Wage Premiums for On-the-Job Computer Use: A Metro and Nonmetro Analysis.

By Lorin D. Kusmin. Food and Rural Economics Division, Economic Research Service,

U.S. Department of Agriculture. Rural Development Research Report No. 95.

Abstract

An analysis of on-the-job computer use shows that such use is more common in metro

areas than in nonmetro areas. A substantial wage premium, 10 to 11 percent, is associated with using a computer on the job, even after other job and worker characteristics

are taken into account. However, this wage premium accounts for only a small proportion of the wage differences between metro and nonmetro areas. In nonmetro areas, the

computer use wage premium is only about 6 percent. This suggests that computer literacy skills may only modestly advance the earnings of low-wage workers within their

current occupations in rural areas.

Keywords: wages, skills, computers, rural, nonmetro.

Acknowledgments

The author wishes to thank Elizabeth Greenberg and Timothy Parker of ERS for assistance with data used in the study, as well as Peter Schaeffer of West Virginia University,

Robert Gibbs and Fred Gale of ERS, and Jennifer Olmsted, formerly of ERS, for helpful

comments.

1800 M Street, NW

Washington, DC 20036-5831 December 2002
ii • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Contents

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Computer Use and the Wage Structure. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Computer Use at Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Estimating Returns to Computer Use at Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Industry, Occupation, and Skill Effects Versus Computer Use Effects . . . . . . . . 6

Computer Use Wage Premium and the Metro-Nonmetro Wage Gap . . . . . . . . 10

Metro Versus Nonmetro Computer Wage Premiums . . . . . . . . . . . . . . . . . . . . . 11

Supply and Demand for Job Skills in Nonmetro Areas . . . . . . . . . . . . . . . . . . . 12

Other Factors Associated With the Size of the Computer Use Wage Premium. 13

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Appendix: Data and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Appendix: Detailed Regression Tables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • iii

Summary

Computer use at work has become widespread. By 1997, almost half of all U.S. workers

used computers on the job, and such workers generally receive higher wages. However,

on-the-job use is less common in nonmetro areas than in metro areas, and wages for

nonmetro workers are generally lower. But is computer use instrumental in explaining

the metro-nonmetro wage gap?

Recent public policy discussions of the digital divide centered on strategies to improve

the economic well-being of certain groups of employees--for instance, nonmetro workers, the less educated, and racial and ethnic minorities--by increasing their access to

computer skills and computer technology. This report, by focusing on metro-nonmetro

differences in computer use and the returns to computer use, addresses several questions

that shed light on the potential effects of efforts to bridge the digital divide. 

A simple comparison of wages for on-the-job computer users versus other workers

shows a wage difference of 32 percent in 1997. Much of this gap reflects differences

between the jobs held by computer users and other workers, or differences in worker

characteristics, and not solely the effect of computer use. However, even after taking

into account differences in industry and occupation, worker education and skill level,

and other worker and job characteristics, there is still a 10-percent to 11-percent wage

premium associated with on-the-job use of computers.

On-the-job computer use is significantly more common in metro areas than in nonmetro

areas (52 percent versus 40 percent in 1997). This higher incidence, together with the

associated wage premium, could help to explain the metro-nonmetro wage gap.

However, taking into account the magnitudes of both the computer use wage premium

and the metro-nonmetro gap in use, the computer effect accounts for only about 5 percent of the overall metro-nonmetro wage gap. (About 30 percent is accounted for by

other differences between jobs and workers in metro and nonmetro areas, particularly

worker education and the distribution of jobs across occupations. The majority of the

gap is explained by factors not included in the analysis.) 

The wage premium associated with computer use in rural areas is only about 6 percent,

about half the computer wage premium found in metro areas in 1997. Little of this difference can be explained by differences in the job and worker characteristics considered

in this study. Perhaps rurality itself dampens returns to worker skills in nonmetro areas,

as suggested in other research.

As returns to computer use on the job are smaller for rural workers, improving such

workers' computer literacy may contribute only slightly to reducing urban-rural wage

inequality, although such training may also allow some workers to move into higher

paying occupations. Computer literacy programs may improve the earnings of some

racial and ethnic minorities, for whom the computer use wage premium appears to be

substantially larger. Some of these conclusions may have to be modified with the recent

explosive growth in the economic significance of the Internet, which is not reflected in

the data used here. 
Introduction

Computer use has become widespread in American

society over the past two decades. One aspect of this

change has been the increasing use of computers at

work. The percentage of U.S. workers using computers

on the job rose from about one-fourth in 1984 to about

one-half by 1997. 

Previous research has indicated that workers who use

computers on the job receive higher wages, and that this

may help to explain changes in the wage distribution

(Krueger, 1993). This study reassesses those findings by

taking into account metro/nonmetro location, industry

wage effects, and the skill content of occupations.

On-the-job computer use is more common in metro

areas than in nonmetro areas (Kusmin, 1996). Metro

and nonmetro workers differ in another important

respect: average wages in nonmetro areas are substantially lower than in metro areas. In 1997, average

weekly earnings for nonmetro wage and salary workers were 79 percent of the metro average (Gibbs and

Parker, 1999). This difference is longstanding (table 1)

and is not fully explained by metro/nonmetro differences in education level; indeed, the metro-nonmetro

wage gap is greater for workers with higher levels of

education (McGranahan and Ghelfi, 1991). Given all

these caveats, does on-the-job computer use partly

explain the current magnitude of the metro-nonmetro

wage gap? 

Finally, recent public policy discussions of the digital

divide raise the question, “How far can improved

access to computer skills and computer technology

improve the economic well-being of groups such as

rural workers and racial/ethnic minorities?” This

report addresses that question by looking at how wage

returns to computer use vary across groups.

Computer Use 

and the Wage Structure

In his 1993 Quarterly Journal of Economics article,

“How Computers Have Changed the Wage Structure,”

Alan Krueger found that workers who used computers

on the job received a wage premium of 10 to 15 percent. This estimate reflected the wage differential

between computer users and others after taking a variety of other personal and job characteristics into

account.

Krueger concluded that this premium represented a

return to their use of computer skills, and further

argued that such premiums helped drive growth during

the 1980s in the apparent wage premium to education.

More highly educated workers are much more likely to

use computers on the job, and their rates of computer

use have risen much further over time.1

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 1

Wage Premiums for 

On-the-Job Computer Use

A Metro and Nonmetro Analysis

Lorin D. Kusmin

Table 1—Real annual earnings in nonfarm jobs,

metro and nonmetro areas, 1970-97

Year Nonmetro Metro Ratio

Dollars Percent

1970 22,147 28,937 76.5

1975 23,543 29,503 79.8

1980 24,025 29,849 80.5

1985 23,798 30,680 77.6

1990 22,737 31,209 72.9

1993 22,925 31,842 72.0

1997 22,986 32,799 70.1

Note: Values are in 1997 dollars.

Source: Calculated by Economic Research Service, USDA, using

data from the Bureau of Economic Analysis.

1 Baldwin and Cain (2000) report that a general equilibrium

analysis suggests "education-biased technical change that is

greater in education-intensive sectors" as a major factor in the

growth of the wage premium for education in the 1979-87 period,

a result which appears consistent with Krueger's argument.
Both conclusions have been challenged. DiNardo and

Pische (1997) find that a wage premium similar to that

for computer use can be estimated for “using pencils

on the job,” and argue that both are serving primarily

as proxies for unobserved differences in jobs and

workers. Goss (2000) also offers some evidence that

unobserved differences between computer users and

other workers may bias the estimation of the computer

use wage premium, although the sources of parameter

identification are unclear in his instrumental variables

analysis. DiNardo and Pische further note that while

the returns to higher skill levels grew most rapidly during the 1980s, there is little evidence of increases in

productivity associated with computer use before the

1990s. 

Both of these positions suggest that a productivity

increase associated with the adoption of computers in

the workplace should be reflected in a return to computer-specific skills that will be captured only by those

actually using computers on the job. However, this

interpretation is open to question. To the extent that

computer skills either include or are correlated with a

broader set of capabilities that are in demand in the

labor market, an increasing demand for computer

skills would bid up the market wages for workers who

possess those capabilities whether or not their jobs

involve direct computer use. (This point is acknowledged in Autor, Katz, and Krueger (1997, p.18fn).)

Further, as suggested by Autor, Katz, and Krueger

(1998), the increasing use of computers in the workplace may also increase the relative wages of more

skilled workers generally because computers have

been able to substitute for humans in performing many

simple, repetitive tasks that previously contributed to

the demand for less-skilled labor (pp. 1185-86).

In addition, the lack of visible productivity gains from

the computer revolution during the 1980s is not inconsistent with a wage premium to workers with 

computer-related skills. Helpman and Trajtenberg

(1995) show that under some reasonable assumptions,

the diffusion of what they term a “general purpose

technology” may engender a two-phase cycle of economic change, with the first phase marked by both

stagnating productivity and an increasing relative wage

for skilled workers, while the productivity benefits of

the new technology emerge only in the second phase.

Objectives

This analysis builds on Krueger’s 1993 wage model to

look further at the wage premium associated with the

use of a computer on the job, and at how that premium

varies with location, job, and personal characteristics.

The study follows Krueger in using data from one of

the periodic supplements to the Current Population

Survey (CPS) that has included questions about subjects' use of computers at work (see appendix).

One objective of this study is to reassess Krueger’s

findings by including additional variables in the wage

model. The study assesses the extent to which taking

account of metro-nonmetro differences affects estimates of the apparent wage premium for on-the-job

(OTJ) computer use.2 Further, some additional data

are brought to bear on the question of the extent to

which omitted occupational or personal characteristics

can “explain” the computer wage premium. However,

as already noted, the returns to such characteristics

may themselves have increased over time in response

to the increasing demand for workers with capabilities

that are associated with computer use. Hence, including some of these characteristics in the wage model

may yield a smaller and more accurate estimate of the

“direct” effect of computer use on wages, but may also

lead to an understatement of the overall effect of computers on the wage distribution. 

A second objective is to assess the relevance of the

computer wage premium for explaining nonmetro

wages and the metro/nonmetro wage gap. In this context, the analysis will consider not only metro/nonmetro differences in rates of computer use, but also

metro/nonmetro differences in the size of the wage

premium to those who do use computers. 

It may be interesting to note whether the results of this

analysis are consistent with Krueger's contention that

OTJ computer use can explain much of the apparent

growth in returns to education during the 1980s. On

the other hand, while of considerable interest, the relationship of the computer use wage premium to productivity growth is not directly addressed here.

Finally, the results may shed some light on the role of

public policy in addressing the metro/nonmetro wage

2 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

2 A more detailed discussion of the difference between on-the-job

computer use rates in metro and nonmetro areas and the factors

accounting for those differences appears in Kusmin (1996).
gap. Would policies to promote computer literacy in

more isolated areas contribute to reducing urban-rural

inequality?

Computer Use at Work

The share of employed U.S. adults using computers at

work more than doubled between 1984 and 1997. By

1997, about 50 percent of all workers used computers

on their jobs. 

The proportion of jobs involving computer use was

higher in metro areas in both 1984 and 1997, and the

absolute size of the gap has grown slightly over time.

In 1984, 18 percent of nonmetro and 28 percent of

metro workers used computers on the job. By 1997, 40

percent of nonmetro and 52 percent of metro workers

used computers on the job (fig. 1).

What accounts for the urban-rural difference in computer use? Predictors of on-the-job computer use in

1997 have been identified using a linear probability

regression that relates the probability of on-the-job

computer use to various job and personal characteristics (appendix table 1).3 An analysis using the parameters of that regression, together with urban-rural differences in the percentage of workers with each of the

relevant characteristics, indicates that differences in

job characteristics such as occupation and industry

account for more than half of the urban-rural computer

use difference (table 2).

