Overview
Please note that all of the numbers in this overview and in the subsequent report are estimates based on U.S. Census survey samples. So all numbers should be read as “approximately” even when the word “approximately” does not appear.
In 2015 there were approximately 148,297,138 workers in the entire U.S. workforce. Approximately 135,475,088 were American citizens; 12,822,050 were foreign workers who moved here to work for their employers. (See Table 1A)
In 2015, there were approximately 4,636,487 workers in the information technology sectors of the 50 states and the District of Columbia; 4,066,773 were Americans; 569,714 were foreign tech professionals .See Chart 1, below
- In 2015 there were approximately 2,916,852 White Americans in U.S. tech; 314,497 Black Americans; 282,946 Hispanic Americans; 443,434 Asian Americans; and 109,044 were from other racial/ethnic groups (See Chart 2, below.)
- Approximately 1,039,074 American techs were female; 3,027,699 were male. Approximately 128,871 American female techs were Asian-American; and 910,203 were non-Asian American. (See Chart 3, below.)
If all other things were equal, one would expect that the percentage of a state’s tech sector that was Black American would be about the same as the percentage of the state’s total workforce that was Black American. One would expect similar parity for the percentages of Hispanic Americans and for American females.
But all things were not equal. The percentage of the tech sector of most states that was American Black, Hispanic, or female was substantially smaller than the percentage of the state’s total workforce that was American Black, Hispanic, or female. By contrast, the percentage of state tech sectors that was Asian American was usually greater than the Asian American percentage of the total workforce population in each state, ranging as high as 5.68 times that percentage, (See Tables 4C and 6)The subgroup of Asian American females occupied disproportionately larger shares of the tech sectors in most states. Indeed, their tech shares ranged as high as 7 times their shares of state workforce populations. (See Table 6.) By contrast, the subgroup of non-Asian American females occupied much smaller shares of state tech sectors than their shares of state workforce populations. (See Table 6.)
The number of tech jobs held by Americans grew by 22.4 percent between 2010 and 2015, i.e., from 3,322,079 to 4,066,773; the number tech jobs held by White Americans grew by 16.8 percent, i.e., from 2,497,966 to 2,916,852; tech jobs held by Black Americans grew by 31.1 percent, i.e., from 239,866 to 314,497; tech jobs held by Asian Americans grew by 36.2 percent, i.e., from 325,603 to 443,434; and tech jobs held by Hispanic Americans grew by 51.9 percent, i.e., from 186,284 to 282,946. (See Chart 4, and Table3EE.)
The number of tech jobs held by American females grew by 15.4 percent between 2010 and 2015, i.e., from 900,283, to 1,039,074. However American females occupied a smaller percentage of the tech jobs held by American tech professionals in 2015 than they did in 2010, i.e., 27.1 percent in 2010 compared to only 25.6 percent in 2015. (See Table 3Z and 3ZZ)
The number of tech jobs held by foreign professionals in U.S. tech grew by 140,417 or 32.7 percent between 2010 and 2015, i.e., from 429,297 to 569,714. (See Chart 4, above.)
The 407,481 professionals imported from Asia in 2015 was substantially larger than the 314,497 Black American or the 282,946 Hispanic American professionals employed by U.S. tech in 2015. Indeed, the 569,714 combined total of foreign professionals imported from Asia and from elsewhere in 2015 was almost as large as the 597,443 combined total of Black and Hispanic American professionals employed by U.S. tech. (See Tables 1D and 1E.)
The best states for Black American tech professionals were HBCU states: Dist of Col, Georgia, Maryland, Virginia and Texas. (See Table 8B)
The best states for Hispanic American tech professionals were Florida, Colorado, New York, and Virginia. (See Table 8D)
The best states for Asian American tech professionals were New Jersey, Massachusetts, Illinois, Texas, and Georgia. (See Table 8C)
Findings
Brief answers to the first three sets of questions provide a broad statistical framwork for the extended responses to the fourth and fifth questions that form the core of this report.
Question 1 – How large was the total U.S. workforce and the U.S. tech sector in 2015?
Question 1A – How many American workers were in the U.S. workforce in 2015? How many foreign workers? (Note: the workforce contained persons 16 and over who held a job or were temporarily unemployed.)
Answer 1A The answers to this question are shown in Table 1A (below).
Note that many of the tables in this section have the same format. The top row, labelled “Num” as an abbreviation for “Number”, shows how many workers fall into the category of the heading of the column: American “Citizen”, “Foreign” worker, and their combined “Total”. The second row, labelled “Per” as short for “Percentage”, shows the percentge of the “Total” for each of the numbers in the first row.
Table 1A – American and Foreign Components of the U.S. Workforce in the 50 states plus the District of Columbia in 2015
Total Citizens Foreign
Num 148,297,138 135,475,088 12,822,050
Per 100 91.4 8.6 Question 1B – How large were the White, Black, Asian, and Hispanic American components of the U.S. workforce in the 50 states plus the District of Columbia in 2015?
Answer 1B … In 2015, as shown in Table 1B (below), there were approximately 135,475,088 citizens in the workforce, a/k/a “labor force”, in the 50 states plus DC. in 2015? The largest of the four racial/ethnic groups discussed in this report was White American (93,526,664); the smallest was Asian American (5,901,978). Hispanic American, the largest minority (16,710,406), was larger than Black American (15,743,152).
Note that the last column “OTHERS” includes workers who described themselves as members of another racial broup not included in the previous categories, or as biracial or multiracial. Also note that “White”, “Black”, and “Asian” include workers who described themselves as “White, but not Hispanic”, “Black, but not Hispnic”, and “Asian, but not Hispanic”.
Table 1B – American Racial/Ethnic Groups in the U.S. Workforce in the 50 states plus the District of Columbia in 2015
Total White Black Asian Hispanic OTHERS
Num 135,475,088 93,526,664 15,743,152 5,901,978 16,710,406 3,592,888
Per 100 69 11.6 4.4 12.3 2.7 Question 1C – How many U.S. citizens and how many foreign employees worked in the the tech sectors of the 50 states plus the District of Columbia in 2015
Answer 1C … Most of the tables in this report will deal with American techs or foreign techs, but not both. Therefore the counts in the second and third columns of this small table will recur in many subsequent tables.
Table 1C – American and Foreign Components of the U.S. Tech Sector in 2015
Total Citizens Foreign
Num 4,636,487 4,066,773 569,714
Per 100 87.7 12.3 Question 1D – How many White, Black, Asian, and Hispanic Americans were in the U.S. tech sector of the 50 states plus the District of Columbia in 2015?
Answer 1D … As shown in Table 1B (below), there were approximately 4,066,773 American tech professionals in the U.S. in 2015. White Americans were a substantial majority (2,916,852) of the tech sector (71.7%); Asians placed second (443,434); Blacks showed third (314,497); and Hispanic Americans came in fourth (282,946)
Table 1D – American Racial/Ethnic Groups in the U.S. Tech Sector in 2015
Total White Black Asian Hispanic OTHERS
Num 4,066,773 2,916,852 314,497 443,434 282,946 109,044
Per 100 71.7 7.7 10.9 7 2.7 Question 1E – How many Asian and non-Asian foreign employees were in U.S. tech in the 50 states plus the District of Columbia in 2015?
Answer 1E … The total number of tech workers imported from Asia and from countries outside of Asia shown in Table 1E (below) will recur in subsequent tables.
Table 1E – Foreign Asian and Non-Asian Components of the U.S. Tech Sector in 2015
Total Asian NonAsian
Num 569,714 407,481 162,233
Per 100 71.5 28.5
Question 2 – What was the American male/female composition of U.S. workforce and tech sector in 2015?
Question 2A – What was the American male/female composition of the U.S. workforce in the 50 states plus the District of Columbia in 2015?
Answer 2A … As shown in Table 2A (below), the U.S. workforce was close to an even split between males and females. But given the focus of this report, the table also breaks the female half into a small Asian component (2.2 percent) and a much larger non-Asian female component (45.8 percent)
Table 2A – American Male/Female Composition of the U.S. Workforce in 2015
Total Male Female FemAsian FemNonAsian
Num 135,475,088 70,447,742 65,027,346 2,931,010 62,096,336
Per 100 52 48 2.2 45.8 Question 2B – What was the American male/female composition of the tech sector in the 50 states plus the District of Columbia in 2015?
Answer 2B … As shown in Table 2B (below), the ratio of American male to American female techs was about 3 to 1, more specifically 74.4 percent to 25.6 percent. The ratio of Non-Asian American females to Asian American females was about 7 to 1, i.e., 22.4 percent to 3.2 percent.
Table 2B – American Male/fFemale Composition of the U.S. Tech Sector in 2015
Total Male Female FemAsian FemNonAsian
Num 4,066,773 3,027,699 1,039,074 128,871 910,203
Per 100 74.4 25.6 3.2 22.4
Question 4 – Where did American tech professionals work in 2015?
Answer 4 … The following sections provide an extensive response to a more specific formulation of this question ==> In which states did American tech professionals work in 2015?
Table 4 (below) lists the six states that employed the most American techs in 2015. These states will also appear in just about every table that follows because they employed such a large share (40.2 percent) of the 4,066,773 American techs in the U.S.
Table 4 … Biggest Half-Dozen Tech States in 2015
ALL STATES CA TX NY FL VA IL perT6
4,066,773 513,031 328,635 240,885 193,697 186,597 173,972 40.2I’ve presented U.S. Census data that shows the geographic distribution of tech professionals in two formats: (1) as a set of choropleth maps and (2) as a corresponding set of tables. I have shown the maps first because it’s easier to see the workplace patterns of the racial/ethnic/female groups by looking at multicolored maps than by scanning columns of data in long tables. Indeed, a quick scan of the maps makes it easier to spot the same patterns in the data tables.
There are six maps (below), one for each racial/ethnic group plus two female groups, Asian American females and non-Asian American females. Map 4A shows the workplace distribution of White techs; Map 4B shows the distribution of Black techs; Map 4C shows Asian techs; Map 4D shows Hispanic; Map E shows Asian female; Map F shows non-Asian females. Each map colors the states with the largest number of tech workers in the darkest colors, and colors the states with the least tech workers in the lightest colors. The same colors represent the same percentages on all eight maps
Maps 4A (Whites), 4B (Blacks), 4C (Asians), 4D (Hispanics), 4E (Asian Females), and 4F (Non-Asian Females) … Lower 48 and DC
Map 4A displays light hues for all states, indicating that White American techs were spread across a large number of states, with California and Texas having concentrations below 10 percent but only marginally higher percentages than the other states. Note that the legend for Map 4A only contains three color blocks; the fourth gray “zero” block is missing because the Census sample indicated that none of the 50 states nor Washington, DC contained zero White American techs. Four legend blocks appear for each of the other five maps because the Census sample contained too few American techs to provide a reliable estimate of the actual number of techs in one or more states. The tables in this section assigned zero values to those states. The few zero states will be the lightest of the gray states, but will be difficult to identify on the maps. The reader is referred to the tables.
Map 4B shows that most Black American techs were located in the Eastern and Southern states, with the largest number working in Georgia, second in Texas, and third in Virginia
Map 4C shows that Asian American techs were highly concentrated in California where their concentration was higher than any other group in any state. Otherwise Asians were spread out over other states with much smaller numbers Asian American professionals.
Map 4D shows that Hispanic American techs were concentrated in California, Texas, and Florida
Map 4E shows that Asian American female techs were highly concentrated in California.
Map 4F shows that non-Asian American female techs had their largest shares of tech in California, Texas, and Florida, but the distribution was more dispersed than the distribution for Asian female techs.
