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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. Why this study?. - PowerPoint PPT Presentation
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Women and Minorities in the IT Workforce
Sharon G. LevinDepartment of Economics
University of Missouri-St. Louisand
Paula E. StephanDepartment of Economics, Andrew
Young School of Policy StudiesGeorgia State University
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
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
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
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
SESTAT Shortcomings1) The SESTAT definition of IT occupations fails
to capture all jobs where IT work is occurring2) SESTAT under-represents 4 groups of
scientists and engineers in the US in 1995 and subsequent years
a) New immigrants with science and engineering (S&E) jobs who entered the US after 1990 and did not subsequently receive a degree in the US
b) College grads without S&E degrees who were not working in S&E occupations in 1993, but were in S&E occupations at a later date
c) Associate degree holders working in S&Ed) Individuals who lack a formal degree but are working in
S&E
SESTAT Shortcomings
3) SESTAT excludes individuals without S&E training who began working in IT occupations after 1993
4) Programming, both as a field of education and occupation, is not defined by SESTAT as being in S&E
5) Degrees awarded from business school are excluded from the definition of S&E fields regardless of their content
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
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
Table 1. Estimating the importance of recruitment and retention to the size of the IT workforce
All
Men
Women
Whites
Asians
URM Total 1999 IT Workforce 1,019,551 740,802
(72.7) 278,749
(27.3) 842,613
(82.6) 106,251
(10.4) 70,687 (6.9)
With formal IT training 375,387 (36.8)
275,746 (37.2)
99,640 (35.7)
294,400 (34.9)
51,271 (48.3)
29,716 (42.0)
Without formal IT training
644,165 (63.2)
465,056 (62.8)
179,109 (64.3)
548,213 (65.1)
54,980 (51.7)
40,971 (58.0)
IT trained, not in IT workforce in either 1993 or 1999
152,039 (14.9)
105,228 (14.2)
46,811 (16.8)
113,642 (13.5)
17,011 (16.0)
21,386 (30.3)
IT trained, working in IT in 1993, not in IT in 1999
78,948 (7.7)
51,035 (6.9)
27,913 (10.0)
63,365 (7.5)
8,810 (8.3)
6,773 (9.6)
Non-IT trained working in IT in 1993, not in IT in 1999
228,632 (22.4)
148,804 (20.1)
79,829 (28.6)
199,681 (23.7)
11,697 (11.0)
17,255 (24.4)
Notes: URM includes African-Americans, Hispanics, Native-Americans and others. The numbers in parentheses show the percent of the 1999 IT workforce. The totals may not add up because of rounding error. All counts are based on weighted data.
Descriptive Analysis
Descriptive Analysis
Descriptive Analysis
MULTIVARIATE ANALYSIS Recruitment of the Non-IT Trained in 1993
All not trained in IT Males Females
WORK IT WORK IT WORK NOT-IT WORK IT WORK IT WORK NOT-IT WORK IT WORK IT WORK NOT-IT
vs. vs. vs. vs. vs. vs. vs. vs. vs.
WORK NOT-IT NO WORK NO WORK WORK NOT-IT NO WORK NO WORK WORK NOT-IT NO WORK NO WORK
Variables Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds
ratio ratio ratio ratio ratio ratio ratio ratio ratio
intercept -2.945 0.053 -6.004 0.002 -3.059 0.047 -2.625 0.072 -8.036 0.000 -5.411 0.004 -3.377 0.034 -5.598 0.004 -2.221 0.108
female 0.024 1.024 -1.271 0.281 -1.294 0.274
african american -0.346 0.707 0.084 1.087 0.430 1.537 -0.379 0.685 -0.446 0.641 -0.067 0.935 -0.253 0.777 0.345 1.412 0.598 1.818