In particular, the concentration of managerial, professional, technical and clerical workers in urban areas

explains much of the urban-rural computer use gap.

Rural workers, in contrast, are more likely than urban

workers to be in those service, blue-collar, or agricultural occupations that are less likely to involve on-thejob computer use.

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 3

3 A linear probability model was used in order to allow comparability with a similar analysis of 1993 data presented in an earlier

piece (Kusmin, 1996) where linear probability was used to make

the analysis more accessible to nontechnical readers. A logit or

probit model would likely be more accurate in assessing the probability of on-the-job computer use for individuals or populations

with extreme characteristics (e.g., non-high school graduates working as laborers in the rural South).

Figure 1

Workers using computers on the job by residence, 1984-97

The percentage of the workforce using computers on the job has remained higher in metro areas

Percent

Source: Calculated by the Economic Research Service, USDA from Current Population Survey, 1984, 1989, 1993, and 1997.

1984 1989 1993 1997

Nonmetro Metro

28

18.2

27.4

39.4

36

48.7

40.2

52

60

50

0

10

20 

30

40
The concentration in urban areas of some industries

with high computer use-such as professional services

and communications-plays a significant but much

smaller role than do occupational differences in

explaining the utilization gap. The higher frequency of

self-employment in rural areas also explains a small

portion of the gap. Since the incidence of part-time

work varies little between urban and rural workers,

lower computer use rates by part-time workers

explains little of the gap.

Among personal characteristics, education plays a substantial role in the urban-rural gap. Even after controlling for occupation, age, and other characteristics, college graduates were 17 to 21 percentage points more

likely to use computers on the job in 1997 than were

high school graduates with no college. Further, urban

residents are more likely than rural residents to hold

college or advanced degrees. In tandem, these differences account for nearly one-fourth of the difference

in the rate of computer use between urban and rural

areas (table 2). 

On the other hand, the higher rate of on-the-job computer use in urban areas occurs despite the lower rates

associated with several population groups that are concentrated in urban areas. Regression analysis shows

that Black, Hispanic, and Asian workers were 4 to 8

percentage points less likely to use computers on the

job than were comparable White workers. In addition,

foreign-born citizens and aliens were 7 to 9 percentage

points less likely to use computers than were otherwise

comparable U.S.-born citizens. All of these groups are

more highly represented in urban than rural populations. However, the concentration of these groups in

urban areas is not sufficient to offset the other factors

that lead to higher computer use rates in urban areas.

Other personal characteristics such as age and gender

appear to have little or no influence on differences in

computer use between urban and rural areas. 

After accounting for job and personal characteristics, a

difference of 3.0 percentage points between urban and

rural rates of on-the-job computer use remains. This

modest difference may reflect slower diffusion of computer skills or computer-based ways of working into

rural areas. Firms in urban areas may have more

opportunities to observe and imitate the adoption of

computer technology by suppliers, customers, or competing firms. The gap may also reflect differences

between urban and rural areas in the detailed mix of

jobs and/or in personal characteristics not taken into

account. 

Estimating Returns 

to Computer Use at Work

Is a wage premium associated with using computer

skills on the job, and if so, how large? A simple comparison of average wages indicates that computer users

earn far more than other workers—32 percent more in

nonmetro areas and 49 percent more in metro areas

(table 3). At the same time, earnings are higher in

metro areas for computer users and nonusers alike,

suggesting that computer use differences are not driving the metro-nonmetro wage gap. 

However, this comparison ignores many other characteristics of workers and jobs that influence wages. A

better assessment of the effect of computer use on

wages should control for such characteristics as education level and labor force experience.

To do this, we estimate a wage regression model, similar to those presented in Krueger (1993), of the form 

ln w = Xβ + γ,

4 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Table 2—Urban-rural gap in computer use at work,

1997

Gap accounted for by Percentage As share

points of total gap

Percent

Job characteristics 7.9 63

Occupational mix 6.3 51

Industrial mix 1.0 8

Other job characteristics 0.5 4

Personal characteristics 1.6 13

Education level 2.9 23

Racial/ethnic background -0.8 -6

Origin/citizenship status -0.8 -7

Other personal 0.3 2

Effect of urban

residence (residual) 3.0 24

Total 12.4 100
where w is the wage rate, X is a vector of job and

worker characteristics, and γ is an error term assumed

to have the standard properties.4 To assess the effect

of computer skills, OTJ computer use is included as

one of the X's that are predictors of the log wage. Data

are taken from the October 1997 CPS subsample (see

appendix), and the hourly wage is defined as reported

weekly earnings divided by usual hours worked.5 The

model controls for a number of personal and job characteristics often found to be associated with wage levels. Personal characteristics taken into account include

sex, marital status, veteran status, race and ethnicity,

national origin and citizenship, region of residence,

metro/nonmetro residence, labor force experience, and

education level. Job characteristics include whether or

not the worker is covered by a union contract, and

whether the job is full-time or part-time. The specification parallels that presented by Krueger (1993, p. 38,

table II) in his analysis of 1984 and 1989 CPS data.6

This analysis suggests that use of a computer on the

job raises hourly earnings by about 21 percent (see

table 4, model 2) when worker characteristics are held

constant.7 This wage effect is considerable, but much

smaller than the raw difference (32-49 percent) shown

in table 3. It is similar to the corresponding estimates

(19-21 percent) found by Krueger for 1984 and 1989.8

Given this, to what extent do differences in computer

use account for metro-nonmetro wage differences? The

otherwise unexplained metro-nonmetro difference in

wages is about 18 percent when OTJ computer use is

left out of the model, and about 16 percent when OTJ

computer use is taken into account. This finding suggests that computer use on the job explains only a

small portion of metro-nonmetro wage differences.

Note, however, that the analysis here assumes that the

returns to computer use are the same for all workers;

this assumption will be relaxed later in this report.

Most of the estimated coefficients on other variables

(experience, education, union membership, and others)

are consistent with past findings in this area. Thus,

labor force experience has a positive but decreasing

effect on wages; higher levels of education are associated with higher wages; and unionized workers earn

more per hour.9,10

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 5

Table 3—Average hourly earnings by metro status

and on-the-job computer use, 1997

Metrononmetro

Item Nonmetro Metro difference

Dollars Percent

Don't use computer 9.83 11.17 13.6

Use computer 12.98 16.62 28.1

Percent

User-nonuser 

difference 32.0 48.8

Source: Current Population Survey, Oct. 1997.

7 The dependent variable in these models is the logarithm of the

hourly wage. In general, for small values of k, a difference of k in

the logarithm of any variable x corresponds approximately to a difference of 100k percent in the level of x. As k grows larger, there

is an increasing divergence between differences in the logarithm of

x and percentage differences in x. Thus, a difference of .192 in the

logarithm of the wage corresponds to a difference of 21.2 percent

in the wage itself.

8 Some earlier analyses, not presented here, suggest that this result

is fairly robust to inclusion of data on home computer use and to

some other variations in specification.

9 Including a control for use of a computer on the job leads to

some reduction in the estimated wage effects of education relative

to an otherwise equivalent model without this computer use variable (model 1). This is consistent with Krueger's suggestion that

returns to OTJ computer use account for some of the apparent

increase in returns to education during the 1980s that had been

reported by other studies. However, the education effects are qualitatively similar in both models.

10 Additional details on the regression results—including coefficients for sex, marital status, part-time status, industry effects, and

several other variables—are reported in the appendix.

4 For another recent example, see Barron et al. (1999).

5 This may lead to some upward bias in the hourly wage values for

managerial employees who are exempt from overtime laws, since

they are probably more likely to often work longer hours than

what they report to CPS as their "usual" hours. This may lead to

some overstatement of the wage premium for managerial employees, and to some overstatement of the wage premium for computer

use in those wage regressions where there is no control for occupation.

6 There are minor differences between Krueger's specification and

the one presented here. Krueger omits controls for Asian race, for

Hispanic origin, for national origin and citizenship status, and for

whether or not data are for self-reporting respondents. In addition,

he models the effect of education as linear in years of schooling,

while here it is captured by dummy variables for several distinct

levels of education.
Industry, Occupation,

and Skill Effects Versus 

Computer Use Effects

Is the apparently large premium to OTJ computer use

(21 percent) actually a return to computer-specific

skills, or is it due to other factors? It might be

explained by higher wages in those occupations or

industries where computer use is more common, perhaps because these are higher-status jobs, or because

some industries are willing to pay more for both desirable workers and new technologies. In this case, the

premium should disappear from a model with appropriate industry and occupational controls. 

Or, as noted earlier, computer use may serve as a

proxy for broader capabilities that are rewarded by the

labor market--perhaps cognitive skills, detail orientation, or a willingness to learn new methods. In this

case, the estimated wage premium may provide some

interesting insight into the returns to such capabilities,

but may not have any implications for the wage effects

of wider computer use.

Some evidence with respect to these issues can be

derived by augmenting the wage regression already

presented with controls for wage differences across

industry, occupation, and skill levels. Including a set of

21 categorical industry variables in the model causes

the computer use premium to fall only slightly, from

21 percent to 18 percent (table 5, model 3). This

result is similar to Krueger's findings with respect to

industry effects (1993, p. 39). Moreover, as Krueger

noted, this approach may lead to an underestimate of

the return to computer skills, since possession of these

6 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Table 4—Wage model with and without computer use

Model 1 Model 2

Wage model without Basic model with

Variable computer use computer use variable

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer NA NA 0.192 18.69***

Metro area 0.162 14.22*** 0.145 12.91***

Labor force experience 0.030 22.40*** 0.029 21.87***

Labor force experience (squared) -0.052 -18.04*** -0.050 -17.42***

Non-HS graduate -0.208 -11.50*** -0.168 -9.36***

Some college 0.140 11.94*** 0.103 8.80***

Bachelor’s degree 0.426 32.71*** 0.360 27.06***

Advanced degree 0.588 33.62*** 0.512 28.96***

Black -0.144 -8.94*** -0.118 -7.44***

Asian 0.004 0.15 0.015 0.57

Native American 0.004 0.09 0.021 0.46

Hispanic -0.154 -8.05*** -0.135 -7.17***

Foreign-born citizen -0.136 -4.13*** -0.111 -3.43***

Noncitizen -0.173 -7.04*** -0.141 -5.82***

Years in U.S. 0.003 3.09** 0.003 3.06**

Union 0.119 9.63*** 0.140 11.44***

Veteran -0.021 -1.34 -0.013 -0.84

Adj. R-squared 0.3574 0.3795

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.

Note: Intercept and coefficients for sex, marital status, region, part-time status, and self-reporting status have been omitted, but are

reported in appendix table 2.
skills may permit individuals to find employment in

higher paying industries.11

The rate computer use varies widely across occupational categories. Occupations that tend to use computers more also tend to pay better, so that some of the

estimated premium to OTJ computer use may reflect

the higher wages associated with these occupations.

