County intelligence
Santa Clara County
California (CA) · FIPS 06085 · 21,710 employers · 524,086 filings with wage data
Source import recorded Sep 2, 2026
What the local records contain / DOL & WARN
What XH1B calculates / Local wage comparisons
How to read the wage comparison
County averages mix occupations and years. Offered wages and the latest available national BLS occupation medians are averaged separately over available records, so their populations can differ. This is a screening comparison, not a comparison of matched filings or a finding about an individual wage.
Definitions & methodology ↗Filings & wages
Annual filing records and a county-level wage comparison.Filing history
Annual DOL filing records. Workers requested are not confirmed hires.
Inspect filing outcomes All available years and DOL statuses
Certified, denied, and withdrawn counts describe application decisions. They do not measure visas issued or workers hired.
Offered-wage distribution
Percentiles across 526,070 positive year-unit LCA offers, all outcomes and visa classes.
Application outcomes all available years
- Certified548,058 · 96.9%
- Denied5,187 · 0.9%
- Withdrawn12,264 · 2.2%
How this distribution is built Year-unit offers, all outcomes and visa classes
The distribution covers positive offers with wage unit Year, across all outcomes and visa classes. It differs from the certified H-1B summary cohort. It does not adjust for occupation, employer, or worksite mix.
Downstream impact
Conditional effects of this county's modeled displacement exposure.Americans displaced (modeled estimate)
1,677,552
The math
208820 of 559693 filings below BLS median
222 notices filed
Σ workers×(1+wage_gap) + Σ WARN layoffs. The 1.0 base rate per certified H-1B worker request is a site model assumption. Wage gap multiplier = (BLS median − offered wage) / BLS median, applied only to filings offering less than the BLS median. Related research provides context, not validation of these exact coefficients. This is a modeled estimate, not a count of actual displacements. WARN layoff counts are government-reported.
Exposure method v1.2 · This is a modeled worker-equivalent count.
Modeled consequence estimates
Conditional impacts associated with modeled displacement
These are conditional population estimates scaled from Santa Clara County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.
Exposure method v1.2 (stored with this aggregate) · benchmarked to the latest BLS survey. Consequence parameters v1.54; build v1.54+engine0.4.1+d84162591c94d06c8148c4834ae705ff3f9a015c29955d95d26749834cd136ed+national-reference1
Excess deaths over 20 years
Illustrative reference calculation · population applicability is not established
48,244.27
Range: 18,893.07 to 84,037.99
20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.
- Source-profile mortality point (deaths)
- 48,244.268307
- No sustained excess from year 7 (deaths)
- 34,428.993198
Declared structural comparison; not a confidence interval or a model probability.
Baseline population: US prime-age men (45-54).
Child lifetime earnings lost
Illustrative reference calculation · population applicability is not established
$803.4B
Range: $208.7B to $1.4T
Canadian father–son firm-closure effect, applied to a US male lifetime-earnings baseline in 2024 dollars. The reference result uses 2 children per worker.
Baseline population: US male median lifetime earnings.
The mortality effect comes from a high-seniority male study cohort. Treat the count as a reference scenario unless the population matches. A mixed population needs a reviewed transport rule.
The range is the engine's parameter-support envelope: it varies declared parameters only and adds none of the exposure input's uncertainty.
The math, limits, and citations
Illustrative reference calculation scaled by the supplied worker-equivalent exposure. Two children per worker; 20-year mortality follow-up. This does not establish that an entity caused these outcomes.
