STATE WORKERS APPLICATIONS 1 CA 184896 76546 2 TX 64105 36797 3 NY 47474 32944 4 NJ 30349 22511 5 WA 29115 16658 6 IL 27605 18638 7 FL 23267 12403 8 MA 22937 14810 9 PA 21127 12520 10 GA 18712 12748 11 NC 17894 11668 12 MI 16233 11673 13 VA 15663 10462 14 OH 15069 9305 15 AZ 9303 6572 16 CT 8951 5924 17 CO 8211 4267 18 MN 7785 5928 19 MO 6608 5156 20 OR 6512 2953 21 MD 6343 5603 22 TN 6266 4103 23 IN 5982 4490 24 WI 5253 4044 25 UT 3345 2288 26 AR 3153 2495 27 LA 3065 984 28 DC 2987 2166 29 DE 2478 1864 30 IA 2474 1896 31 SC 2388 1909 32 KY 2302 1674 33 RI 1893 1440 34 KS 1738 1402 35 NE 1568 1268 36 NH 1390 973 37 AL 1300 1014 38 ID 1289 546 39 OK 1239 957 40 NV 1189 965 41 MS 1130 443 42 NM 658 514 43 WV 360 276 44 ND 326 226 45 ME 326 290 46 HI 299 272 47 SD 232 130 48 VT 198 198 49 MT 185 97 50 GU 174 170 51 WY 107 58 52 AK 82 78 53 PR 61 61 54 MP 50 48 55 VI 17 15 563 3 57 PW 1 1
As stated in an August 5, 2019 Bloomberg Law article, the Labor Department "has begun publishing the names of companies where H-1B holders are working, even if they're employed by another company that provides staffing or consulting services". This is done via H-1B disclosure data and began with fiscal year (FY) 2019.
I've added the capability to look at this data with the R Shiny app at https://labor.shinyapps.io/lca18/. By default, the app will load FY 2019 data. Selecting EMPLOYER_NAME in the "Group by" select list will output a table of which the following is the top 50 employers in FY 2019, sorted by Total Workers:
[1] "Search CASE_STATUS for CERTIFIED"
[1] ""
[1] "H-1B DISCLOSURE DATA, FY 2019"
[1] "(CASE_STATUS=CERTIFIED)"
[1] ""
[1] "SUM(TOTAL_WORKERS) = 643,667"
[1] "NUMBER OF ROWS = 50,725"
[1] "MEDIAN(LOW_WAGE) = NULL"
[1] "MEAN(LOW_WAGE) = NA"
[1] ""
EMPLOYER_NAME WORKERS APPLICATIONS
1 DELOITTE CONSULTING LLP 45622 4007
2 APPLE INC. 22050 737
3 INFOSYS LIMITED 16127 16127
4 COGNIZANT TECHNOLOGY SOLUTIONS US CORP 15619 15619
5 QUALCOMM TECHNOLOGIES, INC. 14412 264
6 KFORCE INC. 13113 504
7 TATA CONSULTANCY SERVICES LIMITED 11116 7191
8 WIPRO LIMITED 10455 3042
9 CISCO SYSTEMS, INC. 9559 515
10 AMAZON.COM SERVICES, INC. 9181 4130
11 HCL AMERICA, INC. 7419 2821
12 GOOGLE LLC 7197 7197
13 CAPGEMINI AMERICA INC 6316 5342
14 NVIDIA CORPORATION 6145 232
15 UBER TECHNOLOGIES, INC. 5374 868
16 FACEBOOK, INC. 5120 1314
17 ERNST & YOUNG U.S. LLP 5115 5067
18 ACCENTURE LLP 5065 3421
19 ORACLE AMERICA, INC. 4743 285
20 SYNOPSYS, INC. 4653 125
21 QUALCOMM ATHEROS, INC. 4560 91
22 INTEL CORPORATION 4042 969
23 MPHASIS CORPORATION 3714 916
24 HEWLETT PACKARD ENTERPRISE COMPANY 3676 140
25 WESTERN DIGITAL TECHNOLOGIES, INC. 3596 142
26 CYIENT, INC. 3544 218
27 BIRLASOFT INC 3460 167
28 IBM CORPORATION 3383 3383
29 HP INC. 3279 93
30 SERVICENOW, INC. 3268 157
31 MICROSOFT CORPORATION 3061 3061
32 LARSEN & TOUBRO INFOTECH LIMITED 2953 2597
33 CGI TECHNOLOGIES AND SOLUTIONS INC. 2747 293
34 APPLIED MATERIALS, INC. 2648 302
35 MASTECH DIGITAL TECHNOLOGIES, INC., A MA 2598 433
36 ORACLE AMERICA, INC 2279 206
37 MANAGEMENT HEALTH SYSTEMS, LLC 2262 55
38 DXC TECHNOLOGY SERVICES LLC 2197 186
39 MENTOR GRAPHICS CORPORATION 2169 91
40 A.T. KEARNEY, INC. 2167 52
41 MCKINSEY & COMPANY, INC. UNITED STATES 2166 274
42 DELOITTE & TOUCHE LLP 2132 677
43 TECH MAHINDRA (AMERICAS),INC. 2118 2118
44 RANDSTAD TECHNOLOGIES, LLC 2014 844
45 UBS BUSINESS SOLUTIONS US LLC 1874 231
46 WORKDAY, INC. 1737 92
47 GILEAD SCIENCES, INC. 1587 92
48 AKT AMERICA, INC. 1563 30
49 AMAZON WEB SERVICES, INC. 1541 785
50 SYNTEL INC 1525 1525
The header specifies that these records are for certified Labor Condition Applications (LCAs). CERTIFIED is the default case status and is specified by setting the Search CASE_STATUS (or leaving it blank for all LCAs). The EMPLOYER_NAME column lists the actual employers of the H-1B workers as given in prior years. Note that most of the employers are either consulting firms or major tech companies. In the top 10, for example, Deloitte, Infosys, Congnizant, KForce, Tata, and Wipro are consulting firms. The other four (Apple, Qualcomm, Cisco, and Amazon) are major tech companies.
