BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF PRODUCTIVITY AND TECHNOLOGY 

 

Which Workers Gain from Computer Use? 

 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics 

 Working Paper 373

 June 2004 

 

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.
Which Workers Gain from Computer Use? * 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics 

May 26, 2004 

Abstract: Workers who use computers earn more than those who do not. Is this a 

productivity effect or merely selection? Using the Canadian Workplace and Employee 

Survey, we control for selection and find a wage premium of 3.8% for the average 

worker upon adopting a computer. This premium, however, obscures important 

differences in returns to computer adoption across education and occupation groups. We 

find that long-run returns to computer use are over 5% for most workers. Differences 

between short-run and long-run returns may suggest that workers share training costs 

through sacrificed wages. 

JEL: J31, O30 

Contact Information: Cindy Zoghi, U.S. Bureau of Labor Statistics, Productivity 

Research and Program Development, 2 Massachusetts Ave., NE, Washington, DC 20212, 

e-mail: Zoghi.Cindy@bls.gov; Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics, 

Productivity Research and Program Development, 2 Massachusetts Ave., NE, 

Washington, DC 20212, e-mail: Pabilonia.Sabrina@bls.gov

*Disclaimer: 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. The 

authors would like to thank B.K. Atrostic, Martha Bailey, Marianne Bitler, Michael 

Harper, Michael Giandrea, Francis Kramarz, Mark Long, Peter Meyer, Anastasiya 

Osborne, David Ribar, Larry Rosenblum, Steve Stillman, Leo Sveikauskas, Aklilu 

Zegeye, and participants at the 2003 NBER/CRIW Summer Institute, 2004 SOLE 

meetings, George Washington University Labor Studies Workshop, and Center for 

Economic Studies seminar for helpful comments. We also thank Tony Fang at Statistics 

Canada for data support.
 1

I. Introduction 

Since the 1980’s, wage inequality between highly educated workers and less 

educated workers has grown substantially. One hypothesis for this increased wage gap is 

skill-biased technological change (Mincer 1993). It has been argued that the 

computerization of work allows workers to shift their focus from routine tasks to problem 

solving. This "upskilling" increases the productivity and wages of workers (Attewell 

1987). Using a cross-section of workers from the 1989 Current Population Survey 

(CPS), Krueger (1993) found that workers who used a computer on the job earned 17.6% 

higher wages than those who did not use a computer. He included a variable measuring 

computer use at home in an attempt to control for unobserved worker heterogeneity. 

However, this did not reduce the size of the returns to computer use on the job. This 

paper sparked debate as to whether there is truly a return for using a computer or if higher 

wages are a result of positive selection into computer use. If workers with high ability or 

unobserved skills are the workers who are given computers on the job, then crosssectional results could falsely attribute a wage premium to computer use. DiNardo and 

Pischke (1997) reached the latter conclusion after finding that workers who used a variety 

of other tools associated with white-collar type work, including a pencil and a hand 

calculator, also received a similar return on these tools. 

A few researchers have used panel data to control for unobserved individual 

heterogeneity. Most, with the exception of Bell (1996) and Dolton and Makepeace 

(2004), found small or insignificant returns on technology use. These studies suggest that 

firms are allocating information technologies to their highest skilled workers, who 

already earn more. Using French employer-employee matched retrospective data on new 
 2

technologies, Entorf and Kramarz (1997) and Entorf, Gollac, and Kramarz (1999) 

confirmed Krueger’s and DiNardo and Pischke’s cross-sectional results. However, after 

controlling for individual fixed-effects, they found that the return to computer use for 

new users is insignificantly different from zero, while prior experience with computers 

earns employees a statistically significant return of two percent. Using retrospective data 

on computer usage from the German Socio-Economic Panel (GSOEP), Haisken-DeNew 

and Schmidt (1999) found that individual fixed-effects reduced the return to computer 

use to one percent. Using first-differences and allowing coefficients to vary over 

individuals and over time, Dolton and Makepeace (2004) found that female workers in 

the U.K. earned 13% more from adopting a computer in 2000 versus 1991 than female 

workers who did not use a computer at either time. 

While proponents of "upskilling" argue that computerization can lead to 

productivity and wage increases, critics such as Braverman (1974) counter that 

computerization can be "deskilling"--the increased mechanization reduces workers' 

control over the production process and simplifies jobs, leading to lower wages. In fact, 

the introduction of new technology may be upskilling for some workers (i.e. because it 

complements them in production) and deskilling for other workers (i.e. because it 

substitutes for them in production), even within a single firm. In a case study of the 

introduction of digital check imaging in a bank, Autor, Levy, and Murnane (2002) found 

that the exceptions processors spent more time on problem solving and less on repetitive 

tasks while the staff of deposit processors in the back room with the same skill 

requirements was reduced. In this case, computers substituted for some routine tasks and 

complemented problem-solving. These differences may be observable between 
 3

occupational groups as computers change skill requirements. For example, word 

processing programs may be deskilling for clerical workers because documents can be 

prepared quicker and with fewer skills, but upskilling for managers because they allow 

them to take on a greater variety of tasks. Bresnahan, Brynjolfsson, and Hitt (2002) 

argue that another reason for differential returns to technology across workers is that 

managers and professionals with high cognitive skills are especially important for the 

implementation of new technologies. They need to be able to transform organizations to 

take advantage of technology and the new information that it enables them to learn about 

their customers. Similarly, Bartel and Lichtenberg (1987) argue that since highly 

educated workers have a comparative advantage in adjusting to new technologies, the 

introduction of new technologies should shift demand away from less educated workers. 

Some evidence on these differences is presented in Krueger (1993), Doms, Dunne and 

Troske (1997) and Tashiro (2003). 

In this paper, we use a panel of workers surveyed in the 1999-2000 Canadian 

Workplace-Employee Survey (WES) to re-examine wage premiums for using a computer 

at work. The panel attribute allows us to control for positive selection into computer use. 

Comparable to other studies, we estimate a fixed-effects specification, which identifies 

effects only through those workers who change their computer use status. We then 

extend the analysis in four directions. First, using a more flexible first-differenced 

model, we identify the return to adopting a computer, as distinct from the negative return 

from ceasing to use a computer. Second, with these selectivity controls we examine the 

returns for specific subgroups of workers by education, occupation and type of computer 

application. Third, using a value-added model, we measure the longer-term returns to 
 4

continued computer use (unmeasurable in the fixed-effects or first-differenced 

specification). Finally, we look at the effects of previous computer experience and 

computer training to determine whether the difference between the small returns for 

adopters and the much larger returns for continued users can be attributed to learning 

costs. 

In the next section, we provide some theoretical motivation to help explain why 

returns to computer use may differ for workers with varying skills and how computer 

training may influence returns. In section III, we discuss the WES and present some 

descriptive statistics on computer users in Canada. In section IV, we present crosssectional wage equations, and then fixed-effects and first-differenced wage equations that 

control for unobserved worker heterogeneity. Section V shows that the differences in 

returns to computer adoption vary depending upon the worker’s occupation, education 

level, and type of computer application used. In section VI, we present long-run returns 

to continued computer use, and investigate whether learning costs explain the difference 

between these long-run returns and the returns to adoption. Section VII concludes the 

paper. 