Input SHA-256: a771d456f24b73ab4a27e51e83ec07640a0d93114d4ffc968aae925e700d843d
Excess deaths over 20 years: {"status":"illustrative_reference_calculation","eligible_for_public_headline":false,"target_population":null,"decision":null,"transport_assumptions":["high-tenure male mass-layoff cohort, ages 45–54","Pennsylvania early-1980s response transported to target place and calendar time","fixed baseline age rate over follow-up; cohort aging is not modeled"],"reason":"Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."}
Child lifetime earnings lost: {"status":"illustrative_reference_calculation","eligible_for_public_headline":false,"target_population":null,"decision":null,"transport_assumptions":["Canadian father-son firm-closure estimate transported to the target families","US male synthetic career earnings convert a relative adult earnings effect to dollars","lifetime dollars are undiscounted; this is not an annual loss"],"reason":"Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."}
Projection eligibility: {"child":{"status":"direct_estimate","eligible_for_validated_direct_contrast":true,"reason":"direct displacement-to-child adult earnings estimate; transport still requires an applicability decision"},"grandchild":{"status":"structural_projection","eligible_for_validated_direct_contrast":false,"reason":"combines a direct child estimate with an intergenerational persistence assumption; not an identified displacement intervention effect"},"greatgrandchild":{"status":"source_withdrawn","eligible_for_validated_direct_contrast":false,"reason":"source withdrawn for grandchild_earnings->greatgrandchild_earnings: The source measures earnings for three generations and education for four. It does not estimate .55 causal gap transmission to a fourth earnings generation. Retaining a projection would require an explicit uncalibrated assumption, not a source-identified default."}}
Exposure source: {"source":"one-worker normalized reference input","method":"scale by the stored warehouse worker-equivalent exposure at read time"}
Exact values: excess deaths over 20 years 48,244.268307 (range 18,893.0718532 to 84,037.9912288); child lifetime earnings lost $803,369,750,569.55 (range $208,667,467,680.40 to $1,356,338,539,922.62);
The displayed range is a parameter-support envelope, not a confidence interval.
Place reference: national reference.
Not included: exposure estimation error; baseline estimation error; unmodeled pathways.
Blocked model paths: local_service_jobs_lost: requires a documented net local loss of tradable jobs; worker displacement alone is not this exposure.
- Excess deaths over 20 years: sullivan2009 (earnings_shock->mortality_peak; canonical) Study population: US high-seniority displaced men born 1930-1959.
- Excess deaths over 20 years: sullivan2009 (earnings_shock->mortality_offset_1; canonical) Study population: US high-seniority displaced men born 1930-1959.
- Excess deaths over 20 years: sullivan2009 (earnings_shock->mortality_offsets_2_3; canonical) Study population: US high-seniority displaced men born 1930-1959.
- Excess deaths over 20 years: sullivan2009 (earnings_shock->mortality_offsets_4_5; canonical) Study population: US high-seniority displaced men born 1930-1959.
- Excess deaths over 20 years: sullivan2009 (earnings_shock->mortality_sustained; canonical) Study population: US high-seniority displaced men born 1930-1959.
- Child lifetime earnings lost: oreopoulos2008 (displacement->child_earnings; EXACT) Study population: Canada 39k father-son pairs firm closures.
Scenario assumptions, sensitivity and source records
Outcome statistics use the displayed exposure. Baselines, coefficients, annual family schedules and component records retain their source normalization.
{
"displayed_workers": 1677552.178,
"source_normalization_workers": 1,
"applicability": {
"excess_deaths": {
"status": "illustrative_reference_calculation",
"eligible_for_public_headline": false,
"target_population": null,
"decision": null,
"transport_assumptions": [
"high-tenure male mass-layoff cohort, ages 45–54",
"Pennsylvania early-1980s response transported to target place and calendar time",
"fixed baseline age rate over follow-up; cohort aging is not modeled"
],
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."
},
"child_lifetime_earnings_lost_usd": {
"status": "illustrative_reference_calculation",
"eligible_for_public_headline": false,
"target_population": null,
"decision": null,
"transport_assumptions": [
"Canadian father-son firm-closure estimate transported to the target families",
"US male synthetic career earnings convert a relative adult earnings effect to dollars",
"lifetime dollars are undiscounted; this is not an annual loss"
],
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."
