BLS WORKING PAPERS 

 

U.S. Department of Labor 

U.S. Bureau of Labor Statistics 

Office of Productivity and Technology 

Which Workers Gain Upon Adopting a Computer? 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

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

Working Paper 395

June 2006 (revised November 2006)

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 Upon Adopting a Computer? * 

Cindy Zoghi, U.S. Bureau of Labor Statistics 

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

September 2006 

Abstract: Using the Canadian Workplace and Employee Survey and controlling 

for individual and establishment fixed-effects, we find that within a year of adopting a 

computer, the average worker earns a 3.6 percent higher wage than a worker who never 

used a computer. Returns are even larger for managers and professionals, highly 

educated workers, and those with significant prior computer experience. Employees who 

adopt computers for use with applications that require high cognitive skills earn the high 

returns. 

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 two anonymous referees, B.K. Atrostic, David Autor, Martha 

Bailey, Marianne Bitler, Joel Elvery, Michael Harper, Michael Giandrea, Francis 

Kramarz, Mark Long, Peter Meyer, Anastasiya Osborne, Renee Morissette, David 

Neumark, David Ribar, Larry Rosenblum, Steve Stillman, Leo Sveikauskas, Madeleine 

Zavodny, Aklilu Zegeye, and participants at the 2003 NBER/CRIW Summer Institute, 

2004 Western Economic Association Meetings, 2004 SOLE meetings, George 

Washington University Labor Studies Workshop, and Center for Economic Studies 

seminar for helpful comments. We also thank Lucy Chung and Yves Decady at Statistics 

Canada for data support.
 1

I. Introduction 

Skill-biased technological change is frequently cited as the leading cause of growing 

wage inequality since the 1980ís. Many believe that the diffusion of computers into the 

workplace during that decade caused an increase in demand for skilled workers, under the 

assumption that computers complement human capital (Bresnahan 1999; Bresnahan, 

Brynjolfsson, and Hitt 2002; Autor, Levy, and Murnane 2003). Increased demand for skilled 

workers resulted in higher wages for skilled workers relative to less skilled workers, thus 

increasing inequality. While an increase in wage inequality happened along with rapid diffusion 

of computers into the workplace, it has proven empirically difficult to link the adoption of 

computers with wage changes. 

Do computers complement human capital and did the introduction of computers thus 

raise the demand for skilled workers, resulting in an increasing wage differential between highskilled and low-skilled workers? The theory motivating this question is described by Bound and 

Johnson (1992), Berman, Bound and Griliches (1994) and Autor, Katz and Krueger (1998). For 

a given state of technology, the relative demand for skilled workers is determined by setting 

equal the ratio of marginal products for skilled and unskilled workers to the ratio of wages for 

these two groups. A skill-biased response to technological change affects the ratio of marginal 

products and increases the demand for skilled workers. In a similar story where land is the 

complementary factor, David (1969) explains that an individual farmer will decide to purchase a 

tractor if the price of the tractor is lower than his expected gain from switching technologies. 

Thus, farmers on large farms will adopt tractors first. 

Berman, Bound and Griliches (1994), Berndt, Morrison and Rosenblum (1992), and 

Autor, Katz and Krueger (1998) all find that skill upgrading has occurred with computerization. 
 2

It is generally believed that computers can substitute for low- and middle-skilled white collar 

workers whose tasks can be regularized and routinized, but the complex tasks performed by 

highly-skilled workers are difficult to automate (Bresnahan 1999; Bresnahan, Brynjolfsson and 

Hitt 2002; Autor, Levy and Murnane 2003). In addition, researchers (e.g. Krueger 1993; 

DiNardo and Pischke 1997; Entorf and Kramarz 1997) have consistently documented that 

workers who use computers earn higher wages. In this paper, we estimate the return to computer 

use immediately following adoption, and examine whether the return varies across skill groups in 

a way that is consistent with the story of a skill-biased response to technological change. 

Several studies (Bartel and Lichtenberg 1987; Bresnahan, Brynjolfsson and Hitt 2002; 

Chun 2003) have suggested that workers with high cognitive and social skills may be especially 

important during the process of implementing new technologies; this could be true regardless of 

whether or not their skills are complements to the use of computers. Thus, in addition to any 

potential long-term changes to labor demand, there may be a short-term increase in the relative 

demand for skilled workers to facilitate the implementation of computers. 

In addition, after adopting computers, individual workers may differ in how quickly they 

learn to use their computers most effectively. Previous researchers have discussed numerous 

dimensions that may affect a workerís learning curve, such as skill (Bartel and Sicherman 1998), 

ability (Galor and Moav 2000), education (Chun 1993; Borghans and ter Weel 2005a), age 

(Borghans and ter Weel 2002; Friedberg 2004; Weinberg 2005; Aubert, Caroli and Roger 2006), 

prior experience with a related technology (Weinberg 2005; Violante 2002), and tasks (Krueger 

1993; Dickerson and Green 2004, Borghans and ter Weel 2005b; Dolton, Makepeace and 

Robinson 2005). For example, a firm may decide that their typists should adopt PCs to do word 

processing because the long-run benefits are greater than the costs. Typists are familiar with the 
 3

QWERTY keyboard and are able to transfer their existing typing skills to the new technology; 

however, ability varies among typists and these differences determine how fast the typists learn 

other aspects of word processing. They may all eventually use the computer effectively; 

however, productivity immediately following PC adoption may vary greatly. If less-skilled 

workers receive more firm-sponsored training, as evidenced by Bartel and Sicherman (1998), we 

would also expect to observe differential initial returns by occupation (and education) as workers 

may be expected to pay for a share of their training costs in terms of sacrificed wages (Zoghi and 

Pabilonia 2005; Valletta 2004). 

