generation versus aging education occupation gender and ethnicity effects in u s digital divides
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Generation versus Aging: Education, Occupation, Gender and Ethnicity Effects in U.S. Digital Divides. Susan Carol Losh, PhD Educational Psychology and Learning Systems Florida State University Tallahassee FL 32306-4453. Prepared for the Atlanta Conference on Science and Innovation

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generation versus aging education occupation gender and ethnicity effects in u s digital divides

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

Susan Carol Losh,PhD


and LearningSystems



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”:

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

[email protected]

Voice (850) 644-8778

Fax (850) 644-8776

Leave a voice message OR contact the

Educational Psychology Department at: (850) 644-4592

this study
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)
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
background basics
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

  • 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

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)
studying change
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

cohort versus aging
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

other background
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
thus this study controls variables such as
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.

the data the nsf surveys of public understanding of science and technology
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

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)

independent variables
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.

independent variables17
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
independent variables generation cohort collapsed age
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
overall analyses
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
overall results
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)

results net of controls
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)

net results cohort age
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

suggestions for it labor force research over time
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
some research questions
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?

  • 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


  • More education for ethnicities more skills
  • More diversity in STEM occupations, more

ethnic equality in Internet use