}
},
"target_population": null,
"projection_eligibility": {
"child": {
"status": "direct_estimate",
"eligible_for_validated_direct_contrast": true,
"reason": "direct displacement-to-child adult earnings estimate; transport still requires an applicability decision"
},
"grandchild": {
"status": "structural_projection",
"eligible_for_validated_direct_contrast": false,
"reason": "combines a direct child estimate with an intergenerational persistence assumption; not an identified displacement intervention effect"
},
"greatgrandchild": {
"status": "source_withdrawn",
"eligible_for_validated_direct_contrast": false,
"reason": "source withdrawn for grandchild_earnings->greatgrandchild_earnings: The source measures earnings for three generations and education for four. It does not estimate .55 causal gap transmission to a fourth earnings generation. Retaining a projection would require an explicit uncalibrated assumption, not a source-identified default."
}
},
"child_applicability": {
"source": "oreopoulos2008",
"source_age_at_displacement": [
10,
14
],
"source_child_sex": "Male",
"source_parent_sex": "Male",
"source_geography": "Canada",
"source_event": "father firm closure 1980-1982",
"target_child_profiles": null,
"incompatible_children": [],
"unknown_children": [],
"source_income_quartile": null,
"income_rank_definition": null,
"eligible_for_reviewed_transport": false,
"status": "unknown",
"limitations": [
"No response curve across child ages is identified by this narrow cohort.",
"Income groups refer to father's average pre-displacement income in 1978-1979; target rank mapping requires a transport argument.",
"A subgroup's nonsignificant coefficient is retained with its uncertainty, not replaced with zero."
]
},
"child_valuation": null,
"assumptions": {
"mortality_method": "odds_survival",
"mortality_timing": "Source-offset profile: displacement, +1, +2–3, +4–5, and +6+; displacement is follow-up year 1, so offset +6 begins in follow-up year 7. Annual baseline rates use a constant-hazard rate-to-risk conversion; odds ratios modify annual risk, and survival compounds across phases. The selected baseline age rate is held fixed over the follow-up horizon.",
"mortality_contract": {
"method": "odds_survival",
"timing": "source_profile",
"baseline_conversion": "1-exp(-annual_rate)",
"phases": [
{
"phase": "displacement",
"start_offset": 0,
"duration_years": 1,
"first_follow_up_year": 1
},
{
"phase": "offset_1",
"start_offset": 1,
"duration_years": 1,
"first_follow_up_year": 2
},
{
"phase": "offsets_2_3",
"start_offset": 2,
"duration_years": 2,
"first_follow_up_year": 3
},
{
"phase": "offsets_4_5",
"start_offset": 4,
"duration_years": 2,
"first_follow_up_year": 5
},
{
"phase": "offset_6_plus",
"start_offset": 6,
"duration_years": null,
"first_follow_up_year": 7
}
],
"description": "Source-offset profile: displacement, +1, +2–3, +4–5, and +6+; displacement is follow-up year 1, so offset +6 begins in follow-up year 7. Annual baseline rates use a constant-hazard rate-to-risk conversion; odds ratios modify annual risk, and survival compounds across phases. The selected baseline age rate is held fixed over the follow-up horizon."