When we include controls for eight occupational

groups in the wage model in addition to the industry

effects already mentioned, the estimated wage premium for computer use falls further—from 18 percent

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 7

Table 5—Wage models with controls for industry and occupation

Model 3 Model 4

Wage model with controls Wage model with controls

Variable for industrial sector for industry and occupation

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer 0.169 16.19*** 0.125 11.22***

Metro area 0.144 12.92*** 0.133 12.19***

Labor force experience 0.026 20.36*** 0.025 19.87***

Labor force experience (squared) -0.045 -16.03*** -0.043 -15.66***

Non-HS graduate -0.167 -9.50*** -0.151 -8.75***

Some college 0.107 9.33*** 0.079 6.92***

Bachelor’s degree 0.373 27.74*** 0.283 20.04***

Advanced degree 0.549 29.47*** 0.434 22.27***

Managers NA NA 0.064 4.30***

Professionals NA NA 0.091 2.83**

Technical NA NA -0.044 -1.84+

Clerical NA NA -0.176 -11.15***

Service NA NA -0.231 -12.79***

Craft occupations NA NA -0.034 -1.74+

Operators NA NA -0.175 -8.87***

Laborers NA NA -0.275 -11.14***

Farmers, fishers, and foresters NA NA -0.199 -1.72+

Black -0.119 -7.59*** -0.097 -6.31***

Asian 0.009 0.35 0.016 0.60

Native American 0.009 0.19 0.017 0.39

Hispanic -0.127 -6.85*** -0.106 -5.82***

Foreign-born citizen -0.102 -3.21** -0.078 -2.50*

Noncitizen -0.130 -5.45*** -0.113 -4.84***

Years in U.S. 0.003 2.98** 0.002 2.19*

Union 0.135 10.46*** 0.156 12.23***

Veteran -0.021 -1.41 -0.018 -1.23

Adj. R-squared 0.4056 0.4324

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.

Note: Intercept and coefficients for sex, marital status, region, part-time status, self-reporting status, and industry effects have been omitted, but

are reported in appendix table 3.

11 The industries for which wage effects were estimated include

agriculture; mining; construction; durable goods manufacturing;

nondurable goods manufacturing; transportation; communications;

utilities and sanitary services; wholesale trade; retail trade; finance,

insurance, and real estate; private household services; business

services; personal services; entertainment and recreation services;

hospitals; medical services (except hospitals); education services;

social services; professional services; forestry and fishing; and

public administration. Estimated individual industry effects for

models 3 through 8 are reported in the appendix tables; industry

effects estimated for models 9 through 11 have been omitted for

reasons of space. When retail trade is treated as the base (omitted)

category, the estimated industry wage differentials range from -21

percent for private household services to +43 percent for mining.
to 13 percent (model 4). This result is also similar to

Krueger's 1993 findings.

But using such broad occupational groups is a crude

proxy for actual skill levels. To better control for work

skills, the Department of Labor's Dictionary of

Occupational Titles (DOT) data file has been used to

compute approximate skill contents for individual

occupations along several dimensions (see "Data and

Methods" appendix). The three DOT occupational

characteristics that have been considered include the

"general educational development" (GED) levels of

the job with respect to math and language skills, and

the extent of "specific vocational preparation" (SVP)

required for a job. The SVP index ranges from 1 to 9

and represents an estimate of the time necessary to

8 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Table 6—Wage models with control for skill level

Model 5 Model 6

Wage model with controls Wage model with controls for 

for industry and for industry, occupation, and

Variable occupational skill level occupational skill level

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer 0.100 9.20*** 0.106 9.45***

Metro area 0.127 11.69*** 0.129 11.90***

Labor force experience 0.025 19.66*** 0.025 19.57***

Labor force experience (squared) -0.042 -15.37*** -0.042 -15.31***

Non-HS graduate -0.142 -8.25*** -0.145 -8.43***

Some college 0.073 6.47*** 0.072 6.36***

Bachelor’s degree 0.278 19.88*** 0.265 18.73***

Advanced degree 0.412 20.99*** 0.397 19.95***

Managers NA NA 0.020 1.23

Professionals NA NA 0.034 0.98

Technical NA NA -0.047 -1.94+

Clerical NA NA -0.088 -4.86***

Service NA NA -0.119 -5.00***

Craft occupations NA NA -0.023 -0.90

Operators NA NA -0.029 -1.08

Laborers NA NA -0.079 -2.41*

Farmers, fishers, and foresters NA NA -0.117 -1.00

GED-Language 0.025 2.29* 0.020 1.36

GED-Math 0.012 1.13 -0.001 -0.12

SVP 0.050 8.95*** 0.043 5.93***

Black -0.092 -6.00*** -0.089 -5.85***

Asian 0.009 0.35 0.013 0.52

Native American 0.022 0.51 0.022 0.51

Hispanic -0.105 -5.82*** -0.102 -5.67***

Foreign-born citizen -0.076 -2.44* -0.071 -2.30*

Noncitizen -0.108 -4.65*** -0.109 -4.68***

Years in U.S. 0.002 2.22* 0.002 2.08*

Union 0.162 12.79*** 0.161 12.68***

Veteran -0.016 -1.06 -0.016 -1.08

Adj. R-squared 0.4362 0.4391

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.

Note: Intercept and coefficients for sex, marital status, region, part-time status, self-reporting status, and industry effects have been omitted, but

are reported in appendix table 4.
achieve average performance in the job (for example,

between 30 days and 3 months are required to achieve

average performance in an occupation with an SVP

rating of 3).12

These measures of the skill levels required for detailed

Census occupations turn out to be fairly powerful as

factors accounting for wage variation. When these three

variables are substituted for the eight occupational categories/variables used in the previous model (table 6,

model 5), the extent of wage variation explained actually increases slightly. All three variables are positively

related to wages, as might be expected.13 The effect of

specific vocational preparation is significant at the 0.1-

percent level, while the effect of language skills is significant at the 5-percent level. The effect of math skills

is not statistically significant. 

Model 5's results are consistent with the suggestion

that a significant share of the previously measured premium to direct computer use is actually a return to

broader associated skills. When the wage model is

augmented with these four occupational skill level

indices, the estimated wage effect of computer use

falls substantially, to slightly over 10 percent.

However, the computer use effect remains statistically

significant at the 0.1-percent level. 

Moreover, if general skills and computer skills can

substitute for each other to some extent as qualification for some skilled occupations, then workers with a

given level of individual general skills will more likely

qualify for higher paying jobs in occupations demanding higher general skills if those workers also possess

specific computer skills. In these cases, a return to the

individual's specific computer skills will appear, at

least in part, to be a return to general skills.

Further, the demand for general skills cannot be neatly

separated from the demand for computer skills in the

labor market as a whole. The increasing demand for

people able to operate computers can also be expected

to raise the returns to other skills and personal characteristics that are necessary for and/or simply correlated

with computer skills, even in those jobs that do not

require computer aptitude. Thus, the return to general

skills may itself be influenced by the increasing role of

computers in the workplace. Both this and the precedRDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 9

Table 7—Wage model with controls for industry,

occupation, and specific vocational preparation

Model 7

Variable Parameter T-statistic

estimate

Uses computer 0.107 9.58***

Metro area 0.130 11.95***

Labor force experience 0.025 19.53***

Labor force experience (squared)-0.042 -15.27***

Non-HS graduate -0.146 -8.48***

Some college 0.073 6.42***

Bachelor’s degree 0.267 18.95***

Advanced degree 0.401 20.46***

Managers 0.013 0.87

Professionals 0.033 1.03

Technical -0.052 -2.21*

Clerical -0.095 -5.48***

Service -0.137 -6.86***

Craft occupations -0.049 -2.51*

Operators -0.051 -2.23*

Laborers -0.099 -3.37***

Farmers, fishers, 

and foresters -0.139 -1.21

SVP 0.051 10.85***

Black -0.090 -5.89***

Asian 0.013 0.50

Native American 0.023 0.51

Hispanic -0.103 -5.71***

Foreign-born citizen -0.072 -2.33*

Noncitizen -0.109 -4.70***

Years in U.S. 0.002 2.10*

Union 0.161 12.67***

Veteran -0.016 -1.07

Adj. R-squared 0.4391

+ = significant at 10-percent level; * = significant at 5-percent level;

** = significant at 1-percent level; *** = significant at 0.1-percent

level.

Note: Intercept and coefficients for sex, marital status, region, parttime status, self-reporting status, and industry effects have been

omitted, but are reported in appendix table 5.

12 In an earlier paper, the effects on wages of variation in the general reasoning GED level (in 1993) were also estimated, but this

variable was not significant in the analysis of 1997 data and has

been dropped to simplify the analysis. 

13 These are measures of occupational characteristics, and not the

skill levels of the particular individuals in the CPS sample. It

seems reasonable that occupational skill levels may be highly correlated with individual skill levels, or at least with individual skill

levels as exercised on the job. However, there is no guarantee that

computer users and non-computer users have the same level of

general skills, even within the same occupation.
ing argument suggest that the overall effect of the

demand for computer skills on the relative wages of

more skilled workers will be understated if we look

only at the individual return on computer skills after

controlling for occupational skill level. 

If both occupational skill levels and broad occupational categories are taken into account, as in model 6,

there is a modest but statistically significant further

increase in the adjusted R-squared of the model. The

estimated direct computer use premium for this specification is about 11 percent, which is a little higher

than in the previous model, indicating that greater

detail on occupational characteristics will not necessarily reduce the estimated size of the OTJ computer use

premium (fig. 2). 

When occupational categories and skill measures are

both included in the model, as in model 6, neither the

language skill nor the math skill variable is significant,

but the specific vocational preparation (SVP) variable

remains highly significant. This suggests that only the

SVP variable among these three is capturing an aspect

of occupational skill that is not already captured by

occupational category or educational attainment.

Accordingly, model 7 drops the language and math

skill variables to yield a more parsimonious model as

the basis for further analysis. This change has almost

no effect on the estimated size of the return to on-thejob computer use. 

Computer Use Wage Premium and

the Metro-Nonmetro Wage Gap

OTJ computer use rates are substantially higher in

metro areas, and the analyses above indicate a wage

premium of 10 to 11 percent associated with using computer skills on the job, even after differences in industry,

occupation, and other skills are taken into account.

Could this wage premium help account for the persistent wage gap between metro and nonmetro areas?

The importance of a particular worker/job characteristic such as OTJ computer use in explaining the metrononmetro wage gap can be assessed using the estimated regression coefficient for that characteristic and

data on the metro/nonmetro distribution of that characteristic. If we multiply the estimated effect of OTJ

computer use on wages by the difference between

metro and nonmetro frequencies of OTJ computer use,

we have an estimate of the metro-nonmetro wage difference accounted for by differing rates of OTJ computer use. Comparing this with the overall metro-nonmetro wage difference yields an estimate of the per10 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Figure 2

Wage premium for computer use, 1997

The wage premium persists when other job and worker differences are considered

Unadjusted differential

Worker characteristics

Industry and worker characteristics

Industry, occupation, and worker characteristics

Industry, skill level, and worker characteristics

Industry, occupation, skill level, and worker characteristics

Industry, occupation, specific skill, and worker characteristics

Percent

Adjusted for differences in:

0 10 20 30 40 50

43.4

21.2

18.4

13.3

10.5

11.2

11.3

Source: Calculated by the Economic Research Service, USDA from Current Population Survey, 1997.
centage of the wage gap explained (see appendix,

"Data and Methods").

Applying this technique and using the results of the

wage regression in model 7, it appears that differences

in computer use rates explain only 5 percent of the

overall wage gap (table 8).14 About 29 percent of the

gap can be explained by differences in education level

and/or specific vocational preparation, but about twothirds of the wage gap is not explained by any variables in the model. The other variables that are

included in the model account for relatively little of

the metro-nonmetro gap, either because their effects on

wages are weak or because the average metro-nonmetro differences in these variables are not large.15

Part of the unexplained wage gap probably reflects

cost-of-living differences between metro and nonmetro

areas.16 However, comprehensive area-specific costof-living indices that would allow us to fully quantify

this factor are not available.