Tables 4A, 4B, 4C, 4D, 4E, 4F … 50 states plus DC
Six data tables impose numeric precision on the qualitative insights provided by the corresponding maps. The tables are ordered by the number of techs from each group that worked in each state. The short versions that only contain the top ten rows are displayed below. To view the full tables that contain data for all 50 states plus the District of Columbia, please click the link below the table name. Each table (full and short versions) has the following eight columns:
1. State – State of workplace for the American techs whose data is shown in the other columns of each row
2. AllTech – Data for all of American techs in the U.S. The data in this column is repeated in each of the six tables, but the order of the rows differs from one table to the next. For example Table 4A shows that the total number of techs in California in 2015 was 513,031, and the total number in Texas was 328,635
3. perSS – The state’s share of each group’s national total. More specifically, the percent of all American techs in a group that worked in each state. For example, Table 4A shows that 9.3 percent of all White techs worked in California in 2015, and 7 percent worked in Texas.
4. WhiTech, BlkTech, AsiTech, HispTech, FemAsiTech, or FemNonAsTech – White techs, Black techs, Asian techs, Hispanic techs, Asian female techs, and Non-Asian female techs in tables A, B, C, D, E, and F, respectively. The data in this column shows the number of techs from the table’s group that worked in each state. For example, Table 4A shows that 270,034 White techs worked in California, and 205,636 White techs worked in Texas. The rows of each table are ordered by data in this column, i.e., by the number of techs in each state; states with higher numbers are at the top of the table; states with lower numbers are at the bottom.
5. perT – Percent of a state’s techs who were in the table’s group. For exampleTable 4A shows that 52.6 percent of the techs in California were White, and 62.6 percent of the techs in Texas were White.
6. WhiPop, BlkPop, AsiPop, HspPop, FemAsiPop, or FemNonAsiPop – White, Black, Asian, Hispanic, female Asian, or female non-Asian components of the workforce in each state. For example, Table 4A shows that the White component of the California workforce in 2015 was 6,833,959, and the White component of the Texas workforce was 5,642,859.
7. perP – Percent of the state’s total workforce that belonged to the table’s group. For example, Table 4A shows that in 2015, 46.5 percent of the total workforce of California was White, and 52 percent of the total workforce of Texas was white
.
8. Par – Parity = The ratio of the percent of the state’s tech workforce that belonged to the table’s group divided by the percent of the state’s total workforce that belonged to that group, i.e., perT / perP. For example, Table 4A shows that the White parity ratio for California was 1.13, which is 52.6% divided by 46.5%
Table 4A – White American Professionals in U.S. Information Technology – Top 10 States
Click here for full version
State AllTech perSS WhiTech perT WhiPop perP Par
ALL STATES 4,066,773 100.0 2,916,852 71.7 93,526,664 69.0 1.04
California 513,031 9.3 270,034 52.6 6,833,959 46.5 1.13
Texas 328,635 7.0 205,636 62.6 5,642,859 52.0 1.20
New York 240,885 5.5 159,512 66.2 5,471,519 64.5 1.03
Pennsylvania 154,066 4.5 130,915 85.0 4,730,650 83.3 1.02
Florida 193,697 4.5 130,007 67.1 4,673,939 59.9 1.12
Illinois 173,972 4.3 126,806 72.9 3,906,703 71.1 1.03
Virginia 186,597 4.2 121,439 65.1 2,499,305 67.3 0.97
Ohio 135,563 4.0 116,168 85.7 4,452,326 84.5 1.01
Massachusetts 136,709 3.7 108,301 79.2 2,585,847 80.4 0.99
Washington 132,266 3.6 104,723 79.2 2,333,814 76.8 1.03
Comments about Table 4A – White American Professionals in Tech
The numbers in the third (perSS) and fourth (WhiTech) columns of this table, i.e., the percentage of the nation’s White techs who worked in each state and the estimated number of techs (WhiTech) in each state are equivalent to the colors on Map 4A. Indeed, the colors on this map are based on the numbers in these columns.
To be specific, the third and fourth columns show that in 2015 the largest number of White techs worked in California; the next largest worked in Texas. White techs were found in every state after that in smaller and smaller numbers.
- This story is captured by the parity values in the last column (Par) of Table 4A (and in its full version). White parity values were all close to 1.0. Therefore the percentage of techs who were White in each state was more or less the same as the percentage of Whites in the state’s total workforce. In other words, if you walked into a tech office in any state in the USA, you would probably encounter the same percentage of White techs as the percentage of White people you would encounter walking down the street in that state – with an important caveat: random encounters with 10 White people on a street would only involve five males; but as per Table 3 (above), random encounters with 10 White techs in an office would probably involve seven or eight males.
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Table 4B – Black American Professionals in U.S. Information Technology – Top 10 states
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State AllTech perSS BlkTech perT BlkPop perP Par
ALL STATES 4,066,773 100.0 314,497 7.7 15,743,152 11.6 0.66
Texas 328,635 10.0 31,437 9.6 1,396,038 12.9 0.74
Georgia 129,978 9.9 31,211 24.0 1,286,685 30.6 0.78
Virginia 186,597 9.0 28,381 15.2 728,267 19.6 0.78
Maryland 119,152 7.3 23,000 19.3 632,387 25.1 0.77
California 513,031 6.1 19,068 3.7 876,211 6.0 0.62
New York 240,885 5.4 17,100 7.1 1,110,478 13.1 0.54
Florida 193,697 5.4 16,999 8.8 1,162,812 14.9 0.59
North Carolina 127,484 5.4 16,986 13.3 888,815 21.0 0.63
Dist of Col 50,301 5.3 16,558 32.9 256,515 34.4 0.96
Illinois 173,972 4.0 12,503 7.2 654,692 11.9 0.61Comments about Table 4B – Black American Professionals in Tech
Although most tech professionals worked in the same states in which they lived, New York City and Washington, DC had large daytime commuter populations. Many techs lived in Maryland and Virginia, but worked in DC; and many who lived in New Jersey and Connecticut worked in New York. There were also substantial commuter flows in the opposite directions, i.e., techs who lived in the core cities but worked in the adjacent states.
Column 4 (BlkTech) of Table 4B (above) shows that in 2015 the largest number of Black techs worked in Texas, second largest in Georgia, third in Virginia, fourth in Maryland, and fifth in California. Why? Because in 2015 the vast majority Black Americans still lived in Eastern and Southern states; so that’s where most of the Black techs were found. This put most Black techs at a considerable disadvantage with regards to learning about and/or being recruited for tech jobs in Silicon Valley.
Table 4C – Asian American Professionals in U.S. Information Technology – Top 10 states
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State AllTech perSS AsiTech perT AsiPop perP Par
ALL STATES 4,066,773 100.0 443,434 10.9 5,901,978 4.4 2.48
California 513,031 33.2 147,206 28.7 2,005,062 13.7 2.09
New York 240,885 8.3 36,973 15.3 581,993 6.9 2.22
Texas 328,635 8.0 35,441 10.8 400,823 3.7 2.92
New Jersey 123,689 5.8 25,614 20.7 241,220 6.8 3.04
Virginia 186,597 5.2 22,847 12.2 175,127 4.7 2.60
Illinois 173,972 4.5 20,000 11.5 226,031 4.1 2.80
Massachusetts 136,709 4.2 18,777 13.7 145,430 4.5 3.04
Maryland 119,152 3.1 13,823 11.6 131,311 5.2 2.23
Washington 132,266 2.8 12,530 9.5 197,762 6.5 1.46
Georgia 129,978 2.7 11,761 9.0 120,709 2.9 3.10Comments about Table 4C – Asian American Professionals in Tech
As per the second row in the fourth column in Table 4C, California was home to 147,206 Asian techs, which represented 33.2 percent of all of the Asian techs in the U.S. This was, by far, the highest concentration of any racial/ethnic group in any state. Asian techs also obtained 28.7 percent of the 513,031 tech jobs in California, the largest share of the tech sector in any state. (See the full version of Tablc 4C)
Although the intensity of Asian invovement in the tech sector of California was impressive, Asian participation in the tech sector of every other state was also remarkable. The parity values in the last column of the table provide concise summaries. The full version of Table 4C shows that Asian parity values ranged from 0.46 to 5.68. In other words, the Asian share of the tech sectors in the 50 states plus DC ranged from 0.46 times their share of the state’s total workforce to 5.68 times their share of the state’s total workforce.
Table 4D – Hispanic American Professionals in U.S. Information Technology – Top 10 states
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State AllTech perSS HspTech perT HspPop perP Par
ALL STATES 4,066,773 100.0 282,946 7.0 16,710,406 12.3 0.57
California 513,031 20.8 58,879 11.5 4,459,531 30.4 0.38
Texas 328,635 16.3 46,261 14.1 3,202,335 29.5 0.48
Florida 193,697 11.7 32,968 17.0 1,623,472 20.8 0.82
New York 240,885 7.7 21,662 9.0 1,147,678 13.5 0.67
Illinois 173,972 4.2 11,901 6.8 625,907 11.4 0.60
Colorado 115,736 3.9 11,158 9.6 369,046 14.6 0.66
New Jersey 123,689 3.6 10,295 8.3 524,280 14.9 0.56
Arizona 77,421 3.1 8,759 11.3 649,936 25.0 0.45
Virginia 186,597 2.8 7,940 4.3 215,705 5.8 0.74
Washington 132,266 2.2 6,245 4.7 234,389 7.7 0.61Comments about Table 4D – Hispanic American Professionals in Tech
As per the third column (perSS) of Table 4D, Hispanic techs were concentrated in three states: California, Texas, and Florida. The table shows that 20.8 percent worked in California, 16.3 percent worked in Texas, and 11.7 percent worked in Florida. Taken together, these three states accounted for 48.8 percent of the Hispanic techs in the entire country, i.e., about half. The other half work in the other 47 states and the District of Columbia.
All of the parity values of the top 10 states for Black Ameican techs shown in the last column of Table 4B are substantially higher than the parity values for all of the top 10 states for Hispanic techs shown in Table 4D … with one exception: Florida, parity = 0.82. Florida’s parity value for Hispanic techs was more than twice the 0.38 parity value for Hispanics in California. Indeed, Table 4D shows that California had the lowest parity value of all of the top ten states for Hispanics. These considerations suggests that Hispanic techs may have encountered larger employment barriers than Black techs, especially in California.
Despite California’s relatively low Hispanic American parity, the fifth column (perT) of Table 4D shows that 11.5 percent of the techs who worked in California were Hispanic. This figure is remarkable because it is two to three times as high as the 2 percent to 4 percent employment rates for Hispanic techs that have been reported by Silicon Valley’s biggest firms in the last three years. It suggests that willingness to hire Hispanic techs was low throughout the state, but substantantially lower in the Valley.
Perhaps the Valley required higher academic qualifications than other employers. But as I pointed out in a recent note on this blog (“The California Pipelines of Silicon Valley”), IPEDS data showed that 20 percent of the combined total of White, Black, Asian, and Hispanic students enrolled in the best colleges and universities in California were Hispanic. So there is reason to be skeptical about claims that few Hispanic techs met the Valley’s higher recruiting standards.