},
"place_application": "initial_only",
"place_counterfactual": "national child-dollar result is the reference"
},
"exposure_provenance": {
"source": "one-worker normalized reference input",
"method": "scale by the stored warehouse worker-equivalent exposure at read time"
},
"place": null,
"place_mix": [],
"input_content_sha256": "a771d456f24b73ab4a27e51e83ec07640a0d93114d4ffc968aae925e700d843d",
"engine_version": "0.4.1",
"bundle_sha256": "d84162591c94d06c8148c4834ae705ff3f9a015c29955d95d26749834cd136ed",
"exposure_input_sha256": "8b4c78f6529f0fbefd8fc3154c9e9b976065d910b4c6e9d55690e36d5c3cdeaa",
"input_hash": null,
"engine_sha256": "d84162591c94d06c8148c4834ae705ff3f9a015c29955d95d26749834cd136ed",
"adapter_sha256": "5fe4b51a9cebfb247cfcd74c734a2b6cb0eae44fce1073917b01455525c6ae52",
"outcomes": {
"excess_deaths": {
"point": 48244.26830695746,
"low": 18893.071853195714,
"high": 84037.99122882204,
"unit": "deaths",
"baseline": {
"value": 0.004944,
"annual_probability": 0.004931798548274205,
"unit": "deaths_per_person_year",
"citation": "cdc_wonder D76 (Underlying Cause of Death, 1999-2020), Male 45-54 years, pooled 2015-2019: 514314 deaths / 104024440 person-years = 494.4 per 100k; pulled 2026-09-06 via scraper.cli wonder-mortality into warehouse wonder_mortality",
"population": "US prime-age men (45-54)",
"notes": "Pre-pandemic window: excludes 2020 (COVID-distorted; 2020 crude rate 601.5/100k). Single-year 2019 = 490.0/100k. Ages 45-54 match the Sullivan & von Wachter prime-age counterfactual population",
"sex": "Male",
"age": "45-54 years",
"years": "2015-2019",
"cause": "All causes",
"geography": "US"
},
"steps": [
{
"link": "earnings_shock->mortality_peak",
"phase": "displacement",
"point": 2.6698,
"low": 2.1144,
"high": 3.3711,
"citation": "sullivan2009",
"tier": "canonical",
"population_scope": "US high-seniority displaced men born 1930-1959",
"evidence_role": "conditional"
},
{
"link": "earnings_shock->mortality_offset_1",
"phase": "offset_1",
"point": 1.9838,
"low": 1.3697,
"high": 2.8732,
"citation": "sullivan2009",
"tier": "canonical",
"population_scope": "US high-seniority displaced men born 1930-1959",
"evidence_role": "conditional"
},
{
"link": "earnings_shock->mortality_offsets_2_3",
"phase": "offsets_2_3",
"point": 1.7315,
"low": 1.3238,
"high": 2.2649,
"citation": "sullivan2009",
"tier": "canonical",
"population_scope": "US high-seniority displaced men born 1930-1959",
"evidence_role": "conditional"
},
{
"link": "earnings_shock->mortality_offsets_4_5",
"phase": "offsets_4_5",
"point": 1.2712,
"low": 0.97,
"high": 1.6661,
"citation": "sullivan2009",
"tier": "canonical",
"population_scope": "US high-seniority displaced men born 1930-1959",
"evidence_role": "conditional"
},
{
"link": "earnings_shock->mortality_sustained",
"phase": "offset_6_plus",
"point": 1.1354,
"low": 1.0335,
"high": 1.2474,
"citation": "sullivan2009",
"tier": "canonical",
"population_scope": "US high-seniority displaced men born 1930-1959",
"evidence_role": "conditional"
}
],
"components": {
"follow_up_years": 20,
"method": "odds_survival",
"timing": "source_profile"
},
"applicability": {
"status": "illustrative_reference_calculation",
"eligible_for_public_headline": false,
"target_population": null,
"decision": null,
"transport_assumptions": [
"high-tenure male mass-layoff cohort, ages 45–54",
"Pennsylvania early-1980s response transported to target place and calendar time",
"fixed baseline age rate over follow-up; cohort aging is not modeled"
],
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."
},
"interpretation": "Conditional modeled expected difference; parameter bands are support envelopes, not a confidence interval.",
"persistence_sensitivity": {
"available": true,
"status": "declared_structural_sensitivity",
"eligible_for_public_headline": false,
"unit": "deaths",
"variants": {
"source_profile": {
"point": 48244.26830695746,
"low": 18893.071853195714,
"high": 84037.99122882204,
"evidence_status": "source_profile_with_transport_assumptions",
"citations": [
"sullivan2009"
]
},
"no_sustained_excess": {
"point": 34428.99319801502,
"low": 15418.156214433404,
"high": 59320.3850705844,
"evidence_status": "structural_assumption",
"motivating_citations": [
"eliasonstorrie2009"
],
"assumption": "Retain source odds through offset +5; set offset +6 onward OR=1.",
"first_changed_follow_up_year": 7,
"fixed_late_odds_ratio": 1
}
},
"source_minus_no_sustained_point": 13815.275108942438,
"structural_point_range": {
"low": 34428.99319801502,
"high": 48244.26830695746,
"interpretation": "Range of point estimates across these two assumptions; not a confidence interval or a bound on the true effect."