Individual returns to computer adoption may also differ when an organization buys 

computers for all or a large portion of its employees. On one hand, the establishment may 

benefit from returns to scale in training and infrastructure development. In this case, wide scale 

implementation would decrease the average costs of adopting a computer, and more workers 

with lower productivity gains would learn to use a computer than if they were adopting 

individually. Analogously, when the price of tractors fell, farmers with less land found it 

advantageous to purchase tractors (David 1969). Alternatively, differences in productivity gains 

may arise due to complementary organizational changes or improved communications associated 

with widespread computer usage (Borghans and ter Weel 2006; Bresnahan 1999; Bresnahan, 

Brynjolfsson and Hitt 2003; Brynjolfsson and Hitt 2003). However, Bresnaham, Brynolfsson, 

and Hitt (2002) point out that the gains from organizational changes may only be realized after a 

period of adjustment. Thus, the effect of the scale of the implementation of computers in a firm 

upon early returns to adoption for an individual worker is an empirical question. 

Critics argue that higher wages among computer users do not prove that computerization 

is the cause of the wage differential. On one hand, if workers (or firms) who use computers have 
 4

unobserved characteristics that are unrelated to computer use but positively correlated with 

higher wages, then a spurious correlation between wages and computer use will appear, if we do 

not control for such characteristics (DiNardo and Pischke 1997; Entorf, Gollac, and Kramarz 

1999). On the other hand, high wage workers may adopt computers first. Borghans and ter 

Weel (2004) describe a simple model in which a high-skilled (high wage) worker will be more 

likely to adopt a computer first, not because she will save more time in performing tasks than the 

less skilled worker, but because the opportunity cost of saved time (her wage) is higher than it is 

for the less skilled worker. In the case of the wide-scale implementation, they argue that the 

average wage of workers will determine the threshold for adoption. 

 If either explanation holds, the wage differential between those who use computers and 

those who do not would diminish with proper controls. However, it would only fully disappear 

if computers were also not complementary to skill. Recently, Pabilonia and Zoghi (2005) used 

an instrumental variables technique to control for the potential endogeneity of computer use and 

found no return to computer use, but rather a return to computer experience, when considering 

both new users and more experienced users. Many researchers (e.g. Haisen-DeNew and Schmidt 

1999; Entorf and Kramarz 1997; Entorf, Gollac, and Kramarz 1999; Bell 1996) have used fixed 

effects to measure the return to computer use for the average worker while controlling for 

unobserved individual and/or firm-level heterogeneity. These studies find small to negligible 

returns and, depending on the time span between years in their panels, these effects could be 

interpreted as an immediate return to adoption or an average of short and long term returns to 

use. However, there are potentially two sources of bias in a standard fixed-effects estimate, 

which could complicate identifying whether there is an immediate return to adoption (Dolton and 

Makepeace 2004; Zoghi and Pabilonia 2005). First, the effects are identified by those 
 5

transitioning both into and out of computer use. To the extent that there are differences in the 

elasticity of wages with respect to these two types of changes (perhaps due to downward wage 

rigidity) or that workers adopting at different times may have different skill sets as suggested by 

the diffusion literature, then the standard fixed-effects estimate does not measure the return to 

adoption. In addition, the effects are measured relative to those who do not have transitions, 

including both those who have a computer in both periods and those who never have a computer. 

Therefore, it is unclear how to interpret the effect of these transitions relative to such a 

heterogeneous group. 

 The purpose of this paper is to determine whether or not there are early wage premiums 

for adopting a computer at work and whether this is a return to complementary skills that shorten 

the learning period. We use a panel of workers and their establishments surveyed in the 1999-

2002 Canadian Workplace and Employee Survey (WES) that allows us to observe transitions 

into computer use, and to control for unobserved worker characteristics that may be correlated 

with both computer use and wages. However, in order to make a comparison with previous 

research studies on this topic, we begin our analysis by estimating a first-differenced 

specification, which identifies effects through all workers who experienced any change in their 

computer use status. We then extend the analysis in several directions. We restrict our sample 

to those workers for whom adopting a computer is possible: in other words, non-computer users 

in the first year of the panel. This restriction allows us to isolate the return to adopting a 

computer relative to other workers who could adopt but did not, and gives us a measure that may 

more closely reflect the return for future adopters. Additionally, we measure the returns to 

adoption for specific subgroups of workers: by worker skills (education, occupation, and 

previous computer experience), by age group, by type of computer application used, by other 
 6

technologies, and by type of diffusion pattern in the establishment. These separate analyses 

suggest that the small return observed for the average worker obscures a tremendous variation in 

the returns to computer adoption and implementation. 

 

II. Data 

The data we use for this analysis come from the first four waves of the Canadian 

Workplace and Employee Survey (WES).1

 This survey was initially conducted in 1999. 

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

years and then re-sampled. For our analysis, we use both currently available two-year panels of 

employees (1999-2000 and 2001-2002) matched with their employer information. The panel 

aspect of the data allows us to control for pre-adoption wages and observable and unobservable 

individual characteristics that might affect the propensity for computer adoption as well as wage 

changes. 

 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 ìemployers operating in crop production and animal production; fishing, hunting, and 

trapping; private households, religious organizations and public administrationî (Statistics 

Canada 2002, 23). At each establishment, a maximum of twenty-four paid employees were then 

randomly sampled from a list of employees. All employees were selected in establishments with 

fewer than four employees. In 1999, 23,540 employees and 5,733 linked establishments were 

interviewed. In 2000, 20,167 of those employees were re-interviewed at 5,453 continuing linked 

establishments. In 2001, employees were re-sampled at continuing workplaces to start a new 

 

1

 These data were used by remote access to Statistics Canada. 
 7

two-year employee panel, which consisted of 20,377 employees and 5,474 linked establishments 

in 2001 and 16,813 employees and 4,834 linked establishments in 2002. 

Wages are measured by 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. Statistics Canada 

derived hourly compensation by dividing wages plus additional compensation by the total 

reported hours.2

 In our analysis, we used their hourly compensation data as the measure of 

hourly wage. 