To see the employers associated with the worksites, change EMPLOYER_NAME to EMPLOYER_NAME2 in the "Group by" select list. One way to do this is to highlight EMPLOYER_NAME, hit the delete key, and then select EMPLOYER_NAME2. This will output a table of which the following is the top 50 "worksite employers" in FY 2019, sorted by Total Workers. This is the same as the prior table except that, if the H-1B worker is working for a client company, the name given is of that of the client company. One other difference is that EMPLOYER_NAME2 is cleaned by default. The default settings for the cleaning are displayed in the bottom section of the left sidepanel and can be changed. For example, note item 19 (ORACLE AMERICA, INC.) and item 36 (ORACLE AMERICA, INC) in the table above. These are both cleaned and combined to form item 8 (ORACLE AMERICA) below. This is done by removing all periods and then removing the trailing INC, one of the items specified in the "Delete Trailer in Employer" select list. Following is the first 50 lines of the cleaned table:
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 DELOITTE CONSULTING 42321 3986
2 APPLE 24758 2955
3 QUALCOMM TECHNOLOGIES 14439 290
4 CISCO SYSTEMS 11136 1547
5 KFORCE 9924 186
6 AMAZON COM 9535 4421
7 GOOGLE 7963 7824
8 ORACLE AMERICA 6974 483
9 NVIDIA 6165 252
10 COGNIZANT TECHNOLOGY SOLUTIONS US 5637 5637
11 FACEBOOK 5528 1708
12 UBER TECHNOLOGIES 5387 881
13 HCL AMERICA 5191 593
14 SYNOPSYS 4659 131
15 QUALCOMM ATHEROS 4560 91
16 INTEL 4389 1205
17 ERNST & YOUNG U S 4159 4122
18 HEWLETT PACKARD ENTERPRISE 4056 314
19 MICROSOFT 3892 3568
20 WESTERN DIGITAL TECHNOLOGIES 3621 167
21 HP 3593 196
22 SERVICENOW 3286 175
23 INFOSYS LIMITED 3277 3277
24 WELLS FARGO 2785 1732
25 AT&T 2692 1922
26 APPLIED MATERIALS 2672 383
27 DXC 2425 392
28 AMERICAN EXPRESS 2246 2021
29 A T KEARNEY 2167 52
30 MCKINSEY & COMPANY INC UNITED STATES 2166 274
31 MENTOR GRAPHICS 2156 102
32 DELOITTE & TOUCHE 2109 670
33 UBS BUSINESS SOLUTIONS US 1967 263
34 CAPITAL ONE 1906 1544
35 IBM 1796 1793
36 WORKDAY 1750 105
37 CAPGEMINI AMERICA 1739 776
38 GILEAD SCIENCES 1738 239
39 TATA CONSULTANCY SERVICES LIMITED 1721 1496
40 CGI TECHNOLOGIES AND SOLUTIONS 1663 198
41 SALESFORCE COM 1617 1019
42 QUALCOMM 1591 84
43 AMAZON WEB 1569 813
44 AKT AMERICA 1563 30
45 ACCENTURE 1497 1168
46 PAYPAL 1491 833
47 ANTHEM 1462 1342
48 GOLDMAN SACHS 1436 1273
49 INFORMATICA 1390 87
50 MPHASIS 1381 71
To see the original employer names, select "Clean None" in the select box above "Ignore in Employer". Following are the first 50 lines in the resulting table:
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 DELOITTE CONSULTING LLP 42049 3758
2 APPLE INC. 22191 874
3 QUALCOMM TECHNOLOGIES, INC. 14412 264
4 KFORCE INC. 9913 184
5 CISCO SYSTEMS, INC. 9617 555
6 AMAZON.COM SERVICES, INC. 9180 4129
7 GOOGLE LLC 7269 7269
8 NVIDIA CORPORATION 6145 232
9 COGNIZANT TECHNOLOGY SOLUTIONS US CORP 5634 5634
10 UBER TECHNOLOGIES, INC. 5374 868
11 HCL AMERICA, INC. 5141 543
12 FACEBOOK, INC. 5122 1316
13 ORACLE AMERICA, INC. 4694 276
14 SYNOPSYS, INC. 4654 126
15 QUALCOMM ATHEROS, INC. 4560 91
16 ERNST & YOUNG U.S. LLP 4151 4114
17 INTEL CORPORATION 4097 1019
18 HEWLETT PACKARD ENTERPRISE COMPANY 3626 139
19 WESTERN DIGITAL TECHNOLOGIES, INC. 3597 143
20 MICROSOFT CORPORATION 3349 3142
21 HP INC. 3320 98
22 SERVICENOW, INC. 3270 159
23 INFOSYS LIMITED 3268 3268
24 APPLIED MATERIALS, INC. 2579 294
25 ORACLE AMERICA, INC 2279 206
26 A.T. KEARNEY, INC. 2167 52
27 MCKINSEY & COMPANY, INC. UNITED STATES 2166 274
28 DXC TECHNOLOGY SERVICES LLC 2153 185
29 MENTOR GRAPHICS CORPORATION 2144 90
30 DELOITTE & TOUCHE LLP 2108 669
31 UBS BUSINESS SOLUTIONS US LLC 1874 231
32 WORKDAY, INC. 1738 93
33 TATA CONSULTANCY SERVICES LIMITED 1719 1494
34 CGI TECHNOLOGIES AND SOLUTIONS INC. 1652 187
35 CAPGEMINI AMERICA INC 1617 654
36 IBM CORPORATION 1616 1616
37 GILEAD SCIENCES, INC. 1594 97
38 AKT AMERICA, INC. 1563 30
39 AMAZON WEB SERVICES, INC. 1541 785
40 QUALCOMM INCORPORATED 1491 38
41 ACCENTURE LLP 1414 1091
42 INFORMATICA LLC 1389 86
43 MPHASIS CORPORATION 1380 70
44 WAL-MART ASSOCIATES, INC. 1361 1361
45 HGST, INC. 1358 58
46 WAYFAIR LLC 1334 223
47 EXPEDIA, INC. 1254 233
48 Quest Diagnostics Incorporated 1239 27
49 AT&T 1207 669
50 PAYPAL, INC. 1197 645
As can be seen, many of the values for EMPLOYER_NAME2 are the same as EMPLOYER_NAME in the initial table in this section. However, the consulting companies generally have fewer workers than before since many of them are assumedly working at client companies. The number of workers for the major tech companies may be slightly higher or lower, depending on how many consultants they may have on site versus employees who may be temporarily lent out to client companies.