},
"shared_inputs": [
"exposure",
"resolved mortality baseline",
"follow-up horizon",
"source odds through offset +5"
],
"limitations": [
"No probabilities or model-average weights are assigned to the structures.",
"A nonsignificant long-run estimate does not establish an exactly zero effect.",
"The Swedish study differs in population, design, effect scale and follow-up; this hybrid does not reproduce its estimates or substitute its hazard ratios for odds ratios.",
"The cutoff follows the source profile's offset +6 phase, not the Swedish study's windows; persistence beyond its 12-year follow-up is an assumption in either direction.",
"Baseline aging and target-population transport remain unresolved in both structures."
]
}
},
"child_lifetime_earnings_lost_usd": {
"point": 803369750569.5533,
"low": 208667467680.4036,
"high": 1356338539922.6223,
"steps": [
{
"link": "displacement->child_earnings",
"kind": "direct",
"citation": "oreopoulos2008",
"tier": "EXACT",
"param_point": 0.9076,
"param_low": 0.844,
"param_high": 0.976,
"value_point": 0.9076,
"value_low": 0.844,
"value_high": 0.976,
"causal_role": "direct_displacement_estimate",
"population_scope": "Canada 39k father-son pairs firm closures",
"evidence_role": "conditional"
}
],
"unit": "usd_2024",
"baseline": {
"value": 2591418,
"unit": "usd_2024",
"citation": "SSA Annual Statistical Supplement 2025, Table 4.B6 (CWHS/Master Earnings File, 1 percent sample): male median earnings by age, 2023 (preliminary) — synthetic career ages 20-64 = 5x(24218+42561+52485+60423+64962+67506+68160+64854)+2x60666+3x56666 = 2517175 usd_2023; CPI-U annual averages 304.702->313.689 (BLS series CUUR0000SA0, pulled 2026-09-06) -> 2591418 usd_2024; warehouse ssa_median_earnings (pulled via ssa-earnings-download)",
"population": "US male median lifetime earnings",
"notes": "Derivation declared: the cross-sectional age profile summed over a 45-year working life (ages 20-64) is a synthetic cohort — the sum of age-band medians approximates rather than equals the median of individual lifetime sums; median-of-workers semantics excludes non-working years; 2023 medians are preliminary (SSA replaces them with final data in a later edition)"
},
"applicability": {
"status": "illustrative_reference_calculation",
"eligible_for_public_headline": false,
"target_population": null,
"decision": null,
"transport_assumptions": [
"Canadian father-son firm-closure estimate transported to the target families",
"US male synthetic career earnings convert a relative adult earnings effect to dollars",
"lifetime dollars are undiscounted; this is not an annual loss"
],
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation."
},
"interpretation": "Conditional modeled expected difference; parameter bands are support envelopes, not a confidence interval."
}
}
}Parameter set v1.54 · model v1.54+engine0.4.1+d84162591c94d06c8148c4834ae705ff3f9a015c29955d95d26749834cd136ed+national-reference1
Local connections
County context, public notices, and linked public records.Community well-being
Food insecurity, SNAP receipt, mental distress, smoking, and overdose mortality in this area. CDC models these from survey data: treat them as local estimates, not headcounts. Bars compare each estimate against population-weighted averages of all reporting areas for the same data year.
Food insecurity
202311.6%: 3.6 points below U.S.
Adults receiving SNAP
20238.4%: 3.4 points below U.S.
Frequent mental distress
202313.7%: 2.3 points below U.S.
Adult smoking
20238.1%: 4.0 points below U.S.
Drug overdose deaths
202116.8: 15.3 below U.S.