asian -0.014 0.986 0.039 1.040 0.053 1.055 -0.140 0.869 -0.260 0.771 -0.120 0.887 0.297 1.346 0.518 1.678 0.220 1.246
hispanothers -0.202 0.817 -0.069 0.933 0.133 1.142 -0.199 0.820 -0.196 0.822 0.002 1.002 -0.209 0.812 0.005 1.005 0.213 1.238
othersci -0.436 0.646 -0.835 0.434 -0.398 0.671 -0.299 0.741 -0.647 0.524 -0.348 0.706 -0.751 0.472 -1.165 0.312 -0.414 0.661
othereng -0.252 0.778 -0.507 0.602 -0.255 0.775 -0.230 0.794 -0.411 0.663 -0.207 0.813 -0.157 0.855 -0.392 0.676 -0.235 0.790
socsci -1.277 0.279 -1.684 0.186 -0.406 0.666 -1.011 0.364 -1.333 0.264 -0.321 0.725 -1.685 0.186 -2.114 0.121 -0.429 0.651
bus 1.209 3.350 0.812 2.252 -0.397 0.672 1.189 3.285 0.720 2.054 -0.469 0.625 1.433 4.191 1.047 2.849 -0.386 0.680
perm93 0.075 1.078 -0.030 0.970 -0.105 0.900 0.005 1.005 -0.140 0.869 -0.145 0.865 0.261 1.298 0.163 1.177 -0.098 0.907
temp93 0.051 1.052 -0.283 0.753 -0.334 0.716 0.179 1.196 -0.208 0.812 -0.387 0.679 -0.372 0.689 -0.641 0.527 -0.269 0.764
age93 0.058 1.060 0.405 1.500 0.347 1.415 0.044 1.045 0.479 1.614 0.435 1.544 0.071 1.074 0.340 1.405 0.269 1.309
agesq93 -0.001 0.999 -0.005 0.995 -0.004 0.996 -0.001 0.999 -0.006 0.994 -0.005 0.995 -0.001 0.999 -0.004 0.996 -0.003 0.997
married93 -0.144 0.866 -0.033 0.968 0.112 1.118 -0.247 0.781 0.723 2.061 0.970 2.638 0.091 1.096 -0.488 0.614 -0.579 0.560
children0693 -0.147 0.863 -0.767 0.464 -0.620 0.538 -0.133 0.876 0.108 1.114 0.240 1.272 -0.196 0.822 -1.113 0.329 -1.016 0.362
n 46443 32386 14057
- 2 log L 40957.1
25047.8 15081.6
Multivariate Analysis
Table 4. The odds of different employment outcomes in 1993 for African-Americans not trained in IT.
(A) (B) WORK IT WORK IT WORK NOT-IT WORK IT WORK IT WORK NOT-IT vs. vs. vs. vs. vs. vs. WORK NOT-IT NO WORK NO WORK WORK NOT-IT NO WORK NO WORK
Variables Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds ratio ratio ratio ratio ratio ratio
Intercept -1.308 0.270 -6.558 0.001 -5.249 0.005 1.275 3.579 -6.708 0.001 -7.983 0.000
female 0.043 1.044 -0.346 0.708 -0.389 0.678 -6.018 0.002 -2.543 0.079 3.475 32.304
othersci -0.632 0.531 -1.186 0.306 -0.554 0.575 -0.290 0.748 -0.044 0.957 -0.149 0.861
othereng -0.278 0.758 0.067 1.069 0.345 1.411 -0.013 0.987 0.443 1.558 0.456 1.578
socsci -1.651 0.192 -2.023 0.132 -0.372 0.690 -1.211 0.298 -1.674 0.188 -0.463 0.630
bus 1.685 5.394 0.995 2.704 -0.690 0.501 2.011 7.471 1.413 4.110 -0.598 0.550
perm93 -2.273 0.103 -2.671 0.069 -0.398 0.672 -1.921 0.147 -2.361 0.094 -0.440 0.644
temp93 -0.747 0.474 -0.830 0.436 -0.083 0.920 -0.700 0.497 -1.342 0.261 -0.642 0.526
age93 -0.004 0.996 0.422 1.525 0.426 1.531 -0.150 0.860 0.372 1.450 0.522 1.685
agesq93 -0.001 0.999 -0.006 0.994 -0.005 0.995 0.001 1.001 -0.005 0.995 -0.006 0.994
married93 0.151 1.163 0.378 1.459 0.227 1.254 0.129 1.138 0.665 1.944 0.536 1.708
children0693 -0.330 0.719 -0.504 0.604 -0.174 0.840 -0.325 0.723 0.190 1.210 0.515 1.674
fothersci -0.637 0.529 -1.413 0.244 -0.776 0.460
fothereng -0.602 0.548 -0.132 0.876 0.469 1.599
fsocsci -0.764 0.466 -0.683 0.505 0.081 1.084
fbus -0.655 0.519 -0.973 0.378 -0.312 0.732
fperm93 na na 0.002 1.002
ftemp93 -0.027 0.973 1.632 5.113 1.659 5.255
fage93 0.351 1.421 0.233 1.263 -0.118 0.889
fagesq93 -0.005 0.995 -0.004 0.996 0.001 1.001
fmarried93 0.091 1.095 -0.531 0.588 -0.622 0.537
fchildren0693 -0.057 0.945 -1.154 0.315 -1.098 0.334
N 2601 2601 - 2 log L 1869.9 1838.6
Multivariate Analysis
Table 5. The odds of different employment outcomes in 1993 for Hispanics and others not trained in IT.