In fact, the 48th and 49th items (Quest and AT&T) are remote sites. Change the select list that reads "All Sites" that's just above "Search WORKSITE CITY" to "Remote Sites Only". This will output a table of which the following is the top 50 "remote sites" in FY 2019, sorted by Total Workers:
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 Quest Diagnostics Incorporated 1239 27
2 AT&T 1207 669
3 Apple, Inc. 928 928
4 Wells Fargo 916 706
5 Apple Inc. 843 428
6 WELLS FARGO 825 426
7 Ford Motor Company 743 689
8 Verizon 736 664
9 Comcast 670 549
10 PNC Bank 631 176
11 CVS 618 150
12 American Express Travel Related Services 591 591
13 Synchrony Financial 578 155
14 American Express 574 522
15 JP MORGAN CHASE 565 181
16 Capital One 562 502
17 AT&T Services Inc 495 481
18 Cisco Systems, Inc. 482 251
19 Fannie Mae 477 291
20 Fidelity Investments 452 398
21 State Street Bank & Trust Company 435 111
22 Capital One Services LLC 418 123
23 N/A 404 116
24 Johnson & Johnson 388 189
25 Aetna Life Insurance Company 378 378
26 Microsoft Corporation 371 262
27 Forum Capital Markets LLC (dba Wells Far 356 145
28 AT&T Services, Inc. 351 291
29 VERIZON 344 334
30 FedEx Corporate Services Inc. 331 87
31 Kaiser Permanente 314 240
32 Apple Inc 314 282
33 Cigna Corporate Services, LLC 314 73
34 Citi Group 312 312
35 BANK OF AMERICA 306 214
36 CHARLES SCHWAB 302 182
37 Metropolitan Life Insurance Company 297 297
38 Cisco Systems Inc 296 281
39 MICROSOFT CORPORATION 289 82
40 FedEx Corporate Services, Inc. 287 287
41 CHARLES SCHWAB & CO., INC. 285 261
42 SOUTHERN CALIFORNIA EDISON COMPANY 284 258
43 THE BOEING COMPANY 284 164
44 Walmart 282 226
45 The PNC Financial Services Group, Inc. 278 27
46 The Capital Group Companies, Inc. 277 269
47 VANGUARD 276 132
48 FORD MOTOR COMPANY 269 184
49 AIG 265 93
50 Allstate Insurance Company 263 235
As can be seen, Quest and AT&T are at the top of this list. Also, note that many of the employer names are in mixed case and that there are numerous listings for the same company. For example, items 4 (Wells Fargo), 6 (WELLS FARGO), and 27(Forum Capital Markets LLC (dba Wells Far), all appear to refer to Wells Fargo. This replication is due to the fact that the client employer name comes from the original LCA form and there is no standardization of the client names. However, it is possible to search for all of the cases by entering "Wells Fargo" (without the quotes) into the text box under "Search EMPLOYER_NAME2". This will output a table of 67 items of which the following are the first 20:
[1] "Search CASE_STATUS for CERTIFIED"
[1] "Search EMPLOYER_NAME2 for Wells Fargo"
[1] ""
[1] "H-1B DISCLOSURE DATA, FY 2019"
[1] "(CASE_STATUS=CERTIFIED, EMPLOYER_NAME2=Wells Fargo)"
[1] ""
[1] "SUM(TOTAL_WORKERS) = 3,033"
[1] "NUMBER OF ROWS = 64"
[1] "MEDIAN(LOW_WAGE) = NULL"
[1] "MEAN(LOW_WAGE) = NA"
[1] ""
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 Wells Fargo 916 706
2 WELLS FARGO 825 426
3 Forum Capital Markets LLC (dba Wells Far 356 145
4 Wells Fargo & Company 247 245
5 Wells Fargo Bank 208 78
6 WELLS FARGO BANK 82 55
7 Wells Fargo Bank, N.A. 79 21
8 WELLS FARGO BANK, N.A. 74 20
9 Forum Capital Markets, LLC (dba Wells Fa 41 12
10 Forum Capital Markets LLC (Wells Fargo) 33 4
11 Wells Fargo Bank N.A. 30 2
12 Wells Fargo Bank N.A. 25 11
13 Wells Fargo Bank, N.A 25 13
14 Wells Fargo Advisors 12 12
15 WELLS FARGO & COMPANY 6 6
16 WELLS FARGO BANK, N.A 5 5
17 Wells Fargo NA 5 1
18 Wells Fargo corporation 4 4
19 WELLS FARGO BANK NA 3 3
20 wells Fargo 2 2
The initial summary gives the total workers for all of these items of 3,033. Now switch the "Remote Sites Only" selected above "Search WORKSITE_CITY" to "Local Sites Only". That will output the following:
[1] "Search CASE_STATUS for CERTIFIED"
[1] "Search EMPLOYER_NAME2 for Wells Fargo"
[1] ""
[1] "H-1B DISCLOSURE DATA, FY 2019"
[1] "(CASE_STATUS=CERTIFIED, EMPLOYER_NAME2=Wells Fargo)"
[1] ""
[1] "SUM(TOTAL_WORKERS) = 230"
[1] "NUMBER OF ROWS = 3"
[1] "MEDIAN(LOW_WAGE) = NULL"
[1] "MEAN(LOW_WAGE) = NA"
[1] ""
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 WELLS FARGO BANK, N.A. 226 111
2 WELLS FARGO SECURITIES, LLC 3 3
3 WELLS FARGO FUNDS MANAGEMENT LLC 1 1
The summary shows only 230 total workers directly employed by Wells Fargo are requested. Hence, the great majority of H-1B workers requested at Wells Fargo are to be hired through consulting companies.
Change the "Local Sites Only" back to "Remote Sites Only". Now add EMPLOYER_NAME before EMPLOYER_NAME2 in the "Group by" box. One way to do this is by placing the cursor in the "Group by" box, hitting the left arrow key to move it in front of the EMPLOYER_NAME2 selection, and then select EMPLOYER_NAME. This will output a table of which the following are the first 20:
EMPLOYER_NAME EMPLOYER_NAME2 WORKERS APPLICATIONS
1 SYNECHRON, INC. Forum Capital Markets LLC (dba Wells Far 356 145
2 RANDSTAD TECHNOLOGIES, LLC WELLS FARGO 280 64
3 INFOSYS LIMITED Wells Fargo & Company 220 220
4 DELOITTE CONSULTING LLP Wells Fargo Bank 141 11
5 BIRLASOFT INC WELLS FARGO 140 7
6 BIRLASOFT INC Wells Fargo 140 7
7 COGNIZANT TECHNOLOGY SOLUTIONS US CORP Wells Fargo 95 95
8 KFORCE INC. Wells Fargo Bank, N.A. 60 6
9 KFORCE INC. WELLS FARGO BANK, N.A. 60 6
10 NTT DATA, INC. WELLS FARGO 52 52
11 MPHASIS CORPORATION WELLS FARGO 51 3
12 SYNECHRON, INC. Forum Capital Markets, LLC (dba Wells Fa 41 12
13 CAPGEMINI AMERICA INC Wells Fargo 38 37
14 MASTECH DIGITAL TECHNOLOGIES, INC., A MA Wells Fargo 36 6
15 EUCLID INNOVATIONS INC. Wells Fargo 35 2
16 SYNECHRON, INC. Forum Capital Markets LLC (Wells Fargo) 33 4
17 SYNECHRON, INC. Wells Fargo 32 32
18 PERSISTENT SYSTEMS, INC. WELLS FARGO 32 32
19 DIVERSANT LLC Wells Fargo 31 31
20 KFORCE INC. WELLS FARGO BANK 30 3
In the above table, EMPLOYER_NAME shows the consulting company and EMPLOYER_NAME2 is the exact name by which Wells Fargo was specified as the client company. As can be seen, Wells Fargo seems to be requesting H-1B workers from a number of different consulting firms. The data could be searched further to see what types of employees are generally contracted from which consulting firms.