Table 4E – Asian American Females in U.S. Information Technology – Top 10 states
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State AllTech perSS AsFemTech perT AsFemPop perP Par
ALL STATES 4,066,773 100.0 128,871 3.2 2,931,010 2.2 1.45
California 513,031 31.9 41,072 8.0 1,001,112 6.8 1.18
Texas 328,635 8.0 10,256 3.1 189,760 1.8 1.72
New York 240,885 6.9 8,933 3.7 280,523 3.3 1.12
New Jersey 123,689 6.7 8,655 7.0 116,934 3.3 2.12
Virginia 186,597 5.3 6,884 3.7 86,427 2.3 1.61
Massachusetts 136,709 4.7 6,065 4.4 70,741 2.2 2.00
Illinois 173,972 3.7 4,739 2.7 111,228 2.0 1.35
Maryland 119,152 3.6 4,693 3.9 66,860 2.7 1.44
Washington 132,266 3.1 3,942 3.0 100,667 3.3 0.91
North Carolina 127,484 2.7 3,485 2.7 39,923 0.9 3.00
Table 4F – Non-Asian American Females in U.S. Information Technology – Top 10 states
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State AllTech perSS NonAsFTech perT NonAsFPop perP Par
ALL STATES 4,066,773 100.0 910,203 22.4 62,096,336 45.8 0.49
California 513,031 8.6 78,525 15.3 5,916,073 40.3 0.38
Texas 328,635 7.8 71,051 21.6 4,903,856 45.2 0.48
Florida 193,697 5.6 50,528 26.1 3,714,566 47.6 0.55
Virginia 186,597 5.3 47,880 25.7 1,685,900 45.4 0.57
New York 240,885 5.2 47,719 19.8 3,869,403 45.6 0.43
Illinois 173,972 4.2 38,235 22.0 2,540,836 46.2 0.48
Pennsylvania 154,066 4.2 37,843 24.6 2,685,949 47.3 0.52
Georgia 129,978 3.6 32,383 24.9 1,999,879 47.5 0.52
Maryland 119,152 3.6 32,333 27.1 1,177,404 46.8 0.58
North Carolina 127,484 3.5 32,183 25.2 2,019,479 47.7 0.53Comments about Tables 4E and 4F
By comparing the California row of the third columns (perSS) of Tables 4C and 4E, the reader will see that that Asian Americanfemale tech professionals were as highly concentrated in California as the entire male and female Asian American cohort. To be specific, 31.9 percent of all Asian American female techs in the U.S. were in California (Table 4E), which is essentially the same as the 33.2 percent of all Asian American tech professionals who were in California (Table 4C).
Non-Asian American females showed no such concentration in California or in any other state. To be sure, California employed the most non-Asian female techs (8.6 %), but this was not much larger than the percentage in Texas (7.8 %), which was not much larger than in New York (5.2 %), etc.
The most striking differences between the distributions of Asian American and non-Asian American female techs were found in the parity values shown in the last columns of these tables. The parity values for non-Asian American female techs were substantially lower than the parity values for Asian American female techs. To be specific, Table 4F (full version) shows that all 51 states plus DC had parity values less than 0.9 for non-Asian female techs; and 24 had values less than 0.5. By contrast, Table 4E (full version) shows that 26 states plus DC had Asian female parity values greater than 1.1 … 22 had Asian female parity values greater than 1.5 … and 9 had Asian female parity values greater than 2.5
Question 5 – Was the size of an American racial/ethnic group’s share of a state’s workforce related to its share of the tech jobs in that state?
Answer 5: As shown in six plots that follow, there was a positive relationship, but the relationship was strongest for Asians, weaker for Asian American females, still weaker for Whites, weaker for Black American, weaker for non-Asian American females, and weakest for Hispanic Americans.
Plots 5 – Plots of American Tech Employment vs Race/Ethnic/Female Group Workforce
Comments about Plots 5A, 5B, 5C, 5D, 5E, 5F
The total workforce of each state was at least 1,000 times the population of the techs in any racial/ethnic group. This suggested simple regressions of techs in a group against the total workforce of that group divided by 1000 in all 50 states plus Washington, DC. The slopes of the regression lines provided estimates of the number of additional techs that would be found in a state if the size of the group’s total workforce population increased by 1000.
The regression lines and scatterplots of the
As per the values of the beta1000 slopes, If each ethnic/racial group’s workforce population increased by 1,000, Asian techs would increase by 73.41; White Americans techs would increase by 33.85, Black Americans would increase by 20.41; and Hispanic Americans would increase by 14.17. The Asian Americann line had the steepest slope; the Hispanic American line had the flattest slope. The Asian American female slope, beta1000 = 40.87, was almost as steep as the White American slope; non-Asian American female techs had the second flattest slope, beta1000 = 13.94.
This kind of simple regression implicitly assumes that the 50 states and DC were all more or less equally receptive or resistant to including more techs from the same groups. This may indeed have been true for most states. However, Table 4B shows that the Black American parity for Texas = 0.74, whereas the Black parity for California = 0.62. This suggests that Texas was more receptive to Black techs than California. And Table 4D shows that the Hispanic parity for Florida = 0.82, while the Hispanic parity for California = 0.38. This suggests that Florida was more receptive to Hispanic techs than California.
Table 5 – Summary of Parity Values for American Groups
Tech perT Population perP Par beta1000
White 2,916,852 71.7 93,526,664 69.0 1.04 33.85
Black 314,497 7.7 15,743,152 11.6 0.66 20.41
Asian 443,434 10.9 5,901,978 4.4 2.48 73.41
Hispanic 282,946 7.0 16,710,406 12.3 0.57 14.17
OTHERS 109,044 2.7 3,592,888 2.7 1.00 33.60
Female 1,039,074 25.6 65,027,346 48.0 0.53 16.28
FemAsian 128,871 3.2 2,931,010 2.2 1.45 40.87
FemNonAsian 910,203 22.4 62,096,336 45.8 0.49 13.94Comments about Table 5
The national parity values and beta1000 statistics shown in Table 5 provide different ways to measure the same concept ==> The relative advantages in the tech sector that the nation as a whole provided to the six groups discussed in this report.
The percentages and ratios shown in Table 5 are national percentages and national parity ratios – not the state percentages and state parity ratios shown in Tables 4A, 4B, 4C, 4D, 4E, and 4F. In other words column 2 (perT) shows the percentage of all techs in the nation who were in each group. Column 4 shows the percentage of the nation’s entire workforce that was in each group. The national parity ratios for each group shown in column 5 (Par) are the result of dividing the national tech percentages in column 2 (perT) by the national workforce percentage shares in column 4 (perP).
As per the plots shown in this section, the more complex beta1000 statistics were the results of regressions that estimated a linear relationship between the percentage of a group’s tech population (perT in Tables A, B, C, D, E, and F) in every state to the group’s total workforce in every state (perSS in Tables A, B, C, D, E, and F).
As can be seen from the last two columns in the table, the two measures provided similar results: Asian Ameicans were the most advantaged (parity = 2.48, beta1000 = 73.41); Asian American females were second ({parity = 1.45, beta = beta1000 = 40.87); White American third (parity = 1.04, beta1000 = 33.85), Black American fourth (parity = 0.66, beta100 = 20.41); non-Asian American females fifth (parity = 0.49, beta1000 = 13.94); and Hispanic Americans were the least advantaged {parity = 0.57, beta1000 = 14.17).
“And the best states are …”
Which states were the best states for White, Black, Hispanic, Asian, and female American professionals in information technology? Given the limited scope of the data that I examined in this study, its selection criteria were rudimentary; so its conclusions should be regarded as tentative first approximations.
Select states that had the largest number of techs in the racial/ethnic groups and the largest number Asian American females and non-Asian American females
This criterion was implemented by selecting the states with the ten highest numbers of techs in each group, i.e., the states that appeared in short versions of Tables 4A through 4F.Select states that had parity values above each group’s median
This criterion was implemented by identifying the states in Tables 4A through 4F that had state level parity values in the top 50% of all 50 states plus DC. Table 6 (below) summarizes the quartile breakdowns of the parity values for each group. For example, Table 6 shows the median Black parity value was 0.65; the median Hispanic parity value was 0.63.
(Note that the first column in Table 6 shows the minimum non-zero parity values for each group. The PUMS sample for a few states had so few techs in some groups that the Census data could not provide reliable estimates of the number of techs in that state based on the sample, so missing values were set to zeros.)
Table 6 – Summary of Parity Values
Min Q1 Median Q3 Max
White 0.92 1.00 1.03 1.10 1.31
Black 0.23 0.53 0.65 0.80 5.71
Asian 0.46 1.47 2.21 2.89 5.68
Hispanic 0.13 0.50 0.63 0.82 1.44
Female 0.15 0.48 0.54 0.59 0.79
FemAsian 0.33 0.48 1.32 1.92 7.00
FemNonAsian 0.15 0.47 0.50 0.57 0.75- Identify the finalists
Table 7A through 7F lists the states whose parity values were greater than or equal to the median parity value for each group.
Tables 7A, 7B, 7C, 7D, 7E, 7F – Finalists 
- Best States
The “Best States” were chosen by sorting the finalists in decreasing order of each group’s percentage share (perT) of the tech sector. The state at the top of each list was the best of the best for its group, the state in the second row was the second best, and so on.
Final Comments
Parity was a crude measure of the opportunities in tech that states provided to the groups considered in this study. Nevertheless, this simple metric suggested which groups should be broken into subgroups in order to obtain more useful insights.
American Female vs. American Female
The low betas and national parity values in Table 5 that were assigned to American female techs (16.28 and 0.53) compared to the high betas and national parity values assigned to Asian American techs as a whole (73.41 and 2.48) suggested that that the American female category be broken into its Asian vs. non-Asian components, as per Tables 4E and 4F.
As shown in Tables 5, the betas and national parity values for Asian American females (40.87 and 1.45) were much higher than the values for non-Asian females (13.94 and 0.49). In other words, Asian American females received substantially greater employment opportunities than non-Asian American females all over the country.
- This is not to say that tech treated Asian American males and Asian American females the same. Not at all. The betas and national parity values assigned to Asian Americans as a whole (73.41 and 2.48) were much higher than the values assigned to Asian females (40.87 and 1.45). Therefore the values assigned to Asian American males would have to be much higher than the values assigned to Asian American females, an indication that Asian American males received substantially greater employment opportunities per capita than Asian American females.
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Hispanic American vs. Hispanic American
As shown in Table 4D, California employed the largest number of Hispanic Americans (58,879), Texas employed the second largest (46,261), and Florida employed the third largest number (32,968). Nevertheless Florida was the “Best State” for Hispanic Americans, whereas California and Texas didn’t make it into the top 3. Indeed Florida had the highest parity value, (0.82), of the ten biggest states in Table 4D, and its parity value was in the top quartile for all 50 states plus the District of Columbia, as shown in Table 6.
In most states, being in tech means working in the information technology sections of government agencies or private corporations in various industries; whereas in California and to a lesser extent in Texas, a substantial number of techs work for computer companies. A large part of the Hispanic American workforce in Florida has Cuban ancestry; whereas large parts of the Hispanic American workforce in California and Texas have Mexican ancestry. Cuban entrepreneurs have established so many prominent businesses in Florida that Miami is often called the “financial capital” of Latin America,
- So it seems reasonable to conjecture that Cuban American tech professionals would have encountered fewer hiring obstacles in Florida because they “looked like/sounded like” many corporate owner/managers; but the same could not be said for Mexican American tech professionals in Texas or in California, and especially in Silicon Valley.
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Black American vs. Black American
Table 5 showed that 11.6 percent of the total U.S. workforce in 2015 was Black American; and Table 5 also showed that the nation’s 314,497 Black American tech professionals represented 7.7 percent of the nation’s 4,066,773 American tech professionals.
Table 5 showed that the national parity rating for Black Americans in tech was 0.66, i.e., 7.7 divided by 11.6, which was more than half, i.e., greater than 0.50. The vast majority of tech jobs required bachelors degrees. This meant that, contrary to popular misconceptions, the nation’s Black academic pipelines were more than half-full.
According to Table 8B, the best states for Black American tech professionals were Dist of Col, Georgia, Maryland, Virginia, and Texas. These states weren’t just Southern states; they were HBCU states, homes to the Historically Black Colleges and Universities (HBCUs) that educated the overwhelming majority of Black college graduates before the Supreme Court’s desegregation decision in 1954 and the Civil Rights legislation in 1964/65. To this day, when over 90 percent of Black American students attend non-HBCUs, HBCUs still educate a large, disproportionate share of the nation’s Black American graduates in the STEM majors that provide entry into careers in information technology. (See my previous note on this blog HBCUs – the Best Producers of Black Graduates in STEM)
- Most people attend colleges that are no more than a few hundred miles from their homes, so the children of HBCU graduates have obtained STEM degrees from some of nation’s best colleges located in the HBCU states, e.g, Georgia Tech, Emory, Duke, Chapel Hill, and Austin. The presence of so many older Black techs in these states created dense, overlapping support networks of role models, mentors, and tutors for Black tech students and young Black tech graduates, networks that were reinforced by memberships in fraternities, sororities, churches, and other social groups, networks that facilitated their entry into the job market and subsequent promotion, networks that were not available to young Black techs in California and other non-HBCU states.