 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 ìDo you use a computer in your job? Please exclude sales 

terminals, scanners, machine monitors, etc.î A help screen further clarified ìBy a computer, we 

mean a microcomputer, mini-computer or mainframe computer that can be programmed to 

perform a variety of operations.î Sixty-two percent of Canadian workers used a computer at 

work in 1999 and 2001.3 Among those who did not use a computer in 1999 (2001), 16 percent 

(14 percent) adopted a computer by 2000 (2002). 

 

2

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

occupational group may be overestimated. 

3

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

figure is the authorsí calculation from the Current Population Survey Supplement (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 Survey (Dolton and Makepeace 2004). Appendix Table A1 shows 

the proportion using computers by other demographic characteristics. These relationships look fairly similar to 

those observed in other studies. 
 8

 Table 1 compares the characteristics of workers who never used a computer to those who 

adopted a computer in 2000 or 2002.4

 Adopters were more likely to hold at least a bachelorís 

degree in both panels. However, in the 2001-2002 panel that difference was much larger with 

5.2% of non-users having at least a bachelorís degree and 13.5% of adopters having at least a 

bachelorís degree. Adopters were also more likely to have some college in 2001-2002 than 

continued non-users ñ 46% versus 55%. Not surprisingly, adopters were almost twice as likely 

to be managers as non-users. They were also more likely to be professionals or in 

marketing/sales and clerical/administrative occupations. Adopters were also less likely to be 

over 40 then continued non-users ñ 46% versus 54% over 40 in 2000 and 44% versus 55% in 

2002. 

In Table 2, we compare wage changes that occur between the first and second years of 

each panel, according to workersí computer use and adoption. On average, workers experienced 

3.3 percent wage growth between 1999 and 2000 and 4.1 percent growth between 2001 and 

2002. Those who did not use a computer in either year had much slower growth, while those 

who used computers in both years had faster wage growth. Adopters had similar wage growth in 

the first panel, but much faster wage growth in the second panel. Workers who stopped using a 

computer by their second year experienced wage growth that was slightly slower than the wage 

growth for the average worker. 

The WES asks workers ìconsidering all jobs you have held, how many years have you 

used a computer in a work environment?î We are thus able to distinguish those workers who 

adopted a computer on their current job but had prior computer experience acquired on other 

jobs from those who adopted a computer with no prior work-related computer experience. Table 

 

4

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

characteristics are measured in the second year of the panel. 
 9

2 does not suggest a clear pattern of wage growth differences between these two groupsóin the 

second panel (2001-2002), the experienced workers have faster growth than those without 

experience, while in the first panel (1999-2000), their wage growth is slower. 

Returns to adopting a computer may also differ depending on how diffusion proceeds in 

an establishment: whether a large group of employees adopts computers simultaneously or 

workers adopt individually. We restrict this part of the analysis to employees of establishments 

that have more than ten employees in order to better examine the effect of a truly large 

implementation, such as when an entire division adopts computers. One way to distinguish those 

who adopt as part of a wide-scale implementation from other adopters comes from the following 

question on the employer survey: ìHow many employees at this location currently use computers 

as part of their normal working hours?î We measure the change in workplace computer usage 

between the two periods as a fraction of the total employment in the second year. An employee 

was considered to be in an establishment that had undergone a wide-scale implementation if the 

value of this statistic was in the top quartile, when the fraction of the establishmentís employees 

using computers rose by at least ten percent within a one year period. A second measure comes 

from a question in the employer survey on whether ìyour workplace has implemented a major 

new software application and/or hardware installationÖthat would affect at least half of the 

users in the workplace?î In general, wage growth for workers who adopt as part of a large 

implementation is faster than for those who do not. The exception is for workers in the second 

panel (2001-2002) and calculated using the second measure. The drawback of the second 

measure, however, is that it combines software and hardware adoption, while this paper focuses 

on new computer adoption. Given the low PC adoption rates within establishments, we suspect 

that this measure includes mostly new software adoptions. 
 10

Table 2 also suggests that a full-sample fixed effects estimate of the return to computer 

use will yield results that are difficult to interpret since there does not appear to be a significant 

wage reduction associated with stopping computer use that would make it reasonable to combine 

transitions into and out of computer use. Nor is the reference group of non-transitioning workers 

homogeneous, since continued non-users experience much slower wage growth than those 

continued users. In order to obtain an estimate of the benefit of adopting a computer relative to 

not adopting, we restrict our sample for most of our analysis to those who do not use a computer 

in the first year of either panel. This shifts the focus of analysis from the question of the return 

to computer use to the more policy-oriented question of what will be the return to future 

computer users. 

III. Estimation and Results 

 As a starting point, we compare results from our data to those used in previous studies of 

the returns to computer use by estimating a model of the effect of all computer use transitions on 

wage growth. We difference the following wage model: 

lnWit = αt + βX it + γCompit + δi + μYear2000it + εit (1) 

to obtain: 

∆lnWit = α + β∆X it + γ∆Compit + μ∆Year2000it + ∆εit (2) 

where Wit is individual iís hourly wage rate at time t; Xit is a vector of observed characteristics of 

i as well as the workplace to which i is linked; Compit is a indicator variable equal to one if i uses 

a computer at time t, and zero otherwise; δi is the non-time varying individual fixed-effect; 

Year2000i is an indicator variable equal to one if the individual was interviewed in 2000, and 
 11

zero otherwise and allows us to control for differences in wage growth between panels5; α, β, γ, 

and μ are parameters to be estimated; and εit is a stochastic disturbance term assumed to follow a 

normal distribution. 

