This paper re-examines the returns to computer use using a new matched workplace-employee data from Canada. We control for potential selection using instrumental variables. Results suggest that it is not merely the employee having a computer on his desk, but rather having complementary computer skills, that causes wages to increase.computers, computer skills, human capital, technology

Cindy Zoghi

Sabrina Wulff Pabilonia

English

Research Papers in Economics

BLS WORKING PAPERS 
U.S. DEPARTMENT OF LABOR 
Bureau of Labor Statistics 
   
OFFICE OF PRODUCTIVITY AND TECHNOLOGY 
  
Returning to the Returns to Computer Use 
                                                            
Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics 
Cindy Zoghi, U.S. Bureau of Labor Statistics 
  Working Paper 377 
 February  2005 
 
 
 
 
 
 
 
 
 
 
   
 
 
 
   
All views expressed in this paper are those of the authors and do not necessarily reflect the views or policies of the U.S. 
Bureau of Labor Statistics.  
 
 
 
 
 
Returning to the Returns to Computer Use 
 
by  
 
Sabrina Wulff Pabilonia and Cindy Zoghi* 
 
 
 
December 21, 2004 
 
 
Forthcoming in the AER Papers and Proceedings May 2005 
 
 
 
 
 
 
Abstract:  This paper re-examines the returns to computer use using a new matched 
workplace-employee data from Canada.  We control for potential selection using 
instrumental variables.  Results suggest that it is not merely the employee having a 
computer on his desk, but rather having complementary computer skills, that causes 
wages to increase. 
 
 
 
 
JEL: J31, O30 
 
 
*U.S. Bureau of Labor Statistics, Division of Productivity Research and Program 
Development, 2 Massachusetts Ave., NE Rm. 2180, Washington, DC 20212. 
Pabilonia.Sabrina@bls.gov and Zoghi.Cindy@bls.gov. We thank Lucy Chung for her 
support with the data, and Joel Elvery, Ed Johnson, Peter Meyer, Anastasiya Osborne, 
Steve Stillman and Leo Sveikauskas for their comments. The findings and opinions 
expressed in this paper do not necessarily reflect the position or policies of the U.S. 
Bureau of Labor Statistics.  1  
Economists have widely debated whether there exists a return to computer use.  If 
computers increase the marginal productivity of labor, workers may realize an economic 
return from using computers.  One reason this might occur is that technology may allow 
firms to automate repetitive tasks and shift the energies of workers toward non-routine 
tasks, such as problem-solving, innovating and developing interpersonal skills; this shift 
may increase overall worker productivity.  It is difficult to find evidence supporting a 
productivity hypothesis, however, since the adoption of computers is also likely to 
increase the relative demand for workers with skills that are complementary to the new 
technology.  
  When a firm adopts a new technology, workers who will use it may require 
general computer skills that are transferable to other firms, and/or firm-specific computer 
skills.  In the case of the former, there will be an increase in demand for workers with 
computer skills.  Those employees lacking the necessary skills may receive training to 
use the new technology, but they are also likely to pay the training costs in terms of lost 
wages.
1 In the case of the latter, all workers will require training to use the new 
technology, but since the skills are not transferable, firms will likely share the costs of 
that training. 
  The earliest studies of the relationship between wages and computerization used 
cross-sectional micro-data and indicated that computer use on the job was associated with 
a 15-20 percent wage premium (Alan B. Krueger 1993; John DiNardo & Jörn-Steffen 
Pischke 1997; David H. Autor, Lawrence F. Katz, & Krueger 1998).  However, in a 
                                                 
