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Technology creates a need for the continuous updating of employee skills through human resource development Wentling, Waight, and King, , This contributes to the stock of human capital in the economy. Information technology has been a contributing factor in rising income inequality over past decades Autor, Katz, and Krueger, ; Acemoglu, This creates a demand for higher levels of education as well as technology use. Information is increasingly important in the economy and leads to competitive advantage Wentling, Waight, and King, , There have been changes within industries as well. Information technology has had contradictory effects, raising the skills needed for some jobs and spurring the development of new occupations, while lowering the skills and compensation for other jobs, or eliminating them entirely Autor, Levy, and Murnane, ; Capelli, Frank Levy and Richard Murnane explain that work can be categorized as consisting of routine tasks that computers can perform at practical costs or as exceptional tasks that entail a higher cost when performed through computers rather than human labor.
Innovations such as online banking have eliminated some routine tasks performed by bank clerks, for example. But such Internet applications have also created new occupations or increased the demand for some existing job categories. More highly skilled technicians, systems analysts, security specialists, Web designers, and others are needed to implement online banking.
The overall effect of technological change has been to raise the level of skill in the workforce. The demand for college graduates has increased within industries and is not just a reflection of a shift away from manufacturing. Occupations with higher average pay and higher educational requirements expanded more rapidly between and in those sectors that adopted computer technology at a faster rate Autor, Katz, and Krueger, ; see also Dunne, Haltiwanger, and Troske, Economists view the spread of computers as not only an increase in the demand for computer users and technicians but more broadly as part of a technological change that has altered the organization of work and thereby affected the need for workers with various skills Autor, Katz, and Krueger, Using CPS data from and , the authors found that technological change had reduced the total of wages paid by industry for all skill levels, except for college graduates.
Networking allows job functions to more easily move to distant locations to capture the lowest prices for labor. This description depicts a complex set of changes in which higher levels of skill include educational attainment as well as computer competencies. The remainder of this chapter will explore the effects of computer and Internet use on wages, reviewing existing research and presenting new analysis.
A growing percentage of workers at differing educational levels use computers and the Internet at the workplace. Frequencies from the most recent CPS data used in the multivariate analysis for this chapter indicate that 72 percent of Americans who are employed and have more than a high school education use computers at work, and 58 percent of employed Americans with more than a high school education use the Internet on the job.
This compares with 35 percent of workers with a high school education or less who use computers at the workplace, and 21 percent of less educated employees who use the Internet at work. Still, more than a third of less educated workers use computers at work, and more than a fifth go online at their jobs. What influence do information technology use and skills have on individual economic opportunity?
Some existing studies indicate that technology use at work increases wages, but this is subject to some debate, and there are real gaps in the research in this area. Yet it is one of the most important questions to ask if we want to justify expanding technology access. Wage growth in occupations in the s and early s was associated with computer use Card, Kramarz, and Lemieux, ; Autor, Katz, and Krueger, Prior research predating the Internet indicates that individual workers enjoy higher wages in return for computer use, beyond what their education and occupation would predict.
He explained these findings as the result of greater productivity for workers with technology skills. Other studies showing increased wages for technology use at work include research on Canada Reilly, ; Morissette and Drolet, , Australia Miller and Mulvey, , the Netherlands Oosterbeek, , and the United Kingdom Arabsheibani, Emami, and Marin, as well as for older workers in the United States Friedberg, Most of these studies indicated that the wage premium attributable to technology use ranged between 10 and 15 percent Arabsheibani, Emami, and Marin, , but there are some exceptions.
The studies cited above examined data from earlier time periods when there was little Internet use outside some scientific and academic circles. Although Krueger controlled for observable differences such as educational attainment and occupation, there may be unobservable factors other than computer use that contribute to higher wages in certain occupations for example, more talented workers being assigned to jobs using computers. In fact, one study using panel data indicated that French workers who were among the first to employ computers and other new technologies on the job tended to be the most qualified workers, and that controlling for this, the wage premium for computer use was approximately two percent rather than 15 percent Entorf, Gollac, and Kramarz, The French research contained some unique data that would be difficult to replicate.