As previously mentioned, this app does some cleaning of EMPLOYER_NAME2 by default. To see its effect, change the select list above "Ignore in Employer" from "Clean None" back to the default "Clean EMPLOYER_NAME2". This will output a table of 31 items of which the following are the first 10:
[1] "Search CASE_STATUS for CERTIFIED"
[1] "Search EMPLOYER_NAME2 for Wells Fargo"
[1] ""
[1] "H-1B DISCLOSURE DATA, FY 2019"
[1] "(CASE_STATUS=CERTIFIED, EMPLOYER_NAME2=Wells Fargo)"
[1] ""
[1] "SUM(TOTAL_WORKERS) = 3,033"
[1] "NUMBER OF ROWS = 29"
[1] "MEDIAN(LOW_WAGE) = NULL"
[1] "MEAN(LOW_WAGE) = NA"
[1] ""
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 WELLS FARGO 2559 1621
2 FORUM CAPITAL MARKETS LLC (DBA WELLS FAR 397 157
3 FORUM CAPITAL MARKETS LLC (WELLS FARGO) 33 4
4 WELLS FARGO ADVISORS 13 13
5 WELLS FARGO SECURITIES 3 3
6 WELLS FARGO COMMUNITY BANKING 2 2
7 WELLS FARGO/TALENT BRIDGE 2 2
8 WELLS FARGO BROKERAGE 2 2
9 THREE WELLS FARGO CENTER 2 2
10 WELLS FARGO/DJ DORAN JONES 1 1
As can be seen, most of the 3,033 workers for Wells Fargo are now in the first group. Blanking out the "Search EMPLOYER_NAME2" box and setting the "Group by" box to just "EMPLOYER_NAME2" will output a table of client companies with cleaned values of EMPLOYER_NAME2 of which the following are the first 50:
EMPLOYER_NAME2 WORKERS APPLICATIONS
1 APPLE 2708 2218
2 WELLS FARGO 2559 1621
3 AT&T 2545 1775
4 AMERICAN EXPRESS 1909 1710
5 CISCO SYSTEMS 1577 1032
6 CAPITAL ONE 1358 996
7 QUEST DIAGNOSTICS 1294 81
8 CITIGROUP 1233 849
9 FORD MOTOR 1174 913
10 PNC 1165 349
11 ANTHEM 1148 1028
12 VERIZON 1136 1042
13 CHARLES SCHWAB 1061 610
14 FEDEX 1052 659
15 COMCAST 1031 819
16 MICROSOFT 832 508
17 JP MORGAN CHASE 829 434
18 GOOGLE 768 629
19 NIKE 759 555
20 SYNCHRONY FINANCIAL 755 294
21 JOHNSON & JOHNSON 697 430
22 FIDELITY INVESTMENTS 636 573
23 CATERPILLAR 619 566
24 CVS 618 150
25 BANK OF AMERICA 614 504
26 FANNIE MAE 606 387
27 CIGNA 587 273
28 WALMART 585 480
29 CHARTER COMMUNICATIONS 536 428
30 UNITED AIRLINES 532 418
31 HUMANA 493 457
32 UNITED SERVICES AUTOMOBILE ASSOCIATION 487 321
33 GENERAL ELECTRIC 484 285
34 BOEING 484 279
35 KROGER 479 439
36 SOUTHERN CALIFORNIA EDISON 475 325
37 MORGAN STANLEY 472 428
38 STATE STREET BANK & TRUST 452 125
39 CITI GROUP 441 343
40 HEWLETT PACKARD ENTERPRISE 430 175
41 VERIZON WIRELESS 428 242
42 CUMMINS 423 362
43 T-MOBILE 418 385
44 VANGUARD 415 261
45 VANGUARD GROUP 413 261
46 N/A 408 120
47 CVS HEALTH 406 383
48 FORUM CAPITAL MARKETS LLC (DBA WELLS FAR 397 157
49 AETNA LIFE INSURANCE 396 396
50 CONDUENT 395 205
Note that about 17 of these 50 companies appear to be financial companies. This is not that surprising given a recent article in the Economist Magazine titled, Core elements of the global banking industry are moving to India.
The details of the disclosure data can be examined by removing the grouping. For example, entering "Wells Fargo" (without the quotes) into the "Search EMPLOYER_NAME2" box amd removing all items from the "Group by" selection list will output the individual application records for workers requested to work at Well Fargo of which the following are the first 20:
[1] "Search CASE_STATUS for CERTIFIED"
[1] "Search EMPLOYER_NAME2 for Wells Fargo"
[1] "Sort by TOTAL_WORKERS, Descending"
[1] ""
[1] "H-1B DISCLOSURE DATA, FY 2019"
[1] "(CASE_STATUS=CERTIFIED, EMPLOYER_NAME2=Wells Fargo)"
[1] ""
[1] "SUM(TOTAL_WORKERS) = 3,033"
[1] "NUMBER OF ROWS = 1,826"
[1] "MEDIAN(LOW_WAGE) = 88,670"
[1] "MEAN(LOW_WAGE) = 90,897"
[1] ""
CASE_STATUS EMPLOYER_NAME JOB_TITLE WORKERS PW_WAGE_LEVEL WAGE_RATE_FROM WAGE_PW WORKSITE_CITY STATE
1 CERTIFIED SYNECHRON, INC. QA ANALYST 30 OES 64000.00 1.0072 Charlotte NC
2 CERTIFIED SYNECHRON, INC. SOFTWARE DEVELOPER 30 OES 89000.00 1.0037 Charlotte NC
3 CERTIFIED SYNECHRON, INC. SOFTWARE DEVELOPER 30 OES 89000.00 1.0037 Charlotte NC
4 CERTIFIED SYNECHRON, INC. SOFTWARE DEVELOPER 30 OES 89000.00 1.0037 Charlotte NC
5 CERTIFIED SYNECHRON, INC. SOFTWARE DEVELOPER 30 OES 89000.00 1.0037 Charlotte NC
6 CERTIFIED SYNECHRON, INC. SOFTWARE DEVELOPER 30 OES 89000.00 1.0037 Charlotte NC
7 CERTIFIED SYNECHRON, INC. QUALITY ASSURANCE ANALYST 30 OES 64000.00 1.0072 Charlotte NC
8 CERTIFIED MPHASIS CORPORATION SOFTWARE PROGRAMMER II 25 OES 87714.00 1.0000 SAN FRANCISCO CA
9 CERTIFIED MPHASIS CORPORATION SOFTWARE PROGRAMMER II 25 OES 87714.00 1.0000 SAN FRANCISCO CA
10 CERTIFIED BIRLASOFT INC SYSTEMS ANALYSTS 20 OES 60382.00 1.0000 MACON GA
11 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 75483.00 1.0000 DANBURY CT
12 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 75483.00 1.0000 DANBURY CT
13 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 75483.00 1.0000 DANBURY CT
14 CERTIFIED BIRLASOFT INC COMPUTER AND INFORMATION SYSTEMS MANAGER 20 OES 113152.00 1.0000 DANBURY CT
15 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 75483.00 1.0000 DANBURY CT
16 CERTIFIED BIRLASOFT INC SYSTEMS ANALYSTS 20 OES 75837.00 1.0000 CHICAGO IL
17 CERTIFIED SYNECHRON, INC. BUSINESS ANALYST 20 OES 64000.00 1.0072 Charlotte NC
18 CERTIFIED BIRLASOFT INC SYSTEMS ANALYSTS 20 OES 75837.00 1.0000 Chicago IL
19 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 80912.00 1.0000 Chicago IL
20 CERTIFIED BIRLASOFT INC SOFTWARE ENGINEER 20 OES 78125.00 1.0000 Southfield MI
As can be seen, the great majority of the requested jobs seem to be software-related. In any event, it should be noted that all of the above data for FY 2019 was obtained from the file named H-1B_FY2019.xlsx linked to at this page.