1 Cindy Zoghi & Sabrina Wulff Pabilonia (2004) find evidence that employees who adopt 
computers and receive training experience slower wage growth.  See Robert G. Valletta 
(2004) for a discussion of the importance of computer training in workers’ wage profiles. 
  2  
cross-section it is difficult to determine whether an observed wage differential represents 
productivity gains or changes in the relative demand for skills.  More recent studies have 
used panel data to control for unobserved worker characteristics in a fixed effects model 
(e.g. Horst Entorf, Michael Gollac, & Francis Kramarz 1999).  They find that workers 
gain only small returns to computer use.  The fixed effects estimate is likely to be small 
or nonexistent, however, because it relies on year-to-year changes in computer use, when 
workers may be bearing the burden of training costs.  Another drawback of the fixed 
effects model is that it cannot control for time-varying unobservable skills. 
  This paper re-examines whether there is a causal effect of computer use on wages 
using a recent matched workplace-employee panel data from Canada.  We control for 
potential selection using instrumental variables.  To the best of our knowledge, no 
published paper has used this method to estimate the returns to computer use.  In 
addition, we use data on the computer experience of each worker to show that computer 
skills, rather than the presence of a computer on an employee’s desk, affect wages. 
  In the next section, we describe the data.  In section III, we discuss our estimation 
strategy and compare estimates from OLS, fixed-effects, and instrumental variables 
models.  The final section of the paper summarizes our findings. 
 
I. Data 
The data come from the first four waves (1999-2002) of the Canadian Workplace 
and Employee Survey (WES).  This matched workplace-employee data includes 
workplaces and a sample of their paid employees aged 18-64.   In 1999, 6,322 employers 
and 23,540 employees were interviewed.  In 2000, 20,167 of those employees were re- 3  
interviewed.  In 2001, employees were re-sampled at continuing workplaces to start a 
new two-year employee panel.  In 2001 and 2002, there were 20,377 and 16,813 
employees, respectively.  WES will continue this sampling pattern with the periodic 
addition of workplaces to ensure a representative cross-section.   
Since attrition may be systematically related to wages and important covariates, 
we use employees from the 1999 and 2001 waves to estimate our OLS and instrumental 
variables models.  In these analyses, we use WES weights to take into account sampling 
design. This pooled sample consists of 43,917 employees with valid responses to the 
variables included in our wage model.  Our fixed-effects analysis is based upon 
employees who were in the sample in both years of either two-year panel (1999-2000 or 
2001-2002).   
  The data are rich in information on technology use, both for the workplace and for 
individual employees.  The two key technology variables in this paper are: an indicator 
for whether or not an employee uses a computer at work, and the number of years of 
experience using computers at work that the employee has acquired (and its square).  The 
computer use variable comes from the question:  “Do you use a computer in your job?  
Please exclude sales terminals, scanners, machine monitors, etc.”  In addition, there is a 
help screen informs the respondent that “By a computer, we mean a microcomputer, 
mini-computer, or mainframe computer that can be programmed to perform a variety of 
operations.”  We believe that this question gives us data on computer use on the job 
comparable to that obtained from other data sources on computer use.  The computer 
experience variable comes from the question: “Considering all the jobs you have held, 
how many years have you used a computer in a work environment?”     4  
  In both 1999 and 2001, 61 percent of workers in Canada used a computer on the 
job.  Most computer users in 1999 were fairly experienced, with on average 8.7 years of 
computer use.  Even those workers who were not currently using a computer had on 
average 1.5 years of computer experience in a work environment.  By 2001, computer 
users had gained on average just over one year of computer experience (9.9 years of 
experience).   
  The dependent variable is the natural logarithm of the employee’s gross hourly 
wage rate.  In addition to computer experience and use, our wage models include human 
capital factors (potential experience and its square, years of education, tenure and its 
square), demographic characteristics (indicators for married, female, female interacted 
with married,  non-European, different language spoken at home than at work, six 
regions), job characteristics (indicators for part-timer, covered by a union, six 
occupations, 14 industries, and the natural logarithm of firm size), the percentage of 
computer users in the workplace, a wave year indicator, and a constant.  In our fixed-
effects model, we also include as a covariate whether or not the employee has been 
promoted within the last year.    
 
