Generation versus Aging: Education, Occupation, Gender and Ethnicity Effects in U.S. Digital Divides

1. Generation versus Aging: Education, Occupation, Gender and Ethnicity Effects in U.S. Digital Divides Susan Carol Losh,PhD EducationalPsychology and LearningSystems FloridaStateUniversity TallahasseeFL32306-4453

2. Prepared for the Atlanta Conference on Science and Innovation Policy 2009. Thanks to continuous assistance and insight from Ryan Wilke, Brandon Nzekwe, Ray Eve, Martin Bauer, Alice Robbin, Lynda Carlson, Robert Bell, Jeri Mulrow, and Melissa Pollak. This research was initially supported by AERA Grant 0310268, although analyses and interpretation of findings, of course, are my own. And thanks to Bil Keane and the Family Circus cartoon strip of October 7 2007 for the following cartoon, an inspiration to us all as we “grow up”:

4. HERE’S HOW YOU CAN CONTACT ME: Susan Carol Losh Department of Educational Psychology & Learning Systems Learning and Cognition Program Florida State University Tallahassee, FL USA 32306-4453 slosh@fsu.edu Voice (850) 644-8778 Fax (850) 644-8776 Leave a voice message OR contact the Educational Psychology Department at: (850) 644-4592

5. This Study • Through its connection with desirable digital skills, information technology (IT) helps create talented employable workers • Potential to upgrade employment desirability among previous “have-not” groups • The “digital divide” refers to differences in IT access and use across a variety of groups: • Gender • Ethnicity • Nationality • Age? (see below)

6. I compare how age and generation affect basic IT access and selected uses across 1 to 24 year periods • Control occupation, educational level, gender (1983-2006), ethnicity (1999-2006) • Results contradict conventional wisdom in several ways • Suggestions relevant for the IT work force

7. Background Basics • Age vs. generation • Stereotype: older people lack new digital skills • And tough to acquire new skills because of aging • Every current IT survey older people access and use IT less (includes international) • Older people may believe the stereotypes • Thus “Seniors” seen as less employable • BUT although it may take older people a bit more time to learn new skills, once learned performance comparable

8. Cohort • Each successive cohort through the 20th century has had more access to IT at earlier ages than prior generations • “Millennials” (“Gen Y”) began using computers in kindergarten (Census) • Also more often use “smart phones”, iPods and other digital equipment

9. SOME NOTES ON CHANGE OVER TIME • Change on concurrent variables (e.g., level of education or age composition) • Genuine social change (e.g., computers are cheaper, people buy more) • Aging effects (e.g., presbyopia, slowing of synaptic transmission) • Changes due to generational composition (cohort change)

10. Studying Change • Are there unique “generational” effects? • We can’t just examine “gross change” over time • Other variables also change over time • E.g., education, IT experience • Without controls, we don’t know if there has been “real” change by generation or simply changes in demographics that predict IT access and use • For example, if those with more formal education use IT more and if median education rises between 1983 and 2006, we would expect more IT access regardless of any generational or age differences

11. Cohort versus Aging • Easy to confuse • Can’t simultaneously estimate age-period-cohort effects (“identification problem”) • In “one shot” studies, inevitably confounded: older workers = earlier generations • BUT interpretation very different • One has implications for discrimination against older workers • Dispirited “why try”? • The other speaks to gaining new skills, “recycling” workers

12. Other Background • Gender: prior IT access and use (esp. at work) = White well-educated men • BUT more women now in life and health sciences • % female—physicians 16 to 32% (1970s – 2000s) • Chemists 23 to 35% • The college educated • In STEM professions • Ethnicity: native English fluency more • Most Web sites in English although this is changing • African-American and Hispanic American less • Asian Americans often not studied separately • statistically samples too small? • but highly educated and disproportionately in STEM professions • These factors also affect type of IT use

13. Thus, this study controls variables such as: • Gender • Highest degree level • Type of occupation • Ethnicity As well as possible interactions with time, age group or generation.