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Asian American vs. Black American
Question: How did Asian American tech professionals attain such high parity values in every state?
Table 4B shows that Black Americans have made good use of the expanded education and employment opportunities provided by the Supreme Court’s 1954 decision and the Civil Rights legislation that followed. But Black American academic pipelines are only half full because we are still overcoming the persistent evil legacy of hundreds of years of slavery and the decades of Jim Crow segregation that followed.
Fortunately for Asian Americans, the oppression they faced before the Supreme Court and Congress outlawed racial exclusions from education and the workplace was orders of magnitude less debilitating. So facing lesser obstacles, Asian Americans have attained greater gains.
Indeed, the remarkable success of Asian Americans in tech’s academic pipelines and subsequent employment reinvigorates the old ideal of America as a “Land of Opportunity”. For example, as per Table 1B, Asians were only 4.4 percent of the nation’s workforce in 2015. But according to U.S. Department of Education, Asian undergraduate enrollments at Cal Tech and M.I.T. – arguably the best technical institutions in the nation, if not the world – are 45 percent and 25 percent, respectively. No one gets admitted to these highly competitive institutions without being very smart and being very well prepared.
- While smarts and thorough preparation are usually sufficient to gain admission to the best colleges and universities, being hired for the best jobs can also be a function of who you know … or more precisely, as Granovetter first observed in one of the most widely cited papers ever published (“The Strength of Weak Ties”), it’s who you know who knows someone who can hire you. So the more Asian Americans that were hired, the more indirect contacts other Asian Americans obtained.
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American vs. Foreign
Question: How did foreign professionals obtain so many jobs in the U.S. tech sector?
Foreign tech professionals had the strongest possible “weak ties” to their future employers because they had been proactively recruited by agents of U.S. companies who persuded them migrate to the U.S. in order to work for their U.S. clients in the U.S., primarily under the H-1B visa program
I am currently developing a supplement to this report – “Best States for Foreign Professionals in U.S. Tech” – that will provide data that focuses on the increasing employment of foreign techs, especially in California. Thereafter I will post a companion op-ed on this blog – “Why Are There So Many Foreign Techs in U.S. tech?” – that will offer a plausible answer to the question in its title based on the data compiled for this report and data gathered from other sources.
Personal Motivation
As the DLL Editor, my motivation for conducting this study influenced my methods. The recent barrage of annual reports from some of the nation’s leading information technology companies in Silicon Valley said the primary reason why those companies employed so few Black, Hispanic, and female information technology professionals was because there were so few available. And the reason they weren’t available was because there were so few Black, Hispanic, and female tech students in the nation’s academic pipelines.
A. Best Practices
While reading the Silicon Valley reports I found myself wondering, “What numbers did they have to back up these assertions? And where did they get them from?” Whereas I would have said that the pipelines were half-full, these reports implied that the pipelines were perilously close to empty. But if the pipelines were close to empty, there shouldn’t be many Black, Hispanic, or female employees in the U.S. tech sector. This led me to wonder how many Black, Hispanic, and female techs were out there and where they were located.
What concerned me the most was the lack of context for assessing Silicon Valley’s diversity numbers. Were they as good as could be expected? Were they worse? … And compared to what? Compared to other states in other parts of the country? The personal data that I had accumulated from more than 50 years in tech informed me that tech was much more diverse in the Eastern and Southern states. But personal data, while a useful source of productive hypotheses, does not provide credible, systematic proof. As a clever tech pundit recently proclaimed, “The plural of personal anecdote is not data” … :-)
One of the most useful lessons that I learned as a consultant to the National Center for Productivity way back in the late 1970s was that the application of performance metrics, even flawed metrics, to just about any significant human activity invariably identified a broad middle tier that was above a small tier of low performers and below another small tier of high performers. Further inquiry usually disclosed that the high performers were doing things quite differently than the middle and low performers. Happily, performers in the lower tiers could improve by adopting the high performers’ best practices provided that they made appropriate adaptations of the best practices to their local conditions. Given this insight, I set out to apply a simple measure of the performance of all 50 states and the District of Columbia with regards to the opportunities their tech sectors provided for Black, Hispanic, and female professionals.
B. Reproducible Reports
One of my most important take-aways from completing the Johns Hopkins “Data Science” online certificate program a few months ago was the notion that reports should be “reproducible”. Authors should not only describe their methods for collecting and analyzing data; they should provide step-by-step descriptions of their methods (including copies of their code) so that interested readers could readily reproduce the results on their own computers. Of course, the best researchers have always tried to achieve this objective and have often succeeded. What was new for me was the specific tools that the statisticians who taught the Hopkins courses recommended ==> the R programming language and GitHub.
Data should be downloaded from original sources, then analyzed, tabulated, mapped, and graphed via programs writen in R. Findings should be summarized in text laid out in a subset of Markdown that an R application called “knitr” would convert to HTML pages suitable for publication on Websites.
Reproducible reports should be as concise as traditional reports, but the writer’s notes, programs, analytical tools, coding guides, data files, tables, maps, and graphs should be shared with the world via GitHub repositories.
Of course Python, MATLAB, or other comparable languages could be used instead of R, and the supplementary files could be stored on other public repositories comparable to GitHub. This being my first attept to write a reproducible report, I wrote it in R and stored my supporting files in a public GitHub repo. If readers disagree with my numbers, they won’t have to wonder how I got them … #OldDogStillLearningNewTricks … :-)
Methods
I used the U.S. Census Bureau’s DataFerret Java application to download two pairs of samples from the Public Use Microdata Sample (PUMS) from the Bureau’s American Community Survey for 2010 and 2015. The first pair of samples was used to estimate the size of the White, Black, Asian, Hispanic, and female components of the total workforce in each of the 50 states plus the District of Columbia in 2010 and 2015
The second, more detailed pair of samples was used to estimate the size of the corresponding components of the tech subset of the total workforce in 2010 and 2015. The number of observations in each of the second pair of samples was usually large enough to make good estimates of the employee counts and percentages of tech employment for Blacks, Whites, Asians, and Hispanics in tech occupations in the 50 states and DC. (Note: For a few states with small tech populations, the ACS subset included missing values which meant that it wasn’t large enough to provide reliable estimates of the racial/ethnic breakdown of tech employment in those states. Missing values were converted to zeros in these cases.) My step-by-step procedures for selecting and downloading the ACS data and its codebook is described in Notes-Data-Download-PUMS-Sample on the GitHub repo. (Note: the GitHub repo page should be opened in a separate window, not in the iFrame on the Tech-Levers blog.)
Selected Data
Occupation
When using the DataFerret app to select a sample of tech professionals from the full PUMS 2010 and 2015 samples, I defined the “information technology sector” by designating the 13 standard occupational categories shown in tables displayed in the answers to Question 3. Twelve categories were software, one was hardwareHispanic vs White (not Hispanic), Black (not Hispanic), Asian (not Hispanic)
I configured the DataFerret to include all races in my sample. However, Hispanic is not regarded as a race, but as an ethnic/national origin, like being Irish, Egyptian, Japanese, or Nigerian. So I included the “Hispanic” variable. This variable designated each respondent’s Hispanic origin, e.g., Mexico, Chile, Puerto Rico, etc … but it also contained a “Not Hispanic” value for respondents who were not descended from Hispanic countries. Using this code, I distinguished between “White” vs. “White (not Hispanic)”, “Black” vs. “Black (not Hispanic)”, and “Asian” vs. “Asian (not Hispanic)” and “Others” vs “Others (non-Hispanic)”. Accordingly, my report contains data about four racial/ethnic groups: Hispanic, White (not Hispanic), Black (not Hispanic), and Asian (not Hispanic); but for brevity it merely calls them Hispanic, White, Black, and Asian.Estimating population values from the sample
Each row in the downloaded PUMS tables contained the anonymized responses of a real employee in the ACS 2010 and 2015 surveys, i.e., sex, race/ethnicity, occupation, and state of Workplace. Each observation also contained a number called “personal weight” which is the Census Bureau’s estimate of how many people in the real population were like each person in the sample. An estimate of the total number of employees in the real population was found by adding up the personal weights of the employees in the sample. This also applied to subgroups, e.g, male vs. female, state of Workplace, etc. The percentage share of a subgroup’s workforce was obtained by dividing the estimated workforce in the subgroup by the estimated workforce in the entire group, i.e., dividing the sum of personal weights in a subgroup by the sum of personal weights in the larger group.
Here’s the Census Bureau’s description of this process:
“Production of Estimates” – The ACS PUMS sample is not self - weighted. To produce estimates or tabulations of characteristics from the ACS PUMS simply add the weights of all persons or housing units that possess the characteristic of interest. For instance, if the characteristic of interest is “total number of black teachers”, simply determine the race and occupation of all persons and cumulate the weights of those who match the characteristics of interest. To get estimates of proportions simply divide the weighted estimate of persons or housing units with a given characteristic by the weighted estimate of the base. For example, the proportion of “black teachers” is obtained by dividing the weighted estimate of black teachers by the PUMS estimate of teachers."
I used the R Studio IDE to write the R scripts and functions that produced the report’s tables, maps, graphs, and stats. My R code can be found in the functions-0.R, Data-1.R, Stats-2A.R, Stats-2B.R, Stats-2C.R., and Report-3.Rmd files in the Best States repo, as well as copies of the codebooks and .csv files I downloaded from the Census. I composed the text of the report in R-Markdown, embedded the data objects, converted the R-markdown to HTML via knitr, pushed the report to git-io pages, then made the pages accessible to readers of the DLL’s Tech-Levers blog via an iframe on a page on the blog.
The scripts and embedded functions should be loaded in numerical order ==> functions-0.R then Data-1.R The Data-1.R module uses the functions to read the Census data files, then create the data frames required by Stats-2A.R, Stats-2B.R, and Stats-2C.R. The Stats modules use the functions to create the tables, maps, graphs, and regressions required by the R-markdown script in the Report-3.Rmd file that generates the report.
Finally, this report represents my honest attempt to shed additional light on some important, troublesome issues. Therefore I would greatly appreciate readers’ comments and suggested improvements … but I would cherish their help in identifying any errors that I made in my data collection or analysis because bad reports don’t support good policies. Thank you … :-)
Appendix – Profiles of Tech Sector Jobs in the Six Biggest Tech States plus Washington, DC in 2015
As per Table 4 in the report, the first six states in this appendix employed the largest number of Americans in their tech sectors – California, Texas, New York, Florida, Virginia, and Illinois. The District of Columbia was also included because it was identified in Table 8B as the Best State for Black Americans in tech.
Three pairs of tables are included for each state:
The first two tables show changes in the employement of American citizens between 2010 and 2015 in the state’s tech sector.
The second pair of tables show changes in employment of Black Americans between 2010 and 2015 in the state’s tech sector.
The third pair of tables show change in employment of Hispanic Americans between 2010 and 2015 in the state’s tech sector.
In a future version of this report, this Appendix will be replaced by an interactive dashboard that will allow users to display pairs of tables for any state and for any American or foreign group.
Note: The column headings and abbreviated row names of the tables in this appendix are explained in the answer to Question 3 in the body of the report.