Employer Records I.A. I.D. C.A. C.D. Workers I.A.R C.A.R T.A.R 1 CA 10871 15642 3498 47774 3367 63416 81.7 93.4 90.2 2 TX 5241 6942 4733 31679 8302 38621 59.5 79.2 74.8 3 NJ 3714 8896 5163 27688 4585 36584 63.3 85.8 79.0 4 NY 6952 9442 1690 16224 1234 25666 84.8 92.9 89.8 5 IL 2535 3779 2442 14738 2391 18517 60.7 86.0 79.3 6 MA 2666 5048 875 11349 1138 16397 85.2 90.9 89.1 7 WA 1061 5341 368 10208 459 15549 93.6 95.7 94.9 8 PA 1872 3583 893 10709 1877 14292 80.0 85.1 83.8 9 MD 1154 2150 473 11154 2014 13304 82.0 84.7 84.3 10 MI 1792 2911 1063 8542 1330 11453 73.3 86.5 82.7 11 VA 1982 2995 1526 7814 979 10809 66.2 88.9 81.2 12 FL 2368 2129 940 6079 1054 8208 69.4 85.2 80.5 13 GA 1677 2014 1132 6146 1145 8160 64.0 84.3 78.2 14 NC 969 1584 388 5527 670 7111 80.3 89.2 87.0 15 OH 1084 1295 427 3585 559 4880 75.2 86.5 83.2 16 AZ 620 1765 161 2925 154 4690 91.6 95.0 93.7 17 TN 485 1224 114 2445 131 3669 91.5 94.9 93.7 18 CT 735 1162 251 2264 207 3426 82.2 91.6 88.2 19 MN 679 876 152 2336 136 3212 85.2 94.5 91.8 20 MO 602 837 221 2101 189 2938 79.1 91.7 87.8 21 WI 468 552 80 1721 112 2273 87.3 93.9 92.2 22 CO 587 685 113 1437 112 2122 85.8 92.8 90.4 23 AR 178 539 96 1119 64 1658 84.9 94.6 91.2 24 IN 470 557 73 945 81 1502 88.4 92.1 90.7 25 DE 229 440 159 1026 106 1466 73.5 90.6 84.7 26 DC 576 542 82 854 64 1396 86.9 93.0 90.5 27 NE 205 314 52 904 59 1218 85.8 93.9 91.6 28 KY 281 297 52 798 99 1095 85.1 89.0 87.9 29 IA 242 421 162 652 66 1073 72.2 90.8 82.5 30 KS 269 329 79 734 72 1063 80.6 91.1 87.6 31 UT 305 354 66 659 44 1013 84.3 93.7 90.2 32 OR 251 247 47 621 41 868 84.0 93.8 90.8 33 SC 317 392 108 400 32 792 78.4 92.6 85.0 34 LA 266 330 45 432 30 762 88.0 93.5 91.0 35 AL 270 318 29 416 28 734 91.6 93.7 92.8 36 RI 117 190 15 491 36 681 92.7 93.2 93.0 37 OK 248 242 64 339 18 581 79.1 95.0 87.6 38 NH 162 198 60 327 41 525 76.7 88.9 83.9 39 NM 118 199 98 214 27 413 67.0 88.8 76.8 40 NV 215 137 34 272 27 409 80.1 91.0 87.0 41 ID 47 148 8 217 11 365 94.9 95.2 95.1 42 ME 71 121 4 164 14 285 96.8 92.1 94.1 43 VT 59 61 8 223 6 284 88.4 97.4 95.3 44 MS 107 107 18 166 3 273 85.6 98.2 92.9 45 WV 81 90 8 123 7 213 91.8 94.6 93.4 46 SD 47 82 7 115 5 197 92.1 95.8 94.3 47 ND 64 74 7 82 5 156 91.4 94.3 92.9 48 HI 68 67 6 87 5 154 91.8 94.6 93.3 49 GU 84 52 17 90 8 142 75.4 91.8 85.0 50 MP 76 92 50 36 4 128 64.8 90.0 70.3 51 PR 45 27 15 25 7 52 64.3 78.1 70.3 52 AK 22 17 1 28 1 45 94.4 96.6 95.7 53 WY 18 24 2 18 3 42 92.3 85.7 89.4 54 MT 24 22 3 17 3 39 88.0 85.0 86.7 55 VI 11 4 2 17 3 21 66.7 85.0 80.8 56 7 3 1 14 0 17 75.0 100.0 94.4 57 AE 2 0 0 2 0 2 NaN 100.0 100.0
Computer software developers (229): 2017, grouped by CTZNSHP and SEX_M_F (percent) Year STATE Count Non.ctzn_M Non.ctzn_F Naturlzd_M Naturlzd_F U.S._Brn_M U.S._Brn_F 1 2017 WY 877 33.4 0.0 0.0 0.0 52.9 13.7 2 2017 WA 94,231 29.6 6.8 8.4 2.1 46.9 6.1 3 2017 NJ 70,269 28.7 7.4 17.6 7.1 33.1 6.2 4 2017 CA 340,950 25.9 7.7 15.7 5.4 38.1 7.1 5 2017 IL 66,867 20.4 3.3 11.9 3.9 50.8 9.8 6 2017 RI 5,858 19.8 3.1 3.1 2.0 61.4 10.6 7 2017 NC 54,871 19.1 4.4 8.0 2.8 53.2 12.4 8 2017 MI 41,988 19.1 3.9 9.1 2.3 58.6 6.9 9 2017 CT 18,030 19.0 3.3 8.1 3.1 49.1 17.4 10 2017 NV 6,256 18.0 0.0 14.2 0.0 58.0 9.8 11 2017 TX 133,351 16.8 5.6 12.8 4.0 51.7 9.1 12 2017 AR 9,064 16.5 6.4 1.6 2.6 63.4 9.5 13 2017 GA 51,081 16.5 6.3 11.1 4.5 51.8 9.8 14 2017 DE 3,662 16.0 5.3 6.1 8.6 58.6 5.5 15 2017 OK 9,088 16.0 1.9 5.4 2.4 61.2 13.1 16 2017 KS 14,361 15.7 2.5 4.7 1.1 64.0 11.9 17 2017 IN 20,973 15.7 6.2 3.3 2.3 59.5 12.9 18 2017 NH 13,140 14.6 2.8 9.9 1.2 58.0 13.4 19 2017 FL 73,765 14.1 3.8 11.1 4.8 52.8 13.3 20 2017 NY 92,198 14.1 2.9 14.3 3.4 54.0 11.2 21 2017 MA 70,723 13.9 3.2 14.7 5.1 48.4 14.6 22 2017 OH 44,869 13.4 2.0 6.1 2.9 63.9 11.7 23 2017 PA 62,516 12.9 2.6 8.1 1.6 63.3 11.4 24 2017 VA 78,219 12.8 3.7 14.8 4.5 49.6 14.7 25 2017 WI 29,884 12.4 7.6 5.3 1.4 57.7 15.5 26 2017 AZ 28,209 12.4 2.9 7.2 3.5 62.8 11.2 27 2017 OR 25,863 11.7 3.4 6.4 2.6 59.0 16.9 28 2017 MN 40,479 10.7 4.9 9.1 1.3 64.9 9.0 29 2017 