Metro Versus Nonmetro Computer

Wage Premiums

Do nonmetro workers receive lower returns for computer skills than metro workers do? The above decomposition analysis assumes that the wage premium for

OTJ computer use is the same in metro and nonmetro

areas. However, the premium is much larger in metro

areas. If a variable representing the interaction

between metro status and OTJ computer use is added

to the wage model, the estimated computer use wage

premium is only about 6 percent in nonmetro areas,

while it is nearly 13 percent in metro areas; this difference is statistically significant at the 1-percent level

(table 9, model 8). This finding implies that the "unexplained" metro-nonmetro wage gap (i.e., the portion of

the gap not accounted for by any variables in the

model other than metro status per se) for those workers who do not use computers on the job is just over

10 percent, while the corresponding value for OTJ

computer users is close to 18 percent.17

So while lower rates of computer use in nonmetro

areas account for relatively little of the metro-nonmetro wage gap, lower returns to computer use are a

substantial component of that gap. In particular, the

two-fifths of all nonmetro workers who use computers

on the job appear to lose out on an additional wage

premium of about 7 percent that they would receive if

employed in metro areas.18

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 11

Table 8—Factors accounting for the metrononmetro wage gap, 1997

Wage difference Wage gap

Variable explained explained

Percentage Percent

points

Education 3.7 18.6

Occupational skill level 2.1 10.4

Race and ethnicity -0.9 -4.8

Industry -0.03 -0.1

Occupation 0.8 4.0

Computer use 1.1 5.5

Other1 0.2 1.0

Total explained 6.8 34.5

Unexplained 13.0 65.5

Total metro-nonmetro gap 19.8 100.0

1 "Other" includes gender, marital status, union membership, veteran status, citizenship and origin, part-time status, labor force

experience, and region.

Source: ERS from Oct. 1997 Current Population Survey.

14 The decomposition analysis presented here is directly applicable

to the unweighted sample of CPS respondents for whom data on

all variables of interest are available. Application of the same

analysis to the entire workforce would produce slightly but not

substantially different results.

15 In particular, while not directly relevant to the topic of this

report, differences in industry mix between metro and nonmetro

areas account for very little net difference in wage levels.

16 For example, Gale (1997) finds that a 1-percent difference in the

average rent for a two-bedroom housing unit is associated with a

0.13-percent difference in manufacturing wages, and that taking

this effect into account reduces the size of the estimated urbanrural wage differential. 

17 The combined logarithmic effect of computer use and the

metro-computer use interaction term is .056 + .065 = .121; the percentage equivalent of this combined effect is 12.9 percent. The

combined logarithmic effect of metro residence and the metrocomputer use interaction term is .097 + .065 = .162; the percentage equivalent of this combined effect is 17.6 percent (see fn. 3).

18 It is also possible that computer-using workers in rural areas are

employing particular computer skills that are less well-rewarded

than those skills employed by computer-using workers in urban

areas. This has not been specifically tested for, in part because preliminary analyses indicated that the estimated returns to particular

uses of computers on the job were difficult to interpret as returns

to particular skills (e.g., these analyses showed a very high rate of

return to using a computer on the job for e-mail).
Supply and Demand for 

Job Skills in Nonmetro Areas

This last result is broadly consistent with previous

work at ERS indicating that returns to education are

greater in metro than nonmetro areas, and that, at least

until recently, those with higher skill levels have been

more likely to migrate to metro areas (McGranahan

and Ghelfi, 1991; McGranahan and Kassel, 1995;

Swaim, 1995; Gibbs, 1998; Nord and Cromartie,

1998.) The gap in skills (including computer use) and

apparently associated wage gap that is seen in rural

areas seem to reflect weaker demand for such skills in

these areas. Stronger demand for skills in urban areas,

as expressed by greater wage premiums for those skills,

encourages those who possess skills to migrate, leaving

lower average skill levels in the remaining rural population. However, perhaps because of imperfect information and/or because some people with skills prefer to

live in rural areas, the migration of skilled workers out

of rural areas does not appear to be sufficient to equalize urban and rural wages for these workers. Observed

12 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Table 9—Metro versus nonmetro premiums and estimates with detailed occupation controls

Model 8 Model 9

Metro versus nonmetro Model with more 

Variable premium detailed occupations

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer 0.056 2.86** 0.061 3.10**

Metro area 0.097 6.39*** 0.099 6.56***

Uses computer x metro area 0.065 3.11** 0.060 2.87**

Labor force experience 0.025 19.59*** 0.024 19.30***

Labor force experience (squared) -0.042 -15.34*** -0.041 -15.07***

Non-HS graduate -0.147 -8.53*** -0.149 -8.69***

Some college 0.073 6.43*** 0.067 5.89***

Bachelor’s degree 0.267 18.95*** 0.260 18.31***

Advanced degree 0.399 20.36*** 0.395 19.32***

Managers 0.012 0.80 NA NA

Professionals 0.032 0.99 NA NA

Technical -0.053 -2.24* NA NA

Clerical -0.095 -5.47*** NA NA

Service -0.137 -6.88*** NA NA

Craft occupations -0.048 -2.50* NA NA

Operators -0.051 -2.24* NA NA

Laborers -0.101 -3.43*** NA NA

Farmers, fishers, and foresters -0.150 -1.30 NA NA

SVP 0.051 10.87*** 0.051 7.66***

Black -0.089 -5.84*** -0.085 -5.59***

Asian 0.013 0.50 0.010 0.39

Native American 0.021 0.47 0.012 0.27

Hispanic -0.102 -5.64*** -0.098 -5.43***

Foreign-born citizen -0.070 -2.28* -0.072 -2.33*

Noncitizen -0.105 -4.53*** -0.103 -4.46***

Years in U.S. 0.002 2.12* 0.002 2.24*

Union 0.162 12.74*** 0.166 12.82***

Veteran -0.015 -1.04 -0.020 -1.39

Adj. R-squared 0.4396 0.4471

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.Note:

Intercept and coefficients for sex, marital status, region, part-time status, self-reporting status, industry effects, and detailed occupation effects

have been omitted, but are reported in Appendix table 6.
rural wages then appear lower than urban wages in part

because the average skill level of rural workers is

lower, and in part because the wage premiums paid to

those more skilled workers who remain in rural areas

are lower. If the skills gap in rural areas instead

reflected a deficit in the ability of rural areas to supply

skilled workers to employers, we would expect to see

greater skill premiums paid to those skilled workers

who are available in rural areas, but in fact we find the

reverse.19 This is consistent with a recent survey of

rural manufacturers, which found that lesser use of

selected advanced technologies in rural areas was primarily a function of the industry mix in these areas,

and thus did not reflect any difficulty specific to rural

areas in obtaining labor or other resources needed to

use these technologies (Gale, 1997).

The sensitivity of our results is further tested by

replacing the nine occupational category variables with

a more detailed list of occupational categories (model

9). This substitution yields a slightly larger coefficient

for the computer wage premium in nonmetro areas

(6.3 percent versus 5.8 percent for the previous model

in table 9) and small changes in other coefficients, but

does not yield substantial changes in any coefficients

except for some of the industry effects (not shown).

Accordingly, the shorter list of nine occupational category variables is again used in the additional specifications discussed below. 

Other Factors Associated with the

Size of the Computer Use Wage

Premium

Other factors are associated with the wage premium,

and we look at the interactions between OTJ computer

use and other job/personal characteristics in the wage

model (table 10, table 11).

The personal monetary return to computer use seems

sensitive to several factors:

Union membership. The premium for computer use is

reduced by about 11 percentage points for unionized

jobs, which will almost wipe it out for the average

worker. This finding is broadly consistent with other

studies that have shown that unionization reduces the

premium to skill differentials.

Race and ethnicity. The premiums to computer use

appear to be greater for some racial and ethnic minorities. The estimated premiums are 10 to 13 percentage

points larger for Hispanics and Asians than for nonHispanic Whites, and nearly 30 percentage points

larger for Native Americans (although the number of

Native Americans in the sample who use computers on

the job is fairly small). However, the coefficient for

Blacks is not significantly different from the coefficient for non-Hispanic Whites. 

Region. The premium to computer use is significantly

greater in the Northeast than in the Midwest; the difference is about 6 percentage points. The estimated

premiums for the South and the West are intermediate

in magnitude and are not significantly different from

the values for other regions.20

Labor force experience. The estimated rate of return

to labor force experience is about 60 percent greater

for those using computers on the job. Thus, the premium for computer use is relatively small for new

workers, while it is much larger for those in their peak

earning years.21

However, further analysis suggests that computer use

here is serving in part as a proxy for employment in

more skilled and higher status jobs where the returns

to experience are greater. When interactions between

skill or education variables and labor force experience

are also included in the model, several of the interactions are significant, while the strength of the interaction between computer use and labor force experience

is cut in half (tables 10 and 11).

Models 10 and 11 also provide more evidence of an

independent effect of metro status on the size of the

computer wage premium, as they show that the estimated size of the metro area-computer use interaction

effect is more or less unchanged when other interactions are taken into account.

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 13

19 Cost-of-living differences may also help to explain the rural

wage gap, but are unlikely to account for the greater rural wage

gap faced by more skilled workers.

20 It is likely that other geographic characteristics also influence

the size of the computer wage premium, especially in rural labor

markets, which are quite heterogeneous. Such influences have not

been explored in this paper, and the limited amount of geographic

detail available in the CPS would make it difficult to do so.

21 Labor force experience has been estimated indirectly based on

age and years of education; see the appendix. The squared experience term included in the specification is typical for wage regression and captures the normal pattern of wage change over a career,

with relatively rapid growth early in a career and little or no

growth near the end of a career.
Conclusion

Some debate has arisen on whether the apparent return

to computer use on the job reflects a return to specific

computer skills or whether computer use is serving as

a proxy for other skills or job characteristics. An

answer to this question would help to determine

whether public expenditures on the development of

computer skills per se are a good investment of education or job training funds.

Results described here suggest that estimated computer wage premiums reflect returns to both computerspecific skills and broader skills. Including controls for

other skill measures in the wage model, as well as

occupational and industry category variables, reduces

the estimated magnitude of the average national computer wage premium by about half, from 21 percent to

11 percent. However, the latter figure is substantial and

statistically significant. 

14 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Table 10—Variation in computer wage premium by characteristics

Model 10 Model 10

Variable Main effects Interactions with computer use:

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer -0.117 -1.65+ NA NA

Metro area 0.094 6.05*** 0.068 3.11**

Labor force experience 0.019 10.80*** 0.012 4.64***

Labor force experience (squared) -0.033 -9.10*** -0.020 -3.58***

Non-HS graduate -0.135 -7.06*** 0.021 0.44

Some college 0.080 4.87*** -0.015 -0.67

Bachelor’s degree 0.240 9.56*** 0.039 1.27

Advanced degree 0.404 9.96*** -0.004 -0.08

Managers 0.028 0.82 -0.032 -0.81

Professionals 0.074 0.62 -0.059 -0.47

Technical -0.046 -0.87 -0.012 -0.20

Clerical -0.047 -1.45 -0.063 -1.61

Service -0.144 -5.41*** 0.027 0.61

Craft occupations -0.043 -1.48 0.011 0.27

Operators -0.044 -1.45 0.031 0.61

Laborers -0.108 -2.93** 0.039 0.54

Farmers, fishers, and foresters -0.180 -1.49 0.549 1.26

SVP 0.050 8.07*** 0.004 0.41

Black -0.095 -4.53*** 0.012 0.39

Asian -0.058 -1.57 0.119 2.31*

Native American -0.090 -1.57 0.252 2.80**

Hispanic -0.138 -5.79*** 0.091 2.46*

Foreign-born citizen -0.043 -1.06 -0.045 -0.72

Noncitizen -0.101 -3.43*** 0.028 0.56

Years in U.S. 0.003 2.27* -0.002 -1.12

Union 0.211 11.83*** -0.105 -4.13***

Veteran -0.014 -0.67 -0.003 -0.11

Adj. R-squared 0.4444

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.