Yet the French study also focused on employees who were in the vanguard of the early diffusion of a technology. During a period of widespread use, unobserved individual differences among workers may be less of a threat to validity. Another criticism is that technology use represents only one part of the rising skill requirements in the workforce. Timothy Bresnahan concludes that cognitive abilities and people skills account for more of the return to increases in education and skills than information technology use, although he does find some positive effects for technology use as well.
While we are most concerned here with the impact of technology skills, we acknowledge that they may be just one part of the changing skill set demanded in the new economy. Phil Moss and Chris Tilly conclude from a review of the literature that skill needs are indeed rising for jobs at all levels, not just managerial or professional jobs. Forty percent of the employers who were surveyed mentioned some increase in the level of skills needed for jobs requiring a high school diploma or less, and computer use was cited by about 70 percent of these employers as the reason for the rising requirements.
The multicity survey cited by Moss and Tilly in the preceding paragraph was also analyzed by Harry Holzer Between and , employers were randomly sampled in four cities: Boston, Detroit, Atlanta, and Los Angeles. The dependent variable in this study was the log of the weekly starting wage of the last person hired in each of the firms responding to the survey.
While suggestive, these data are neither as comprehensive nor as precise as the national CPS, which is based on the current wages of individual respondents. Additionally, there may have been considerable change since the early s. With the emergence of the Internet and the more widespread use of information technology in the workforce, a more recent assessment of the impact of technology is needed.
There is some initial research on Internet use in the United States. Controlling for other factors influencing pay, Internet use was still a significant predictor of higher wages. This study was based on the CPS and was limited to one economic sector. The work by Goss and Phillips , and earlier research on computers by Krueger and his colleagues, all indicate that Internet use at work might have similar effects across the economy. Using more recent and complete data, we test whether the income gap due to Internet use is significant beyond the manufacturing sector.
Does the frequency of use matter, given that we have defined digital citizenship as regular and effective use? We are also interested in how information technology affects workers in different occupations. Women, African Americans, and Latinos are even more likely than others to view information technology as an avenue for increasing economic opportunities in the United States Mossberger, Tolbert, and Stansbury, Can Internet skills confer some advantages in the job market that might offset, to some extent, other inequities?
There is currently a lack of recent national research that directly evaluates the effects of the Internet on the wages of U.
First, it allows us to test whether technology use at work is consequential for this group, which is also most likely to experience digital inequality. Next, we supplement this analysis with survey data collected by the Pew Internet and American Life Project in and to examine the significance of the frequency of Internet use at work for income. This is important, given our emphasis on the frequency of use for digital citizenship.
We explore the impact of Internet use at work using the CPS, which is the most recent survey conducted by the U. Census Bureau that includes a supplement on information technology use. The CPS does not, however, include data on the frequency of computer or Internet use at work. Additionally, we examine the effects of frequent use and online training using national opinion data collected in and by the Pew Internet and American Life Project: These surveys feature questions about Internet use at work, job training activities, and income.
Census Bureau collected the data for the CPS.
Our primary hypothesis is that Internet use at work leads to higher incomes for employees, controlling for other factors, including education, occupation, and age. The following section provides a detailed explanation of our methods and variable coding for all three surveys. In order to explore the impact of technology access at work on wages, we turn to the CPS March Supplement on information technology conducted by the U.
The national random sample survey includes over , respondents. This sample a hundred times larger than a typical national opinion survey provides accurate estimates of the population as a whole, with detailed questions about occupations and employment as well as technology use.
This unique data set allows a rigorous empirical test of whether computer and Internet use at work leads to increased income, especially among subpopulations, such as those with a limited education. We begin by filtering our sample population for only employed workers in the labor force. These individuals are included in the analysis. The remaining respondents in the sample are unemployed due to a layoff. These respondents were excluded from the analysis. The primary dependent or outcome variable measures weekly earnings of the respondent in dollars.