California … California …California … California … California …
Table 3.CA – American Male/Female Tech in California in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 513,031 119,597 23.3
SOFTWARE DEVELOPERS 30.1 154,590 30,563 19.8
COMP SUPPORT SPECIALISTS 12.5 64,055 13,969 21.8
COMP/INFO SYSTEMS MANAGERS 11.2 57,446 17,617 30.7
COMP SYSTEMS ANALYSTS 9.9 50,927 18,547 36.4
COMP PROGRAMMERS 9.7 49,869 7,744 15.5
COMP OCCUPATIONS OTHER 9.7 49,770 10,902 21.9
WEB DEVELOPERS 4.9 25,128 8,833 35.2
NET/COMP SYS ADMINS 3.8 19,632 3,231 16.5
COMP NET ARCHITECTS 3.0 15,341 1,395 9.1
DATABASE ADMINISTRATORS 1.9 9,878 3,937 39.9
COMP HARDWARE ENGINEERS 1.8 9,363 1,388 14.8
INFO SECURITY ANALYSTS 0.9 4,868 868 17.8
COMP/INFO RSRCH SCIENTISTS 0.4 2,164 603 27.9Table 3.CACA – Changes in American Tech in California 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 403,554 513,031 109,477 27.1 24.7
SOFTWARE DEVELOPERS 103,979 154,590 50,611 48.7 21.2
COMP SUPPORT SPECIALISTS 41,983 64,055 22,072 52.6 23.8
COMP/INFO SYSTEMS MANAGERS 57,132 57,446 314 0.5 29.6
COMP SYSTEMS ANALYSTS 44,473 50,927 6,454 14.5 37.8
COMP PROGRAMMERS 50,463 49,869 -594 -1.2 21.0
COMP OCCUPATIONS OTHER 29,657 49,770 20,113 67.8 16.6
WEB DEVELOPERS 21,866 25,128 3,262 14.9 33.7
NET/COMP SYS ADMINS 23,622 19,632 -3,990 -16.9 16.9
COMP NET ARCHITECTS 7,416 15,341 7,925 106.9 10.6
DATABASE ADMINISTRATORS 9,200 9,878 678 7.4 39.8
COMP HARDWARE ENGINEERS 9,403 9,363 -40 -0.4 15.5
INFO SECURITY ANALYSTS 2,425 4,868 2,443 100.7 25.4
COMP/INFO RSRCH SCIENTISTS 1,935 2,164 229 11.8 25.1
Table 3.CAblack – Black American Male/Female Tech in California in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 19,068 5,603 29.4
COMP SUPPORT SPECIALISTS 23.5 4,488 1,888 42.1
COMP SYSTEMS ANALYSTS 17.8 3,403 1,581 46.5
COMP/INFO SYSTEMS MANAGERS 12.2 2,334 766 32.8
COMP OCCUPATIONS OTHER 11.4 2,171 203 9.4
SOFTWARE DEVELOPERS 9.9 1,885 196 10.4
COMP NET ARCHITECTS 7.7 1,461 72 4.9
COMP PROGRAMMERS 5.9 1,127 234 20.8
WEB DEVELOPERS 4.9 939 275 29.3
DATABASE ADMINISTRATORS 4.0 760 388 51.1
COMP HARDWARE ENGINEERS 1.0 199 0 0.0
NET/COMP SYS ADMINS 0.9 172 0 0.0
INFO SECURITY ANALYSTS 0.7 129 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.CACAblack – Changes in Black American Tech in California 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 16,971 19,068 2,097 12.4 24.8
COMP SUPPORT SPECIALISTS 1,943 4,488 2,545 131.0 7.1
COMP SYSTEMS ANALYSTS 3,151 3,403 252 8.0 32.7
COMP/INFO SYSTEMS MANAGERS 2,909 2,334 -575 -19.8 40.1
COMP OCCUPATIONS OTHER 2,373 2,171 -202 -8.5 3.6
SOFTWARE DEVELOPERS 1,688 1,885 197 11.7 21.9
COMP NET ARCHITECTS 163 1,461 1,298 796.3 0.0
COMP PROGRAMMERS 1,511 1,127 -384 -25.4 40.1
WEB DEVELOPERS 421 939 518 123.0 21.1
DATABASE ADMINISTRATORS 554 760 206 37.2 11.4
COMP HARDWARE ENGINEERS 382 199 -183 -47.9 19.9
NET/COMP SYS ADMINS 1,422 172 -1,250 -87.9 15.4
INFO SECURITY ANALYSTS 366 129 -237 -64.8 77.3
COMP/INFO RSRCH SCIENTISTS 88 0 -88 -100.0 100.0
Table 3.CAhispanic – Hispanic American Male/Female Tech in California in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 58,879 14,999 25.5
COMP SUPPORT SPECIALISTS 20.9 12,334 2,396 19.4
SOFTWARE DEVELOPERS 16.1 9,500 1,761 18.5
COMP OCCUPATIONS OTHER 15.4 9,084 2,257 24.8
COMP SYSTEMS ANALYSTS 11.8 6,955 2,377 34.2
COMP/INFO SYSTEMS MANAGERS 8.3 4,901 1,833 37.4
WEB DEVELOPERS 6.5 3,825 1,672 43.7
NET/COMP SYS ADMINS 6.0 3,517 440 12.5
COMP PROGRAMMERS 5.7 3,360 857 25.5
COMP NET ARCHITECTS 3.9 2,296 238 10.4
COMP HARDWARE ENGINEERS 2.1 1,232 713 57.9
DATABASE ADMINISTRATORS 1.8 1,048 293 28.0
INFO SECURITY ANALYSTS 1.4 827 162 19.6
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.CACAhispanic – Changes in Hispanic American Tech in California 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 32,820 58,879 26,059 79.4 27.0
COMP SUPPORT SPECIALISTS 4,989 12,334 7,345 147.2 28.3
SOFTWARE DEVELOPERS 5,286 9,500 4,214 79.7 18.3
COMP OCCUPATIONS OTHER 4,667 9,084 4,417 94.6 21.4
COMP SYSTEMS ANALYSTS 4,086 6,955 2,869 70.2 53.3
COMP/INFO SYSTEMS MANAGERS 3,674 4,901 1,227 33.4 36.1
WEB DEVELOPERS 1,422 3,825 2,403 169.0 26.4
NET/COMP SYS ADMINS 2,038 3,517 1,479 72.6 6.4
COMP PROGRAMMERS 3,767 3,360 -407 -10.8 29.5
COMP NET ARCHITECTS 912 2,296 1,384 151.8 0.0
COMP HARDWARE ENGINEERS 801 1,232 431 53.8 20.1
DATABASE ADMINISTRATORS 909 1,048 139 15.3 15.0
INFO SECURITY ANALYSTS 269 827 558 207.4 21.9
COMP/INFO RSRCH SCIENTISTS 0 0 0 NaN 0.0
Texas … Texas … Texas … Texas … Texas … Texas … Texas …
Table 3.TX – American Male/Female Tech in Texas in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 328,635 81,307 24.7
SOFTWARE DEVELOPERS 20.8 68,305 14,562 21.3
COMP SUPPORT SPECIALISTS 16.3 53,409 13,519 25.3
COMP OCCUPATIONS OTHER 14.4 47,318 11,636 24.6
COMP/INFO SYSTEMS MANAGERS 13.4 44,156 10,923 24.7
COMP SYSTEMS ANALYSTS 10.5 34,598 13,502 39.0
COMP PROGRAMMERS 8.6 28,301 4,890 17.3
NET/COMP SYS ADMINS 4.8 15,891 3,101 19.5
WEB DEVELOPERS 3.6 11,905 4,450 37.4
COMP NET ARCHITECTS 2.8 9,212 1,088 11.8
DATABASE ADMINISTRATORS 2.0 6,696 2,542 38.0
INFO SECURITY ANALYSTS 1.9 6,224 607 9.8
COMP HARDWARE ENGINEERS 0.5 1,795 110 6.1
COMP/INFO RSRCH SCIENTISTS 0.3 825 377 45.7Table 3.TXTX – Changes in American Tech in Texas 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 261,026 328,635 67,609 25.9 25.8
SOFTWARE DEVELOPERS 48,308 68,305 19,997 41.4 20.1
COMP SUPPORT SPECIALISTS 47,218 53,409 6,191 13.1 26.7
COMP OCCUPATIONS OTHER 19,494 47,318 27,824 142.7 25.4
COMP/INFO SYSTEMS MANAGERS 33,424 44,156 10,732 32.1 30.6
COMP SYSTEMS ANALYSTS 34,302 34,598 296 0.9 37.0
COMP PROGRAMMERS 31,589 28,301 -3,288 -10.4 18.0
NET/COMP SYS ADMINS 14,817 15,891 1,074 7.2 19.4
WEB DEVELOPERS 9,231 11,905 2,674 29.0 29.4
COMP NET ARCHITECTS 6,626 9,212 2,586 39.0 11.8
DATABASE ADMINISTRATORS 7,249 6,696 -553 -7.6 34.8
INFO SECURITY ANALYSTS 3,565 6,224 2,659 74.6 26.3
COMP HARDWARE ENGINEERS 3,982 1,795 -2,187 -54.9 20.1
COMP/INFO RSRCH SCIENTISTS 1,221 825 -396 -32.4 74.1
Table 3.TXblack – Black American Male/Female Tech in Texas in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 31,437 11,489 36.5
COMP SUPPORT SPECIALISTS 27.3 8,583 2,275 26.5
COMP OCCUPATIONS OTHER 17.6 5,530 1,991 36.0
SOFTWARE DEVELOPERS 13.6 4,268 1,180 27.6
COMP SYSTEMS ANALYSTS 12.6 3,964 2,935 74.0
COMP/INFO SYSTEMS MANAGERS 10.2 3,208 1,222 38.1
WEB DEVELOPERS 4.1 1,284 946 73.7
COMP PROGRAMMERS 3.8 1,208 456 37.7
INFO SECURITY ANALYSTS 3.2 993 171 17.2
NET/COMP SYS ADMINS 2.7 861 0 0.0
DATABASE ADMINISTRATORS 2.5 786 134 17.0
COMP NET ARCHITECTS 2.1 663 179 27.0
COMP HARDWARE ENGINEERS 0.3 89 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.TXTXblack – Changes in Black American Tech in Texas 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 18,942 31,437 12,495 66.0 37.7
COMP SUPPORT SPECIALISTS 5,558 8,583 3,025 54.4 39.7
COMP OCCUPATIONS OTHER 1,885 5,530 3,645 193.4 56.0
SOFTWARE DEVELOPERS 1,376 4,268 2,892 210.2 17.2
COMP SYSTEMS ANALYSTS 3,824 3,964 140 3.7 40.8
COMP/INFO SYSTEMS MANAGERS 1,737 3,208 1,471 84.7 30.7
WEB DEVELOPERS 806 1,284 478 59.3 39.3
COMP PROGRAMMERS 1,310 1,208 -102 -7.8 17.8
INFO SECURITY ANALYSTS 650 993 343 52.8 69.5
NET/COMP SYS ADMINS 500 861 361 72.2 13.4
DATABASE ADMINISTRATORS 568 786 218 38.4 48.8
COMP NET ARCHITECTS 372 663 291 78.2 0.0
COMP HARDWARE ENGINEERS 0 89 89 Inf 0.0
COMP/INFO RSRCH SCIENTISTS 356 0 -356 -100.0 57.3
Table 3.TXhispanic – Hispanic American Male/Female Tech in Texas in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 46,261 11,594 25.1