II. Estimation Strategy and Results 
  We first estimate the following pooled OLS wage regression: 
lnWit = α + βX it + γ1Compit + γ2Compexpit + γ3Compexp
2
it + εit   (1) 
where Wit is individual i’s gross hourly wage rate at time t; Xit is a vector of observed 
characteristics of i at time t described in the previous section; Compit is an indicator 
variable that is equal to one if i uses a computer at time t, and zero otherwise; Compexpit  5  
and Compexp
2
it are years of computer experience and its square, respectively; α, β, γ1, γ2, 
and γ3 are parameters to be estimated; and εit is a stochastic disturbance term assumed to 
follow a normal distribution.  The coefficient γ1 is the return to computer use. 
    OLS results presented in Table 1 indicate a wage premium of 6.6 percent 
(exp(0.064)-1) for computer users when controlling for computer experience; however, 
there likely exist other unobservable skills correlated with computer use and wages.  We 
also find that employees with computer experience earn a return for their skills.  A 
computer user with average computer experience would earn 16.9 percent higher wages 
than the non-computer user.  Previous cross-sectional results that do not control for 
computer experience attribute a comparable size differential entirely to computer use 
(Krueger 1993). 
  We next estimate a fixed-effects model on the full sample by including individual 
intercepts in equation (1).  Identification in this model comes from changes in computer 
use status for employees, which combines the effects of adopting a computer and of no 
longer using a computer.  Downward wage rigidity may prevent reductions in wages 
when employees stop using a computer, as indicated in Zoghi & Pabilonia (2004).  
Therefore, the fixed-effects estimate is likely to underestimate the return to computer 
adoption.  In addition, fixed-effects estimates from this paper and in prior research are 
likely to be smaller than the average effect of computers on wages if workers indirectly 
pay the cost of training in the years following adoption.  Indeed, the fixed-effects 
estimate indicates a return to computer adoption of only 1.2 percent.  However, there 
remains a small but highly significant return to computer experience of 0.3 percent.  Our  6  
results on the return to computer use and computer experience are similar in magnitude to 
those found by Entorf, Gollac, & Kramarz (1999). 
Assuming that skilled workers are positively selected into computer use, 
measurement of γ1, the return on computer use in the OLS model, would be biased 
upwards.  In order to distinguish the real return to using computers, we must account for 
selection into computer use.   Therefore, we treat Compit in equation (1) as an 
endogenous dummy variable and replace this variable with the predicted probability of an 
employee using a computer, which is modeled as  
Compit = δ + ρZit + ηit.       (2) 
and is estimated using maximum likelihood estimation.  Zit is a vector of exogenous 
covariates explaining computer use.  We assume that the error, ηit, is normally distributed 
and that εit and ηit are jointly bivariate normally distributed.  In order to separately 
identify the probability that an employee receives a computer, we rely on a set of 
questions on innovation from the workplace survey.  Our instrumental variable is whether 
or not the workplace has implemented a new process or has improved existing processes 
in production within the past year.  Approximately 31 percent of employers reported a 
new or improved process.  It is likely that this process involves a change in technology 
and increases the chance that an employee will currently be using a computer.  At the 
same time, it is not obvious that these changes would affect contemporaneous wages.  
Mark Doms, Timothy Dunne and Kenneth R. Troske (1997) find evidence suggesting 
that the adoption of new technologies does not alter wages within manufacturing plants. 
  We perform several endogeneity and misspecification tests.  They largely 
confirm the importance of controlling for selection into computer use.  For example, the  7  
t-value for a Hausman t-test for the endogeneity of computer use is 4.14.  Using a 
Davidson and MacKinnon endogeneity test, we can reject at the 13% level the exogeneity 
of the computer use variable in an OLS wage model.  Using a Wald test, we reject at the 
5% level the independence of the error terms in the instrumental variables model, which 
indicates that OLS is misspecified.   
When we account for selection into computer use, we find that the effect of 
computer use on wages is statistically insignificant.
2  Our instrument, an improved or 
new process in production, is associated with a 3.8% increased likelihood of an employee 
using a computer, and is significant at the 5% level.  In addition, we find that a computer 
user with average computer experience would earn 13.5 percent higher wages than the 
non-computer user.  Comparing this result to the cross-sectional estimates obtained here 
and elsewhere, we find that the wage differential is almost entirely attributable to 
computer skills, rather than computer use independent of skills. 
 