HI 2,935 9.9 0.0 0.0 4.2 75.0 10.9 30 2017 TN 22,638 9.8 5.9 5.4 0.4 58.4 20.0 31 2017 NE 10,468 9.2 4.3 4.5 2.7 63.5 15.8 32 2017 MD 58,240 8.9 2.2 10.7 4.4 49.8 24.0 33 2017 MO 24,758 7.9 3.0 5.1 1.1 64.8 18.1 34 2017 LA 7,395 7.6 3.0 1.9 1.7 71.0 14.9 35 2017 DC 6,818 7.6 2.4 7.3 1.9 48.3 32.5 36 2017 CO 50,474 6.8 0.6 5.8 2.6 71.8 12.3 37 2017 IA 14,940 6.8 2.4 7.1 0.0 66.0 17.8 38 2017 SC 14,240 6.3 0.8 3.1 0.0 73.7 16.2 39 2017 KY 14,512 6.3 1.2 4.3 0.2 62.6 25.5 40 2017 UT 27,182 5.9 1.8 2.5 1.1 78.4 10.3 41 2017 MS 4,546 3.4 1.8 3.3 0.0 63.6 27.8 42 2017 AL 16,830 3.4 0.8 3.5 0.9 63.8 27.7 43 2017 ME 3,541 2.2 0.0 3.0 0.0 71.1 23.7 44 2017 ID 5,281 1.9 2.3 1.4 5.0 80.5 8.8 45 2017 AK 806 0.0 0.0 0.0 0.0 100.0 0.0 46 2017 MT 2,241 0.0 0.0 0.0 0.0 82.2 17.8 47 2017 ND 1,623 0.0 0.0 0.0 0.0 78.7 21.3 48 2017 NM 5,047 0.0 0.0 16.4 0.0 60.0 23.6 49 2017 SD 1,739 0.0 0.0 0.0 0.0 88.7 11.3 50 2017 VT 2,563 0.0 0.0 8.7 0.0 74.5 16.8 51 2017 WV 2,847 0.0 0.0 2.1 0.0 73.6 24.3 URL parameters (short)= ?STATE=&geo=STATE&occ=Computer%20software%20developers%20(229)&group=CTZNSHP|SEX_M_F&sortn=4&mincount=0
Computer software developers (229): 2017, grouped by CTZNSHP and SEX_M_F, 5000 or more workers (percent) Year COUNTY Count Non.ctzn_M Non.ctzn_F Naturlzd_M Naturlzd_F U.S._Brn_M U.S._Brn_F 1 2017 Hudson County NJ 8,836 59.4 18.5 6.0 3.1 11.3 1.8 2 2017 Middlesex County NJ 15,402 41.0 6.6 24.5 9.3 13.9 4.7 3 2017 Mecklenburg County NC 10,694 40.6 7.0 2.9 3.5 36.3 9.7 4 2017 Santa Clara County CA 90,412 38.8 10.6 17.6 6.0 22.4 4.5 5 2017 Alameda County CA 39,393 38.5 9.4 16.0 6.1 25.1 4.9 6 2017 Lake County IL 6,757 37.4 4.6 17.0 5.8 30.9 4.5 7 2017 King County WA 72,589 34.0 8.3 8.4 1.9 41.8 5.6 8 2017 Snohomish County WA 7,859 33.6 4.1 7.5 3.9 45.8 5.0 9 2017 Oakland County MI 14,694 33.5 6.4 11.4 2.9 43.5 2.3 10 2017 DuPage County IL 9,767 31.5 6.6 13.9 4.0 38.6 5.4 11 2017 New York County NY 13,179 31.1 3.5 7.9 1.6 47.5 8.3 12 2017 Hillsborough County FL 7,446 28.3 7.7 10.8 0.7 41.2 11.3 13 2017 Dallas County TX 16,730 28.2 7.2 14.8 1.6 41.4 6.7 14 2017 Collin County TX 19,963 27.8 8.7 12.6 6.4 36.7 7.8 15 2017 Hartford County CT 6,529 26.5 1.3 9.8 6.0 35.7 20.8 16 2017 San Mateo County CA 19,108 25.9 16.4 16.2 7.3 30.9 3.4 17 2017 Denton County TX 7,218 24.0 6.2 19.3 5.8 37.7 7.1 18 2017 Duval County FL 6,645 23.4 5.7 13.9 9.4 38.4 9.3 19 2017 Washington County OR 11,096 22.8 5.9 6.5 4.4 44.6 15.8 20 2017 Sacramento County CA 9,603 22.4 1.2 11.4 2.2 45.7 17.0 21 2017 Johnson County KS 8,509 22.0 2.5 7.0 1.9 56.0 10.6 22 2017 Bergen County NJ 5,208 21.4 6.0 19.7 11.9 38.3 2.7 23 2017 Suffolk County MA 7,205 21.2 0.0 16.6 9.6 42.2 10.5 24 2017 Monmouth County NJ 5,383 20.9 2.6 11.1 3.5 55.5 6.4 25 2017 Orange County CA 29,277 20.0 4.2 20.2 6.6 42.6 6.4 26 2017 Cobb County GA 6,891 19.8 0.0 17.4 1.6 56.3 4.9 27 2017 Suffolk County NY 6,851 19.6 2.9 18.0 1.9 43.0 14.6 28 2017 Broward County FL 7,696 19.2 11.0 16.3 9.6 31.1 12.9 29 2017 San Francisco County CA 25,559 18.9 5.4 7.7 2.9 52.8 12.3 30 2017 San Diego County CA 27,569 18.6 6.2 16.2 7.6 42.6 8.9 31 2017 Cook County IL 28,419 18.6 2.9 11.9 2.9 53.0 10.7 32 2017 Williamson County TX 9,423 18.4 7.0 19.4 2.9 45.0 7.3 33 2017 Harris County TX 16,997 17.8 3.5 17.9 4.0 45.5 11.3 34 2017 Los Angeles County CA 46,009 17.8 5.2 14.3 4.8 49.7 8.2 35 2017 Wake County NC 20,160 17.5 4.3 9.6 3.6 51.2 13.7 36 2017 Contra Costa County CA 13,197 17.5 7.9 27.2 7.9 33.0 6.6 37 2017 St. Louis County MO 6,718 17.4 8.4 5.6 1.8 53.6 13.2 38 2017 Montgomery County MD 15,992 17.4 3.0 19.4 9.7 34.3 16.2 39 2017 Loudoun County VA 12,867 17.3 9.9 22.7 5.5 35.7 8.9 40 2017 Cuyahoga County OH 6,256 16.5 0.0 10.2 2.7 59.4 11.1 41 2017 Montgomery County PA 7,636 16.3 2.9 18.1 0.9 51.8 10.0 42 2017 Franklin County OH 10,146 15.6 7.4 6.4 4.7 58.6 7.4 43 2017 Queens County NY 12,032 15.0 1.1 26.9 8.0 44.9 4.2 44 2017 Hennepin County MN 14,438 14.7 6.8 4.5 1.5 66.5 6.0 45 2017 Dane County WI 10,832 14.6 15.1 4.2 2.3 45.7 18.2 46 2017 Monroe County NY 6,009 14.5 3.3 4.3 2.2 55.3 20.4 47 2017 Chester County PA 7,853 14.3 3.4 14.8 1.7 61.7 4.0 48 2017 Maricopa County AZ 22,527 14.2 3.6 7.2 4.4 59.2 11.3 49 2017 Howard County MD 7,434 13.7 5.1 15.4 7.3 37.9 20.6 50 2017 Gwinnett County GA 7,028 13.1 5.7 18.3 6.6 48.0 8.3 URL parameters (short)= ?STATE=&geo=COUNTY&occ=Computer%20software%20developers%20(229)&group=CTZNSHP|SEX_M_F&sortn=4