II. Theoretical Motivation 

In this section, we present a model with workers of differing observable skill 

levels in order to show how selection may affect the returns to computer use, to explain 

why workers may still earn differential “true” returns to computer use, and how this 

“true” return may change over time as the price of computers falls. This model will also 

help to explain why training may lead to unequal returns at the time of computer 
 5

adoption, which we test in section VI. The model follows largely from Borghans and 

Weel (2003). 

Let us assume there exists a firm with two labor inputs, who differ in their 

education levels. One type (H) is highly educated, the other type (L) is less educated. For 

simplicity, these are the only two inputs to production, and they are measured in effective 

labor units rather than man hours. Output (Y) is produced according to the following 

CES production function1

: 

 [ ] ρ ρ ρ μ μ

1

Y = f (H, L) = H + (1− )L (1) 

 ; is the factor share distribution parameter that can vary over time; and the 

elasticity of substitution  1− ρ

1 ). Given a competitive labor market, the wage 

rates per effective labor unit for each type of worker equal their respective marginal 

products: 

 [ ] (1 ) ( , )

1

1 w H H L T H L T

E = + − ∈ − − ρ

ρ

ρ ρ ρ μ μ μ (2) 

For any given worker, however, the number of effective labor units per man hour may 

vary, depending on the worker’s ability, experience, training, quality of the job match, 

and other factors. Each worker’s wage will depend on his own productivity parameter, 

denoted ai , which is (at least imperfectly) known by the manager. It is likely that ai

varies both across the two types of workers (with ai higher on average among type H 

workers than among type L workers) and within each worker type. Therefore, the typical 

worker i at time t earns the following wage per man hour: 

 w a w T (H, L) T

i Et

T

it = ∈ (3) 

 

1

 Results hold for a general constant returns to scale production function as well. 
 6

 Suppose further that a worker’s productivity depends not only on her productivity 

parameter, but also on whether or not she uses a computer. The productivity of computer 

user j is given by j a j. We assume that 	

	

		
	 jt > 1 for 

all workers, i.e. computers are complements in production. This means that there is some 

task the worker performs that could be done more productively with computerization. As 

will be discussed below, this does not necessarily indicate that all workers will adopt 

computers, since the cost of computerizing that task may be higher than the value of the 

additional output. The computing productivity parameter is likely to be high for tasks 

that computers are well-designed to perform, such as routine tasks. The computing 

productivity parameter may also be higher for workers with more computer experience 

(i.e. changes over time) or for workers who learn more quickly. It is commonly 

assumed that the average computing productivity is higher among type H workers than 

type L workers, i.e. L

t

H θ t > θ . 

 If labor markets are competitive, employers demand quantities of H and L such 

that the value of the marginal product of each effective unit equals its cost, irrespective of 

whether the worker uses a computer or not. We treat the computer itself as an asset 

chosen by the worker, rather than a distinct input to production. Although this may not 

be especially realistic in terms of the decision-making process, it is more realistic that 

decision-makers behave as if the computer were an asset of the worker. Thus, for any 

worker to use a computer, the productivity gains from computerization must at least 

offset the costs of computerizing. When computers are expensive, they are used by the 

worker who is likely to achieve the highest productivity gains from using the computer. 

As prices fall, diffusion spreads to other workers who achieve successively smaller 
 7

productivity gains. So while non-computer users’ wages are given by (3), computer 

users’ wages are: 

 jt jt

T

j Et

T

wjt = a w θ − C (4) 

where Cjt is the cost of computerizing (at least some aspect of) an individual worker’s job 

at time t. Although part of Cjt is fixed across workers and reflects the (falling) market 

price of personal computers, there are also job and worker characteristics that may affect 

the size of Cjt for an individual worker. For very complex tasks, software design is likely 

to be more complicated and expensive. Thus, we might see differences in computer 

adoption over occupations. Some workers may require formal computer training or onthe-job training, which increases Cjt. Differences in returns may be particularly stark in 

the first year of computer use when employers provide formal training programs to 

workers. Since the ability to use many of these applications adds to the workers’ general 

transferable skills rather than firm-specific skills, workers would be expected to share the 

costs of training. It is not clear whether costs are likely to be higher or lower on average 

for type H workers relative to type L, since type H workers may perform more complex 

tasks, but type L workers may require more training. However, the literature usually 

assumes that H L C < C . 

 The within-group wage differential between a typical computer user j and noncomputer user i is given by: 

 w (a a ) C T (H, L) j jt i jt

T

Et θ − − ∈ (5) 

Thus, there are five factors that increase the wage differential: 1) high individual 

productivity for computer users, 2) higher market efficiency wages, 3) higher computing 

productivity, 4) lower cost of computerizing, and 5) lower individual productivity of the 
 8

non-computer user. The first two factors are the typical selection biases that result in an 

overestimate of the returns to computer use in cross-sectional regressions, i.e. computers 

are used by high-wage, high-ability workers. The third factor, however, is in a sense the 

“true” return to computer use computers make workers more productive. Thus, if 

computers do not increase productivity at all ( = 1), wage differentials reflect only 

ability differentials, less the cost of computerization. We test for selection effects and/or 

productivity effects in section IV where we control for both observed and unobserved 

worker heterogeneity.  jt > 1, then there will be a return to computer use 

after controlling for differences across workers. In the empirical section, a significant 

wage premium for adopting a computer would be interpreted as the net benefit of 

computerizing. 

 There may also be difference between groups of workers. Replacing the 

individual worker parameters with their group means (where H

a j is the mean of highly 

educated computer users, H

i a is the mean of highly educated non-computer users, L

a j is 

the mean of less educated computer user, L

i a is the mean of the less educated noncomputer user), we find that the double difference in wages can be expressed as: 

 ( ) ( ) ( ) H

t

L

t

L

i

L

t

L

j

L

Et

H

i

H

t

H

j

H

wEt a θ − a − w a θ − a + C − C (6) 

If L

t

H θ t > θ and L

t

H Ct < C , the difference may be positive, so that more highly educated 

workers earn a higher return to computer use than less educated workers. However, this is 

largely an empirical question—even if highly educated workers have on average higher 

computing productivity and lower costs of using a computer than less educated workers, 

the distribution of workers between the groups also determines which effect will 
 9

dominate. Equation (6) also demonstrates how computers can affect wage inequality 

between the two groups over time, even with relative demand held constant. The relative 

return to computer use for type H workers could decrease (reducing inequality over time) 

if computerization were to become relatively less costly for type L workers. For 

example, the diffusion of computers into K-12 classrooms may decrease the amount of on 

the job computer training necessary for less educated workers to effectively use 

computers relative to the training necessary for highly educated workers. Similarly, 

changes over time in either parameter will affect the size of the between group wage 

differential. Changes might occur through a redesign of jobs, perhaps, if firms shifted 

some easily computerized tasks to less educated workers. Therefore, we test in section V 

whether the return to computer use is higher for more educated workers than less 

educated workers. Then, we test whether workers earned lower differential returns if 

they received computer training (to proxy for the costs of computerizing) when adopting 

a computer. 