 Since our sample only includes workers who do not change establishments, this 

specification controls for both individual and establishment-level time-invariant effects. In our 

initial specification, ∆Xit includes time-varying controls for changes in four indicators for the 

highest level of education obtained (high school degree, some college, college degree, and 

advanced degree with less than high school as the omitted group).6 Additionally, we include age 

squared, tenure squared, and binary variables for whether or not the worker speaks a different 

language at home than at work, is a part-time worker, is married, is married interacted with being 

female, and is covered by a union, since these variables can change from year to year. The size 

of the establishment, defined as the natural logarithm of the number of employees, can also 

increase or decrease between years. 

 Column I of Table 3 shows ordinary least squares estimates for equation (2) using a 

sample of all workers with non-missing data in both years of each panel, resulting in a sample 

size of 35,033 observations. The estimated return to computer use is a statistically significant 

1.44 percent, which is comparable to the results by Entorf, Gollac and Kramarz (1999). We 

interpret this estimate as the return to transitioning into or out of computer use relative to those 

who either never used computers or always used computers. 

 Our objective, however, is to measure the effect of extending the technology to those who 

do not currently use computers. Therefore, we now restrict the sample to those who were not 

 

5

 It is possible that there may be differences in wage growth between panels due to lower economic growth in 2001-

2002 than 1999-2000 and not just due to sample differences. 

6

 It is possible that some of this change is due to measurement error in one or both of the years. The survey asks for 

highest grade completed in high school and for levels of education thereafter; therefore, we use levels of education 

instead of years of education. 
 12

using computers in the first period, so that the first-difference estimation in equation (2) 

measures the return to computer adoption conditional upon being able to adopt. This reduces the 

sample size to 24,392 worker-year observations. This restriction eliminates those who used a 

computer in both years as well as those who stopped using a computer. Other papers (Dolton 

and Makepeace 2004; Zoghi and Pabilonia 2005) have presented estimates from a more flexible 

first-differenced specification, which includes all workers and also provides separate wage 

growth estimates for both those using a computer in both periods and those who stop using a 

computer over the period compared to continuing non-users.7

 However, this latter method 

forces all the other coefficients to be identical for those who used computers and those who did 

not use computers in the first period, although these groups of workers may, in fact, be very 

different, as evidenced by a comparison of coefficients between columns I and II of Table 3. 

Identification in our model comes from the 16.1 percent of workers in our restricted sample who 

adopt computers in the second period. Column II of Table 3 shows that the average worker who 

adopts a computer experiences 3.4 percent higher wage growth in the first year of adoption 

conditional upon not using a computer in the first year. 

 In column III we add binary controls for whether or not the worker was recently 

promoted ñ sometime during either the first or second year ñ or changed occupations within 

establishments (as defined by a change in SOC code), which help to control for the potential 

endogenity of adopting a computer as part of a job change.8

 Both variables have a significant 

positive effect upon wage growth and thus the estimated return to computer use falls slightly to 

2.96 percent. 

 

7

 Using this alternative specification, the estimate of the return to adopting is similar. We also estimated a 

specification among computer users in the first period to estimate the wage growth for those who stopped using a 

computer compared to those who used computers in both periods. We find that workers who stop using a computer 

have 3 percent slower wage growth than continuing users. 

8

 Appendix Table A2 shows the mean between-year changes in all the controls in each panel. 
 13

 In column IV of Table 3 we also include establishment fixed-effects to control for 

characteristics of the establishments that affect wage growth. We now find a slightly higher 

wage growth of 3.63 percent for workers who adopt computers conditional upon not using a 

computer in the first period. All of the estimates presented in this paper henceforth are based 

upon the sample of first period non-users and do not include establishment fixed-effects in order 

to maximize degrees of freedom needed to identify the effects of adopting computers for smaller 

subsamples. 

 To test whether there are differential returns to computer adoption depending on the 

workerís education, occupation, computer experience, age and the scale of computer 

implementation in the establishment, we run a series of ordinary least squares regressions based 

upon equation (2) with the addition of some interaction effects to estimate how adopting a 

computer and being in a particular group affects wage growth.9

 Results are reported in Tables 4, 

5, and 6. The specification including the interaction of adopting and education groups (Table 4) 

provides strong evidence that highly-skilled workers do see an immediate and large return to 

computer adoption, even after controlling for wages prior to adoption and the demographic 

characteristics of workers. Workers with an advanced degree have a statistically significant 12.9 

percent higher wage growth in the year following adoption than workers with the same education 

level who do not adopt; computer adoption wage premiums for those with a bachelorís degree 

are a statistically significant 8.6 percent, and for those with some college or a vocational degree 

are 2.7 percent, though insignificant. Less educated workers, those with only a high school 

degree or less, do not earn significantly higher wages in the year they adopt compared to their 

counterparts who do not adopt. Borghans and ter Weel (2005a), in contrast to our findings, 

concluded that highly-educated workers in the U.S. do not benefit more from using a computer 

 

9

 All of the time-varying groups are measured in the second year of each two-year panel. 
 14

than less-educated workers. They find small and insignificant returns on a computer use dummy 

interacted with years of education in cross-sectional regressions; however, their result combines 

returns for both adopters and longer-term computer users whereas we focus on adopters 

compared to those who could adopt but did not. 

 The results across occupation groups (see Table 4) show similar heterogeneity. The over 

9 percent premium for managers and professionals who adopt computers is much larger than that 

for all other occupations. Technical and trade workers are the only other workers who have 

higher, though insignificant, wage growth (2 percent) following adoption than comparable 

workers who never use a computer. 

 These results show that education and skills affect whether a worker can immediately 

increase his productivity when adopting a computer. Highly-skilled individuals are likely to 

learn more quickly and spend less on computer training in order to become computer proficient. 

