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 fixedeffects 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 + γ3Compexp2

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 Compexp2

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) 

R2

 .56 

Adjusted R2 .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.