III. Data 

The data we use for this analysis come from the Canadian Workplace and 

Employee Survey (WES). This survey was initially conducted in 1999. Establishments 

in the WES are being followed each year, while employees are followed for only two 

years and then re-sampled. For our analysis, we use a panel of employees with their 

matched employer information from 1999 and 2000 – the data currently available. The 

panel aspect of the data allows us to control for unobserved individual characteristics that 

might affect the propensity for computer use as well as wages. 
 10

 In 1999, 23,540 employees in 5,733 establishments were interviewed. 

Establishments were first selected from employers in Canada with paid employees in 

March of the survey year with the exception of the Yukon, Nunavut, and Northwest 

Territories and “employers operating in crop production and animal production; fishing, 

hunting, and trapping; private households, religious organizations and public 

administration” (Statistics Canada 2002, 23). At each establishment, a maximum of 

twelve paid employees were then randomly sampled from a list of employees. All 

employees were selected in establishments with fewer than four employees. In 2000, 

20,167 employees were re-interviewed. For some of our main econometric analysis, we 

use a restricted sample composed of those 19,364 employees who responded in both 

years, remained with the same employer in both years, and had non-missing observations 

on the dependent and independent variables. Sample means and proportions for the 1999 

restricted analysis sample are in Table A1 in the Appendix.2

 The dependent variable in our analysis is the natural logarithm of the hourly 

wage. In the compensation section of the WES, employee respondents reported their 

wage or salary before taxes and other deductions in any frequency they preferred (e.g. 

hourly, daily, weekly, annually). They were also asked about additional variable pay 

earned from tips, commissions, bonuses, overtime pay, profit-sharing, productivity 

bonuses and piecework in the last twelve months. Hourly compensation was derived by 

Statistics Canada by dividing wages plus additional variable pay by total reported hours.3

 

This derived hourly compensation is the measure of hourly wage used in our analysis. 

 

2

 Although not reported here, there do not appear to be any significant differences between the full sample 

and restricted sample employee characteristics. 

3

 Managers may be more likely to work unreported hours than other workers. Thus, hourly wages for this 

occupational group would be overestimated. 
 11

 The WES is rich in questions concerning the use of technology by establishments 

and their employees. One of the central variables in our study is computer use by 

employees. Specifically, employees were asked “Do you use a computer in your job? 

Please exclude sales terminals, scanners, machine monitors, etc.” Identification in the 

fixed-effects analysis comes from users that changed their computer use status (see 

Appendix Table A1). Table 1 describes the proportion of workers who used a computer 

at work in 1999 and 2000.4

 Sixty-one percent of Canadian workers used a computer at 

work in 1999 and 65% used a computer at work in 2000.5

 Women were more likely than 

men to use a computer in either year. In 1999, 63% of women and 58% of men used a 

computer. Though the percentages are larger in magnitude in Canada, the relative 

computer use by gender is similar to that found in the United States in 2001 (U.S. Bureau 

of Labor Statistics 2001) and the United Kingdom in 2000 (Dolton and Makepeace 

2004). 

Employees aged 25-54 (approximately 65% in 1999) were much more likely to 

use a computer than the youngest employees aged 18-24 (44% in 1999) and employees 

aged 55+ (47% in 1999). By occupation groups, managers, professionals, and 

clerical/administrative employees had the largest number of computer users, with at least 

82% of the workers in each of these occupation groups using a computer in 1999. 

Managers and marketing/sales had the largest gains (6% gains) in the percentage of new 

computer users between 1999 and 2000. More educated workers were more likely to use 

a computer at work than less educated workers. Eighty-one percent of workers with a 

 

4 Survey means and proportions throughout the paper have been weighted using the employee weights. 

5

 This is comparatively larger than the 53% of U.S. workers who used a computer at work in 2001. This 

figure is the authors’ calculation from the Current Population Survey Supplements (U.S. Bureau of Labor 

Statistics 2001). The percentage is comparatively lower than the 75% of U.K. workers who reported using 

a computer at work in 2000 in the National Child Development Study (Dolton and Makepeace 2004). 
 12

bachelor’s degree used a computer at work while only 52% of workers whose highest 

degree was a high school diploma used a computer at work in 1999. Workers not 

covered by a union were more likely to use a computer than those who were covered 

under a collective bargaining agreement (64% versus 52%), and full-time workers were 

more likely to use a computer than part-time workers (65% versus 45%) in 1999.6

 

Employees were also asked about their years of experience using computers, the 

number and types of computer applications used, the number of hours per week spent 

using their computer, and the use of other technologies, such as industrial robots or 

computer-aided design (CAD) systems. Table 2 presents means and proportions for 

selected technology-related questions from the WES. In 1999, 11% of employees did not 

use a computer for their current position but had some prior experience using a computer. 

Among employees who used a computer, the majority were experienced computer users. 

Sixty-four percent had used a computer for five or more years, while 44% had used a 

computer for nine or more years. On average, computers users spent half of their work 

week using computers (about 19 hours per week) and used 2.6 computer applications. 

Clerical and administrative workers spent the most hours on average per week using their 

computers (24.05 hours) while managers used their computers on average about 20 hours 

per week. On average, employers reported that 46% of their employees used computers. 

Besides computers, 12% of employees used computer-assisted technology, such as 

industrial robots, and 27% used other technology or machinery, such as cash registers or 

sales terminals. 

 

6

 In the WES, union coverage is defined as being either a member of a union or covered by a collective 

bargaining agreement. 
 13

The respondents were also asked which software application they spent more time 

on. While they were free to answer any specific application, the answers were then 

coded into one of fourteen types of software applications listed in Table 3. There are 

significant differences in the most frequently used applications by occupation. Managers 

and professionals were most likely to use word processors as a primary application. 

Other occupations were most likely to use specialized office applications. Computer 

usage is thus a fairly heterogeneous concept across workers. 

IV. Wage Differential for Computer Use 

A. Cross-section estimation 

 In order to verify that our data yield similar results to those used in previous work, 

we first estimate a pooled cross-sectional wage equation with the computer use variable 

as the explanatory variable of interest. Specifically, we estimate: 

 lnWit =  X it + Compit + it (7) 

where Wit is individual i’s hourly wage rate at time t; Xit is a vector of observed 

characteristics of i at time t; Compit is an dummy variable equaling one if i uses a 

computer at time t, and zero otherwise; ,   are parameters to be estimated it

is a stochastic disturbance term assumed to follow a normal distribution. 