Likewise, a worker who has previously used a computer on the job should adapt to using a 

computer on their current job more quickly than a worker without prior experience. In our 

restricted sample, nearly 28 percent of those who were not using a computer in the first year of 

the survey reported previous experience using a computer. Some had quite a bit of experience ñ 

over 7 years. We therefore estimate equation (2) including interactions for adopting with 1-2 

years of prior computing experience, adopting with 3 to 6 years of prior computing experience 

and adopting with 7 or more years of experience. The results in Table 5 suggest that workers 

who adopt computers and have more than 7 years of prior computer experience earn 

approximately 3 percent higher wages than workers who have never used a computer at work or 

workers adopting with fewer years of experience, although the estimate is imprecise. These 

results provide additional support for the skill-biased technical change explanation for growing 
 15

wage inequality: workers with complementary skills earn wage premiums for adopting 

computers. 

 Age may also affect adaptability to new technologies either due to lower learning 

capabilities or an inability to transfer existing skills. We test whether age has an effect on the 

return to computer use by including interactions of the computer dummy variable with indicators 

for age 18-24, 25-39, and 40-54. The omitted group is workers aged 55+. Results in Table 5 

indicate that workers aged 55+ and 18-24 earn a significant 5.6 percent return to computer use 

while middle-aged workers earn less. This specification suggests that older workers do not have 

slower adaptability. However, older workers are also likely to include a higher percentage of 

workers with computer experience (and experience in general) and who are managers and 

professionals; both these groups earn high returns to computer use, which may confound the 

effect of age. Therefore, we estimate two additional specifications, results of which are 

presented at the bottom of Table 5. The first specification includes interactions between 

adoption and three indicators for years of computer experience categories as well as interactions 

between adoption and three indicators for the age categories specified previously. Controlling 

for the potentially confounding effect of computer experience, the effect of age at adoption 

remains the same. The second specification includes interactions between adoption and three 

indicators for the age categories as well as an interaction between adoption and an indicator for 

being a manager or professional. Controlling for these occupation groups eliminates the effect of 

age at adoption on wage growth, confirming our prior suspicion that age was a proxy for another 

worker characteristic. 

 We examine how the returns to adopting a computer differ depending on how diffusion 

proceeds in the establishment by including an interaction between adopting and wide-scale 
 16

implementation in our specification. As discussed in section II, we measure wide-scale 

implementation in two ways: first, we examine whether the establishment had a ten percent or 

greater change in the number of computer users relative to the total employment, and then 

whether the establishment recently had a major software or hardware implementation that 

affected a majority of workers. In both cases, we also add a control for whether all workersí 

wages rose in an establishment with a large implementation, regardless of computer usage. The 

results of these separate estimations are in Table 6. Again, the amount of variation to exploit in 

these models is low, so it is difficult to obtain statistically significant differences between the 

scales of implementation. However, contrary to descriptive statistics in Table 2, the return to 

adoption is lower for those workers who adopt a computer as part of either type of wide-scale 

implementation. In both implementation cases, the implementation itself had no direct effect on 

the average wage growth in the establishment in the first year. This would be consistent with 

diffusion theory that an implementation will take place at the break-even point where the cost of 

the implementation equals the gain from computerization. 

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

from the number and complexity of tasks that a worker performs using a computer. Autor, Levy 

and Murnane (2002) show that technology may complement a worker who performs problem 

solving tasks but substitute for a worker who performs routine tasks. If this is the case, then it 

may be important to examine more detailed questions of technology use. Recently, Dickerson 

and Green (2004), Borghans and ter Weel (2005b), and Dolton, Makepeace, and Robinson 

(2005) used British cross-sectional data to examine how different types of computer use affect 

the return. They all find that workers may receive wage premiums for performing the most 

sophisticated computerized tasks while Dolton, Makepeace, and Robinson (2005) also find that 
 17

workers earn more if they use e-mail and the internet. Therefore, we re-examine this issue 

applying panel data methods and making use of information from the WES on the categories of 

software applications which employees reported they used the most and also whether or not 

employees used technologies other than computers. While employees were free to name any 

specific software application, their answers were coded into one of fourteen aggregate categories 

of software. Table 7 shows the share of workers using each type of software as their main 

application in the first year of the panel and the share of workers adopting each type of software 

by the second year as well. Word processing and specialized office programs are the two most 

commonly used applications. Adoptions are highest for those two applications as well. 

To obtain estimates of the return to adopting a new computer and using a particular 

software application, we estimate the following specification using ordinary least squares: 

∆lnWit = α + β∆X it + γ1∆Soft1it + γ2∆Soft2it +Ö γ14∆Soft14it + μ∆Year2000it + ∆εi (3) 

where all variables are defined as in equation (2), and Softjit is an indicator variable that equals 

one if worker i used software j as her main application in time t. Since the sample is restricted to 

those workers who do not use a computer in the first year, Softjit will be zero for all workers in 

year one and will change to one for any worker who both adopted a computer and also used j as 

her main application. As in equation (2), the excluded group contains those workers who do not 

adopt a computer. 

Results of these estimations are in Table 8. We find statistically significant wage 

premiums for those adopting data analysis, computer-assisted design, word processing, expert 

systems, and graphics applications (13.2%, 6.1%, 5.9%, 4.7% and 2.5% respectively) compared 

to non-adopters. All of these applications either require or complement critical thinking or 

problem-solving skills and may be central to their job tasks, with perhaps the exception of expert 
 18

systems. In contrast to the findings of Dolton, Makepeace, and Robinson (2005), adopting a 

computer primarily for use with communications applications, such as e-mail and web browsing, 

which do not require higher-level skills, results in a insignificant, below average return. These 

results suggest that applications do not earn a return if they are neither of primary importance to 

the individualís job nor require advanced skills. We find considerable differences in the wage 

premium depending upon which primary application was adopted. 