14. The DataThe NSF Surveys of Public Understanding of Science and Technology • Entire archive 12 nationally representative USA surveys: 1979, 1981, 1983, 1985, 1988, 1990, 1992, 1995, 1997, 1999, 2001, 2006 with ~24,000 interviews total non-institutionalized adults. • THIS STUDY uses NSF Surveys and the 2002 General Social Survey (telephone sample only) • Nine surveys: 1983, 1985, 1988, 1990, 1992, 1997, 1999, 2002, 2006 (NSF = GSS for 2006) • 18,125 adults 18 years and older (total possible) • Case limitations below

15. Measures Information Technology • PC home ownership 1983-2006 • Home Internet access 1995-2006 • Estimated annual hours online access* • Primary news source: Internet 2006 • Primary science news source: Internet 2006 (earlier surveys have virtually no cases) *1995-2002 several questions, amalgamated; 2006 a single question (overall estimates drop Slightly for 2006)

16. Independent Variables Education and Occupation • Completed Education • High school at most • Two year AA or vocational degree • Four year degree • Graduate degree • Occupation • Science, technology, engineering, math professional (STEM) • Other professional • Managers • Clerical workers and retail sales • Blue-collar workers • Not in labor force. The rationale for these categories is to capture nuances among white collar workers who use ICT more than blue-collar employees.

17. Independent Variables • Survey year • Years of age • Gender (male =1) • Labor force status (in CLF =1) • Ethnicity: • White (Euro) American • African American • Asian American • Hispanic American

18. Independent VariablesGeneration (Cohort) & Collapsed Age* • Cohort: • The Early Years (born 1891 – 1929) • The Lucky Few (1930 – 1945) • Baby Boomers (1946 – 1961) • Generation X (1962 – 1978) • Millennials (“Gen Y”), 1979 to 1988 • Age group (collapsed for ANOVA; cross tabs) • 18-24 • 25-34 • 35-44 • 45-64 • 65 and over • Age group and cohort (across all studies) r = 0.65

19. Overall Analyses • Multivariate cross-tabulations • Test for gender-cohort, degree level-cohort (etc) interaction effects (with control variables) • Multiple classification analyses produce adjusted simple effects (MCA through ANOVA) creates figures • Net contributions age and generation assessed

20. Overall Results • Home PC ownership fuses with online access by 2002 (100% in 2006) • Gender “fusion” by cohort and time • Ethnic differences remain • Educational differences • No B.A. fall further behind with each cohort • Occupational differences: STEM workers most • First set gives birth years for each cohort; second set cohort labels (get a feel for the years the labels represent)

29. Results (net of controls) • Gender gap in PC ownership basically gone • More women in STEM professions • Thus Web home access (general) equal • Education gap widened across generations • STEM professionals most IT access • Ethnic gap has widened across generations • Especially young Asian-Americans (up) • BUT controlling occupation mitigates ethnic effects (chicken and egg causal issues)

30. Net Results Cohort & Age • Millennials most often have access and use • Each successive generation more often has access and uses more • However, recall education and ethnic effects which can widen or remain static by generation • Within each generation, older adults more often own a computer and spend more time online • Older adults more occupationally established and canafford IT equipment and broadband

31. Suggestions for IT Labor Force Research over Time • ALWAYS separate age and generation effects if at all possible • Smaller time intervals (and more of them) preferable for such analyses • Do NOT collapse into “White” and “Non White” categories (there are ethnic differences) • Control occupational categories if possible and measures exist • Income should prove useful

32. Some Research Questions • What do employers believe about digital skills and age • What do workers believe about digital skills and age? • How do worker beliefs about age and digital skills translate into training and upgrading skills?

33. CONCLUSIONS • Some digital divides dissolving, others not • What we have called “age effects” ARE NOT • Rather these are generation effects • Age (net) has POSITIVE effects on IT access • Ethnic and education effects widen some by generation • More education for ethnicities more skills • More diversity in STEM occupations, more ethnic equality in Internet use

34. Dilbert 9-17-2009