 Results for the return to computer use for the pooled cross-section regression, 

estimated by ordinary least squares, are reported in column I of Table 4. Included in the 

Xit’s are employee characteristics: years of education, potential experience, potential 

experience squared, has parents or grandparents who descended from non-European 

countries, speaks different language at work than at home, part-time status, marital status, 
 14

gender, gender interacted with marital status, is covered by a union, regional indicators, 

five occupational indicators, worker’s tenure with the establishment, and a year 

indicator.7 In addition, we control for two establishment characteristics from the linked 

employer files: the natural logarithm of establishment size and the percentage of 

computer users in the establishment. The resulting wage premium for computer use is 

16.9% (exp(.1565)-1). This result is comparable to that found by Krueger for the U.S. 

(1993). 

B. Controlling for Worker Heterogeneity 

 There may also be unobserved worker characteristics, such as ability, that make 

computer-users different from other workers. If these unobservables are correlated with 

wages, the previously reported wage premiums would be incorrectly attributed to 

computer use. Indeed, many other researchers have found that the wage premium for 

computer use is greatly diminished or no longer exists when they have controlled for 

unobserved individual heterogeneity.8

 

 In order to control for potential unobserved individual heterogeneity, we first 

estimate the following traditional fixed-effects specification on an unbalanced panel of 

workers in the 1999-2000 WES to replicate previous researchers results: 

 lnWit =  X it + Compit + i it (8) 

w i is the non-time varying individual fixed-effect. Many of the demographic 

variables are time invariant and consequently do not appear in the fixed-effects model. 

 

7

 It may be inappropriate to include occupational dummies in these regressions because employees with 

computer skills may be more likely to obtain jobs in higher paying occupations (Krueger 1993). Results 

were similar excluding occupational dummies. 

8

 See, for example, Bell (1996), Entorf, Gollac and Kramarz (1999), and Entorf and Kramarz (1997). 
 15

However, education does change for quite a few workers, possibly due to measurement 

error in one or both of the years. Additionally, marital status, work-home language 

differences, part-time status, and union coverage can change from one year to the next for 

some workers. For many of the establishments, both the number of employees and the 

percentage of computer users within the establishment change between 1999 and 2000. 

We also include whether the worker was recently promoted in 2000 – sometime in 1999 

or 2000 – since a promotion may be correlated with both changes in computer use and 

changes in wages.9

 Confirming previous researchers results, we find a fixed-effects estimate of only 

1.68% (column II in Table 4).10 Identification in this specification comes from the 9% of 

workers who changed computer status 6% adopted and 3% ceased to use a computer in 

2000 (see Appendix Table A1).11 Equation (8) assumes that the absolute value of the 

return to computer use is the same for both adopters and ceasers, which may not be the 

case. In addition, it does not tell us anything about the return to computer use for 

workers who used the computer in both 1999 and 2000 or even for many years prior to 

1999 (Dolton and Makepeace 2004). 

 Therefore, we separately identify the four possible computer use transitions a 

worker can experience over time and allow returns to computer use to vary between these 

 

9

 The simple correlation between adopting a computer and a recent promotion is 0.0317 while the 

correlation between ceasing to use a computer and promotion is -0.0054. 

10 We have also tried a random-effects specification and establishment fixed-effects specification. 

According to results of the Hausman test, we can reject the null hypothesis that the individual effects are 

uncorrelated with the other regressors in the model. The return to computer use controlling for 

establishment heterogeneity, but not worker heterogeneity, was 7.7%. 

11 Some may be concerned with the large number of ceasers in the data. Dolton and Makepeace (2004) 

suggest two possible reasons why workers may stop using a computer. One is that they may do so as they 

move up the promotion ladder; however, in Canada, the simple correlation between ceasing to use a 

computer and promotion is -0.0054 and we have controlled for promotion in this specification and those 

that follow. The other reason is that ceasers are not very good at using a computer, i.e. low i. In a fixedeffects regression using only non-computer users in 1999, we found a 3.9% return. 
 16

groups of individuals and over time. The four transitions are: those who never used a 

computer, those who used a computer in both periods (Mi), those who ceased using a 

computer in 2000 (Ci), and those who adopted a computer between 1999 and 2000 (Ai). 

Using a balanced panel of 19,364 Canadian employees, we control for unobserved 

individual heterogeneity by differencing the following two equations: 

 lnWi1999 = 1999 + X i1999 + m

1999Mi + c1999Ci + i + i1999 (9a) 

 lnWi2000 = 2000 + X i2000 + m

2000Mi + a2000Ai + i + i2000 (9b) 

in order to estimate the following first-differenced model12

: 

Wi =  βi + ( m)Mi + a

2000Ai - c1999Ci + εi (10) 

where  is the change in each variable/coefficient between 1999 and 2000; Mi , Ai, Ci are 

indicator variables for maintaining computer use, adopting a computer, and ceasing to use 

a computer, respectively. We allow the return to computer use to vary over time for 

continued users by allowing m

1999  m2000. 

 Column III of Table 4 reports results for this general first-differenced model. In 

this specification, the effect of computer use on wages for the average worker in the first 

year of computer adoption is a statistically significant 3.8%. This result is larger than 

that found by Haisken-DeNew and Schmidt (1999) who combined the effect of all 

movements into or out of computer use, since our estimate is specifically a measure of 

the return to adopting a computer. Our coefficient on ceasing to use a computer is not 

statistically significantly different from zero, perhaps due to downward wage rigidity. 

This first-differenced model reveals that the small, but insignificant, return to adopting a 

computer reported by Entorf et al. (1999), whose fixed-effects estimates did not account 

 

12 We have tried including establishment fixed-effects in the model; results, which are available from the 

authors, did not change. 
 17

for information on ceasing to use a computer, may be biased upward since the wage loss 

from ceasing to use a computer is quite asymmetric. 

 The small wage premium in our panel does not necessarily indicate returns to 

computer use are this small but merely that returns to the average worker in the first year 

of computer use are small. Returns might be small in the first year if employers pass 

along some or all of the costs of computer training to their employees. We find that the 

change in the return for workers who maintained their computer use over the period,  m, 

was 3.75%. This coefficient, however, can not tell us the return to long-run computer 

experience for maintainers m

2000. We explore this issue in section VI. 

V. Wage Differential for Computer Use by Worker Heterogeneity and Technology 

Use 

 The evidence thus far implies that the average worker does not earn the high wage 

premiums initially associated with computers at least in the short run although the 

premium is still positive and economically significant. Nevertheless, as suggested in 

section II, certain workers may earn higher than average returns. We look for evidence 

of such differential effects by re-estimating the first-differenced model for workers 

separately by occupational groups, educational groups, and type of application used most 

frequently. 