Besides computers, workers use a wide array of other computerized technologies on their 

jobs. We distinguish between two additional technologies used by workers, which we refer to as 

computer-aided technologies, such as industrial robots and retail scanning systems, or other 

technologies, such as cash registers, sales terminals, scanners, etc. These alternative 

technologies are especially likely to substitute for routine tasks and are unlikely to require 

advanced skills for use. To estimate the return to adopting either of these technologies, we 

replace the computer use indicator in equation (1) with indicators for whether or not either of 

these technologies were used. We find no wage premium for adopting these alternative 

technologies (Table 9). No effect here is in contrast to the significant 3.6 percent wage growth 

experienced with adopting a computer, which supports our argument that skill is required in 

learning to use a computer and workers are thus receiving a return to this skill. 

IV. Conclusion 

 In this paper, we show that using fixed-effects to estimate a traditional wage model with 

an indicator variable for computer use does not accurately measure the returns to computer 

adoption. Rather, it measures the return to transitioning into or out of computer use relative to 

not making such a transition. As a result, the effect is averaged over workers who adopt a new 
 19

computer, as well as over workers who stop using a computer. The reference group is likewise 

averaged over workers who have never used a computer as well as those who used a computer 

throughout the period. Therefore, it cannot address the policy question of what would be the 

effect of extending computer use to those who are not currently using computers. To obtain a 

more meaningful measure of the return to adoption for future users, we restrict our sample to 

those workers who did not use a computer in the first period. 

 We use two panels of the Canadian Workplace and Employee Survey from 1999-2000 

and 2001-2002, allowing us to control for both individual and establishment level heterogeneity. 

We find a significant immediate return to adoption for the average worker. In addition, we find 

that this return varies considerably across different types of workers and types of adoptions. 

Highly skilled workers, such as college graduates, managers, professionals, and workers with 

more than seven years of previous computer experience, earn quite high premiums for computer 

adoption whereas less educated and lower skilled workers do not earn wage premiums for 

adoption. Finally, returns vary depending upon the complexity of tasks a worker performs using 

a computer. Those adopting a computer for data analysis, computer-assisted design, and word 

processing earn large returns, while those who primarily use communications applications that 

require less skill earn no significant return. The persistent finding of a return to computer 

adoption among high-skilled workers--even after controlling for worker observable and 

unobservable skill and establishment characteristics--implies that the computer is not merely a 

proxy for human capital, but is also a complement to that human capital, at least in the short 

term. Our findings are consistent with a skill-biased technical change explanation for increasing 

wage inequality. 
 20

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 23

Table 1. Demographic characteristics of adopters and continued non-users 

 

 Continued 

non-users 

1999-2000 

Adopters 

1999-2000 

Continued 

non-users 

2001-2002 

Adopters 

2001-2002 

Advanced degree .0139 .0087 .0165 .0199 

Bachelorís degree .0571 .0791 .0354 .1148 

Some college/vocational degree .5138 .5447 .4600 .5521 

High school degree .2162 .2188 .2619 .1976 

Less than high school degree .1990 .1487 .2262 .1158 

Managers .0450 .0882 .0483 .0906 

Professionals .0549 .1353 .0522 .1712 

Technical/trade .5744 .3790 .5812 .4821 

Marketing/sales .1158 .1740 .1033 .1070 

Clerical/administrative .0471 .1194 .0449 .1110 

Production/no trade .1629 .1040 .1702 .0381 

Age 18-24 .1227 .1923 .1271 .1473 

Age 25-39 .3343 .3455 .3269 .4120 

Age 40-54 .4088 .3671 .4071 .3025 

Age 55+ .1342 .0950 .1389 .1382 

Number of observations 5,740 1,094 4,607 755 

Note: Proportions are weighted to account for survey design. 
 24

Table 2. Wage growth by computer use and transitions 

 

 1999-2000 2001-2002 

All workers .0331 

(.0059) 

[19,364] 

.0412 

(.0032) 

[15,669] 

Continued non-user .0035 

(.0105) 

[5,740] 

.0339 

(.0058) 

[4,607] 

Continued user .0502 

(.0059) 

[11,895] 

.0436 

(.0042) 

[9,742] 

Adopter .0309 

(.0497) 

[1,094] 

.0601 

(.0139) 

[755] 

 Adopter with previous computer experience -.0321 

(.0978) 

[461] 

.0631 

(.0198) 

[383] 

 Adopter with no previous computer experience .0868 

(.0275) 

[633] 

.0572 

(.0195) 

[372] 

 Adopter in wide-scale implementation 

 (measured by >10% ∆ in establishment computer use) 

.0822 

(.0483) 

[208] 

.0390 

(.0267) 

[106] 

 Adopter not in wide-scale implementation 

 (measured by ≤ 10% ∆ in establishment computer use ) 

.0129 

(.0774) 

[675] 

.0504 

(.0170) 

[533] 

 Adopter in wide-scale implementation 

 (measured by major new software/hardware 

 installation in previous year) 

.0290 

(.0261) 

[303] 

.0500 

(.0261) 

[150] 

 Adopter not in wide-scale implementation 

 (measured by no major new software/hardware 

 installation in previous year) 

.0017 

(.0959) 

[550] 

.0381 

(.0148) 

[479] 

Stopped using computer .0257 

(.0234) 

[635] 

.0378 

(.0146) 

[565] 

Notes: Means are weighted to account for survey design. Standard errors are in parentheses. 