COMP SUPPORT SPECIALISTS 21.9 10,129 2,834 28.0
COMP OCCUPATIONS OTHER 19.0 8,782 2,133 24.3
SOFTWARE DEVELOPERS 14.9 6,879 1,664 24.2
COMP SYSTEMS ANALYSTS 11.4 5,252 1,891 36.0
COMP/INFO SYSTEMS MANAGERS 9.2 4,249 901 21.2
NET/COMP SYS ADMINS 8.4 3,880 816 21.0
COMP PROGRAMMERS 5.6 2,592 312 12.0
WEB DEVELOPERS 3.6 1,654 372 22.5
COMP NET ARCHITECTS 2.0 918 356 38.8
DATABASE ADMINISTRATORS 1.6 763 229 30.0
INFO SECURITY ANALYSTS 1.1 525 36 6.9
COMP/INFO RSRCH SCIENTISTS 0.8 390 50 12.8
COMP HARDWARE ENGINEERS 0.5 248 0 0.0Table 3.TXTXhispanic – Changes in Hispanic American Tech in TExas 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 37,149 46,261 9,112 24.5 24.6
COMP SUPPORT SPECIALISTS 10,156 10,129 -27 -0.3 21.4
COMP OCCUPATIONS OTHER 3,154 8,782 5,628 178.4 19.7
SOFTWARE DEVELOPERS 5,088 6,879 1,791 35.2 29.7
COMP SYSTEMS ANALYSTS 3,921 5,252 1,331 33.9 40.3
COMP/INFO SYSTEMS MANAGERS 3,286 4,249 963 29.3 29.1
NET/COMP SYS ADMINS 2,997 3,880 883 29.5 17.2
COMP PROGRAMMERS 4,373 2,592 -1,781 -40.7 22.6
WEB DEVELOPERS 1,191 1,654 463 38.9 1.4
COMP NET ARCHITECTS 693 918 225 32.5 11.0
DATABASE ADMINISTRATORS 1,125 763 -362 -32.2 14.2
INFO SECURITY ANALYSTS 526 525 -1 -0.2 41.3
COMP/INFO RSRCH SCIENTISTS 305 390 85 27.9 100.0
COMP HARDWARE ENGINEERS 334 248 -86 -25.7 0.0
New York … New York … New York … New York …
Table 3.NY – American Male/Female Tech in New York in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 240,885 56,652 23.5
SOFTWARE DEVELOPERS 18.0 43,378 7,333 16.9
COMP SUPPORT SPECIALISTS 14.4 34,799 8,196 23.6
COMP SYSTEMS ANALYSTS 12.8 30,898 10,715 34.7
COMP OCCUPATIONS OTHER 12.6 30,397 6,420 21.1
COMP/INFO SYSTEMS MANAGERS 12.2 29,324 7,688 26.2
COMP PROGRAMMERS 11.7 28,158 6,621 23.5
WEB DEVELOPERS 5.7 13,849 4,810 34.7
NET/COMP SYS ADMINS 4.1 9,928 1,317 13.3
COMP NET ARCHITECTS 3.0 7,240 205 2.8
DATABASE ADMINISTRATORS 2.5 5,948 1,771 29.8
INFO SECURITY ANALYSTS 1.3 3,219 1,156 35.9
COMP HARDWARE ENGINEERS 1.0 2,368 284 12.0
COMP/INFO RSRCH SCIENTISTS 0.6 1,379 136 9.9Table 3.NYNY – Changes in American Tech in New York 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 200,259 240,885 40,626 20.3 25.4
SOFTWARE DEVELOPERS 31,844 43,378 11,534 36.2 19.9
COMP SUPPORT SPECIALISTS 22,859 34,799 11,940 52.2 23.8
COMP SYSTEMS ANALYSTS 28,850 30,898 2,048 7.1 35.4
COMP OCCUPATIONS OTHER 20,920 30,397 9,477 45.3 18.2
COMP/INFO SYSTEMS MANAGERS 24,694 29,324 4,630 18.7 31.1
COMP PROGRAMMERS 30,846 28,158 -2,688 -8.7 21.6
WEB DEVELOPERS 12,842 13,849 1,007 7.8 35.4
NET/COMP SYS ADMINS 12,450 9,928 -2,522 -20.3 20.6
COMP NET ARCHITECTS 5,133 7,240 2,107 41.0 6.8
DATABASE ADMINISTRATORS 4,340 5,948 1,608 37.1 45.0
INFO SECURITY ANALYSTS 2,152 3,219 1,067 49.6 30.8
COMP HARDWARE ENGINEERS 3,164 2,368 -796 -25.2 13.7
COMP/INFO RSRCH SCIENTISTS 165 1,379 1,214 735.8 52.7Table 3.NYblack – Black American Male/Female Tech in New York in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 17,100 4,467 26.1
COMP OCCUPATIONS OTHER 17.9 3,069 743 24.2
COMP SUPPORT SPECIALISTS 17.5 3,000 1,331 44.4
COMP SYSTEMS ANALYSTS 14.2 2,434 827 34.0
COMP/INFO SYSTEMS MANAGERS 12.8 2,188 475 21.7
SOFTWARE DEVELOPERS 9.1 1,548 273 17.6
COMP PROGRAMMERS 8.8 1,509 599 39.7
WEB DEVELOPERS 7.0 1,202 0 0.0
DATABASE ADMINISTRATORS 6.8 1,155 219 19.0
COMP NET ARCHITECTS 3.8 654 0 0.0
NET/COMP SYS ADMINS 0.9 153 0 0.0
COMP HARDWARE ENGINEERS 0.7 120 0 0.0
INFO SECURITY ANALYSTS 0.4 68 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.NYNYblack – Changes in Black American Tech in New York 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 15,492 17,100 1,608 10.4 25.7
COMP OCCUPATIONS OTHER 2,902 3,069 167 5.8 31.0
COMP SUPPORT SPECIALISTS 3,986 3,000 -986 -24.7 19.6
COMP SYSTEMS ANALYSTS 2,859 2,434 -425 -14.9 33.7
COMP/INFO SYSTEMS MANAGERS 939 2,188 1,249 133.0 29.1
SOFTWARE DEVELOPERS 1,104 1,548 444 40.2 35.7
COMP PROGRAMMERS 690 1,509 819 118.7 24.6
WEB DEVELOPERS 640 1,202 562 87.8 0.0
DATABASE ADMINISTRATORS 377 1,155 778 206.4 0.0
COMP NET ARCHITECTS 350 654 304 86.9 38.9
NET/COMP SYS ADMINS 1,106 153 -953 -86.2 33.1
COMP HARDWARE ENGINEERS 162 120 -42 -25.9 0.0
INFO SECURITY ANALYSTS 377 68 -309 -82.0 0.0
COMP/INFO RSRCH SCIENTISTS 0 0 0 NaN 0.0
Table 3.NYhispanic – Hispanic American Male/Female Tech in New York in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 21,662 5,663 26.1
COMP SUPPORT SPECIALISTS 22.2 4,813 1,759 36.5
SOFTWARE DEVELOPERS 17.7 3,832 414 10.8
COMP/INFO SYSTEMS MANAGERS 16.6 3,593 1,074 29.9
COMP OCCUPATIONS OTHER 10.7 2,316 0 0.0
COMP SYSTEMS ANALYSTS 8.7 1,875 1,289 68.7
COMP PROGRAMMERS 6.5 1,399 206 14.7
NET/COMP SYS ADMINS 6.0 1,301 230 17.7
WEB DEVELOPERS 4.4 955 368 38.5
DATABASE ADMINISTRATORS 2.4 516 171 33.1
COMP NET ARCHITECTS 2.0 439 0 0.0
COMP HARDWARE ENGINEERS 1.1 234 0 0.0
INFO SECURITY ANALYSTS 0.9 201 55 27.4
COMP/INFO RSRCH SCIENTISTS 0.9 188 97 51.6Table 3.NYNYhispanic – Changes in Hispanic American Tech in New York 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 12,681 21,662 8,981 70.8 27.9
COMP SUPPORT SPECIALISTS 1,652 4,813 3,161 191.3 16.9
SOFTWARE DEVELOPERS 749 3,832 3,083 411.6 38.7
COMP/INFO SYSTEMS MANAGERS 2,308 3,593 1,285 55.7 43.8
COMP OCCUPATIONS OTHER 2,417 2,316 -101 -4.2 1.2
COMP SYSTEMS ANALYSTS 1,858 1,875 17 0.9 44.6
COMP PROGRAMMERS 890 1,399 509 57.2 47.1
NET/COMP SYS ADMINS 987 1,301 314 31.8 15.7
WEB DEVELOPERS 552 955 403 73.0 26.4
DATABASE ADMINISTRATORS 377 516 139 36.9 57.8
COMP NET ARCHITECTS 364 439 75 20.6 0.0
COMP HARDWARE ENGINEERS 334 234 -100 -29.9 21.3
INFO SECURITY ANALYSTS 193 201 8 4.1 49.7
COMP/INFO RSRCH SCIENTISTS 0 188 188 Inf 0.0
Florida … Florida … Florida … Florida …
Table 3.FL – American Male/Female Tech in Florida in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 193,697 51,910 26.8
COMP SUPPORT SPECIALISTS 17.6 34,021 10,410 30.6
SOFTWARE DEVELOPERS 16.9 32,703 6,518 19.9
COMP OCCUPATIONS OTHER 14.8 28,670 7,890 27.5
COMP/INFO SYSTEMS MANAGERS 13.0 25,177 7,364 29.2
COMP PROGRAMMERS 10.1 19,519 4,203 21.5
COMP SYSTEMS ANALYSTS 8.9 17,209 6,176 35.9
WEB DEVELOPERS 5.8 11,212 4,849 43.2
NET/COMP SYS ADMINS 5.3 10,249 1,948 19.0
DATABASE ADMINISTRATORS 2.6 4,973 1,598 32.1
COMP NET ARCHITECTS 2.4 4,567 135 3.0
INFO SECURITY ANALYSTS 1.5 2,856 466 16.3
COMP HARDWARE ENGINEERS 1.1 2,158 295 13.7
COMP/INFO RSRCH SCIENTISTS 0.2 383 58 15.1Table 3.FLFL – Changes in American Tech in Florida 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 145,995 193,697 47,702 32.7 24.6
COMP SUPPORT SPECIALISTS 23,895 34,021 10,126 42.4 27.3
SOFTWARE DEVELOPERS 27,357 32,703 5,346 19.5 21.2
COMP OCCUPATIONS OTHER 14,017 28,670 14,653 104.5 20.4
COMP/INFO SYSTEMS MANAGERS 18,104 25,177 7,073 39.1 27.5
COMP PROGRAMMERS 17,698 19,519 1,821 10.3 19.4
COMP SYSTEMS ANALYSTS 15,317 17,209 1,892 12.4 36.5
WEB DEVELOPERS 7,497 11,212 3,715 49.6 23.0
NET/COMP SYS ADMINS 9,803 10,249 446 4.5 19.2
DATABASE ADMINISTRATORS 4,666 4,973 307 6.6 38.3
COMP NET ARCHITECTS 3,130 4,567 1,437 45.9 8.3
INFO SECURITY ANALYSTS 1,346 2,856 1,510 112.2 34.5
COMP HARDWARE ENGINEERS 3,035 2,158 -877 -28.9 18.3
COMP/INFO RSRCH SCIENTISTS 130 383 253 194.6 44.6
Table 3.FLblack – Black American Male/Female Tech in Florida in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 16,999 5,916 34.8
COMP SUPPORT SPECIALISTS 29.3 4,975 2,227 44.8
COMP OCCUPATIONS OTHER 19.8 3,365 1,527 45.4
COMP/INFO SYSTEMS MANAGERS 14.6 2,481 786 31.7
SOFTWARE DEVELOPERS 13.9 2,361 185 7.8
NET/COMP SYS ADMINS 7.1 1,210 39 3.2
COMP SYSTEMS ANALYSTS 6.8 1,164 645 55.4
COMP PROGRAMMERS 3.2 544 237 43.6