 While the WES does not provide detailed occupational information on workers, it 

does contain a variable for broad occupation groups: managers, professionals, technical 

and skilled production workers, marketing and sales, clerical and administrative workers, 

and unskilled production workers with no trade or certification. Results for these groups 
 18

are reported in Table 5. Group samples are restricted to those who were in the same 

occupation in both years. Even controlling for individual heterogeneity, managers earned 

a statistically significant 7.3% higher wages in the first year of computer use, while 

technical/trade workers earned 4% higher wages in the first year of computer use. The 

remaining occupational groups, however, earned no statistically significant wage 

premium for adopting computers, and only the return to professionals using a computer 

was an economically significant 4.5%. Results of a Chow test confirm that these are 

statistically different returns. These results coincide with our expectations, since white 

collar workers are likely to possess more problem-solving skills than other workers. If, 

as suggested by Autor et al. (2002), Bresnahan et al.(2002), and Doms et al. (1997), 

computers are a complement to high-skilled workers and a substitute for low-skilled 

workers, it makes sense that the adoption of computers would affect the wages of these 

groups of workers differently. Estimations of the wage effect for the average worker 

obscure important differences between types of workers. 

 A second way to test whether there are differential effects of computerization for 

particular types of workers is to estimate the models separately by education. We divide 

the sample into groups of workers with less than a high school diploma, with only a high 

school diploma, with some college or a vocational degree, with a bachelor’s degree and 

those with advanced degrees. Wage premiums are quite high for workers with an 

advanced degree (19.2%) or a bachelor’s degree (10.9%), still positive for those with 

some college or a vocational degree (2.9%), and not statistically different from zero for 

those with only a high school diploma or less. Again, a Chow test confirms that these 
 19

returns are statistically different. Thus, we find that returns to computer use are higher 

for more educated workers than for less educated workers in the first year of adoption. 

Another source of heterogeneity that may affect the returns to computer use stems 

from the different tasks that a worker performs using a computer. Autor et al. (2002) 

showed that technology may complement a worker who performs problem solving tasks 

but may be a substitute for a worker who performs repetitive tasks. If this is the case, 

then it may be important to look at more detailed questions of technology use. To do 

this, we estimate a first-differenced model similar to (10), but disaggregate the adoption 

indicator Ai into a set of 14 indicators representing the primary software application used 

by the adopter. In addition, we re-estimate equation (10) for two other types of 

technology--computer-aided tools (e.g. industrial robots) and other non-computer 

technologies (e.g. cash registers and scanners). 

 Results of these estimations are in Table 6. The wage premium is largest for 

those adopting desktop publishing, data analysis, and programming (22%, 11.5%, and 

9.3% respectively) compared to continued non-users. These applications tend to be 

applications in which workers must use critical thinking or problem-solving skills. 

However, the variance for the coefficients in this model comes from individual workers 

who adopt a computer and this particular software. The number of workers in each group 

is quite small, resulting in large standard errors in most instances. Adopters who use 

word processing, database, communication, and specialized office applications also earn 

significant wage premiums (7.5%, 5.2%, 7.2%, and 3.5% respectively). Thus while some 

of the estimates in the first-differenced model are quite noisy, there do appear to remain 

some differences in the wage premium depending upon the primary application adopted. 
 20

It does not appear that workers using technologies other than computers earn a wage 

premium for that usage. The three different groups of workers--by occupation, by 

education, and by software application used--seem to largely confirm that technology can 

affect workers differently. 

VI. Long-Run Results 

 One reason the traditional fixed-effects and general first-differenced models 

might yield small estimates of the return to computer use is that they measure the wage 

change within the first year of adopting/ceasing to use a computer. In order to estimate 

the return for maintaining computer use, we estimate equation (9b) by OLS using the 

lagged wage to try to capture the individual fixed-effects: 

 lnWi2000 = 2000 lnWi1999 + X i2000 + m

2000Mi + a2000Ai + i2000 (9b’). 

This value-added approach was first advocated for panel data by Todd and Wolpin 

(2003). 

 Results in Table 7 show that the average return to computer use for those who 

used computers in both periods (maintainers) was 8.3% in 2000. This large and 

significant return suggests that those with computer skills are earning higher wages than 

those who are first learning to use their new computers at this establishment. This higher 

return for maintainers is still lower than that found by Dolton and Makepeace (2004) for 

either men or women (14.3% and 9.3% respectively); however, their respondents had to 

maintain computer use over a nine year period and their minimum experience with a 

computer was over nine years, while the maintainers in our data had on average 10.29 

years of computer experience. The return to adopting (4.2%) using the value-added 
 21

approach was only slightly higher than that obtained using first-differences (3.8%), 

suggesting that lagged wages are good proxies for the individual fixed-effects at least 

for adopters. 

 We re-estimate this equation for the occupational and educational groups. Among 

the occupational groups, we find that most maintainers earned a return to computer use. 

Even though workers in the marketing/sales and clerical/administrative occupations did 

not earn a return to adopting a computer, workers in these occupational groups who 

maintained their computer use earned an economically significant return of 10% and 8%, 

respectively. Among the educational groups, maintainers all earned an economically 

large return to computer use. Maintainers among high school graduates, one of the lower 

educational levels, earned one of highest returns – 10.6%. The coefficient on maintainers 

in the advanced degree group was imprecise. These results suggest that previous fixedeffects models dramatically understate the “true” returns to computer use, and in fact, 

only represent the much smaller average returns to adopting/ceasing to use a computer. 

 It is not too surprising that the long-run returns are in most cases much larger than 

the short-run ones, since most workers will not immediately become more productive the 

instant a computer appears on their desk. The worker must learn to use the computer and 

to incorporate it into the way she performs her job.13 In the first year of using a computer 

on the job, there may be high learning costs for workers, especially for those with no 

prior experience. These may be pecuniary costs of training courses or on-the-job 

training, or opportunity costs of lost productivity while adapting to using a computer. 

While some of these learning costs will be paid by the employer, workers may be 

 

13 Bresnahan (1999) discusses how important the re-organization of the workplace is to effectively use 

computers. 
 22

expected to implicitly share these costs, since learning many of these applications adds to 

the workers’ general transferable skills rather than firm-specific skills. 

 The data provide two ways to assess why returns are lower for adopters than 

maintainers. First we compare the returns to adoption for those who received and did not 

receive computer training. Employees were asked if they participated in any on-the-job 

training or classroom training on computer hardware or software related to their job and 

paid for by their employers. We expect that the 15% of adopters who received (and 

implicitly required) training will see lower wages while they pay their share of the cost of 

that training, resulting in lower returns in the presence of training. In order to make this 

comparison, we add to equation (10) the interaction between this training variable and the 

adoption indicator. The second way we analyze learning costs is to compare the returns 

to adoption for workers with and without prior computer experience. We expect that 

workers who have prior experience using computers may be able to reap higher 

productivity in their first year of computer use than workers who have no prior computer 

experience. Thus, we expect experienced adopters will earn a higher return than adopters 

with no experience. We estimate this by adding to equation (10) the interaction between 

prior experience and the adoption indicator. 

