Leaving Careers in IT: Differences in Retention by Gender and Minority Status

1
Leaving Careers in IT Differences in Retention
by Gender and Minority Status

• Paula Stephan
• Sharon Levin
• January 2005

2
Acknowledgements

• Supported by National Science Foundation ELA
  0089995 SEWP-NBER
• Uses data from Sciences Resources Statistics,
  National Science Foundation

3
Focus

• Considerable interest in recent years concerning
  low prevalence of women and underrepresented
  minorities in the IT workforce.
• Initial focus motivated by concerns regarding
  equity
• Interest augmented in 1990s because of key role
  IT sector played in economic expansion and
  concern that shortage of IT workers existed.

4
Size of IT Workforce Depends onPipeline In

• Much discussion in 1990s concerned how pipeline
  could be expanded, making careers in IT more
  attractive and possible for women and minorities.
• Case in point Carnegie Mellon initiative,
  unlocking the clubhouse door which focused on
  recruiting and attracting women and minorities
  into IT programs at CM.

5
Size of IT Workforce Also Depends onPipeline Out

• IT workforce is diminished when trained
  individuals leave either for
• Careers outside of IT or
• Leave the labor force
• IT workforce is diminished when recruited
  individuals leave.
• Focus of this research is whether retention
  varies by gender and minority status.
• Interest is on retention subsequent to working in
  occupation not retention while in a degree
  program.

6
If those working in IT in 93 had been retained
in 99 . . .

• IT workforce would have had 40 more women
• 50 more underrepresented minorities
• 25 more men
• Conclude
• IT workforce would have been bigger
• More balanced by gender and underrepresented
  minority status

7
Plan for Todays Presentation

• Overview of data used
• What we mean by IT trained
• What we mean by IT occupations
• Descriptive Data
• Logit Analysis

8
Data

• Drawn from SESTAT (college degree or higher,
  focus in SE)
• Integrated database built on three different NSF
  surveys
• Years 1993, 1995, 1997, 1999
• National Survey of College Graduates
• National Survey Recent College Graduates
• Survey of Doctorate Recipients

9
NSCG

• Sampling frame is college educated (BA or higher)
  1990 Census
• Surveyed in 1993 to determine if degree held in
  1990 is in SE or whether working in an SE
  occupation in 1990
• SE identified sample followed in 1995, 1997, 1999

10
NSRCG

• Sampling frame is individuals who earn bachelors
  or masters SE degrees during the decade of 1990s
• Refreshes NSCG but only adds those educated in
  U.S.

11
SDR

• Sampling frame is individuals who earn Ph.D.
  degree in U.S. and indicate plan to stay in U.S.
• Note excludes individuals who earn Ph.D.s
  outside U.S.

12
Shortcomings of Data

• Excludes scientists and engineers trained outside
  U.S. after 1990
• Excludes college-trained individuals working in
  SE after 1993 but not trained in SE
• Excludes associate degree holders
• Does not consider programming to be a field of
  training in SE or an occupation in SE

13
Definition of IT Trained IT Work

• Follow lead of IT Data Project concerning
  definition of IT trained
• Follow lead of IT Data Project and IT Workforce
  report for definition of IT work
• Available on our web page
• http//www.gsu.edu/ecopes/itworkforce/index.htm

14
Definition of IT Trained One or More Degree in

• Computer/information sciences
• Computer science
• Computer system analysts
• Information service and systems
• Other computer and information sciences
• Computer and systems engineers
• Electrical, electronics and communications
  engineering if recipient also minored or did
  second major in area of computer or information
  sciences.

15
Definition of IT Occupations

• Computer analyst
• Computer scientists except system analysts
• Information system scientists and analysts
• Other computer and information science
  occupations
• Other computer and information sciences
• Computer engineers software engineers
• Computer engineershardware
• Computer programmers (Noteonly programmers
  picked up in SESTAT are those trained in an SE
  field who work as a programmer or individuals not
  trained in SE but working in an SE occupation
  in 1993.)

16
Big Picture

• Find about 1 million individuals (weighted data)
  working in IT in 1993 were in SESTAT in 1999.
• 30 women
• 84 white
• 9 Asian
• 4 African American
• 3 Hispanic Other

17
Big Picture Continued

• About 70 of those working in IT in 1993 were
  retained in 1999.
• Retention rate higher for those trained (80 vs
  65)
• Retention rate higher for men than women (73 vs.
  66)
• Retention rate higher for whites than African
  Americans (70 vs. 66)
• Retention rate higher for Asians (70) than
  whites (70)