Number of observations in brackets. 
 25

Table 3. First-difference wage regressions

 I II III IV 

∆ Computer use .0144**

(.0068) 

.0340***

(.0071) 

.0296***

(.0070) 

.0363***

(.0100) 

∆ Education high school grad .0093 

(.0231) 

.0169 

(.0285) 

.0180 

(.0282) 

.0260 

(.0372) 

∆ Education some college -.0019 

(.0217) 

.0065 

(.0277) 

.0056 

(.0275) 

.0354 

(.0327) 

∆ Education Bachelorís degree -.0213 

(.0297) 

-.0354 

(.0457) 

-.0393 

(.0456) 

.0142 

(.0606) 

∆ Education advanced degree -.0126 

(.0370) 

-.0893 

(.1392) 

-.0945 

(.1405) 

-.1410 

(.1282) 

∆ Age2

 -.0007***

(.0001) 

-.0004***

(.0001) 

-.0003***

(.0001) 

-.0002 

(.0002) 

∆ Tenure2 .0082*

(.0045) 

.0092 

(.0083) 

.0129 

(.0085) 

.0097 

(.0096) 

∆ Home language not work 

language 

-.0029 

(.0070) 

-.0080 

(.0123) 

-.0080 

(.0123) 

.0008 

(.0153) 

∆ Part-time worker .0691***

(.0081) 

.0459***

(.0132) 

.0483***

(.0132) 

.0301**

(.0153) 

∆ Married .0419***

(.0113) 

.0571***

(.0165) 

.0564***

(.0165) 

.0594***

(.0199) 

∆ Married*female -.0223 

(.0158) 

-.0028 

(.0309) 

-.0011 

(.0308) 

.0016 

(.0359) 

∆ Union member .0472***

(.0088) 

.0640***

(.0139) 

.0643***

(.0139) 

.0552***

(.0162) 

Recently promoted .0199***

(.0061) 

.0200***

(.0082) 

∆ Occupation .0252***

(.0102) 

.0226*

(.0120) 

∆ ln (Establishment size) .0149***

(.0034) 

.0156***

(.0061) 

.0158***

(.0061) 

.0124 

(.0123) 

1999-2000 panel .0035 

(.0025) 

-.0192***

(.0045) 

-.0178***

(.0046) 

-.0227***

(.0063) 

Restricted to those not initially 

using computers? 

No Yes Yes Yes 

Establishment fixed-effects No No No Yes 

Number of observations 35,033 12,196 12,196 12,196 

R-squared .0114 .0140 .0157 .0630 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to those 

employees who responded to the survey in both years and remained with the same employer. 

Significance levels: *** = p<.01; ** = p<.05; * = p<.10. The return to computer use in column I 

is not comparable to that in columns II - IV since the return in column I is for transitions into and 

out of computer use relative to always or never using a computer while the return in columns II ñ 

IV is for adopting a computer conditional on not using a computer in the first period. 

Regressions also include a constant. 
 26

Table 4. Wage regressions with interactions for occupation and education groups 

 

 Adoption return 

Adopter .0066 

(.0175) 

Adopter * Advanced degree .1222***

(.0422) 

Adopter * Bachelorís degree .0796***

(.0319) 

Adopter * Some college/vocational .0208 

(.0196) 

Adopter * High school degree .0074 

(.0209) 

R-squared .0172 

 

Adopter .0010 

(.0193) 

Adopter * Manager .0947***

(.0308) 

Adopter * Professional .0925***

(.0297) 

Adopter * Tech/trade .0190 

(.0207) 

Adopter * Marketing/sales -.0410 

(.0404) 

Adopter * Clerical/administrative .0053 

(.0300) 

R-squared .0185 

Number of observations 12,196 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to 

employees who responded to the survey in both years and remained with the same 

employer. Regressions also include levels of education, age squared, speaks different 

language at work, part-time status, marital status, gender interacted with marital status, is 

covered by a union, the natural log of establishment size, tenure squared, a recent 

promotion, occupation change, a panel indicator, and a constant. Significance levels: *** = 

p<.01; ** = p<.05; * = p<.10. 
 27

Table 5. Wage regressions with interactions for experience and age 

 Adoption return 

Adopter .0234***

(.0091) 

Adopter * 1-2 years of computer experience -.0013 

(.0215) 

Adopter * 3-6 years of computer experience .0074 

(.0171) 

Adopter * 7 or more years of computer experience .0324 

(.0207) 

R-squared .0160 

Adopter .0558*** 

(.0182) 

Adopter * Age 18-24 -.0010 

(.0347) 

Adopter * Age 25-39 -.0384*

(.0216) 

Adopter * Age 40-54 -.0301 

(.0201) 

R-squared .0162 

Adopter .0471***

(.0188) 

Adopter * 1-2 years of computer experience -.0008 

(.0216) 

Adopter * 3-6 years of computer experience .0100 

(.0171) 

Adopter * 7+ years of computer experience .0350*

(.0208) 

Adopter * Age 18-24 .0077 

(.0351) 

Adopter * Age 25-39 -.0364*

(.0217) 

Adopter * Age 40-54 -.0293 

(.0201) 

R-squared .0165 
 28

Table 5 Continued. Wage regressions with interactions for experience and age 

Adopter .0300 

(.0185) 

Adopter * Manager/Professional .0828***

(.0182) 

Adopter * Age 18-24 .0194 

(.0344) 

Adopter * Age 25-39 -.0276 

(.0217) 

Adopter * Age 40-54 -.0228 

(.0201) 

R-squared .0186 

Number of observations 12,196 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to 

employees who responded to the survey in both years and remained with the same 

employer. Regressions also include levels of education, age squared, speaks different 

language at work, part-time status, marital status, gender interacted with marital status, is 

covered by a union, the natural log of establishment size, tenure squared, a recent 

promotion, occupation change, a panel indicator, and a constant. Significance levels: *** = 

p<.01; ** = p<.05; * = p<.10. 
 29

Table 6. Wage regressions with interactions for scale of implementation 

 

 Adoption return 

Adopter .0342***

(.0083) 

Adopter * 10% increase in establishment-wide computer use -.0135 

(.0179) 

R-squared .0145 

Adopter .0375***

(.0089) 

Adopter * new software/hardware introduced -.0203 

(.0151) 

R-squared .0146 

Number of observations 9,897 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to 

employees in establishments with more than 10 employees, who responded to the survey in 

both years and remained with the same employer. Regressions also include levels of 

education, age squared, speaks different language at work, part-time status, marital status, 

gender interacted with marital status, is covered by a union, the natural log of establishment 

size, tenure squared, a recent promotion, occupation change, a panel indicator, a control for 

wide-scale implementation, and a constant. Significance levels: *** = p<.01; ** = p<.05; * 