 Table 8 shows the results of these models. Although results are imprecise for the 

interaction terms due to the small number of adopters with either prior experience or 

training,14 the coefficients suggest that learning costs do affect the short-term returns to 

computer use. A worker who does not receive training earns a return of around 4%, 

while one who receives training earns only 3% (Model I). An inexperienced worker 

 

14 Only 1.2% percent of the sample both adopted a computer and received some type of training. 
 23

earns a return to adopting a computer of 2.9% while the worker with prior experience 

earns 5% in the year of adoption (Model II). 

 The theoretical model in section II allows the size of these learning costs and the 

extent to which workers share the costs may vary across types of workers, and shows that 

these variations can help explain the differential returns to computer adoption found in 

section V. For example, if low-skilled workers require more learning than high-skilled 

workers to master a particular computer application, then it might take longer for any 

premium to be reflected in their wages. Table 8 shows the same models as above, 

estimated separately for the different occupational and educational subgroups. Again, 

these estimations are likely to be quite noisy, as the variance is derived from a one year 

wage change for workers of a given type who adopt a computer and receive training. 

There is nevertheless some evidence that the sharing of these costs is especially high for 

particular groups of workers, although the pattern is not clearly related to skill level. The 

one significant result in the training interaction is for the marketing and sales occupation, 

which is consistent with the fairly large return to maintainers for this occupational group 

shown in Table 7. Other groups, such as professionals, clerical and administrative, and 

the highly educated pay economically large costs of training. While these are not all 

intuitive, it is important to keep in mind that the first-differencing method does not 

control for unobservables that might cause one worker to receive training in the second 

period and another worker not to receive the training. Thus, although the large negative 

effect on the interaction term for workers who hold bachelor’s and advanced degrees is 

somewhat surprising (10.7% and 8.4%, respectively), it is likely that many of these 

degree holders do not require formal training and those who do require it are different in 
 24

some important unobservable way.15 Alternatively, it is possible that their training 

programs are expensive due to the complexity of the applications workers must master. 

 Importantly, the size of the wage premium for those who do not receive formal 

training is larger for several of the low-skilled groups (e.g. marketing/sales, 

clerical/administrative) than in the models that do not control for training. It is plausible 

that if we observed workers a few years after adopting computers, their wages would be 

higher than for similar workers who did not adopt a computer between 1999 and 2000. 

In fact, the effect should be larger than was measured here, since much of the learning 

costs are not reflected in formal training but in on-the-job experience using the computer. 

 Most groups also demonstrated a larger return for experienced adopters, as 

evidenced by the positive return on the interaction, although again these estimates are 

imprecisely measured. The exceptions are workers with some college or no high school 

degree and those in the technical and trade occupation, which may indicate that the 

applications used by these workers tend to be firm specific and prior general computer 

skills are not readily transferable. 

VI. Conclusion 

In this paper, we re-examine the issue as to whether or not there is a return to 

using a computer, using the 1999-2000 panel of the Canadian Workplace and Employer 

Survey. In a pooled cross-section wage regression, we find that workers who used a 

computer earned 16.9% higher wages in 2000 than those who did not use a computer. 

When we control for unobserved worker heterogeneity using a flexible first-differenced 

 

15 See Appendix Table A2 for observable ways that adopters who receive training differ from those who do 

not receive training. 
 25

model, the wage growth for the first year of computer use is a statistically significant 

3.8%. This estimate is larger than the previously reported 1% return to computer use 

because using a general first differenced model instead of a traditional fixed-effects 

model allows us to separately identify the return to adopting a computer from the 

asymmetric wage loss associated with ceasing to use a computer, which is not statistically 

different from zero. 

This panel estimate, however, obscures important differences between types of 

workers and returns from using different computer applications. We find that technical 

workers, professionals and managers earn higher wages in the first year of computer use, 

while other occupational groups, whose skills may be substitutes for computer 

technologies, earn no statistically significant return. Similarly, workers with a bachelor’s 

or advanced degree earn 11-19% higher wages when adopting a computer, while those 

with some college earn around 3% and those with a high school diploma or less do not 

earn a wage premium. We also find important differences in returns to using different 

software applications, which suggest that there is a return to computerizable tasks that 

allow creative and/or cognitive skills to be better utilized. Workers who use other 

machinery or computer-controlled technology do not earn a return. These results suggest 

that computers are a complement to high-skilled workers performing problem solving 

tasks and a substitute for low-skilled workers performing repetitive tasks. 

 These results indicate small but significant returns for some workers to the first 

year of computer use. We extend the analysis by using the lagged wage as an alternative 

means of controlling for individual fixed-effects, which allows us to estimate returns to 

computer use for those who used a computer both years. We find that the average worker 
 26

who maintains his computer use between 1999 and 2000 earns an 8.3% wage premium, 

more than double the return for the average adopter. In addition, maintainers in most 

skill groups earn more than a 5% return to computer use in 2000. We conclude that this 

return is a return to computing skills. 

 The result that continued users earn more than adopters may represent greater 

productivity. We also explored to what extent the differences between short-run and 

long-run returns within and between subgroups may reflect differences across employees 

in the costs of learning the new technology and the sharing of these costs between 

employers and employees. We find a negative wage effect associated with receiving 

training on a new computer, which suggests either that workers pay for training in terms 

of slower wage growth or that workers who receive training are different than workers 

who do not receive training. Controlling for computer training does increase wages for 

many of the low-skilled groups whose premia were small or zero in previous models. In 

addition, computer adopters with prior computer experience earn more in the first year 

than those lacking experience. 
 27

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 29

Table 1. Computer Usage Among Employees in the 1999 and 2000 

Canadian Workplace and Employee Survey 

 1999 Sample 2000 Sample 

Total .61 .65 

 Men .58 .61 

 Women .63 .68 

 European .61 .63 

 Non-European .58 .65 

 Ages 18-24 .44 .46 

 Ages 25-39 .66 .68 

 Ages 40-54 .63 .67 

 Ages 55+ .47 .56 

 Managers .82 .88 

 Professionals .85 .87 

 Technical/trades .46 .50 

 Marketing/sales .41 .47 

 Clerical/administrative .85 .88 

 Production/no trade .18 .21 

 Advanced degree .89 .91 

 Bachelor’s degree .81 .84 

 Some college/vocational degree .62 .61 

 High school graduate .52 .55 

 Less than high school graduate .28 .34 

 Union coverage .52 .55 

 No union coverage .64 .69 

 Full-time .65 .68 

 Part-time .45 .50 

Number of Observations 23,540 20,167 

Note: Proportions are weighted. 
 30

Table 2. Selected Technology-Related Characteristics of Workers 

Computer experienced non-user .11 

Conditional on using a computer 

4 years of computer experience .14 

5-8 years of computer experience .20 

9+ years of computer experience .44 

Hours per week spent at computer 19.18 

(.24) 

Hours by occupation 

 Managers 19.92 

(.52) 

 Professionals 18.99 

(.47) 

 Technical/trades 17.16 

(.41) 

 Marketing/sales 14.85 

(1.19) 

 Clerical/administrative 24.05 

(.43) 

 Production/no trade 9.72 

(2.54) 

Number of applications 2.62 

(.03) 

Computer users in employee’s workplace .46 

Computer-assisted technology .12 

Other technology .27 

Number of observations 23,450 

Source: 1999 Canadian Workplace and Employee Survey. 