Source: CDC PLACES 2025 release (BRFSS survey year 2023) and NCHS drug poisoning mortality. PLACES values are model-based small-area estimates, not survey counts. Benchmarks are population-weighted averages across all reporting areas.
Medical workforce context
Santa Clara County in context
Read the county map County-level H-1B intensity
The map gives geographic context. A county average does not classify every filing, employer, or worker within it.
Employers & occupations
The sponsors and job categories recorded in this county.Employers (21,710)
Job categories (405)
- Electronics Engineers, Except Computer$1.9B · 51.7%
- Computer Hardware Engineers$627.2M · 16.8%
- Computer and Information Systems Managers$291.7M · 7.8%
- Electrical Engineers$236.6M · 6.4%
- Software Developers$218.9M · 5.9%
- Accountants and Auditors$177.2M · 4.8%
- Architectural and Engineering Managers$125.4M · 3.4%
- Marketing Managers$119.5M · 3.2%
217,635 workers · 29,147 filings · $1.9B wage theft
22,128 workers · 6,266 filings · $627.2M wage theft
36,204 workers · 12,744 filings · $291.7M wage theft
28,277 workers · 11,289 filings · $236.6M wage theft
170,418 workers · 60,733 filings · $218.9M wage theft
18,087 workers · 6,005 filings · $177.2M wage theft
17,757 workers · 3,259 filings · $125.4M wage theft
8,408 workers · 2,650 filings · $119.5M wage theft
Labor condition applications (584,827)
Apple Inc.(1 position requested)
SoC Architecture · Santa Clara 95051 · 2026-03-31
Google LLC(1 position requested)
Software Engineer · Sunnyvale 94089 · 2026-03-31
Google LLC(1 position requested)
Senior Software Engineer · Sunnyvale 94089 · 2026-03-31
Wal-Mart ASSOCIATES, Inc.(1 position requested)
Senior, Software Engineer · Sunnyvale 94089 · 2026-03-31
Docusign, Inc.(1 position requested)
Lead Workplace Identity Engineer · San Jose 95129 · 2026-03-31
Google LLC(1 position requested)
Software Engineer · Mountain View 94043 · 2026-03-31
Google LLC(1 position requested)
Software Engineer · Sunnyvale 94089 · 2026-03-31
Sandisk Technologies, Inc.(25 positions requested)
Senior Engineer, VLSI Design Engineering · Milpitas 95035 · 2026-03-31
A negative % sits below the BLS market median; a positive % sits above.
What the filing rows show One application per row
Each row links to the disclosure record. A wage comparison is a screening calculation, not a finding of a wage violation.
Records to inspect
Open the linked public records behind this county's filings.Immigration law firms (3,499)
Federal contracts (18,397)
Evidence & sources
Scope, methods, and source coverage for this county record.Public source records and site analysis. We do not attest to accuracy. Read the full disclaimer.
Dataset guides and site methods
- [1] Dataset documentation · DOL OFLC. LCA public disclosure record layout (FY2026 Q3). Pages 1–4: application, DOL outcome and dates, requested positions, employer, representatives, worksites and pay unitsThis layout covers determinations issued October 1, 2025 through June 30, 2026. It documents employer-provided application fields and DOL decisions, not USCIS decisions, payroll verification, legal bills or worker-linked moves. Older layouts can differ.Checked 2026-10-17.
- [2] Dataset documentation · BLS. OEWS technical notes (May 2025). Scope of the survey; occupational coding; concepts and estimation methodologyDocuments occupation and geographic wage estimates, not an individual worker’s pay. Use the estimate year and geography stated by the site; older releases can differ.Checked 2026-10-17.
- [3] XH1B method or registry · XH1B. WARN Notices (mass layoffs): source registry; Calculation and Matching Methods; Reading Public Records and Derived Indicators. Source registry entry: warn; Filing units, annualization, weighting, wage comparisons, and inference limits; Reading records: units, source coverage, wage gaps, entity links, and inference limitsXH1B dataset description: One row per source_notice_id. The definition of a mass layoff and the layoff threshold vary by state. The threshold is typically 50 or more employees.; These are the site's review methods, not validated predictions of wrongdoing. Inspect underlying records and alternative explanations.