Computer software developers (229): 2017, grouped by AGE10, 5000 or more workers (percent) Year COUNTY Count 0-24 25-34 35-44 45-54 55-64 65-99 1 2017 Dane County WI 10,832 5.4 60.1 20.0 1.5 10.1 2.9 2 2017 Hudson County NJ 8,836 4.3 55.8 33.6 5.7 0.7 0.0 3 2017 San Francisco County CA 25,559 10.4 54.7 25.8 6.0 2.4 0.7 4 2017 New York County NY 13,179 9.5 54.2 26.9 4.9 2.8 1.7 5 2017 Mecklenburg County NC 10,694 5.8 54.0 20.6 11.8 6.1 1.7 6 2017 Arlington County VA 5,053 6.2 51.1 24.4 14.9 1.1 2.2 7 2017 District of Columbia DC 6,818 9.3 46.2 22.2 8.7 9.6 3.9 8 2017 King County WA 72,589 7.9 43.9 28.0 13.8 5.4 1.0 9 2017 Oakland County MI 14,694 4.4 43.7 25.4 17.0 9.5 0.0 10 2017 San Mateo County CA 19,108 6.3 43.2 27.4 14.0 6.6 2.6 11 2017 Allegheny County PA 10,960 6.9 43.0 23.8 16.6 8.0 1.7 12 2017 Bexar County TX 8,500 8.5 42.8 21.8 13.8 13.1 0.0 13 2017 Kings County NY 14,519 12.3 41.8 25.9 11.9 3.6 4.5 14 2017 Duval County FL 6,645 0.6 40.5 23.3 21.7 13.2 0.6 15 2017 Travis County TX 21,567 5.9 40.3 23.6 17.6 10.1 2.6 16 2017 Suffolk County MA 7,205 17.5 39.7 26.2 8.2 8.4 0.0 17 2017 Santa Clara County CA 90,412 8.3 39.4 27.6 16.7 7.3 0.6 18 2017 Alameda County CA 39,393 4.1 38.9 30.6 18.9 6.2 1.3 19 2017 Multnomah County OR 6,378 3.0 37.9 35.1 16.9 5.2 1.9 20 2017 Cook County IL 28,419 2.9 36.5 31.4 15.4 10.9 2.8 21 2017 Dallas County TX 16,730 6.0 35.9 23.4 19.2 12.4 3.1 22 2017 Snohomish County WA 7,859 2.7 35.6 30.4 16.9 14.4 0.0 23 2017 Montgomery County PA 7,636 1.5 33.7 30.4 21.9 12.6 0.0 24 2017 Johnson County KS 8,509 3.1 33.5 31.8 17.9 11.6 2.1 25 2017 Montgomery County MD 15,992 4.6 33.4 20.0 21.6 19.6 0.8 26 2017 Ramsey County MN 6,373 1.4 33.2 28.2 20.2 12.5 4.4 27 2017 Orange County FL 8,688 7.5 33.1 28.0 14.5 16.3 0.7 28 2017 Hennepin County MN 14,438 2.8 33.0 25.5 26.4 11.8 0.4 29 2017 Franklin County OH 10,146 4.6 32.7 33.9 17.2 10.5 1.2 30 2017 Hartford County CT 6,529 4.7 32.7 22.6 28.9 11.1 0.0 31 2017 Utah County UT 8,735 16.0 32.4 19.0 22.4 8.5 1.6 32 2017 Suffolk County NY 6,851 4.5 32.1 23.6 23.6 13.7 2.4 33 2017 Los Angeles County CA 46,009 3.8 32.1 29.1 18.5 13.3 3.1 34 2017 San Diego County CA 27,569 4.2 32.1 30.5 19.9 11.9 1.4 35 2017 Hillsborough County FL 7,446 4.2 31.9 27.9 19.1 10.2 6.8 36 2017 Queens County NY 12,032 9.0 31.4 28.5 13.3 12.0 5.7 37 2017 Cuyahoga County OH 6,256 12.1 31.2 20.1 16.1 16.0 4.5 38 2017 DuPage County IL 9,767 3.9 30.6 27.0 23.3 11.5 3.7 39 2017 Broward County FL 7,696 1.0 30.0 28.4 18.1 16.1 6.4 40 2017 Orange County CA 29,277 4.6 29.7 27.4 24.6 10.6 3.1 41 2017 Monmouth County NJ 5,383 1.5 29.1 22.8 12.1 28.5 5.9 42 2017 Monroe County NY 6,009 9.3 28.9 19.5 28.8 13.5 0.0 43 2017 Collin County TX 19,963 4.5 28.7 31.5 22.9 8.9 3.5 44 2017 Harris County TX 16,997 5.3 27.9 34.3 17.3 13.2 2.0 45 2017 Maricopa County AZ 22,527 7.2 27.3 32.2 20.2 9.0 4.1 46 2017 Wayne County MI 5,581 4.4 26.8 40.6 17.9 10.3 0.0 47 2017 Pinellas County FL 5,742 3.5 26.5 9.8 26.2 28.8 5.1 48 2017 Anne Arundel County MD 6,835 0.0 25.5 27.2 19.5 22.9 5.0 49 2017 (NA) 407,221 6.7 25.3 25.6 23.7 15.4 3.2 50 2017 Sacramento County CA 9,603 7.2 25.2 33.4 21.2 9.4 3.6 URL parameters (short)= ?STATE=&geo=COUNTY&occ=Computer%20software%20developers%20(229)&group=AGE10&sortn=5