Note: Intercept and coefficients for sex, marital status, region, part-time status, self-reporting status, and industry effects, and for interactions

involving these variables have been omitted, but are reported in appendix table 7.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 15

Table 11—Variation in computer wage premium and experience wage premium 

by characteristics (model 11)

Variable Main effects Interactions with computer use

Parameter T-statistic Parameter T-statistic

estimate estimate

Uses computer -0.075 -1.04 NA NA

Metro area 0.094 6.04*** 0.068 3.12**

Labor force experience 0.011 2.57* 0.007 2.30*

Labor force experience (squared) -0.025 -2.73** -0.011 -1.66+

Non-HS graduate -0.034 -0.67 0.017 0.36

Some college 0.092 2.76** -0.016 -0.70

Bachelor’s degree 0.267 6.22*** 0.041 1.31

Advanced degree 0.418 6.49*** -0.014 -0.29

Managers 0.024 0.70 -0.031 -0.80

Professionals 0.059 0.50 -0.049 -0.39

Technical -0.047 -0.89 -0.013 -0.21

Clerical -0.047 -1.43 -0.065 -1.64

Service -0.142 -5.34*** 0.028 0.64

Craft occupations -0.044 -1.54 0.012 0.30

Operators -0.041 -1.35 0.030 0.60

Laborers -0.114 -3.11** 0.037 0.51

Farmers, fishers, and foresters -0.186 -1.54 0.573 1.31

SVP 0.018 1.82+ 0.005 0.51

Black -0.095 -4.56*** 0.011 0.36

Asian -0.060 -1.63 0.121 2.36*

Native American -0.089 -1.56 0.251 2.80**

Hispanic -0.135 -5.66*** 0.090 2.42*

Foreign-born citizen -0.043 -1.06 -0.046 -0.74

Noncitizen -0.101 -3.43*** 0.029 0.59

Years in U.S . 0.003 2.33* -0.002 -1.19

Union 0.211 11.84*** -0.103 -4.04***

Veteran -0.014 -0.66 -0.002 -0.07

Interactions with LFE Interactions with LFE2

Parameter T-statistic Parameter T-statistic

Variable estimate estimate

Non-HS graduate -0.009 -2.09* 0.015 1.83+

Some college 0.002 0.55 -0.009 -1.19

Bachelor’s degree 0.002 0.47 -0.012 -1.27

Advanced degree 0.010 1.71+ -0.042 -2.95**

SVP 0.002 2.57* -0.002 -1.02

Adj. R-squared 0.4469

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level;

LFE = labor force experience.

Note: Intercept and coefficients for sex, marital status, region, part-time status, self-reporting status, and industry effects, and for interactions

involving these variables, have been omitted, but are reported in appendix table 8.
Is this a factor in explaining the metro-nonmetro wage

gap? Rates of computer use on the job are higher in

metro areas, a finding explained primarily by differences in occupational mix and educational attainment

between metro and nonmetro areas. This study finds

that this gap, combined with the computer wage premium, explains only a small percentage of the metrononmetro wage gap.

In general, nonmetro workers may benefit less than

metro workers from computer training, as the premium

paid for working with a computer appears to be substantially less outside metro areas, a result which persists even after other differences between metro and

nonmetro workers are taken into account. This finding

is consistent with past work (McGranahan and Ghelfi,

1991; McGranahan and Kassel, 1995), which has indicated that the demand for worker skills is weaker in

nonmetro areas. This difference is likely to be accentuated by trends in industry mix in metro and nonmetro

areas, with skill-intensive sectors such as producer

services and high-tech manufacturing growing more

rapidly in metro areas (Gale and McGranahan, 2001.)

Such results suggest that, while training in computer

skills may benefit workers who now live in nonmetro

areas, those workers may have to relocate to obtain the

most benefit from such training. These estimates do

not take into account the potential for some workers to

increase their earnings after computer training by moving into new, higher paying occupations; however,

these opportunities also seem likely to be greater in

metro areas.

Results also suggest that the returns to computer training may be greater for members of some racial and

ethnic minorities than for otherwise comparable nonminorities. While the reasons for this are unclear, this

result suggests that computer skills training might be

particularly valuable for some disadvantaged minority

group members.

However, these conclusions may have to be modified

with the recent explosive growth in the economic significance of the Internet, which is not reflected in the

data used here. It seems likely that the increasing

importance of the Internet has increased the relevance

of computer skills in many occupations. Further, the

Internet may lessen the importance of physical proximity to customers, clients, and information resources

in some industries, allowing firms in isolated areas to

participate in the economy in ways that previously

required location in metro areas. In turn, this may

increase the demand for workers with computer skills

and other skills in less densely settled areas.

References

Autor, David H., Lawrence F. Katz, and Alan B. Krueger,

Computing Inequality: Have Computers Changed the

Labor Market? National Bureau of Economic Research

Working Paper 5956, March 1997. 

Autor, David H., Lawrence F. Katz, and Alan B. Krueger.

"Computing Inequality: Have Computers Changed the

Labor Market?” Quarterly Journal of Economics, Vol.

113, No. 4 (Nov. 1998), pp. 1169-1213.

Baldwin, Robert E. and Glen G. Cain. “Shifts in Relative

U.S. Wages: The Role of Trade, Technology, and Factor

Endowments,” Review of Economics and Statistics, Vol.

82, No. 4 (Nov. 2000), pp. 580-595.

Barron, John M., Mark C. Berger, and Dan A. Black. “Do

Workers Pay for On-the-Job Training?” Journal of Human

Resources, Vol. 34, No. 2 (Spring 1999), pp. 235-252.

DiNardo, John E., and Jörn-Steffen Pische. “The Returns to

Computer Use Revisited: Have Pencils Changed the Wage

Structure Too?” Quarterly Journal of Economics, Vol. 112,

No. 1 (Feb. 1997), pp. 291-303. 

Gale, H. Frederick. Is There a Rural-Urban Technology

Gap?: Results of the ERS Rural Manufacturing Survey,

U.S. Dept. Agr., Econ. Res. Serv., Agriculture Information

Bulletin No. 736-01, Aug. 1997.

Gale, H. Frederick. “Labor Productivity and Wages in Rural

and Urban Manufacturing Plants,” Review of Regional

Studies, Vol. 28, No. 1 (1998), pp. 13-26.

Gale, Fred, and David McGranahan. “Latest Trends in

Nonmetro Jobs and Earnings: Nonmetro Areas Fall

Behind in the 'New Economy',” Rural America, Vol. 16,

No. 1 (May 2001), pp. 44-52.

Gibbs, Robert, and Timothy Parker. “Rural Earnings

Continue A Slow, Steady Rise," Rural Conditions and

Trends, Vol. 9, No. 2 (Feb. 1999), pp. 59-62.

Gibbs, Robert M. “College Completion and Return

Migration Among Rural Youth,” Rural Education and the

New Economy: The Myth of the Rural Skills Gap, Robert

16 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95
M. Gibbs, Paul L. Swaim, and Ruy Teixeira (eds.), Ames:

Iowa State University Press, 1998.

Goss, Ernest, and Joseph Philip. “The Impact of Internet

Usage on Wages.” Presented at Southern Regional Science

Association Meeting, Miami, FL, April 2000.

Helpman, Elhanan, and Manuel Trajtenberg. A Time to Sow

and a Time to Reap: Growth Based on General Purpose

Technologies, Tel Aviv Foerder Institute for Economic

Research Working Paper, Nov. 1995.

Krueger, A.B. “How Computers Have Changed the Wage

Structure: Evidence from Microdata, 1984-1989,”

Quarterly Journal of Economics, Vol. 108, No.1 (Feb.

1993), pp. 33-60.

Kusmin, Lorin D. “Computer Use by Rural Workers Is

Rapidly Increasing,” Rural Development Perspectives, Vol.

11, No. 3 (June 1996), pp.11-16.

Kusmin, Lorin D. Wage Premiums for On-the-Job Computer

Use in Metro and Nonmetro Areas. ERS Staff Report

AGES 00-02, U.S. Dept Agr., Econ. Res. Serv., Oct. 2000.

Kusmin, Lorin D., John M. Redman, and David W. Sears.

Factors Associated with Rural Economic Growth: Lessons

from the 1980's. Technical Bulletin No. 1850, U.S. Dept

Agr., Econ. Res. Serv., Sept. 1996.

McGranahan, David A., and Linda M. Ghelfi. “The

Education Crisis and Rural Stagnation in the 1980's,”

Education and Rural Economic Development: Strategies

for the 1990's, ERS Staff Report No. AGES 9153, Sept.

1991, pp. 40-92.

McGranahan, David A., and Kathleen Kassel. “Rural-Urban

Migration Patterns Shift,” Rural Conditions and Trends,

Vol. 6, No. 1 (Spring 1995), pp. 10-13.

McGranahan, David A., and Kathleen Kassel. “Education

and Rural Minority Job Opportunities,” Racial/Ethnic

Minorities in Rural Areas: Progress and Stagnation 1980-

1990, Linda L. Swanson (ed.), U.S. Dept. Agr., Econ. Res.

Serv., AER No. 731, Aug. 1996, pp. 5-25.

Newburger, Eric C. Computer Use in the United States,

October 1997, Current Population Report P20-522, U.S.

Census Bureau, Sept. 1999.

Nord, Mark, and John Cromartie. “Migration in the

Nonmetropolitan South.” Presented at Southern Rural

Labor Force Conference, New Orleans, LA, Oct. 1998.

Oaxaca, Ronald L. “Male-Female Differentials in Urban

Labor Markets,” International Economic Review, Vol. 14,

No. 3 (Oct. 1973), pp. 693-709.

Swaim, Paul. “Rural Earnings Holding Steady in the Early

1990's,” Rural Conditions and Trends, Vol. 6, No. 1

(Spring 1995), pp. 18-21.

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 17
Appendix: Data and Methods

Data Sources

Data for this analysis have been taken from responses

to the Current Population Survey (CPS). The CPS is

conducted monthly by the U.S. Census Bureau to collect data on employment and unemployment. The

October 1997 CPS asked a variety of questions about

computer use on the job, at home, and at school, and

specifically "Does (person's name) directly use a computer at work?" According to the interviewers' instructions quoted in Krueger (1993), "Using a computer

refers only to the respondent's 'DIRECT' or 'HANDSON' use of a computer with typewriter-like keyboards"

(p. 35). Thus, use of an electronic cash register or

hand-held data-entry device with a more limited keypad is excluded.

Data for this survey were collected from a sample of

approximately 48,000 households, chosen to represent

the civilian noninstitutional population of the United

States. The sample covered by this analysis includes

respondents who were employed, who were asked

about weekly earnings in the October survey (a quarter

of all respondents are asked about earnings in any single month), and who responded to all of the questions

that are used in the analysis, for a total of about 10,000

unweighted observations.

Metro and Nonmetro Areas

In this article, "metro" refers to metropolitan areas as

designated by the Office of Management and Budget

as of 1993, while "nonmetro" refers to all other areas.

The metro or nonmetro status of respondents is based

on their place of residence, and not on their place of

work. For 1997, the metro-nonmetro designation of

residences in the CPS was based on population and

commuting patterns from the 1990 Census of

Population. The terms “metro/nonmetro” and

“urban/rural” are used interchangeably.