A limitation of these data is missing values for the variable measuring income. Of the , respondents, 90 percent had missing values on the weekly earnings question, because the CPS rotates the percentage of panel respondents who are asked about earnings. Unlike weekly earnings, almost all respondents in the survey answered questions about annual household income yielding a full sample of a hundred thousand cases. Three questions are used as the primary explanatory independent variables, each measuring technology use at work.
The latter question was included to find out whether Internet use for increasing skills had any effect on wages. Affirmative responses to each question were coded 1 yes and 0 no. These three binary variables serve as our explanatory variables, and separate our sample among those who use technology on the job and those who do not. Separate regression models are estimated for the three types of technology use on the job.
Beyond technology use at work, many other factors are known predictors of income and earnings, especially occupation. An advantage of the CPS data beyond standard surveys is detailed employment information. We expect that management and professional occupations will have the highest earnings. As an additional control, we include a binary variable measuring whether the respondent is employed in the job sector that the U.
We would expect those employed in the information industry to have a higher probability of using computers and the Internet at work. Our models also include standard demographic controls given known earnings gaps based on gender, race, age, and education. We expect that white males who are older with a higher education will earn more than minority females who are younger with a lower education. By including these demographic variables in the models, we control or hold constant the effect of demographic factors on earnings.
A binary variable measures gender, with females coded 1 and males 0.
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Compared to standard surveys, our national data include large and representative samples of African Americans and Latinos. Of the , total sample, 10 percent or 10, reported being of Hispanic origin, and almost 10 percent or 9, reported being black. Additionally, almost 5 percent or 5, were Asian American.
Age is measured in years. It serves as a proxy for experience; we presume that older employees have greater job experience and will earn more. Including a different grouping of binary variables for the job sector does not change the substantive findings reported here. We use two alternative measures of Internet use at work. Internet use at work is measured with a binary variable, where yes responses are coded 1, and no responses are coded 0.
This coding is comparable to the coding used in the above CPS analysis. Because we have emphasized the importance of the frequency of Internet use as a preferable way to measure skills and digital citizenship, we measure the use of technology at work on an ordinal scale. In the survey question, the wording was: For the survey, the question wording was: The models also include a number of demographic and socioeconomic factors that are known to influence income, which are coded to be similar across the two years of the national opinion data and comparable with the analysis of the CPS data.
Gender is measured using a binary variable coded 1 for males and 0 for females. We expect males to earn higher incomes than females. Like the CPS data, the Pew survey included a question on the occupation of the respondent. Variables for the respondents who named each one of these job categories are coded 1, and 0 for the respondents who did not name this as their job type. The reference group is composed of service workers, skilled trades, semiskilled labor and laborers, all coded 0.
Unique in this survey is a series of binary variables that measure employer type and size. The employees of these organizations are coded 1, and if the individual did not work for this type of organization they are coded 0. The reference group is government workers, including federal, state, and local government employees. State unemployment rates in and are from the Economic Census. States with a larger share of workers trained and skilled in the use of information technology are expected to foster higher incomes than states with a smaller share. The Progressive Policy Institute explains that this measure includes workers in a variety of industries.
The variable used in this analysis measures the number of information technology jobs in the information technology sectors and then subtracts this number from the total number of workers in information technology occupations in a state. This creates a more accurate measure of the extent to which traditional industries employ information technology professionals. Since the dependent variables in Tables 2.
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Column 1 Table 2. Finally, column 3 includes a variable measuring whether the respondent took courses online. Across the three models in Table 2. The substantive magnitude of the effects of technology use at work on economic opportunity is substantial. This is a This is strong and consistent evidence that technology use at work may increase wages for the U. Many of the control variables are in the expected direction, lending validity to our findings.
Occupation also matters significantly for wages, with those in management and professional occupations earning considerably more than the reference category production. Sales and construction occupations also earn more than our baseline occupation production. So far the analysis provides fairly robust evidence that technology use at work is associated with increased economic opportunity among the employed segment of the U.
The models in Table 2. The models control for a wide range of occupations, as shown in Tables 2.