WEB DEVELOPERS 1.5 263 99 37.6
COMP NET ARCHITECTS 1.5 248 0 0.0
COMP HARDWARE ENGINEERS 1.3 217 0 0.0
DATABASE ADMINISTRATORS 0.6 103 103 100.0
INFO SECURITY ANALYSTS 0.4 68 68 100.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.FLFLblack – Changes in Black American Tech in Florida 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 10,896 16,999 6,103 56.0 33.0
COMP SUPPORT SPECIALISTS 2,206 4,975 2,769 125.5 33.9
COMP OCCUPATIONS OTHER 1,558 3,365 1,807 116.0 30.3
COMP/INFO SYSTEMS MANAGERS 633 2,481 1,848 291.9 43.8
SOFTWARE DEVELOPERS 1,735 2,361 626 36.1 19.4
NET/COMP SYS ADMINS 586 1,210 624 106.5 38.7
COMP SYSTEMS ANALYSTS 1,355 1,164 -191 -14.1 44.3
COMP PROGRAMMERS 1,209 544 -665 -55.0 51.9
WEB DEVELOPERS 659 263 -396 -60.1 0.0
COMP NET ARCHITECTS 50 248 198 396.0 0.0
COMP HARDWARE ENGINEERS 120 217 97 80.8 61.7
DATABASE ADMINISTRATORS 638 103 -535 -83.9 24.8
INFO SECURITY ANALYSTS 75 68 -7 -9.3 100.0
COMP/INFO RSRCH SCIENTISTS 72 0 -72 -100.0 0.0
Table 3.FLhispanic – Hispanic American Male/Female Tech in Florida in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 32,968 7,862 23.8
COMP SUPPORT SPECIALISTS 22.8 7,510 2,163 28.8
COMP OCCUPATIONS OTHER 16.5 5,431 1,213 22.3
SOFTWARE DEVELOPERS 15.5 5,115 761 14.9
COMP PROGRAMMERS 10.7 3,541 851 24.0
COMP/INFO SYSTEMS MANAGERS 9.5 3,142 870 27.7
COMP SYSTEMS ANALYSTS 8.7 2,865 1,181 41.2
NET/COMP SYS ADMINS 6.6 2,163 392 18.1
WEB DEVELOPERS 4.3 1,416 174 12.3
DATABASE ADMINISTRATORS 3.4 1,128 257 22.8
COMP NET ARCHITECTS 1.3 425 0 0.0
COMP HARDWARE ENGINEERS 0.5 174 0 0.0
INFO SECURITY ANALYSTS 0.2 58 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.FLFLhispanic – Changes in Hispanic American Tech in Florida 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 21,882 32,968 11,086 50.7 20.6
COMP SUPPORT SPECIALISTS 4,011 7,510 3,499 87.2 31.0
COMP OCCUPATIONS OTHER 2,832 5,431 2,599 91.8 14.4
SOFTWARE DEVELOPERS 2,306 5,115 2,809 121.8 23.3
COMP PROGRAMMERS 2,677 3,541 864 32.3 5.7
COMP/INFO SYSTEMS MANAGERS 3,152 3,142 -10 -0.3 15.9
COMP SYSTEMS ANALYSTS 2,098 2,865 767 36.6 38.0
NET/COMP SYS ADMINS 1,714 2,163 449 26.2 18.9
WEB DEVELOPERS 1,344 1,416 72 5.4 14.4
DATABASE ADMINISTRATORS 520 1,128 608 116.9 8.7
COMP NET ARCHITECTS 577 425 -152 -26.3 35.7
COMP HARDWARE ENGINEERS 520 174 -346 -66.5 17.7
INFO SECURITY ANALYSTS 131 58 -73 -55.7 0.0
COMP/INFO RSRCH SCIENTISTS 0 0 0 NaN 0.0
Virginia … Virginia … Virginia …
Table 3.VA – American Male/Female Tech in Virginia in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 186,597 54,764 29.3
SOFTWARE DEVELOPERS 26.4 49,194 11,316 23.0
COMP OCCUPATIONS OTHER 13.9 26,027 6,919 26.6
COMP SYSTEMS ANALYSTS 12.7 23,725 11,015 46.4
COMP/INFO SYSTEMS MANAGERS 11.8 22,092 6,681 30.2
COMP SUPPORT SPECIALISTS 11.3 21,022 5,693 27.1
NET/COMP SYS ADMINS 5.3 9,975 2,465 24.7
COMP PROGRAMMERS 5.2 9,640 4,077 42.3
WEB DEVELOPERS 3.3 6,192 2,079 33.6
DATABASE ADMINISTRATORS 2.9 5,427 2,403 44.3
INFO SECURITY ANALYSTS 2.7 5,043 831 16.5
COMP NET ARCHITECTS 2.7 4,957 482 9.7
COMP/INFO RSRCH SCIENTISTS 1.0 1,811 746 41.2
COMP HARDWARE ENGINEERS 0.8 1,492 57 3.8Table 3.VAVA – Changes in American Tech in Virginia 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 166,793 186,597 19,804 11.9 27.7
SOFTWARE DEVELOPERS 41,159 49,194 8,035 19.5 25.0
COMP OCCUPATIONS OTHER 12,942 26,027 13,085 101.1 29.6
COMP SYSTEMS ANALYSTS 22,155 23,725 1,570 7.1 32.8
COMP/INFO SYSTEMS MANAGERS 21,288 22,092 804 3.8 34.7
COMP SUPPORT SPECIALISTS 21,441 21,022 -419 -2.0 29.4
NET/COMP SYS ADMINS 14,544 9,975 -4,569 -31.4 19.8
COMP PROGRAMMERS 13,257 9,640 -3,617 -27.3 22.9
WEB DEVELOPERS 3,801 6,192 2,391 62.9 38.4
DATABASE ADMINISTRATORS 3,343 5,427 2,084 62.3 45.8
INFO SECURITY ANALYSTS 5,061 5,043 -18 -0.4 20.3
COMP NET ARCHITECTS 5,042 4,957 -85 -1.7 6.6
COMP/INFO RSRCH SCIENTISTS 968 1,811 843 87.1 73.3
COMP HARDWARE ENGINEERS 1,792 1,492 -300 -16.7 9.8
Table 3.VAblack – Black American Male/Female Tech in Virginia in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 28,381 11,356 40.0
COMP OCCUPATIONS OTHER 20.8 5,892 1,897 32.2
SOFTWARE DEVELOPERS 17.7 5,030 2,342 46.6
COMP SUPPORT SPECIALISTS 15.7 4,455 1,588 35.6
COMP SYSTEMS ANALYSTS 11.4 3,227 1,644 50.9
COMP/INFO SYSTEMS MANAGERS 10.2 2,891 1,805 62.4
NET/COMP SYS ADMINS 7.3 2,076 809 39.0
COMP PROGRAMMERS 6.9 1,965 752 38.3
INFO SECURITY ANALYSTS 3.2 895 155 17.3
COMP/INFO RSRCH SCIENTISTS 2.8 787 179 22.7
COMP NET ARCHITECTS 2.2 620 126 20.3
WEB DEVELOPERS 0.8 228 59 25.9
DATABASE ADMINISTRATORS 0.7 202 0 0.0
COMP HARDWARE ENGINEERS 0.4 113 0 0.0Table 3.VAVAblack – Changes in Black American Tech in Virginia 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 22,087 28,381 6,294 28.5 40.9
COMP OCCUPATIONS OTHER 2,340 5,892 3,552 151.8 27.2
SOFTWARE DEVELOPERS 5,374 5,030 -344 -6.4 39.2
COMP SUPPORT SPECIALISTS 3,723 4,455 732 19.7 45.8
COMP SYSTEMS ANALYSTS 2,165 3,227 1,062 49.1 34.5
COMP/INFO SYSTEMS MANAGERS 3,494 2,891 -603 -17.3 67.0
NET/COMP SYS ADMINS 2,162 2,076 -86 -4.0 22.3
COMP PROGRAMMERS 985 1,965 980 99.5 10.1
INFO SECURITY ANALYSTS 581 895 314 54.0 20.5
COMP/INFO RSRCH SCIENTISTS 67 787 720 1,074.6 100.0
COMP NET ARCHITECTS 207 620 413 199.5 0.0
WEB DEVELOPERS 128 228 100 78.1 100.0
DATABASE ADMINISTRATORS 761 202 -559 -73.5 79.1
COMP HARDWARE ENGINEERS 100 113 13 13.0 0.0
Table 3.VAhispanic – Hispanic American Male/Female Tech in Virginia in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 7,940 2,852 35.9
SOFTWARE DEVELOPERS 19.5 1,551 84 5.4
COMP OCCUPATIONS OTHER 14.4 1,142 199 17.4
COMP SYSTEMS ANALYSTS 14.0 1,111 729 65.6
COMP/INFO SYSTEMS MANAGERS 13.4 1,062 731 68.8
NET/COMP SYS ADMINS 9.2 731 244 33.4
COMP SUPPORT SPECIALISTS 8.5 676 60 8.9
DATABASE ADMINISTRATORS 6.4 508 508 100.0
INFO SECURITY ANALYSTS 4.9 386 0 0.0
WEB DEVELOPERS 4.3 342 239 69.9
COMP NET ARCHITECTS 2.9 234 0 0.0
COMP PROGRAMMERS 2.5 197 58 29.4
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0
COMP HARDWARE ENGINEERS 0.0 0 0 0.0Table 3.VAVAhispanic – Changes in Hispanic American Tech in Virginia 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 4,888 7,940 3,052 62.4 24.4
SOFTWARE DEVELOPERS 1,413 1,551 138 9.8 12.7
COMP OCCUPATIONS OTHER 159 1,142 983 618.2 42.1
COMP SYSTEMS ANALYSTS 1,231 1,111 -120 -9.7 59.8
COMP/INFO SYSTEMS MANAGERS 444 1,062 618 139.2 30.9
NET/COMP SYS ADMINS 377 731 354 93.9 19.4
COMP SUPPORT SPECIALISTS 584 676 92 15.8 0.0
DATABASE ADMINISTRATORS 0 508 508 Inf 0.0
INFO SECURITY ANALYSTS 337 386 49 14.5 0.0
WEB DEVELOPERS 0 342 342 Inf 0.0
COMP NET ARCHITECTS 176 234 58 33.0 0.0
COMP PROGRAMMERS 113 197 84 74.3 0.0
COMP HARDWARE ENGINEERS 54 0 -54 -100.0 0.0
COMP/INFO RSRCH SCIENTISTS 0 0 0 NaN 0.0
Illinois … Illinois …
Table 3.IL – American Male/Female Tech in Illinois in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 173,972 42,974 24.7
SOFTWARE DEVELOPERS 17.2 29,982 4,866 16.2
COMP SUPPORT SPECIALISTS 14.7 25,593 6,042 23.6
COMP OCCUPATIONS OTHER 13.9 24,172 6,150 25.4
COMP SYSTEMS ANALYSTS 13.2 23,045 9,856 42.8
COMP/INFO SYSTEMS MANAGERS 12.9 22,452 6,900 30.7
COMP PROGRAMMERS 10.1 17,634 3,849 21.8
NET/COMP SYS ADMINS 5.1 8,935 1,164 13.0
WEB DEVELOPERS 4.1 7,068 2,309 32.7
COMP NET ARCHITECTS 3.0 5,139 173 3.4
DATABASE ADMINISTRATORS 2.4 4,212 973 23.1
INFO SECURITY ANALYSTS 1.9 3,345 254 7.6
COMP HARDWARE ENGINEERS 1.3 2,263 306 13.5
COMP/INFO RSRCH SCIENTISTS 0.1 132 132 100.0Table 3.ILIL – Changes in American Tech in Illinois 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 149,428 173,972 24,544 16.4 27.3
SOFTWARE DEVELOPERS 21,772 29,982 8,210 37.7 16.4
