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Are computer skills rewarded in the labor market?: new empirical evidence by gender US

Are computer skills rewarded in the labor market?: new empirical evidence by gender US. Ferran Mane (Universitat Rovira i Virgili and Cornell University) John Bishop (Cornell University). MOTIVATION. Research interest: payoff to different types of skills. Are computer skills important?

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Are computer skills rewarded in the labor market?: new empirical evidence by gender US

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  1. Are computer skills rewarded in the labor market?: new empirical evidence by genderUS Ferran Mane (Universitat Rovira i Virgili and Cornell University)John Bishop (Cornell University)

  2. MOTIVATION • Research interest: payoff to different types of skills. Are computer skills important? • Current consensus in the economic literature is that computer skills don’t pay off: just need basic skills to be able to use computers (people easily trained) • Literature on information systems management, behavioral psychology or end-user computing stress differences in the effectiveness of computing use: skills and attitudes are important • Anecdotal data points to individuals approaching the use of computers in different ways

  3. MOTIVATION • New evidence on old questions? • Is there a true return to computer use at work? What generates it? • Will computer technology help close racial and gender wage-gaps? • Educational policy: Should students be required to take courses in computer skills?

  4. INDEX • Literature review • Strategy of the paper • Key concepts • Data: description main variables • Empirical results • Conclusions

  5. LITERATURE REVIEW • Krueger (1993): computer use increases productivity due to specific computer skills • DiNardo and Pischke (1997): mainly unobserved worker heterogeneity. Panel data research confirms • Autor-Levy-Murnane (2003): computers “complement” some skills flexibility, creativity, generalized problem-solving capabilities, and complex communications

  6. LITERATURE REVIEW • Dolton and Makepeace (2004): with panel data find positive impact of computer use on wages due to better physical capital endowment • Dickerson and Green (2004): computer skills carry positive wage premium • Controlling for omitted variable bias important • Measures of computer skills very poor

  7. STRATEGY OF THE PAPER • Decompose the effect of computer use into its different parts • Direct increase in productivity due to the effect of physical capital • Direct impact of TECHNICAL computer specific human capital (skills) • Direct impact of ATTITUDINAL-computer-specific human capital (skills) • Indirect impact of computer use through raising the return to general human capital eg. math

  8. STRATEGY OF THE PAPER • ATTITUDINAL-computer-specific human capital • Computer special “machine”: broad range of potential use complexity • Entry barriers low, getting better difficult: positive attitude towards learning • Learning on the job is intentional not just by default • Specific experience, not just “experience”

  9. DATA • NELS-88 • Longitudinal data: 1988 (14 years old), 1990, 1992, 1994 and 2000 • Information from students, parents, teachers and schools • Detailed information on pre-labor market use of computer and use of computer on the job • Detailed information on family background and personal characteristics • Cohort data set – recent information

  10. DATA • Use of computer at work • Information on intensity of use: no use, occasionally and frequently. We will use a dummy variable indicating frequent use of computer at work • Definition of “computer” not provided • Question only for people currently working • Measure direct productivity impact

  11. DATA • TECHNICAL-computer-specific human capital: we created two variables • Number of computer courses - business related: computer courses intended to provide skills to be used in the business and office area • Number of computer courses – computer science: computer courses intended to provide skills in the computer and information sciences area • Basic courses represent a large part of the total (Keyboarding 65% in business related; Computer Appreciation 55% in computer science)

  12. DATA • ATTITUDINAL-computer-specific human capital: we created a composite from three questions

  13. DATA • From parents’ questionnaire in 1988: • Has your eight grader attended classes outside of his or her regular school to study computer skills?: coded as dummy variable with 1 if the answer was yes. • Do you have a computer in your home that your child uses for educational purposes?: coded as dummy variable with 1 if the answer was yes. • From student’s questionnaire in 1992: • How often do you spend time using personal computers, not including school-related work or video/computer games outside of school?. Coded as 0, 0.33, 0.66 and , representing, respectively: Rarely or never, Less than once a week, Once or twice a week, Every day or almost.