- [4] Dataset documentation · USAspending / U.S. Treasury. Federal Spending Guide: contract transactions. Pages 3, 5, 9 and 17: transactions, amount fields and recipient identifiersContract downloads contain prime award transactions. XH1B’s use of recipient identifiers for employer links is its own processing step.Checked 2026-10-18.
- [5] XH1B method or registry · XH1B. Displacement Model and Assumptions. Eligibility, requested-position weighting, scopes, WARN matching, and counting limitsModel outputs are estimates. A matched layoff notice does not prove that an H-1B worker replaced a laid-off worker.
- [6] XH1B method or registry · XH1B. Downstream Research and Methods. Study populations, parameter bounds, baselines, and expected outcomes; linked model card
- [7] Dataset documentation · HRSA. Area Health Resources Files: 2024–2025 technical documentation. County User Guide: physicians, Table 10 NPI professions, population estimates and missing values; Technical Documentation: variable years and sourcesClinician categories and variable years differ. County APRN counts use NPI taxonomy; physician active and federal status matter. Release year is not the year of every measure.Checked 2026-10-18.
Questions about the data & methods
How many H-1B filings are in Santa Clara County, CA?
559,693 H-1B labor condition applications requesting 1,554,271 workers have been filed in Santa Clara County, CA, per Department of Labor disclosure records.
How many employers have H-1B filings in Santa Clara County, CA?
21,122 distinct employers have filed H-1B labor condition applications in Santa Clara County, CA. The employer list on this page is ranked by filings and wage impact.
What worker-equivalent exposure does the displacement model estimate for Santa Clara County, CA?
The model estimates 1,677,552 worker-equivalents of displacement exposure in Santa Clara County, CA: one per requested worker on a certified H-1B filing, raised where the offered wage sits below the occupation's BLS median, plus government-reported WARN layoffs matched to filings. This is a modeled exposure estimate, not a count of documented job losses; the formula is on the About page.
What is the median H-1B wage in Santa Clara County, CA?
$130,894 per year is the median wage offered on H-1B labor condition applications filed for work in Santa Clara County, CA. The wage-distribution chart on this page shows the spread around that median.
Who files the most H-1B filings in Santa Clara County, CA?
Google LLC leads with 64,380 filings, about 12% of all Santa Clara County, CA filings. Its dossier page carries the full filing, wage, and layoff history.
What share of Santa Clara County, CA H-1B filings does DOL deny?
DOL disclosure records show 548,058 certified, 5,187 denied, 12,264 withdrawn of 565,509 labor condition applications with reported outcomes in Santa Clara County, CA. The denial share is 1% across the reported outcomes.
How many WARN layoff notices has Santa Clara County, CA recorded?
226 Worker Adjustment and Retraining Notification (WARN) notices. Where a layoff employer then filed H-1B labor condition applications in the same window, the pair is recorded as a displacement event with an evidence score.
When did H-1B use peak in Santa Clara County, CA?
Filings peaked in 2019 at 48,188 labor condition applications. The filings-by-year chart on this page shows the full trend since records began.
Which immigration law firms file the most H-1B filings in Santa Clara County, CA?
3,499 distinct law firms and agents have filed H-1B applications for employers in Santa Clara County, CA. The law-firm table on this page ranks them by filings and by the wage gap their filings carry.
Do Santa Clara County, CA H-1B employers hold federal contracts?
18,397 federal contract actions in Santa Clara County, CA name employers that also file H-1B labor condition applications. The contracts table links each award to the employer dossier. A shared employer name does not show how award funds were spent.
Where does the Santa Clara County, CA H-1B data come from?
Every figure on this page traces to a public government record: DOL OFLC disclosure files (LCA filings, wages, outcomes), BLS OES wage data (occupation medians), state WARN notices, and federal contract awards. The archive is read-only evidence; nothing is crowdsourced or anonymous.