COMP SUPPORT SPECIALISTS 21,451 25,593 4,142 19.3 33.7
COMP OCCUPATIONS OTHER 13,404 24,172 10,768 80.3 29.5
COMP SYSTEMS ANALYSTS 21,484 23,045 1,561 7.3 34.2
COMP/INFO SYSTEMS MANAGERS 24,536 22,452 -2,084 -8.5 28.8
COMP PROGRAMMERS 19,094 17,634 -1,460 -7.6 27.3
NET/COMP SYS ADMINS 9,163 8,935 -228 -2.5 19.6
WEB DEVELOPERS 6,232 7,068 836 13.4 28.0
COMP NET ARCHITECTS 4,167 5,139 972 23.3 4.8
DATABASE ADMINISTRATORS 4,599 4,212 -387 -8.4 48.6
INFO SECURITY ANALYSTS 1,936 3,345 1,409 72.8 6.2
COMP HARDWARE ENGINEERS 1,199 2,263 1,064 88.7 20.3
COMP/INFO RSRCH SCIENTISTS 391 132 -259 -66.2 11.8
Table 3.ILblack – Black American Male/Female Tech in Illinois in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 12,503 3,283 26.3
COMP OCCUPATIONS OTHER 20.0 2,505 356 14.2
COMP SUPPORT SPECIALISTS 16.8 2,099 903 43.0
COMP SYSTEMS ANALYSTS 13.9 1,737 725 41.7
SOFTWARE DEVELOPERS 10.6 1,328 165 12.4
COMP/INFO SYSTEMS MANAGERS 9.8 1,226 273 22.3
COMP PROGRAMMERS 6.8 846 534 63.1
COMP NET ARCHITECTS 6.7 838 86 10.3
NET/COMP SYS ADMINS 6.0 750 77 10.3
DATABASE ADMINISTRATORS 3.7 461 164 35.6
INFO SECURITY ANALYSTS 3.5 441 0 0.0
WEB DEVELOPERS 1.6 204 0 0.0
COMP HARDWARE ENGINEERS 0.5 68 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.ILILblack – Changes in Black American Tech in Illinois 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 10,998 12,503 1,505 13.7 50.1
COMP OCCUPATIONS OTHER 1,391 2,505 1,114 80.1 42.3
COMP SUPPORT SPECIALISTS 2,200 2,099 -101 -4.6 53.0
COMP SYSTEMS ANALYSTS 1,307 1,737 430 32.9 53.0
SOFTWARE DEVELOPERS 697 1,328 631 90.5 27.8
COMP/INFO SYSTEMS MANAGERS 1,346 1,226 -120 -8.9 70.0
COMP PROGRAMMERS 1,586 846 -740 -46.7 34.0
COMP NET ARCHITECTS 259 838 579 223.6 24.7
NET/COMP SYS ADMINS 363 750 387 106.6 62.5
DATABASE ADMINISTRATORS 866 461 -405 -46.8 100.0
INFO SECURITY ANALYSTS 325 441 116 35.7 36.9
WEB DEVELOPERS 598 204 -394 -65.9 19.1
COMP HARDWARE ENGINEERS 0 68 68 Inf 0.0
COMP/INFO RSRCH SCIENTISTS 60 0 -60 -100.0 0.0
Table 3.ILhispanic – Hispanic American Male/Female Tech in Illinois in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 11,901 2,649 22.3
COMP SUPPORT SPECIALISTS 21.9 2,607 894 34.3
COMP OCCUPATIONS OTHER 16.6 1,975 163 8.3
COMP SYSTEMS ANALYSTS 15.7 1,870 711 38.0
COMP PROGRAMMERS 13.0 1,543 112 7.3
NET/COMP SYS ADMINS 10.2 1,208 138 11.4
SOFTWARE DEVELOPERS 5.4 639 180 28.2
DATABASE ADMINISTRATORS 5.1 604 152 25.2
WEB DEVELOPERS 4.7 559 0 0.0
COMP/INFO SYSTEMS MANAGERS 3.0 360 207 57.5
COMP NET ARCHITECTS 1.9 228 0 0.0
INFO SECURITY ANALYSTS 1.3 156 0 0.0
COMP HARDWARE ENGINEERS 1.3 152 92 60.5
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0Table 3.ILILhispanic – Changes in Hispanic American Tech in Illinois 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 9,102 11,901 2,799 30.8 23.7
COMP SUPPORT SPECIALISTS 1,614 2,607 993 61.5 36.6
COMP OCCUPATIONS OTHER 372 1,975 1,603 430.9 58.1
COMP SYSTEMS ANALYSTS 616 1,870 1,254 203.6 9.3
COMP PROGRAMMERS 1,372 1,543 171 12.5 23.7
NET/COMP SYS ADMINS 1,302 1,208 -94 -7.2 5.3
SOFTWARE DEVELOPERS 1,324 639 -685 -51.7 16.5
DATABASE ADMINISTRATORS 615 604 -11 -1.8 17.2
WEB DEVELOPERS 83 559 476 573.5 0.0
COMP/INFO SYSTEMS MANAGERS 928 360 -568 -61.2 62.3
COMP NET ARCHITECTS 644 228 -416 -64.6 0.0
INFO SECURITY ANALYSTS 0 156 156 Inf 0.0
COMP HARDWARE ENGINEERS 78 152 74 94.9 0.0
COMP/INFO RSRCH SCIENTISTS 154 0 -154 -100.0 0.0
Dist. of Columbia
Table 3.DC – American Male/Female Tech in DC in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 50,301 16,894 33.6
SOFTWARE DEVELOPERS 24.6 12,386 4,829 39.0
COMP OCCUPATIONS OTHER 15.3 7,708 1,802 23.4
COMP SUPPORT SPECIALISTS 12.6 6,349 2,011 31.7
COMP/INFO SYSTEMS MANAGERS 12.4 6,218 2,527 40.6
COMP SYSTEMS ANALYSTS 11.3 5,671 1,859 32.8
COMP PROGRAMMERS 6.1 3,067 980 32.0
NET/COMP SYS ADMINS 3.7 1,848 699 37.8
WEB DEVELOPERS 3.2 1,629 775 47.6
DATABASE ADMINISTRATORS 3.0 1,519 554 36.5
COMP NET ARCHITECTS 2.8 1,411 172 12.2
INFO SECURITY ANALYSTS 2.5 1,262 424 33.6
COMP HARDWARE ENGINEERS 1.6 820 262 32.0
COMP/INFO RSRCH SCIENTISTS 0.8 413 0 0.0Table 3.DCDC – Changes in American Tech in DC 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 47,524 50,301 2,777 5.8 38.6
SOFTWARE DEVELOPERS 9,962 12,386 2,424 24.3 54.7
COMP OCCUPATIONS OTHER 4,629 7,708 3,079 66.5 21.8
COMP SUPPORT SPECIALISTS 4,195 6,349 2,154 51.3 34.3
COMP/INFO SYSTEMS MANAGERS 8,264 6,218 -2,046 -24.8 45.5
COMP SYSTEMS ANALYSTS 6,959 5,671 -1,288 -18.5 36.7
COMP PROGRAMMERS 1,888 3,067 1,179 62.4 31.4
NET/COMP SYS ADMINS 2,354 1,848 -506 -21.5 17.9
WEB DEVELOPERS 1,655 1,629 -26 -1.6 40.8
DATABASE ADMINISTRATORS 2,578 1,519 -1,059 -41.1 37.0
COMP NET ARCHITECTS 1,345 1,411 66 4.9 7.4
INFO SECURITY ANALYSTS 2,724 1,262 -1,462 -53.7 38.5
COMP HARDWARE ENGINEERS 569 820 251 44.1 33.0
COMP/INFO RSRCH SCIENTISTS 402 413 11 2.7 37.6
Table 3.DCblack – Black American Male/Female in DC in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 16,558 6,924 41.8
SOFTWARE DEVELOPERS 27.0 4,476 2,122 47.4
COMP OCCUPATIONS OTHER 15.8 2,622 868 33.1
COMP PROGRAMMERS 12.5 2,077 611 29.4
COMP SUPPORT SPECIALISTS 10.8 1,793 644 35.9
COMP SYSTEMS ANALYSTS 10.1 1,671 601 36.0
COMP/INFO SYSTEMS MANAGERS 10.0 1,651 1,205 73.0
NET/COMP SYS ADMINS 4.2 702 381 54.3
COMP NET ARCHITECTS 3.1 517 0 0.0
WEB DEVELOPERS 2.3 373 205 55.0
DATABASE ADMINISTRATORS 1.8 304 0 0.0
INFO SECURITY ANALYSTS 1.7 287 287 100.0
COMP HARDWARE ENGINEERS 0.3 51 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.2 34 0 0.0Table 3.DCDCblack – Changes in Black American Tech in DC 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 13,292 16,558 3,266 24.6 50.0
SOFTWARE DEVELOPERS 3,020 4,476 1,456 48.2 85.0
COMP OCCUPATIONS OTHER 1,495 2,622 1,127 75.4 29.1
COMP PROGRAMMERS 174 2,077 1,903 1,093.7 0.0
COMP SUPPORT SPECIALISTS 1,436 1,793 357 24.9 56.4
COMP SYSTEMS ANALYSTS 2,384 1,671 -713 -29.9 27.4
COMP/INFO SYSTEMS MANAGERS 2,336 1,651 -685 -29.3 43.7
NET/COMP SYS ADMINS 763 702 -61 -8.0 38.5
COMP NET ARCHITECTS 237 517 280 118.1 0.0
WEB DEVELOPERS 0 373 373 Inf 0.0
DATABASE ADMINISTRATORS 808 304 -504 -62.4 59.0
INFO SECURITY ANALYSTS 549 287 -262 -47.7 70.5
COMP HARDWARE ENGINEERS 90 51 -39 -43.3 0.0
COMP/INFO RSRCH SCIENTISTS 0 34 34 Inf 0.0
Table 3.DChispanic – Hispanic American Male/Female Tech in DC in 2015
Occupation perTS Tech15 Fem perF15
All Occupations 100.0 2,156 486 22.5
SOFTWARE DEVELOPERS 26.9 579 226 39.0
COMP OCCUPATIONS OTHER 23.0 496 0 0.0
COMP SYSTEMS ANALYSTS 18.8 406 119 29.3
COMP/INFO SYSTEMS MANAGERS 12.1 261 0 0.0
COMP SUPPORT SPECIALISTS 9.2 198 76 38.4
DATABASE ADMINISTRATORS 4.5 96 0 0.0
NET/COMP SYS ADMINS 3.0 65 65 100.0
COMP NET ARCHITECTS 2.6 55 0 0.0
COMP/INFO RSRCH SCIENTISTS 0.0 0 0 0.0
INFO SECURITY ANALYSTS 0.0 0 0 0.0
COMP PROGRAMMERS 0.0 0 0 0.0
WEB DEVELOPERS 0.0 0 0 0.0
COMP HARDWARE ENGINEERS 0.0 0 0 0.0Table 3.DCDChispanic – Changes in Hispanic American Tech in DC 2010 to 2015
Occupation Tech10 Tech15 Change perCh perF10
All Occupations 1,641 2,156 515 31.4 17.0
SOFTWARE DEVELOPERS 362 579 217 59.9 28.7
COMP OCCUPATIONS OTHER 209 496 287 137.3 0.0
COMP SYSTEMS ANALYSTS 344 406 62 18.0 20.1
COMP/INFO SYSTEMS MANAGERS 344 261 -83 -24.1 0.0
COMP SUPPORT SPECIALISTS 123 198 75 61.0 0.0
DATABASE ADMINISTRATORS 0 96 96 Inf 0.0
NET/COMP SYS ADMINS 0 65 65 Inf 0.0
COMP NET ARCHITECTS 0 55 55 Inf 0.0
COMP HARDWARE ENGINEERS 60 0 -60 -100.0 0.0
COMP PROGRAMMERS 106 0 -106 -100.0 100.0
COMP/INFO RSRCH SCIENTISTS 0 0 0 NaN 0.0
INFO SECURITY ANALYSTS 93 0 -93 -100.0 0.0
WEB DEVELOPERS 0 0 0 NaN 0.0