Categorical Variables

Wage regressions have been computed using standard

multivariate linear regression. Categorical variables

such as region of the country, or whether a worker is

covered by a union contract, are normally represented

in regression analysis by dichotomous variables, commonly referred to as dummy variables. These are variables that take on only the values 0 or 1, depending on

whether an observation satisfies a particular condition.

A categorical variable with two categories (for example, union versus nonunion) can be represented with

one dummy variable (1=union); a categorical variable

with n categories (for example, the nine major occupation groups used in this report) can be represented with

n-1 independent dummy variables. Inclusion of a

dummy variable for the nth category--for example,

inclusion of separate 0,1 dummy variables for union

and nonunion--would add redundant information to the

list of independent variables (Kusmin, Redman, and

Sears, 1996, p. B-6). The nth category, which is not

included in the list of regressors, is referred to as the

"omitted" category. Omitted categories for regressions

here are: female (sex); not married (marital status);

nonveteran; nonunion; non-Hispanic White (race/ethnicity); U.S.-born citizen (citizenship/origin); Midwest

(region); nonmetro; not self-reporting; full-time; high

school graduate (education); does not use computer;

and sales occupations.

Labor Force Experience

Labor force experience (LFE) is not directly measured

in the Current Population Survey. Thus, LFE (in years)

has been estimated from the formula,

LFE = Age in Years - Estimated Years of Education - 6

where estimated years of education are derived from

the reported highest level of education completed. The

term LFE2 is commonly included in wage regressions

to capture the widely observed nonlinear relationship

between experience and wages (on average, wages rise

rapidly early in working life and then begin to level

off, and may even decline near the end of working

life). 

“Self-Reporting”

For each household, data are collected on all household members within the sample universe, although

not all may be available for interview. Each individual

record contains an item indicating whether this individual reported for himself/herself, or whether a parent, spouse, other relative, or nonrelative responded for

him/her. Reporting may be less accurate for some variables (such as whether a computer is used on the job)

when reported by one household member on behalf of

another. Therefore, the analysis reported here includes

a variable reflecting whether an individual observation

18 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95
is self-reported or reported on behalf of another individual. 

Assignment of Skill Levels 

to Occupations

The Dictionary of Occupational Titles (DOT) file,

which was used to assign skill levels to occupations,

contains quantitative assessments of the characteristics

of a large number of narrowly defined occupations.22

To associate skill levels with individuals in the CPS

data, these occupations were aggregated to correspond

to the level of occupational detail available in the CPS.

The DOT data set was originally developed for job

placement purposes, and not for statistical purposes;

thus no data on employment totals in DOT occupations are readily available for weighting. Hence, equal

weights were assigned to each DOT occupation in estimating the average characteristics of individual CPS

occupations. This weighting means that the estimated

skill content of any CPS occupation will be underestimated (overestimated) to the extent that the DOT component occupations with higher skill levels in reality

have more (fewer) workers than are found in those

with lower skill levels. However, for the skills considered in this study, the dispersion of skill level values

among the various DOT occupations within a single

CPS occupation was usually small relative to the dispersion among CPS occupations.

Decomposition of Urban-Rural Differences

Into Explained and Unexplained

Components

The decomposition of urban-rural computer use differences and wage differences into explained and unexplained components (tables 2 and 8) follows the model

of McGranahan and Kassel (1996). To illustrate this

method, in the sample studied for the wage analysis,

47.5 percent of nonmetro workers and 57.7 percent of

metro workers used computers, a difference of 10.2

percentage points. In the regression model used for this

calculation, the estimated effect of computer use on

wages is 10.7 percent. Thus, the estimated contribution

of differing levels of computer use to metro-nonmetro

wage differences is (.107 x .102 = .0109), or 1.09 percent. The overall difference between metro and nonmetro wage levels is 19.8 percent. Thus, differing levels

of computer use account for 5.5 percent of the overall

wage level difference (.0109/.198 = 5.5 percent). If a

similar computation is carried out for each independent

variable in the wage equation and the results are

summed, 34.5 percent of the overall wage difference

can be explained; the remaining 13.0-percent gap is

unexplained (20 percent x .655 = 13.0 percent).

This method appears to differ from the traditional

Oaxaca decomposition of intergroup wage differences

(Oaxaca, 1973). In that approach, group-specific wage

equations are estimated separately for each group of

interest (in the present study, those groups would be

metro and nonmetro residents). Then, the difference

between average log wages for the groups is decomposed into two components. One component reflects

intergroup differences in the average level of the

regression variables, assuming a common set of coefficients--typically the coefficients estimated for one of

the two groups. The second component is explained by

intergroup differences in estimated regression coefficients, and is often viewed as the "discriminatory"

component of the difference. 

However, the "explained" and "unexplained" components of the intergroup wage difference derived in the

present study are equivalent to the two components of

the wage difference that would be derived from a

Oaxaca-style decomposition if the common set of

coefficients used in that decomposition were derived

from running the wage equation on the pooled sample,

rather than being chosen from one of the groups.

RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 19

22 The Department of Labor has replaced the DOT with a system

of occupational skill descriptions called O*NET. O*NET is

intended to be more useful in the primary function of both DOT

and O*NET, which is to assess the match between an individual's

skills and potential careers. However, O*NET may not be as well

suited as the DOT for assessing "how skilled" a particular occupation is. In particular, while the DOT rates occupational skill

demands using multipoint scales on a number of dimensions, the

O*NET combines a single scale that rates the amount of education

and/or training required for an occupation with several dozen (0,1)

variables that rate whether or not a particular skill is required for

that occupation.
20 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 1—Predictors of on-the-job computer use

Parameter T-statistic

Variable estimate

Intercept 0.5292 48.91*** 

Northeast -0.0298 -4.77*** 

South 0.0003 -0.05

West 0.0183 2.97**

Nonmetro Northeast 0.0144 1.06

Nonmetro Midwest -0.0225 -2.63**

Nonmetro South -0.0472 -6.32*** 

Nonmetro West -0.0276 -2.32*

Age 18-21 -0.0596 -5.13*** 

Age 25-34 0.0303 3.60*** 

Age 35-44 0.0316 3.78*** 

Age 45-54 0.0024 0.28

Age 55-64 -0.0268 -2.74**

Age 65+ -0.1532 -10.00*** 

Non-HS graduate -0.0860 -12.45*** 

Some college 0.1018 21.13*** 

Bachelor’s degree 0.1731 28.97*** 

Advanced degree 0.2134 26.64*** 

Managers 0.0995 16.18*** 

Professionals 0.1743 12.05*** 

Technical 0.0727 6.69*** 

Clerical 0.1337 19.41*** 

Service -0.3360 -44.17*** 

Craft occupations -0.2634 -33.27*** 

Operators -0.3626 -44.70*** 

Laborers -0.3510 -33.39*** 

Farmers, fishers, and foresters -0.2611 -11.15*** 

Black -0.0837 -13.51*** 

Asian -0.0351 -3.07**

Native American -0.0314 -1.45

Hispanic -0.0564 -7.52*** 

Part-time -0.1757 -16.89*** 

Self-employed -0.1232 -18.79*** 

Male -0.0379 -8.70*** 

Foreign-born citizen -0.0709 -7.24*** 

Noncitizen -0.0879 -9.94*** 

Self-reporting 0.0399 10.65*** 

--Continued

Appendix: Detailed Regression Tables
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 21

Appendix table 1—Predictors of on-the-job computer use--continued

Parameter T-statistic

Variable estimate

Industry effects:

Agriculture 0.0041 0.24

Mining 0.0828 3.31*** 

Construction -0.1039 -10.98*** 

Durable goods 0.1047 12.80*** 

Nondurables 0.0907 10.14*** 

Transportation 0.0157 1.54

Communication 0.2180 14.27*** 

Utilities & sanitary services 0.1275 7.75*** 

Wholesale Trade 0.0852 8.34*** 

Finance, insurance, & real estate 0.1793 19.99*** 

Private household services -0.0787 -2.82**

Business services 0.1094 12.37*** 

Personal services -0.0100 -0.78

Entertainment services -0.0370 -2.33*

Hospital services 0.1153 10.76*** 

Medical services -0.0238 -2.37*

Educational services 0.0362 4.05*** 

Social services -0.0707 -5.28*** 

Professional services 0.1188 11.69*** 

Forestry & fishing 0.0766 1.25

Public service 0.2046 20.49*** 

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level;

*** = significant at 0.1-percent level.
22 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 2—Wage model with and without computer use

Model 1 Model 2

Wage model without Basic model with

Variable computer use computer use variable

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 6.349 296.85*** 6.280 294.24***

Uses computer NA NA 0.192 18.69***

Metro area 0.162 14.22*** 0.145 12.91***

Labor force experience 0.030 22.40*** 0.029 21.87***

Labor force experience (squared) -0.052 -18.04*** -0.050 -17.42***

Non-HS graduate -0.208 -11.50*** -0.168 -9.36***

Some college 0.140 11.94*** 0.103 8.80***

Bachelor’s degree 0.426 32.71*** 0.360 27.06***

Advanced degree 0.588 33.62*** 0.512 28.96***

Black -0.144 -8.94*** -0.118 -7.44***

Asian 0.004 0.15 0.015 0.57

Native American 0.004 0.09 0.021 0.46

Hispanic -0.154 -8.05*** -0.135 -7.17***

Foreign-born citizen -0.136 -4.13*** -0.111 -3.43***

Noncitizen -0.173 -7.04*** -0.141 -5.82***

Years in U.S. 0.003 3.09** 0.003 3.06**

Union 0.119 9.63*** 0.140 11.44***

Male 0.142 9.37*** 0.170 11.38***

Married 0.032 2.30* 0.022 1.59

Married male 0.122 6.43*** 0.116 6.25***

Veteran -0.021 -1.34 -0.013 -0.84

Northeast 0.060 4.38*** 0.061 4.56***

South -0.027 -2.17* -0.028 -2.24*

West 0.035 2.67** 0.029 2.24*

Self-reporting 0.043 4.57*** 0.032 3.40***

Part-time -0.234 -7.64*** -0.180 -5.95***

Adj. R-squared 0.3574 0.3795

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 23

Appendix table 3—Wage models with controls for industry and occupation

Model 3 Model 4

Wage model with controls Wage model with controls

Variable for industrial sector for industry and occupation

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 6.191 269.67*** 6.348 259.91***

Uses computer 0.169 16.19*** 0.125 11.22***

Metro area 0.144 12.92*** 0.133 12.19***

Labor force experience 0.026 20.36*** 0.025 19.87***

Labor force experience (squared) -0.045 -16.03*** -0.043 -15.66***

Non-HS graduate -0.167 -9.50*** -0.151 -8.75***

Some college 0.107 9.33*** 0.079 6.92***

Bachelor’s degree 0.373 27.74*** 0.283 20.04***

Advanced degree 0.549 29.47*** 0.434 22.27***

Managers NA NA 0.064 4.30***

Professionals NA NA 0.091 2.83**

Technical NA NA -0.044 -1.84+

Clerical NA NA -0.176 -11.15***

Service NA NA -0.231 -12.79***

Craft occupations NA NA -0.034 -1.74+

Operators NA NA -0.175 -8.87***

Laborers NA NA -0.275 -11.14***

Farmers, fishers, and foresters NA NA -0.199 -1.72+

Black -0.119 -7.59*** -0.097 -6.31***

Asian 0.009 0.35 0.016 0.60

Native American 0.009 0.19 0.017 0.39

Hispanic -0.127 -6.85*** -0.106 -5.82***

Foreign-born citizen -0.102 -3.21** -0.078 -2.50*

Noncitizen -0.130 -5.45*** -0.113 -4.84***

Years in U.S . 0.003 2.98** 0.002 2.19*

Union 0.135 10.46*** 0.156 12.23***

Male 0.143 9.48*** 0.136 9.05***

Married 0.022 1.62 0.010 0.77

Married male 0.105 5.73*** 0.095 5.34***

Veteran -0.021 -1.41 -0.018 -1.23

Northeast 0.063 4.82*** 0.059 4.62***

South -0.027 -2.22* -0.032 -2.71**

West 0.035 2.76** 0.024 1.93+

Self-reporting 0.032 3.51*** 0.029 3.19**

Part-time -0.136 -4.58*** -0.131 -4.48***

--Continued
24 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 3—Wage models with controls for industry and occupation, continued

