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

Leaving Careers in IT: Differences in Retention by Gender and Minority Status. Paula Stephan & Sharon Levin January 2005. Acknowledgements. Supported by National Science Foundation: ELA 0089995; SEWP-NBER Uses data from Sciences Resources Statistics, National Science Foundation. Focus.

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

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  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 Today’s 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 S&E) • 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 S&E or whether working in an S&E occupation in 1990 • S&E identified sample followed in 1995, 1997, 1999

  10. NSRCG • Sampling frame is individuals who earn bachelors or masters S&E 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 S&E after 1993 but not trained in S&E • Excludes associate degree holders • Does not consider programming to be a field of training in S&E or an occupation in S&E

  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 engineers—hardware • Computer programmers (Note:only programmers picked up in SESTAT are those trained in an S&E field who work as a programmer or individuals not trained in S&E but working in an S&E 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.

  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%) • Conclude—as does Preston—that retention is a major issue

  20. Table III. Weighted means for individuals employed in engineering occupations in 1993 and in SESTAT in 1999.

  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 force…especially 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. 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

  25. 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.

  26. 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.

  27. 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.

  28. 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

  29. 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.

  30. 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 fragile—related to “thinness” of sample

  31. 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.

  32. 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

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