  14. DATA • Index of Computer Familiarity • Capturing interest in “exploring” how computers work (being into computers). Time when measured important • Important not to include “just” use: controls for gaming • Important not to capture family wealth

  15. Preview of Findings • Effect of frequent use of computers is significant & important but smaller than previously reported • Pre-labor market Technical & Attitudinal computer skills increase wages but only for men. • Recreational use of computers lowers future wages. • The payoff for attitudinal computer skills is highest for men who use computers frequently.

  16. Empirical model Yi =  Ci +  Ati +  Bcci +  CScci +  Xi + i • Yi : log of hourly wage • Ci : use computer frequently (=1) at work • Ati : Index of computer familiarity (ICF) • Bcci : # of computer courses - business • CScci : # of computer courses – computer science • Xi : covariates

  17. NO CONTROLS Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  18. Spend time with computers outside school 1990 # hours per week playing games in 1988 Extracurricular activities in 1988 Computer at home (not used for education) (1=yes) Index sport activities in 1988 Index religious activities Index artistic activities Introductory vocational Occupation-Vocational Academic Courses Other Courses BASIC CONTROL VARIABLES

  19. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  20. Years of education Tenure Tenure squared Hispanic Black Indian Asian Part time Female Married # of dependents Married*Female Dependents*Female Part time student “KRUEGER’S” CONTROL VARIABLES

  21. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  22. Grade Point Average in 8th Grade 8th Grade Mathematics’ Test Scores Early learning problem Read for fun Locus of control Self esteem Watch TV (# hours per day) Socio Economic Status Home Capital Home Culture Family Size Uses Internet at home (# of days per week) Uses computer at home (# of days per week) PERSONAL AND FAMILY CONTROL VARIABLES

  23. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  24. High school in urban area High school in rural area High school in Mid-West High school in South High school in West Mean school test score Mean school SES Enrolment Enrolment square Pupil teacher ratio % white students % lunch free Salary teachers Catholic school Private non-religious Private religious SCHOOL AND STATE CONTROL VARIABLES

  25. State Minimum Competency Exam Academic courses required to graduate State minimum credits to graduate State unemployment rate in 1992 State mean wage in 1992 State manufacturing wage in 1992 SCHOOL AND STATE CONTROL VARIABLES (2)

  26. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  27. Perceived autonomy at work Read letters, memos or reports (frequently and occasionally) Write letter, memos or reports (frequently and occasionally) Read manuals or reference books, including catalogues (frequently and occasionally) JOB CONTROL VARIABLES • Read or fill out bills, invoices, spreadsheets or budgets (frequently and occasionally) • Measure or estimate the size or weight of objects (frequently and occasionally) • Calculate prices, costs or technical specifications (frequently and occasionally)

  28. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  29. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  30. Variables deviated from the mean and divided by std

  31. WHY IMPORTANT? Men considered to be more technologically minded than women: is that true? Can computers make a difference? If “attitudes” are important, does it penalize women? if women can get access to more skilled non-manual work, would this overcome any “less favorable attitude” towards technology? All in all, can computers be a factor to reduce men-women wage gap? ANALYSIS BY GENDER

  32. Estimations using OLS. * Statistically significant at 10 percent level on a two tail test; ** 5 percent level; *** 1 percent level.

  33. Variables deviated from the mean and divided by std

  34. As far as our estimates are true returns to what we think are measuring, we have obtained a direct impact on productivity and a return to having some computer skills, but: are they directly related? Re-estimate model with INTERACTIONS Yi =  Ci +  Ati +  (Ci*Ati) + Bcci +  (Ci*Bcci) + CScci +  (Ci*CScci) +  Xi + i Does productivity using a computer depend on having “skills”?

  35. Variables not centered

  36. Variables not centered

  37. Skill variables centered and divided by std

  38. Returns to computer use economically significant Males and females have different payoff to their levels of computer technical skills and attitude towards learning how to use a computer Using computers for “educational purposes” pays off, using for “recreational” activities doesn’t CONCLUSIONS

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