tests at age 7 are used to proxy for innate ability.  
Economists have often interpreted these scores as measures of cognitive ability or innate 
ability as these tests are much less likely to be affected by schooling than later tests.  As 
in Cawley et al. (1996) we measure intelligence by the first Principal Component from 
the two tests. 
  Occupation is measured from the variables describing socio-economic group and 
introduced as five dummy variables: professional, managerial, skilled non-manual, semi-
skilled non manual, semi-skilled manual, and unskilled (the omitted category). 
  Firm size is grouped into five categories: 10–24 employees, 25–99 employees, 100–499 
employees, 500 or more. The omitted category is 1-9 employees in the 2000 survey and 1–10 
in 1991 survey. 
  Dummy variables for married, children and union affiliation are also added to our 
wider earnings specifications. 
 
   9
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Computer Use and Earnings

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