18
Table II. Weighted means for individuals
employed in IT occupations in 1993 and in SESTAT
in 1999.
All Females Males Whites Asians African Americans Hispanic Others
Ittrain93 0.387 0.366 0.395 0.384 0.571 0.454 0.387 0.454 0.387
retained 0.710 0.658 0.732 0.703 0.790 0.660 0.716 0.660 0.716
retained IT trained 0.804 0.735 0.824 0.800 0.840 0.778a 0.778a
retained not IT trained 0.651 0.604 0.672 0.649 0.725 0.618a 0.618a
work out of IT 0.232 0.247 0.225 0.236 0.155 0.316 0.237 0.316 0.237
no work 99 0.059 0.095 0.043 0.061 0.055 0.025 0.047 0.025 0.047
Unemployed 99 0.012 0.011 0.013 0.012 0.006 0.018 0.015 0.018 0.015
out of labor force lf99 0.046 0.084 0.031 0 0.049 0.049 0.007 0.032 0.007 0.032
n 1,058,989 314,564 744,425 8 885,600 97,688 44,914 30,786 44,914 30,786
of sample 100 29.7 70.3 8 83.6 9.2 4.2 2.9 4.2 2.9
All Females Males Whites Asians African Americans Hispanic Others
0.387 0.366 0.395 0.384 0.571 0.454 0.387 0.454 0.387
0.710 0.658 0.732 0.703 0.790 0.660 0.716 0.660 0.716
0.804 0.735 0.824 0.800 0.840 0.778a 0.778a
0.651 0.604 0.672 0.649 0.725 0.618a 0.618a
work out of IT 0.232 0.247 0.225 0.236 0.155 0.316 0.237 0.316 0.237
no work 99 0.059 0.095 0.043 0.061 0.055 0.025 0.047 0.025 0.047
Unemployed 99 0.012 0.011 0.013 0.012 0.006 0.018 0.015 0.018 0.015
out of labor force lf99 0.046 0.084 0.031 0 0.049 0.049 0.007 0.032 0.007 0.032
n 1,058,989 314,564 744,425 8 885,600 97,688 44,914 30,786 44,914 30,786
of sample 100 29.7 70.3 8 83.6 9.2 4.2 2.9 4.2 2.9
19
Compared to Engineering

• Retention in IT is higher (71 vs. 66)
• Higher for women (66 vs. 52)
• Higher for African Americans (66 vs. 54)
• Concludeas does Prestonthat retention is a
  major issue

20
Table III. Weighted means for individuals
employed in engineering occupations in 1993 and
in SESTAT in 1999.
All Females Males Whites Asians African Americans Hispanic Others
engtrain93 1.000 0.387 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
retained 0.659 0.521 0.673 0.656 0.705 0.544 0.694 0.544 0.694
wknoeng99 0.282 0.340 0.276 0.283 0.248 0.396 0.254 0.396 0.254
nowork99 0.059 0.139 0.051 0.061 0.048 0.060 0.052 0.060 0.052
unempl99 0.012 0.010 0.012 0.011 0.016 0.019 0.018 0.019 0.018
outlf99 0.047 0.129 0.039 0 0.048 0.032 0.041 0.035 0.041 0.035
n 1,159,923 108,188 1,051,735 8 977,662 111,061 32,665 38,535 32,665 38,535
of sample 100.0 9.3 90.7 8 84.3 9.6 2.8 3.3 2.8 3.3
21
What Do the IT trained do when they leave IT?

• Top and mid-level managers (32.4)
• Electrical and Electronic Engineering (9.2)
• Accountants (7.2)
• Other Management (6.4)
• Other Administrative (4.0)
• They also leave the labor forceespecially true
  of women (8 for women vs. 3 for men)

22
Retention Analysis

• Look at those in IT occupation in 1993 (trained
  and untrained)
• Determine IT workforce status in 1999
• In IT
• In another occupation
• Not working (unemployed or out of labor force)
• Estimate a multinomial logit model

23
Right hand side variables

• Training variables
• Family status variables
• Change in family status variables
• Citizenship status and change in citizenship
  status
• Age
• Self employment
• Race/ethnicity
• Gender

24
(No Transcript)
25
Findings Staying in IT vs. Moving to non-IT
occupation

• Positively related to whether IT is latest
  degree
• Negatively related to whether self-employed had
  taken additional training in a non-IT field and
  African American.
• Note female is not significant

26
Findings Working in IT vs. Not Working

• Negatively related to being self employed and
  being female and, for women, whether one began
  parenting a child under six during the interval.

27
Findings Working Not in IT vs. Not Working

• Positively related to being African American
• Negatively related to being female and, for
  women, beginning to parent a child under six
  during the interval.

28
Summarize

• African Americans leave IT occupations for other
  occupations do not leave the labor force or
  become unemployed.
• Women leave IT occupations to leave the labor
  force or become unemployed, not to move into
  another occupation
• Results consistent with Xie Shauman No
  evidence that marriage per se affects the
  retention of women IT workers but the arrival of
  young children makes women less likely to remain
  in the labor force.

29
Do African American Women Respond the Same as
White Women and/or African American Men?

• Interact variable female and African American
• Find African American women are significantly
  more likely to remain in the labor force than are
  white females.
• Cannot reject hypothesis that African American
  women are any more or less likely to leave IT for
  another job than African American men

30
Re-estimate, splitting the sample by training

• Find that change in visa status is related to
  leaving IT for another occupation for the
  non-trained.
• Suggests that IT occupations are used as an
  entrée to getting an H-1B visa.
• Change in visa status does not affect probability
  of retention for those trained in IT.

31
Gender Effects

• In both trained and un-trained samples, the
  female result holds
• The female-get children result only holds for
  those without formal training.
• African American results become more
  fragilerelated to thinness of sample

32
Policy Implications

• Policies directed towards retention will have
  differential outcomes depending upon group in
  question
• Women would be likely to respond to initiatives
  that provide on-site child care.
• African Americans more likely to respond to
  initiatives that make IT occupations more
  attractive relative to non-IT jobs.

33
Usual Caveats

• Data thin for URM especially when split by
  gender.
• Data does not include certain groups working in
  IT.
• Results may be clouded by strong labor market for
  IT workers in late 1990s.
• Labor force patterns are fluid some of those who
  have left will return