Notes: Means are calculated using employee weights. Standard errors are in parentheses. 
 31

Table 3. Primary Computer Application Used by Workers, by Occupation 

All Manager Professional Technical Sales Clerical Production 

Word processing .1427 .2109 .3054 .0785 .0285 .1903 .0267 Spreadsheets .0584 .1163 .0772 .0460 .0165 .0562 .0165 Databases .0587 .0656 .0589 .0470 .0555 .1078 .0165 Desktop publishing .0049 .0077 .0093 .0032 .0033 .0048 0 Mgmt. applications .0130 .0230 .0059 .0109 .0195 .0180 .0026 Communications .0445 .1206 .0826 .0247 .0063 .0160 .0072 Programming .0062 .0029 .0229 .0035 0 .0047 .0006 Specialized office .1454 .1443 .1189 .1301 .1555 .2620 .0538 Data analysis .0044 .0080 .0063 .0027 .0013 .0061 .0015 Graphics .0094 .0150 .0173 .0076 .0011 .0089 .0003 Computer-aided design .0062 .0063 .0086 .0093 .0028 .0002 0 Computer-aided engineering .0023 .0027 .0061 .0023 0 0 0 Expert systems .0146 .0111 .0144 .0106 .0373 .0218 .0038 Other .0970 .0840 .1133 .0869 .0845 .1511 .0539 Source: 1999 Canadian Workplace and Employee Survey (N = 23,540). Notes: Means are calculated using employee weights. Communications includes e-mail and web browsers. 
 32

Table 4. The Effect of Using A Computer on Wages 

 

Pooled OLS 

Model 

(I) 

Individual 

FE Model 

(II) 

First-differenced 

Model 

(III) 

Dependent Variable ln(hourly wage) ln(hourly wage) ln(hourly wage) 

Computer use (1=yes) .1565*** 

(.0062) 

 .0160** 

(.0071) 

Computer user in both 

years (Maintainers) 

 .0375*** 

(.0054) 

Computer user in 1999 

only (Ceasers) 

 .0029 

(.0123) 

Computer user in 2000 

only (Adopters) 

 .0377*** 

(.0097) 

R2 .4285 .0879 

Adjusted R2 .0243 

Number of 

observations 

42,904 42,904 19,364 

Notes: Standard errors are in parentheses. * = p<.10; ** = p<.05; *** = p<.01. The OLS model 

includes a constant, years of education, potential experience (and its square), has parents or 

grandparents who descended from non-European countries, speaks different language at work 

than at home, part-time status, marital status, gender, gender interacted with marital status, is 

covered by a union, regional indicators, five occupational indicators, worker’s tenure with the 

establishment, a year indicator, the natural log of establishment size, and the percentage of 

computer users in the firm. Specifications II and III include the before-mentioned variables 

except for those that are constant over time and recent promotion in 2000.
 33

Table 5. First-Differenced Estimates of the Effect of 

Adopting a Computer on Wages, by Occupational and 

Educational Groups 

Occupation 

 Managers 

 (N = 2,477) 

.0704* 

(.0391) 

 Professionals 

 (N = 2,660) 

.0437 

(.0354) 

 Technical/trade 

 (N = 8,143) 

.0389*** 

(.0128) 

 Marketing/sales 

 (N = 603) 

-.0026 

(.0590) 

 Clerical/administrative 

 (N = 2,899) 

.0118 

(.0309) 

 Production/no trade 

 (N = 1,108) 

.0214 

(.0367) 

Education 

 Advanced degree 

 (N = 1,056) 

.1760** 

(.0745) 

 Bachelor’s degree 

 (N = 2,543) 

.1031*** 

(.0389) 

 Some college/vocational degree 

 (N = 10,367) 

.0289** 

(.0130) 

 High school graduate 

 (N = 3,280) 

.0310 

(.0206) 

 Less than high school graduate 

 (N = 2,118) 

.0146 

(.0267) 

F-statistic for pooling by occupation 10.10 

F-statistic for pooling by education 8.02 

Notes: The sample is restricted to those employees who responded to the 

survey in both years, remained with the same employer for both years, and 

remained in the same occupation both years. Standard errors are in 

parentheses. * = p<.10; ** = p<.05; *** = p<.01.
 34

Table 6. The Effect of Adopting a Specific Technology Use on Wages

First-Differenced Model 

Computer-aided technologies -.0072 

(.0076) 

Other technologies -.0034 

(.0062) 

Main application used 

(conditional on adopting a computer) 

 Word processing .0729*** 

(.0224) 

 Spreadsheets .0189 

(.0278) 

 Databases .0511** 

(.0258) 

 Desktop publishing .1996* 

 (.1107) 

 Management applications .0246 

(.0504) 

 Communications .0694** 

(.0281) 

 Programming .0890 

(.0845) 

 Specialized office .0343* 

(.0190) 

 Data analysis .1091 

(.1035) 

 Graphics -.0152 

(.0691) 

 Computer-assisted 

 design 

.0289 

(.0812) 

 Computer-assisted 

 engineering 

.0171 

(.1195) 

 Expert systems .0866 

(.0575) 

 Other -.0173 

(.0217) 

Number of Observations 19,364 

Notes: The sample is restricted to those employees who responded to the survey 

in both years and also remained with the same employer for both years. Standard errors are in parentheses. * = p<.10; ** = p<.05; *** = p<.01. 
 35

Table 7. The Long-Run Effect of Using a Computer on Wages Value-Added Approach 

OLS 

Maintainers Adopters 

All workers 

(N = 19,364) 

.0796*** 

(.0005) 

.0410*** 

(.0089) 

Occupation 

 Managers 

 (N = 2,477) 

.0664*** 

(.0221) 

.0836** 

(.0358) 

 Professionals 

 (N = 2,660) 

.0243 

(.0185) 

.0523 

(.0324) 

 Technical/trade 

 (N = 8,143) 

.0862*** 

(.0069) 

.0445*** 

(.0119) 

 Marketing/sales 

 (N = 603) 

.1043*** 

(.0341) 

.0823 

(.0538) 

 Clerical/administrative 

 (N = 2,899) 

.0771*** 

(.0150) 

.0333 

(.0279) 

 Production/no trade 

 (N = 1,108) 

.0563** 

(.0245) 

.0580* 

(.0315) 

Education 

 Advanced degree 

 (N = 1,056) 

.0601 

(.0389) 

.1465** 

(.0682) 

 Bachelor’s degree 

 (N = 2,543) 

.0829*** 

(.0210) 

.1018*** 

(.0353) 

 Some college/vocational degree 

 (N = 10,367) 

.0831*** 

(.0067) 

.0360*** 

(.0117) 

 High school graduate 

 (N = 3,280) 

.1008*** 

(.0115) 