Computer software developers (229): 2017, grouped by AGE10 (percent) Year COUNTY Count x0.24 x25.34 x35.44 x45.54 x55.64 x65.99 1 2017 Hudson County NJ 8,836 4.3 55.8 33.6 5.7 0.7 0.0 2 2017 Arlington County VA 5,053 6.2 51.1 24.4 14.9 1.1 2.2 3 2017 San Francisco County CA 25,559 10.4 54.7 25.8 6.0 2.4 0.7 4 2017 New York County NY 13,179 9.5 54.2 26.9 4.9 2.8 1.7 5 2017 Kings County NY 14,519 12.3 41.8 25.9 11.9 3.6 4.5 6 2017 Middlesex County NJ 15,402 4.7 21.7 43.3 26.2 3.7 0.4 7 2017 Williamson County TX 9,423 7.4 24.3 38.6 23.8 4.0 2.0 8 2017 Multnomah County OR 6,378 3.0 37.9 35.1 16.9 5.2 1.9 9 2017 King County WA 72,589 7.9 43.9 28.0 13.8 5.4 1.0 10 2017 Mecklenburg County NC 10,694 5.8 54.0 20.6 11.8 6.1 1.7 11 2017 Alameda County CA 39,393 4.1 38.9 30.6 18.9 6.2 1.3 12 2017 San Mateo County CA 19,108 6.3 43.2 27.4 14.0 6.6 2.6 13 2017 Santa Clara County CA 90,412 8.3 39.4 27.6 16.7 7.3 0.6 14 2017 Loudoun County VA 12,867 2.5 24.3 37.5 26.9 7.5 1.4 15 2017 Washington County OR 11,096 6.7 24.6 36.4 23.1 7.6 1.7 16 2017 Allegheny County PA 10,960 6.9 43.0 23.8 16.6 8.0 1.7 17 2017 Nassau County NY 6,180 7.7 12.5 33.0 32.6 8.1 6.1 18 2017 Suffolk County MA 7,205 17.5 39.7 26.2 8.2 8.4 0.0 19 2017 Utah County UT 8,735 16.0 32.4 19.0 22.4 8.5 1.6 20 2017 Collin County TX 19,963 4.5 28.7 31.5 22.9 8.9 3.5 21 2017 Maricopa County AZ 22,527 7.2 27.3 32.2 20.2 9.0 4.1 22 2017 Bergen County NJ 5,208 6.6 22.3 34.1 23.8 9.1 4.1 23 2017 Sacramento County CA 9,603 7.2 25.2 33.4 21.2 9.4 3.6 24 2017 Oakland County MI 14,694 4.4 43.7 25.4 17.0 9.5 0.0 25 2017 District of Columbia DC 6,818 9.3 46.2 22.2 8.7 9.6 3.9 26 2017 Fort Bend County TX 5,700 0.0 6.5 37.4 44.4 9.7 2.1 27 2017 Salt Lake County UT 11,825 7.5 20.4 43.3 17.4 10.0 1.3 28 2017 Travis County TX 21,567 5.9 40.3 23.6 17.6 10.1 2.6 29 2017 Dane County WI 10,832 5.4 60.1 20.0 1.5 10.1 2.9 30 2017 Hillsborough County FL 7,446 4.2 31.9 27.9 19.1 10.2 6.8 31 2017 Wayne County MI 5,581 4.4 26.8 40.6 17.9 10.3 0.0 32 2017 Riverside County CA 5,659 6.2 15.5 35.3 32.6 10.4 0.0 33 2017 Franklin County OH 10,146 4.6 32.7 33.9 17.2 10.5 1.2 34 2017 Orange County CA 29,277 4.6 29.7 27.4 24.6 10.6 3.1 35 2017 Cook County IL 28,419 2.9 36.5 31.4 15.4 10.9 2.8 36 2017 Hartford County CT 6,529 4.7 32.7 22.6 28.9 11.1 0.0 37 2017 DuPage County IL 9,767 3.9 30.6 27.0 23.3 11.5 3.7 38 2017 Johnson County KS 8,509 3.1 33.5 31.8 17.9 11.6 2.1 39 2017 Hennepin County MN 14,438 2.8 33.0 25.5 26.4 11.8 0.4 40 2017 San Diego County CA 27,569 4.2 32.1 30.5 19.9 11.9 1.4 41 2017 Queens County NY 12,032 9.0 31.4 28.5 13.3 12.0 5.7 42 2017 Dallas County TX 16,730 6.0 35.9 23.4 19.2 12.4 3.1 43 2017 Ramsey County MN 6,373 1.4 33.2 28.2 20.2 12.5 4.4 44 2017 Montgomery County PA 7,636 1.5 33.7 30.4 21.9 12.6 0.0 45 2017 Bexar County TX 8,500 8.5 42.8 21.8 13.8 13.1 0.0 46 2017 Denton County TX 7,218 2.1 21.8 37.4 23.3 13.2 2.2 47 2017 Duval County FL 6,645 0.6 40.5 23.3 21.7 13.2 0.6 48 2017 Harris County TX 16,997 5.3 27.9 34.3 17.3 13.2 2.0 49 2017 Los Angeles County CA 46,009 3.8 32.1 29.1 18.5 13.3 3.1 50 2017 Monroe County NY 6,009 9.3 28.9 19.5 28.8 13.5 0.0 URL parameters (short)= ?STATE=&geo=COUNTY&occ=Computer%20software%20developers%20(229)&group=AGE10&sortn=8&sortdir=Ascending
Annual Growth in Optional Practical Training (OPT); Science, Technology, Engineering and Mathematics (STEM) OPT; and
Curricular Practical Training (CPT) Authorizations, 2007 - 2017
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Approved Employment Count of Approved Authorizations With Employment Start Dates in Indicated Calendar Year
Authorizations 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
-------------------- -------- -------- -------- -------- -------- -------- -------- -------- -------- -------- --------
OPT 83,077 83,029 83,489 92,316 103,656 112,962 121,994 134,315 160,623 192,841 219,635
STEM OPT* 2 2,169 5,939 9,391 13,516 16,055 18,918 21,673 27,649 40,771 60,410
CPT 57,679 61,430 48,765 57,629 64,105 68,767 76,541 92,792 111,472 122,920 132,796
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*STEM OPT is a subset of OPT
Sources: U.S. Immigration and Customs Enforcement,
SEVP publishes 2017 international student data, SEVP Data Library,
2007 to 2017 Annual Growth in OPT, STEM OPT and CPT Authorizations
Additional OPT Data