Women and Minorities in the IT Workforce

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Women and Minorities in the IT Workforce

• Sharon G. Levin
• Department of Economics
• University of Missouri-St. Louis
• and
• Paula E. Stephan
• Department of Economics, Andrew Young School of
  Policy Studies
• Georgia State University

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Why this study?

• Low incidence in IT was initially motivated by
  concerns regarding equity
• The interest heightened during the 1990s as the
  IT sector boomed and shortages of workers were
  perceived to exist
• The increased participation of women and
  underrepresented minorities WURM was seen as
  one way by which the IT workforce could be grown

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Why this study?

• Much of the policy discussion focused on how the
  pipeline leading to careers in IT could be
  expanded making IT careers more attractive and
  accessible to WURM
• Often focused on why WURM leave STEM fields while
  students
• Studies related to recruitment, almost without
  exception, focused on pipeline issues related to
  recruiting WURM into degree programs in STEM few
  examined retention after the career had begun

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Why this study?

• The present work is one of the few to examine the
  recruitment of college educated individuals
  without formal IT training into the IT workforce
  and how both recruitment and retention vary by
  gender and minority status
• As we will see shortly, the importance of
  recruitment and retention in determining the size
  and diversity of the IT workforce is substantial

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Data

• The SESTAT Database is used
• College educated individuals living in the US in
  1990 who had a degree in Science and Engineering,
  or
• Individuals working in Science and Engineering
  occupations in 1993 who did not possess Science
  and Engineering degrees

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SESTAT Shortcomings

• The SESTAT definition of IT occupations fails to
  capture all jobs where IT work is occurring
• SESTAT under-represents 4 groups of scientists
  and engineers in the US in 1995 and subsequent
  years
• New immigrants with science and engineering (SE)
  jobs who entered the US after 1990 and did not
  subsequently receive a degree in the US
• College grads without SE degrees who were not
  working in SE occupations in 1993, but were in
  SE occupations at a later date
• Associate degree holders working in SE
• Individuals who lack a formal degree but are
  working in SE

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SESTAT Shortcomings

• SESTAT excludes individuals without SE training
  who began working in IT occupations after 1993
• Programming, both as a field of education and
  occupation, is not defined by SESTAT as being in
  SE
• Degrees awarded from business school are excluded
  from the definition of SE fields regardless of
  their content

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Defining IT training

• Individuals are formally trained in IT if they
  received one or more degrees in
• Computer/information sciences, computer science,
  computer system analysis
• Information service and systems, other computer
  and information sciences
• Computer and system engineering, electrical,
  electronics and communications engineering
• Individuals were also considered formally trained
  in IT if they had minored or did a second major
  in computer/information sciences

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Defining IT occupations

• Individuals are in the IT workforce if they are
  employed as
• Computer analysts or computer scientists
  (excluding system analysts)
• Information system scientists and analysts, or
  other computer and information scientist
• Computer engineers, software engineers, and
  post-secondary teachers in computer or
  mathematical sciences
• Computer engineers, including both hardware and
  computer programmers

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Descriptive Analysis
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Descriptive Analysis
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Descriptive Analysis
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Multivariate Analysis
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Multivariate Analysis
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Multivariate Analysis
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Multivariate Analysis
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Conclusions

• WURM have different recruitment and retention
  patterns in the IT workforce than do men and
  whites
• These differences persist after controlling for
  family structure, age, citizenship status and
  field of training
• URM are more likely than whites to work in non-IT
  occupations relative to IT occupations
• This is not evident for women
• There are substantial differences in the odds of
  working for men compared to women

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Conclusions

• In terms of recruitment, marriage and family play
  different roles for men and women
• For men, marriage decreases the odds those
  without formal IT training work in IT rather than
  in other occupations
• For men, marriage increases the odds they remain
  in the workforce

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Conclusions

• For women, marriage increases the odds that they
  will leave the workforce rather than work in IT
  or other occupations
• Women with young children are less likely to work
  in IT than in other occupations, but more likely
  to leave the workforce.
• Men with young children are also less likely to
  work in IT than in other occupations, but they
  are more likely to work in a non-IT occupation
  than to not work

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Conclusions

• In terms of retention,
• Women and African Americans have lower odds of
  retention than do white males.
• For women, this holds for women with and without
  formal IT training.
• For African Americans, this holds only for the
  IT-trained.
• Those who were not IT-trained who gained
  permanent status had lower odds of retention than
  those who still held temporary status.

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Conclusions

• Overall these results suggest that policies
  directed towards recruitment and retention will
  have different outcomes depending on the group in
  question
• With regards to recruitment, underrepresented
  women, but not men, would more likely be in the
  IT workforce if initiatives such as on-site child
  care and flex-time were provided

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Conclusions

• With regards to retention, women and
  African-Americans would be more likely to respond
  to selected initiatives than would Hispanics and
  others.
• One must also question the extent to which
  temporary residents chose IT occupations as a
  means (H1-B visas) by which to enter the US
  workforce