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

Status

Paula Stephan & Sharon LevinJanuary 2005

Acknowledgements

Supported by National Science Foundation: ELA 0089995; SEWP-NBER

Uses data from Sciences Resources Statistics, National Science Foundation

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.

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.

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.

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

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

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

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

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.

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.

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

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

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.

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.)

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”

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%)

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

retained 0.710 0.658 0.732 0.703 0.790 0.660 0.716

retained & IT trained

0.804 0.735 0.824 0.800 0.840 0.778a

retained & not IT trained 0.651 0.604 0.672 0.649 0.725 0.618a

work out of IT 0.232 0.247 0.225 0.236 0.155 0.316 0.237

no work 99 0.059 0.095 0.043 0.061 0.055 0.025 0.047

Unemployed 99

0.012

0.011

0.013 0.012

0.006 0.018 0.015

out of labor force lf99 0.046 0.084 0.031 0 0.049 0.049 0.007 0.032

n 1,058,989 314,564 744,425 8 885,600 97,688 44,914 30,786

% of sample 100% 29.7% 70.3% 8 83.6% 9.2% 4.2% 2.9%

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

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

retained 0.659 0.521 0.673 0.656 0.705 0.544 0.694

wknoeng99 0.282 0.340 0.276 0.283 0.248 0.396 0.254

nowork99 0.059 0.139 0.051 0.061 0.048 0.060 0.052

unempl99

0.012

0.010

0.012 0.011

0.016 0.019 0.018

outlf99 0.047 0.129 0.039 0 0.048 0.032 0.041 0.035

n 1,159,923 108,188 1,051,735 8 977,662 111,061 32,665 38,535

% of sample 100.0% 9.3% 90.7% 8 84.3% 9.6% 2.8% 3.3%

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)

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

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

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

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.

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.

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.

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

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.

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

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.

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