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