COMPUTERS HAVE CHANGED THE WAGE STRUCTURE 37 A linear probability regression of a computer-use dummy on experience and its square,education,and demographic variables indicates that the likelihood of using a computer increases with experience in the first fifteen years of experience,and declines thereafter. Tabulations of the 1989 CPS show that relatively few employ- ees (less than 5 percent of employees)use computers in the agriculture,construction,textile,lumber,and personal services industries,whereas computer use is widespread (exceeding 60 percent of employees)in the banking,insurance,real estate, communications,and public administration industries.The Octo- ber CPS does not contain information on employer size,but a 1989 establishment survey by the Gartner Group found that computer use is not strongly related to establishment size for establishments with more than twenty employees [Statistical Abstract of the United States,1990,p.951].And the growth in personal computers per worker between 1984 and 1989 was not strongly related to establishment size for establishments with more than twenty employees. II.COMPUTER USE AND WAGES I have estimated a variety of statistical models to try to answer the question:Do employees who use computers at work receive a higher wage rate as a result of their computer skills?I begin by summarizing some simple ordinary least squares(OLS)estimates. The analysis is based on data from the October 1984 and 1989 CPS. The sample consists of workers age 18-65.(See Appendix A for further details of the sample.) My initial approach is to augment a standard cross-sectional earnings function to include a dummy variable indicating whether an individual uses a computer at work.Let C:represent a dummy variable that equals one if the ith individual uses a computer at work,and zero otherwise.Observation i's wage rate W:is assumed to depend on Ci,a vector of observed characteristics Xi,and an error e.Adopting a log-linear specification, (1) lnW=X:β+C:a+ei, where B and a are parameters to be estimated.Section III considers the effect of bias because of possible correlation between C:and e. Table II reports results of fitting equation(1)by OLS,with varying sets of covariates(X).In columns(1)and(4)a computer- use dummy variable is the only right-hand-side variable.In theseCOMPUTERS HAVE CHANGED THE WAGE STRUCTURE 37 A linear probability regression of a computer-use dummy on experience and its square, education, and demographic variables indicates that the likelihood of using a computer increases with experience in the first fifteen years of experience, and declines thereafter. Tabulations of the 1989 CPS show that relatively few employ￾ees (less than 5 percent of employees) use computers in the agriculture, construction, textile, lumber, and personal services industries, whereas computer use is widespread (exceeding 60 percent of employees) in the banking, insurance, real estate, communications, and public administration industries. The Octo￾ber CPS does not contain information on employer size, but a 1989 establishment survey by the Gartner Group found that computer use is not strongly related to establishment size for establishments with more than twenty employees [Statistical Abstract of the United States, 1990,p. 9511. And the growth in personal computers per worker between 1984 and 1989 was not strongly related to establishment size for establishments with more than twenty employees. I have estimated a variety of statistical models to try to answer the question: Do employees who use computers at work receive a higher wage rate as a result of their computer skills? I begin by summarizing some simple ordinary least squares (OLS) estimates. The analysis is based on data from the October 1984 and 1989 CPS. The sample consists of workers age 18-65. (See Appendix A for further details of the sample.) My initial approach is to augment a standard cross-sectional earnings function to include a dummy variable indicating whether an individual uses a computer at work. Let Ci represent a dummy variable that equals one if the ith individual uses a computer at work, and zero otherwise. Observation i's wage rate W, is assumed to depend on Ci, a vector of observed characteristics Xi, and an error E~. Adopting a log-linear specification, (1) lnWi =Xi@ +Cia + ei, where f3 and a are parameters to be estimated. Section I11 considers the effect of bias because of possible correlation between Ci and E,. Table I1 reports results of fitting equation (1)by OLS, with varying sets of covariates (X). In columns (1)and (4) a computer￾use dummy variable is the only right-hand-side variable. In these