III. Concluding remarks 
In this paper, we have re-examined the returns to computer use controlling for 
observed and unobserved skills.  Using instrumental variables, we find that it is not 
merely the employee having a computer on his desk, but rather having complementary 
computer skills, that causes wages to increase.  These results suggest that the wage 
differential observed between computer users and other workers is largely due to 
increased demand for workers with both observable computer skills and other, 
                                                 
2 Although computer experience may be endogenous, results are robust to excluding 
computer experience from the instrumental variables model, suggesting that this is not 
likely to bias the result.   
  8  
unobservable skills.  Once we control for these skills, the wage differential disappears.  
We believe our instrumental variable estimation is preferable to either fixed effects, 
which estimates the wage changes from transitions into and out of computer use, or the 
cross-section, which fails to control for selection.  In future work, we intend to analyze 
the importance of formal and informal technology training and specific tasks workers are 
performing with their computers to the return to computer use. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  9  
 
References 
 
Autor, David H.; Katz, Lawrence F. and Krueger, Alan B.  “Computing Inequality:  Have 
Computers Changed the Labor Market?”  The Quarterly Journal of Economics, 
November 1998, 113 (4), pp.1169-1213. 
 
DiNardo, John and Pischke, Jörn-Steffen. “The Return to Computer Use Revisited:  Have 
Pencils Changed the Wage Structure Too?”  The Quarterly Journal of Economics, 
February 1997, 112(1), pp.291-303. 
 
Doms, Mark; Dunne, Timothy and Troske, Kenneth R.  “Workers, Wages, and 
Technology.”  The Quarterly Journal of Economics, February 1997, 112(1), 
pp.253-289. 
 
Entorf, Horst; Gollac, Michael and Kramarz, Francis.  “New Technologies, Wages, and 
Worker Selection.”  Journal of Labor Economics, July 1999, 17(3), pp. 464-91. 
 
Krueger, Alan B. “How Computers Have Changed the Wage Structure:  Evidence from 
Microdata, 1984-1989.”  The Quarterly Journal of Economics, February 1993, 
108(1), pp.33-60. 
 
Valletta, Robert G.  “The Computer Evolution: Diffusion and Changing Returns in the 
U.S., 1984-2001.”  Manuscript, Federal Reserve Bank of San Francisco, 2004. 
 
Zoghi, Cindy and Pabilonia, Sabrina Wulff.  “Which Workers Gain from Computer 
Use?”  Working Paper No. 373, Bureau of Labor Statistics, 2004. 
 
 
 
 
 
 
 
 
 
 
  10 
Table 1― Log Hourly Wage Estimations 
Independent Variable  OLS  FE  IV 
Computer Use  .064** 
(.013) 
.012**   
(.005) 
-.036  
(.051) 
Computer Experience  .012** 
(.002) 
.003** 
(.001) 
.020** 
(.005) 
(Computer Experience)
2 / 1000  -.291** 
(.085) 
-.100*  
(.0439) 
-.545** 
(.154) 
R
2 .56     
Adjusted  R
2   .88   
Wald χ
2(42)     9,014.86 
Number of Observations  43,917  78,950  43,917 
Notes:  For the OLS and IV models, standard errors in parentheses are adjusted for complex 
survey design effects.  All standard errors were corrected for workplace clustering.  
Regressions include potential experience and its square, years of education, tenure and its 
square, married, non-European, different language spoken at home than work, female, 
female*married, region, part-timer, covered by union, occupation, industry, ln(firm size), % 
computer users in the workplace, a wave year indicator, and a constant.  In the FE model, we 
also include a recent promotion indicator. 
*Statistically significant at the 5 percent level. 
**Statistically significant at the 1 percent level. 
 
 
 
 
 
 
 
 
 
 
 

Computing Inequality:

How Computers Have Changed the Wage Structure: Evidence from Microdata,

New Technologies, Wages, and Worker Selection.”

The Computer Evolution: Diffusion and Changing Returns in the U.S., 1984-2001.” Manuscript, Federal Reserve Bank of San Francisco,

The Return to Computer Use Revisited: Have Pencils Changed

Wages,

Returning to the Returns to Computer Use

Returning to the Returns to Computer Use

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