Model 3 Model 4

Wage model with controls Wage model with controls

Variable for industrial sector for industry and occupation

Parameter T-statistic Parameter T-statistic

estimate estimate

Industry effects:

Agriculture -0.054 -1.38 0.064 1.54

Mining 0.355 7.17*** 0.360 7.33***

Construction 0.253 11.28*** 0.243 10.20***

Durable goods 0.209 11.69*** 0.221 11.48***

Nondurables 0.193 9.70*** 0.214 10.31***

Transportation 0.148 6.04*** 0.203 8.09***

Communication 0.226 6.13*** 0.230 6.34***

Utilities & sanitary services 0.285 7.34*** 0.301 7.82***

Wholesale trade 0.146 5.94*** 0.154 6.33***

Finance, insurance, & real estate 0.175 8.51*** 0.193 9.36***

Private household services -0.231 -3.35*** -0.132 -1.95+

Business services 0.123 5.68*** 0.135 6.26***

Personal services -0.009 -0.28 0.037 1.18

Entertainment services 0.044 1.09 0.087 2.21*

Hospital services 0.196 8.16*** 0.222 9.37***

Medical services 0.152 6.49*** 0.182 7.80***

Educational services 0.010 0.48 0.028 1.35

Social services -0.005 -0.15 -0.008 -0.23

Professional services 0.174 7.03*** 0.180 7.30***

Forestry & fishing 0.208 0.83 0.237 0.97

Public service 0.220 9.87*** 0.269 12.02***

Adj. R-squared 0.4056 0.4324

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 25

Appendix table 4—Wage models with control for skill level

Model 5 Model 6

Wage model with controls Wage model with controls for 

for industry and for industry, occupation, and

Variable occupational skill level occupational skill level

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 5.929 235.73** 6.056 156.15***

Uses computer 0.100 9.20*** 0.106 9.45***

Metro area 0.127 11.69*** 0.129 11.90***

Labor force experience 0.025 19.66*** 0.025 19.57***

Labor force experience (squared) -0.042 -15.37*** -0.042 -15.31***

Non-HS graduate -0.142 -8.25*** -0.145 -8.43***

Some college 0.073 6.47*** 0.072 6.36***

Bachelor’s degree 0.278 19.88*** 0.265 18.73***

Advanced degree 0.412 20.99*** 0.397 19.95***

Managers NA NA 0.020 1.23

Professionals NA NA 0.034 0.98

Technical NA NA -0.047 -1.94+

Clerical NA NA -0.088 -4.86***

Service NA NA -0.119 -5.00***

Craft occupations NA NA -0.023 -0.90

Operators NA NA -0.029 -1.08

Laborers NA NA -0.079 -2.41*

Farmers, fishers, and foresters NA NA -0.117 -1.00

GED-Language 0.025 2.29* 0.020 1.36

GED-Math 0.012 1.13 -0.001 -0.12

SVP 0.050 8.95*** 0.043 5.93***

Black -0.092 -6.00*** -0.089 -5.85***

Asian 0.009 0.35 0.013 0.52

Native American 0.022 0.51 0.022 0.51

Hispanic -0.105 -5.82*** -0.102 -5.67***

Foreign-born citizen -0.076 -2.44* -0.071 -2.30*

Noncitizen -0.108 -4.65*** -0.109 -4.68***

Years in U.S. 0.002 2.22* 0.002 2.08*

Union 0.162 12.79*** 0.161 12.68***

Male 0.138 9.39*** 0.133 8.91***

Married 0.009 0.70 0.007 0.55

Married male 0.093 5.24*** 0.094 5.28***

Veteran -0.016 -1.06 -0.016 -1.08

Northeast 0.061 4.76*** 0.061 4.75***

South -0.032 -2.68** -0.033 -2.76**

West 0.027 2.20* 0.025 1.98*

Self-reporting 0.026 2.93** 0.027 3.04**

Part-time -0.114 -3.93*** -0.114 -3.94***

--Continued
26 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 4—Wage models with control for skill level, continued

Model 5 Model 6

Wage model with controls Wage model with controls for 

for industry and for industry, occupation, and

Variable occupational skill level occupational skill level

Parameter T-statistic Parameter T-statistic

estimate estimate

Industry effects:

Agriculture -0.040 -1.05 -0.019 -0.45

Mining 0.347 7.18*** 0.342 6.99***

Construction 0.210 9.29*** 0.207 8.61***

Durable goods 0.199 11.38*** 0.193 9.91***

Nondurables 0.201 10.33*** 0.189 9.06***

Transportation 0.175 7.32*** 0.180 7.15***

Communication 0.190 5.31*** 0.201 5.55***

Utilities & sanitary services 0.270 7.16*** 0.280 7.29***

Wholesale trade 0.146 6.08*** 0.139 5.73***

Finance, insurance, & real estate 0.146 7.21*** 0.160 7.63***

Private household services -0.165 -2.46* -0.120 -1.75+

Business services 0.104 4.90*** 0.111 5.12***

Personal services 0.001 0.04 0.023 0.75

Entertainment services 0.042 1.07 0.060 1.53

Hospital services 0.155 6.55*** 0.179 7.31***

Medical services 0.111 4.78*** 0.141 5.82***

Educational services -0.033 -1.55 -0.017 -0.76

Social services -0.047 -1.45 -0.041 -1.27

Professional services 0.113 4.61*** 0.131 5.15***

Forestry & fishing 0.188 0.77 0.217 0.90

Public service 0.181 8.19*** 0.214 9.20***

Adj. R-squared 0.4362 0.4391

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 27

Appendix table 5—Wage model with controls for industry, occupation,

and specific vocational preparation

Model 7

Variable Parameter T-statistic

estimate

Intercept 6.079 175.19***

Uses computer 0.107 9.58***

Metro area 0.130 11.95***

Labor force experience 0.025 19.53***

Labor force experience (squared) -0.042 -15.27***

Non-HS graduate -0.146 -8.48***

Some college 0.073 6.42***

Bachelor’s degree 0.267 18.95***

Advanced degree 0.401 20.46***

Managers 0.013 0.87

Professionals 0.033 1.03

Technical -0.052 -2.21*

Clerical -0.095 -5.48***

Service -0.137 -6.86***

Craft occupations -0.049 -2.51*

Operators -0.051 -2.23*

Laborers -0.099 -3.37***

Farmers, fishers, and foresters -0.139 -1.21

SVP 0.051 10.85***

Black -0.090 -5.89***

Asian 0.013 0.50

Native American 0.023 0.51

Hispanic -0.103 -5.71***

Foreign-born citizen -0.072 -2.33*

Noncitizen -0.109 -4.70***

Years in U.S. 0.002 2.10*

Union 0.161 12.67***

Male 0.132 8.89***

Married 0.008 0.58

Married male 0.094 5.27***

Veteran -0.016 -1.07

Northeast 0.061 4.75***

South -0.032 -2.75**

West 0.025 1.99*

Self-reporting 0.027 3.05**

Part-time -0.115 -3.96***

--Continued
28 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 5—Wage model with controls for industry, occupation,

and specific vocational preparation, continued

Model 7

Variable Parameter T-statistic

estimate

Industry effects:

Agriculture -0.012 -0.28

Mining 0.344 7.04***

Construction 0.207 8.66***

Durable goods 0.196 10.18***

Nondurables 0.193 9.29***

Transportation 0.185 7.42***

Communication 0.205 5.67***

Utilities & sanitary services 0.283 7.38***

Wholesale trade 0.143 5.90***

Finance, insurance, & real estate 0.166 8.03***

Private household services -0.107 -1.59

Business services 0.116 5.37***

Personal services 0.026 0.84

Entertainment services 0.066 1.70+

Hospital services 0.188 7.89***

Medical services 0.150 6.42***

Educational services -0.009 -0.42

Social services -0.036 -1.12

Professional services 0.139 5.58***

Forestry & fishing 0.222 0.91

Public service 0.221 9.75***

Adj. R-squared 0.4391

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level;

*** = significant at 0.1-percent level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 29

Appendix table 6—Metro versus nonmetro premiums and detailed occupations

Model 8 Model 9

Metro versus Model with more 

Variable nonmetro premium detailed occupations

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 6.104 171.34*** 6.054 153.65***

Uses computer 0.056 2.86** 0.061 3.10**

Metro area 0.097 6.39*** 0.099 6.56***

Uses computer x metro area 0.065 3.11** 0.060 2.87**

Labor force experience 0.025 19.59*** 0.024 19.30***

Labor force experience (squared) -0.042 -15.34*** -0.041 -15.07***

Non-HS graduate -0.147 -8.53*** -0.149 -8.69***

Some college 0.073 6.43*** 0.067 5.89***

Bachelor’s degree 0.267 18.95*** 0.260 18.31***

Advanced degree 0.399 20.36*** 0.395 19.32***

Managers 0.012 0.80 NA NA

Professionals 0.032 0.99 NA NA

Technical -0.053 -2.24* NA NA

Clerical -0.095 -5.47*** NA NA

Service -0.137 -6.88*** NA NA

Craft occupations -0.048 -2.50* NA NA

Operators -0.051 -2.24* NA NA

Laborers -0.101 -3.43*** NA NA

Farmers, fishers, and foresters -0.150 -1.30 NA NA

SVP 0.051 10.87*** 0.051 7.66***

Black -0.089 -5.84*** -0.085 -5.59***

Asian 0.013 0.50 0.010 0.39

Native American 0.021 0.47 0.012 0.27

Hispanic -0.102 -5.64*** -0.098 -5.43***

Foreign-born citizen -0.070 -2.28* -0.072 -2.33*

Noncitizen -0.105 -4.53*** -0.103 -4.46***

Years in U.S. 0.002 2.12* 0.002 2.24*

Union 0.162 12.74*** 0.166 12.82***

Male 0.132 8.89*** 0.123 8.25***

Married 0.008 0.63 0.010 0.78

Married male 0.093 5.24*** 0.090 5.09***

Veteran -0.015 -1.04 -0.020 -1.39

Northeast 0.061 4.74*** 0.060 4.70***

South -0.034 -2.87** -0.034 -2.94**

West 0.025 1.98* 0.024 1.92+

Self-reporting 0.027 3.01** 0.027 3.00**

Part-time -0.116 -4.02*** -0.111 -3.84***

Industry effects:

Agriculture -0.017 -0.42 0.024 0.41

Mining 0.342 7.00*** 0.305 6.14***

Construction 0.206 8.63*** 0.129 4.37***

Durable goods 0.195 10.10*** 0.163 7.42***

Nondurables 0.190 9.19*** 0.167 7.12***

Transportation 0.183 7.34*** 0.109 3.92***

Communication 0.202 5.57*** 0.149 3.96***

Utilities & sanitary services 0.283 7.38*** 0.244 6.19***

Wholesale trade 0.142 5.85*** 0.079 2.95**

Finance, insurance, & real estate 0.164 7.94*** 0.114 4.90***

Private household services -0.108 -1.60 0.143 0.83

Business services 0.113 5.27*** 0.064 2.65**

Personal services 0.027 0.85 -0.021 -0.63

Entertainment services 0.067 1.72+ 0.038 0.94

Hospital services 0.187 7.85*** 0.094 3.22**

--Continued
30 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 6—Metro versus nonmetro premiums and detailed occupations, continued