.0559*** 

(.0189) 

 Less than high school graduate 

 (N = 2,118) 

.0588*** 

(.0173) 

.0175 

(.0241) 

F-statistic for pooling by occupation 17.24 

F-statistic for pooling by education 10.52 

Notes: Standard errors are in parentheses. *=p<.10; ** = p<.05; *** = p<.01. The OLS model (using the 2000 

sample) includes lagged wage, a constant, years of education, potential experience (and its square), has parents or 

grandparents who descended from non-European countries, speaks different language at work than at home, parttime status, marital status, gender, gender interacted with marital status, is covered by a union, regional indicators, 

five occupational indicators, worker’s tenure with the establishment, the natural log of establishment size, the 

percentage of computer users in the establishment, and recent promotion. The other specifications exclude from 

this list the occupational indicators. 
 36

Table 8. First-differenced Estimates of the Effect of Training and Previous Computer 

Experience on the Computer Adoption Wage Premium 

Model I Model II 

Independent variable Adopt 

in 2000 

Adopt in 2000 

*Training 

Adopt 

in 2000 

Adopt in 2000 

*Prior Experience

All workers 

(N = 19,364) 

.0395*** 

(.0105) 

-.0101 

(.0234) 

.0289** 

(.0123) 

.0210 

(.0179) 

Occupation 

 Managers 

 (N = 2,477) 

.0630 

(.0411) 

.0544 

(.0933) 

.0451 

(.0479) 

.0590 

(.0648) 

 Professionals 

 (N = 2,660) 

.0626 

(.0399) 

-.0663 

(.0655) 

.0189 

(.0420) 

.0673 

(.0612) 

 Technical/trade 

 (N = 8,143) 

.0340** 

(.0138) 

.0322 

(.0331) 

.0379 

(.0159) 

.0026 

(.0247) 

 Marketing/sales 

 (N = 603) 

.0459 

(.0634) 

-.2908** 

(.1415) 

-.0205 

(.0826) 

.0332 

(.1067) 

 Clerical/administrative 

 (N = 2,899) 

.0305 

(.0339) 

-.0877 

(.0652) 

.0048 

(.0399) 

.0149 

(.0534) 

 Production/no trade 

 (N = 1,108) 

.0178 

(.0379) 

.0541 

(.1412) 

.0048 

(.0459) 

.0433 

(.0720) 

Education 

 Advanced degree 

 (N = 1,056) 

.1901*** 

(.0798) 

-.0796 

(.1620) 

.1274 

(.0899) 

.1236 

(.1280) 

 Bachelor’s degree 

 (N = 2,543) 

.1210*** 

(.0416) 

-.1018 

(.0837) 

.0834 

(.0540) 

.0339 

(.0646) 

 Some college/vocational degree 

 (N = 10,367) 

.0314** 

(.0141) 

-.0136 

(.0303) 

.0319** 

(.0167) 

-.0065 

(.0235) 

 High school graduate 

 (N = 3,280) 

.0245 

(.0224) 

.0329 

(.0478) 

.0094 

(.0255) 

.0534 

(.0382) 

 Less than high school graduate 

 (N = 2,118) 

.0129 

(.0281) 

.0156 

(.0801) 

.0152 

(.0305) 

-.0023 

(.0578) 

Notes: The sample is restricted to those employees who responded to the survey in both years, remained with the 

same employer for both years, and remained in the same occupation both years. Standard errors are in parentheses. 

* = p<.10; ** = p<.05; *** = p<.01.
 37

Table A1. 1999 Sample Means and Proportions for Employees, by Computer Use 

Transition 

All 

Employees

Continued 

Use 

Adopted 

Computer 

Ceased 

Computer Use

Continued 

Non-Use 

Ln(wage) 2.80 

(.01) 

2.94 

(.01) 

2.63 

(.04) 

2.66 

(.04) 

2.60 

(.01) 

2000 Ln(wage) 2.80 

(.01) 

2.99 

(.01) 

2.64 

(.04) 

2.68 

(.04) 

2.59 

(.01) 

Potential Experience 19.87 

(.19) 

19.34 

(.22) 

18.37 

(.83) 

19.01 

(1.08) 

21.27 

(.39) 

Tenure 8.79 

(.13) 

9.16 

(.17) 

7.97 

(.47) 

8.62 

(.81) 

8.29 

(.22) 

Non-European .13 .13 .13 .13 .14 

Spoke different language 

at work than home 

.08 .07 .08 .06 .10 

Part-time status .20 .15 .31 .21 .29 

Married .58 .62 .48 .54 .55 

Female .53 .56 .51 .45 .48 

Union coverage .29 .24 .35 .32 .37 

Managers .15 .20 .13 .13 .05 

Professionals .17 .24 .12 .12 .06 

Technical/trade .39 .29 .41 .44 .57 

Marketing/sales .08 .05 .13 .11 .11 

Clerical/administrative .14 .20 .11 .10 .05 

Production/no trade .07 .02 .10 .10 .17 

Less than high school 

graduate 

.10 .04 .14 .14 .20 

High school graduate .17 .14 .20 .24 .21 

Some college/vocational 

degree 

.52 .53 .54 .45 .52 

Bachelor’s degree .14 .19 .09 .12 .06 

Advanced degree .07 .10 .03 .04 .01 

Ln(establishment size) 4.37 

(.03) 

4.61 

(.05) 

4.03 

(.10) 

4.30 

(.17) 

3.92 

(.06) 

Percentage of Computer 

Users 

.47 

(.01) 

.61 

(.01) 

.30 

(.12) 

.49 

(.05) 

.23 

(.01) 

Promoted by 2000 .19 .21 .24 .17 .13 

Number of observations 19,364 11,895 1,094 635 5,740 

Note: The sample is restricted to those employees who responded to the survey in both years and also remained with 

the same employer for both years. Means are calculated using employee weights. Weighted standard errors are in 

parentheses. 
 38

Table A2. Selected Characteristics of Computer Adopters in the 2000 Canadian 

Workplace and Employee Survey, by Whether Received Computer Training 

Training 

Received 

Training 

Not Received 

Years of Education 13.40 

(.30) 

13.29 

(.14) 

Potential experience 15.74 

(2.26) 

19.18 

(.91) 

Female .66 .49 

Part-time status .34 .24 

Tenure 7.49 

(1.50) 

7.72 

(.53) 

Managers .05 .13 

Professionals .26 .13 

Technical/trade .34 .41 

Marketing/sales .14 .14 

Clerical/administrative .08 .10 

Production/no trade .13 .09 

Recent promotion .23 .23 

Union coverage .49 .32 

Ln(establishment size) 4.64 

(.11) 

3.79 

(.27) 

1999 ln(wage) 2.60 

(.12) 

2.60 

(.04) 

Number of observations 164 930 

Note: The sample is restricted to those adopters who responded to the survey in both years and also 

remained with the same employer for both years. Means are calculated using employee weights. Standard 

errors are in parentheses.