= p<.10. 
 30

Table 7. Proportion using and adopting computers, by primary software type 

 

 Use in 

1999 

Adopt by 

2000 

Use in 

2001 

Adopt by 

2002 

Word processing .1481 .0116 .1258 .0099 

Specialized office .1388 .0174 .1629 .0145 

Databases .0613 .0084 .0797 .0059 

Spreadsheets .0576 .0063 .0647 .0059 

Communications .0453 .0042 .0544 .0081 

Expert systems .0154 .0015 .0151 .0010 

Management applications .0132 .0022 .0196 .0022 

Graphics .0094 .0009 .0087 .0011 

Computer-assisted design .0069 .0008 .0081 .0004 

Programming .0066 .0011 .0077 .0002 

Desktop publishing .0057 .0003 .0062 .0001 

Data analysis .0047 .0003 .0069 .0020 

Computer-assisted engineering .0029 .0003 .0022 .0006 

Other .1002 .0101 .0610 .0087 

Number of observations 19,364 15,669 

Note: All proportions are weighted to account for survey design. Communications includes email and web browsers. 
 31

Table 8. Wage regressions for adopting a computer and main software application used 

 

 Adoption 

return 

Word processing .0591***

(.0182) 

Specialized office .0084 

(.0232) 

Databases .0255 

(.0178) 

Spreadsheets .1045 

(.0789) 

Communications .0259 

(.0281) 

Expert systems .0474***

(.0189) 

Management applications .0692 

(.0644) 

Graphics .0253*

(.0140) 

Computer-assisted design .0605**

(.0261) 

Programming .0370 

(.0535) 

Desktop publishing .0391 

(.0511) 

Data analysis .1313*

(.0778) 

Computer-assisted engineering .0604 

(.0457) 

Other -.0088 

(.0170) 

Number of observations 12,196 

R-squared .0170 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to employees 

who responded to the survey in both years and remained with the same employer. Regressions 

also include levels of education, age squared, speaks different language at work, part-time status, 

marital status, gender interacted with marital status, is covered by a union, the natural log of 

establishment size, tenure squared, a recent promotion, occupation change, a panel indicator, and 

a constant. Significance levels: *** = p<.01; ** = p<.05; * = p<.10. 
 32

Table 9. Wage regression for adopting other computerized technologies 

 

 Adoption return 

Computer-aided technologies -.0171 

(.0107) 

Other technologies -.0013 

(.0067) 

R-squared .0144 

Number of observations 12,196 

Notes: White-corrected standard errors are in parentheses. The sample is restricted to employees 

who responded to the survey in both years and remained with the same employer. Regressions 

also include levels of education, age squared, speaks different language at work, part-time status, 

marital status, gender interacted with marital status, is covered by a union, the natural log of 

establishment size, tenure squared, a recent promotion, occupation change and a panel indicator. 

Significance levels: *** = p<.01; ** = p<.05; * = p<.10. 
 33

Table A1. Proportion using computers, by demographics 

 

 1999 2000 2001 2002 

All workers .6248 .6426 .6231 .6309 

Male .5915 .6058 .5786 .5978 

Female .6544 .6768 .6649 .6636 

Married .6529 .6691 .6521 .6559 

Not married .5850 .6053 .5850 .5975 

European background .6072 .6251 .6095 .6264 

Not European background .6275 .6452 .6255 .6317 

Ages 18-24 .4513 .4453 .4714 .4495 

Ages 25-39 .6756 .6806 .6584 .6667 

Ages 40-54 .6453 .6639 .6545 .6546 

Ages 55+ .5016 .5558 .5269 .5843 

Home language not work language .5414 .5621 .5563 .5584 

Home language is work language .6319 .6501 .6310 .6397 

Union .5296 .5468 .5701 .5806 

Non-union .6644 .6817 .6437 .6508 

Part-time worker .4644 .5032 .4519 .4934 

Full-time worker .6669 .6739 .6685 .6659 

Workplace ≤ 20 employees .5851 .6065 .5571 .5661 

Workplace 20-99 employees .5774 .5940 .5908 .6200 

Workplace 100-499 employees .6163 .6213 .5772 .5981 

Workplace 500+ employees .7486 .7379 .7624 .7660 

Number of observations 19,364 15,669 

Note: Proportions are weighted to account for survey design. 
 34

Table A2. Means of differenced variables 

 

 Continued non-users Adopters

 1999-2000 2001-2002 1999-2000 2001-2002 

∆ ln(Wage) .0035 .0332 .0455 .0521 

∆ Education less than high school grad -.0109 -.0272 -.0372 -.0326 

∆ Education high school grad -.0166 -.0247 -.0003 .0071 

∆ Education some college .0223 .0451 .0199 .0009 

∆ Education Bachelorís degree .0042 .0064 .0149 .0226 

∆ Education advanced degree .0009 .0004 .0028 .0020 

∆ (Age2) 80.94 79.94 74.77 74.97 

Recently promoted .1334 .2001 .2654 .2694 

∆ Occupation .1246 .1254 .2796 .3082 

∆ Home language not work language .0102 .0050 .0041 .0020 

∆ Part-time worker -.0255 -.0158 -.1180 -.1295 

∆ Married -.0040 .0030 -.0103 .0020 

∆ Married * female -.0077 -.0029 -.0006 -.0060 

∆ Union .0077 .0187 -.0097 -.0183 

∆ ln(Establishment size) -.0390 -.0303 -.0492 -.0144 

∆ (Tenure2) .1439 .1196 .0703 .1271 

Number of observations 5,740 4,607 1,094 755 

Notes: Means are weighted to account for survey design. Standard errors in parentheses.