(A) (B)
WORK IT WORK IT WORK NOT-IT WORK IT WORK IT WORK NOT-IT vs. vs. vs. vs. vs. vs. WORK NOT-IT NO WORK NO WORK WORK NOT-IT NO WORK NO WORK
Var iables Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds Coeff. Odds ratio ratio ratio ratio ratio ratio
Intercept -2.469 0.085 -7.234 0.001 -4.766 0.009 1.583 4.869 -5.878 0.003 -3.705 0.025
female 0.040 1.040 -1.053 0.349 -1.093 0.335 -2.817 0.060 -5.367 0.005 -2.550 0.078
othersci -0.226 0.798 -0.853 0.426 -0.627 0.534 0.025 1.025 -0.215 0.807 -0.239 0.787
othereng 0.422 1.525 -0.089 0.915 -0.511 0.600 0.620 1.860 0.151 1.162 -0.470 0.625
socsci -1.438 0.238 -2.423 0.089 -0.986 0.373 -0.780 0.458 -1.770 0.170 -0.990 0.372
bus 1.933 6.913 2.571 13.074 0.637 1.891 2.118 8.310 2.178 8.826 0.060 1.062
perm93 0.102 1.107 -0.032 0.968 -0.134 0.875 -0.025 0.975 0.101 1.106 0.127 1.135
temp93 -0.982 0.375 -1.182 0.307 -0.200 0.818 -0.934 0.393 -1.181 0.307 -0.248 0.781
age93 0.008 1.008 0.467 1.595 0.459 1.582 -0.032 0.968 0.321 1.379 0.354 1.424
agesq93 0.000 1.000 -0.006 0.994 -0.006 0.995 0.000 1.000 -0.004 0.996 -0.004 0.996
married93 -0.167 0.846 -0.586 0.556 -0.419 0.658 -0.292 0.747 0.728 2.071 1.019 2.771
children0693 -0.230 0.795 -0.677 0.508 -0.447 0.640 -0.227 0.797 1.058 2.881 1.285 3.615
fothersci -0.569 0.566 -1.087 0.337 -0.518 0.596
fothereng -0.182 0.833 -0.208 0.812 -0.026 0.975
fsocsci -0.166 0.847 -1.560 0.210 0.096 1.101
fbus -0.104 0.901 na na
fperm93 0.420 1.521 -0.004 0.996 -0.423 0.655
ftemp93 -0.218 0.804 -0.038 0.962 0.180 1.197
fage93 0.098 1.103 0.291 1.338 0.194 1.214
fagesq93 -0.001 0.999 -0.003 0.997 -0.002 0.998
fmarried93 0.427 1.532 -2.488 0.083 -2.915 0.054
fchildren0693 0.026 1.026 -2.278 0.102 -2.304 0.100
N 2693 2693
- 2 log L 2143.7 2033.2
Multivariate Analysis
Multivariate AnalysisRETENTION IN IT OCCUPATIONS, 1993-1999
Table 6.
All IT-trained Not IT-trained WORK IT WORK IT WORK IT vs. vs. vs. NOT WORK IT NOT WORK IT NOT WORK IT Odds Odds Odds
Variables Coeff. ratio Coeff. ratio Coeff. ratio
Intercept -3.390 0.034 -0.445 0.641 -4.897 0.007
ittrain93 0.973 2.645
Othersci 0.628 1.874 0.640 1.896
Othereng 0.263 1.300 0.313 1.368
Bus 0.050 1.051 0.072 1.075
Socsci 0.186 1.204 0.191 1.210
Female -0.258 0.773 -0.410 0.664 -0.174 0.840
african american -0.250 0.778 -0.421 0.656 -0.146 0.864
Asian 0.167 1.181 0.067 1.069 0.244 1.276
Hispanothers 0.052 1.053 0.526 1.692 -0.107 0.899
perm93 0.065 1.067 0.267 1.305 -0.049 0.953
temp93 0.282 1.325 0.075 1.078 0.530 1.699
native93
age93 0.210 1.233 0.122 1.130 0.276 1.318
agesq93 -0.003 0.997 -0.002 0.998 -0.003 0.997
Addit 0.670 1.955 0.580 1.786 0.733 2.081
Addnoit -0.996 0.369 -1.173 0.309 -0.840 0.432
Gotmarried 0.061 1.063 -0.071 0.932 0.132 1.141
gotchildren06 0.218 1.243 -0.072 0.930 -0.073 0.930
Gotsingle -0.075 0.928 0.457 1.579 0.099 1.104
Gotperm -0.375 0.688 -0.173 0.841 -0.586 0.557
N 5208 2110 3098
-2 log L 5746.9 1979.7 3746.4
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
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
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
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.
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
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