Model 8 Model 9

Metro versus Model with more 

Variable nonmetro premium detailed occupations

Parameter T-statistic Parameter T-statistic

estimate estimate

Medical services 0.149 6.35*** 0.092 3.22**

Educational services -0.008 -0.38 0.004 0.17

Social services -0.037 -1.15 -0.059 -1.72+

Professional services 0.137 5.50*** 0.102 3.74***

Forestry & fishing 0.233 0.96 0.184 0.76

Public service 0.222 9.78*** 0.184 6.74***

Detailed occupations:

Public administration NA NA 0.079 1.27

Other executives and managers NA NA 0.122 3.23**

Management-related occupations NA NA 0.105 2.67**

Engineers NA NA 0.138 2.90**

Mathematical and computer scientists NA NA 0.272 5.29***

Natural scientists NA NA 0.032 0.48

Health diagnosing occupations NA NA 0.076 1.00

Health assessment/treating occupations NA NA 0.288 6.15***

College and university teachers NA NA 0.054 0.68

Other teachers NA NA 0.002 0.05

Lawyers and judges NA NA 0.207 3.04**

Other professional specialties NA NA 0.021 0.52

Health technologists and technicians NA NA 0.065 1.33

Engineering and science technicians NA NA -0.011 -0.23

Other technicians NA NA 0.123 2.46*

Sales supervisors and proprietors NA NA 0.080 2.24*

Sales repr.: finance and business service NA NA 0.139 3.11**

Sales repr.: commod., exc. retail NA NA 0.222 4.52***

Sales-related occupations NA NA -0.033 -0.17

Supervisors-administrative support NA NA 0.063 1.09

Computer equipment operators NA NA -0.014 -0.19

Secretaries and typists NA NA -0.046 -1.21

Financial records processing occup. NA NA 0.001 0.02

Mail and message distributing NA NA 0.137 2.54*

Other administrative support NA NA 0.003 0.09

Private household services NA NA -0.377 -2.02*

Protective service occupations NA NA -0.029 -0.66

Food service occupations NA NA -0.087 -2.71**

Health service occupations NA NA -0.071 -1.61

Cleaning and building services NA NA -0.008 -0.20

Personal service occupations NA NA 0.033 0.63

Mechanics and repairers NA NA 0.071 1.91+

Construction trades NA NA 0.090 2.18*

Other craft occupations NA NA 0.027 0.69

Machine operators and tenders NA NA 0.014 0.39

Fabricators, assemblers, etc. NA NA 0.025 0.64

Motor vehicle operators NA NA 0.100 2.75**

Other transp. & material moving occup. NA NA 0.100 1.96+

Construction laborers NA NA 0.163 2.65**

Freight, stock, and material handlers NA NA -0.071 -1.52

Other handlers and laborers NA NA 0.007 0.16

Farm operators and managers NA NA -0.337 -1.98*

Farm workers and related occupations NA NA -0.094 -1.51

Forestry and fishing occupations NA NA 0.160 0.99

Adj. R-squared 0.4396 0.4471

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent

level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 31

Appendix table 7—Variation in computer wage premium by characteristics

Model 10 Model 10

Variable Main effects Interactions with computer use

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 6.186 130.04*** NA NA

Uses computer -0.117 -1.65+ NA NA

Metro area 0.094 6.05*** 0.068 3.11**

Labor force experience 0.019 10.80*** 0.012 4.64***

Labor force experience2 -0.033 -9.10*** -0.020 -3.58***

Non-HS graduate -0.135 -7.06*** 0.021 0.44

Some college 0.080 4.87*** -0.015 -0.67

Bachelor’s degree 0.240 9.56*** 0.039 1.27

Advanced degree 0.404 9.96*** -0.004 -0.08

Managers 0.028 0.82 -0.032 -0.81

Professionals 0.074 0.62 -0.059 -0.47

Technical -0.046 -0.87 -0.012 -0.20

Clerical -0.047 -1.45 -0.063 -1.61

Service -0.144 -5.41*** 0.027 0.61

Craft occupations -0.043 -1.48 0.011 0.27

Operators -0.044 -1.45 0.031 0.61

Laborers -0.108 -2.93** 0.039 0.54

Farmers, fishers, and foresters -0.180 -1.49 0.549 1.26

SVP 0.050 8.07*** 0.004 0.41

Black -0.095 -4.53*** 0.012 0.39

Asian -0.058 -1.57 0.119 2.31*

Native American -0.090 -1.57 0.252 2.80**

Hispanic -0.138 -5.79*** 0.091 2.46*

Foreign-born citizen -0.043 -1.06 -0.045 -0.72

Noncitizen -0.101 -3.43*** 0.028 0.56

Years in U.S. 0.003 2.27* -0.002 -1.12

Union 0.211 11.83*** -0.105 -4.13***

Male 0.154 7.06*** -0.053 -1.75+

Married 0.014 0.62 -0.011 -0.39

Married male 0.081 3.00** 0.029 0.81

Veteran -0.014 -0.67 -0.003 -0.11

Northeast 0.028 1.48 0.058 2.26*

South -0.044 -2.47* 0.020 0.86

West 0.015 0.81 0.022 0.89

Self-reporting 0.002 0.18 0.044 2.46*

Part-time -0.101 -2.90** -0.073 -1.15

--Continued
32 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 7—Variation in computer wage premium by characteristics, continued

Model 10 Model 10

Variable Main effects Interactions with computer use

Parameter T-statistic Parameter T-statistic

estimate estimate

Industry effects:

Agriculture -0.018 -0.37 -0.012 -0.13

Mining 0.357 5.31*** -0.065 -0.66

Construction 0.198 6.51*** -0.033 -0.62

Durable goods 0.160 5.68*** 0.057 1.45

Nondurables 0.162 5.55*** 0.053 1.26

Transportation 0.194 5.53*** -0.045 -0.90

Communication 0.195 2.08* 0.015 0.15

Utilities & sanitary services 0.259 4.17*** 0.027 0.34

Wholesale trade 0.134 3.69*** 0.006 0.12

Finance, insurance, & real estate 0.140 3.22** 0.030 0.58

Private household services -0.087 -1.25 -0.178 -0.42

Business services 0.040 1.25 0.130 2.99**

Personal services 0.064 1.62 -0.098 -1.52

Entertainment services 0.085 1.66+ -0.037 -0.47

Hospital services 0.191 4.55*** -0.009 -0.17

Medical services 0.171 5.05*** -0.035 -0.75

Educational services 0.034 1.04 -0.051 -1.16

Social services -0.049 -1.12 0.036 0.56

Professional services 0.000 0.01 0.166 2.58**

Forestry & fishing 0.288 0.97 -0.271 -0.53

Public service 0.244 5.88*** -0.029 -0.57

Adj. R-squared 0.4444

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.
RDRR-95 • Wage Premiums for On-the-Job Computer Use USDA/Economic Research Service • 33

Appendix table 8—Variation in computer wage premium and experience wage premium by characteristics

Model 11 Model 11

Variable Main effects Interactions with computer use

Parameter T-statistic Parameter T-statistic

estimate estimate

Intercept 6.309 108.04*** NA NA

Uses computer -0.075 -1.04 NA NA

Metro area 0.094 6.04*** 0.068 3.12**

Labor force experience 0.011 2.57* 0.007 2.30*

Labor force experience2 -0.025 -2.73** -0.011 -1.66+

Non-HS graduate -0.034 -0.67 0.017 0.36

Some college 0.092 2.76** -0.016 -0.70

Bachelor’s degree 0.267 6.22*** 0.041 1.31

Advanced degree 0.418 6.49*** -0.014 -0.29

Managers 0.024 0.70 -0.031 -0.80

Professionals 0.059 0.50 -0.049 -0.39

Technical -0.047 -0.89 -0.013 -0.21

Clerical -0.047 -1.43 -0.065 -1.64

Service -0.142 -5.34*** 0.028 0.64

Craft occupations -0.044 -1.54 0.012 0.30

Operators -0.041 -1.35 0.030 0.60

Laborers -0.114 -3.11** 0.037 0.51

Farmers, fishers, and foresters -0.186 -1.54 0.573 1.31

SVP 0.018 1.82+ 0.005 0.51

Black -0.095 -4.56*** 0.011 0.36

Asian -0.060 -1.63 0.121 2.36*

Native American -0.089 -1.56 0.251 2.80**

Hispanic -0.135 -5.66*** 0.090 2.42*

Foreign-born citizen -0.043 -1.06 -0.046 -0.74

Noncitizen -0.101 -3.43*** 0.029 0.59

Years in U.S. 0.003 2.33* -0.002 -1.19

Union 0.211 11.84*** -0.103 -4.04***

Male 0.151 6.91*** -0.048 -1.59

Married 0.009 0.41 -0.005 -0.20

Married male 0.085 3.16** 0.024 0.66

Veteran -0.014 -0.66 -0.002 -0.07

Northeast 0.028 1.48 0.058 2.25*

South -0.042 -2.39* 0.021 0.87

West 0.017 0.90 0.021 0.83

Self-reporting 0.002 0.15 0.046 2.56*

Part-time -0.100 -2.88** -0.068 -1.08

--Continued
34 • USDA/Economic Research Service Wage Premiums for On-the-Job Computer Use • RDRR-95

Appendix table 8—Variation in computer wage premium and experience wage premium 

by characteristics, continued

Model 11 Model 11

Variable Main effects Interactions with computer use

Parameter T-statistic Parameter T-statistic

estimate estimate

Industry effects:

Agriculture -0.019 -0.39 -0.007 -0.08

Mining 0.356 5.32*** -0.063 -0.64

Construction 0.206 6.78*** -0.041 -0.76

Durable goods 0.159 5.68*** 0.059 1.52

Nondurables 0.162 5.55*** 0.053 1.27

Transportation 0.197 5.61*** -0.044 -0.88

Communication 0.190 2.03* 0.016 0.15

Utilities & sanitary services 0.255 4.11*** 0.033 0.42

Wholesale trade 0.135 3.71*** 0.007 0.14

Finance, insurance, & real estate 0.139 3.20** 0.034 0.67

Private household services -0.100 -1.44 -0.093 -0.22

Business services 0.039 1.24 0.132 3.03**

Personal services 0.066 1.69+ -0.092 -1.42

Entertainment services 0.094 1.82+ -0.044 -0.56

Hospital services 0.196 4.69*** -0.011 -0.21

Medical services 0.168 4.99*** -0.033 -0.70

Educational services 0.038 1.15 -0.052 -1.19

Social services -0.045 -1.03 0.034 0.53

Professional services 0.018 0.32 0.151 2.34*

Forestry & fishing 0.272 0.92 -0.264 -0.51

Public service 0.244 5.88*** -0.032 -0.63

Interactions with labor force Interactions with labor force

experience experience (squared)

Parameter T-statistic Parameter T-statistic

estimate estimate

Non-HS graduate -0.009 -2.09* 0.015 1.83+

Some college 0.002 0.55 -0.009 -1.19

Bachelor’s degree 0.002 0.47 -0.012 -1.27

Advanced degree 0.010 1.71+ -0.042 -2.95**

SVP 0.002 2.57* -0.002 -1.02

Adj. R-squared 0.4469

+ = significant at 10-percent level; * = significant at 5-percent level; ** = significant at 1-percent level; *** = significant at 0.1-percent level.