# El Dorado County, CA (FIPS 06017): H-1B profile
> County profile: https://www.xh1b.org/county/06017
Employers in El Dorado, CA submitted 1,780 certified H-1B applications requesting 4,692 workers across 366 companies. The median offered wage is $86,695/yr. The largest filers are R Systems Inc., Mphasis Corporation and Infosys Limited. The displacement model assigns 5.95K worker-equivalent exposures to these filings. Separately, 230 layoffs appear in matched WARN notices.
| Metric | Value |
|---|---|
| H-1B filings | 1,780 |
| H-1B filings with wage data | 1,681 |
| Workers requested | 4,692 |
| US workers displaced (modeled) | 5,954 |
| Distinct employers | 366 |
| Filings below prevailing wage (share of compared filings) | 0% |
| Wage gap vs BLS | -7% |
| Metric | Value |
|---|---|
| Average offered wage | $94K |
| Average prevailing wage | $84K |
| Average BLS market wage | $101K |
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.
## US workers displaced (modeled)
| Metric | Value |
|---|---|
| Base rate (1.0 per certified worker) | 4,692 |
| Wage-gap multiplier | 1,032 |
| Matched WARN notice layoff counts | 230 |
| Certified filings | 1,780 |
| Filings below BLS median | 1,355 |
| **Total (modeled)** | **5,954** |
| Exposure method version | v1.2 |
These are derived estimates, not raw counts. The formula and every component are shown on the full HTML page.
## Modeled consequence estimates
**Conditional impacts associated with modeled displacement**
These are conditional population estimates scaled from El Dorado County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.
[Explore El Dorado County’s scenario: Earnings · children · health · adjust the worker count](https://www.xh1b.org/downstream?workers=5954.337&entity=El%20Dorado%20County#downstream-explorer)
| Metric | Value |
|---|---|
| Exposure method version | 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` |
| Outcome | Applicability | Estimate | Range |
|---|---|---|---|
| Excess deaths over 20 years | Illustrative reference calculation · population applicability is not established | 171.239163577 | 67.0594442632 to 298.286114222 |
| Child lifetime earnings lost | Illustrative reference calculation · population applicability is not established | $2,851,496,539.56 | $740,648,451.83 to $4,814,214,936.92 |
20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.
Source-profile mortality point (deaths): 171.239163577
No sustained excess from year 7 (deaths): 122.202951872
Declared structural comparison; not a confidence interval or a model probability.
Baseline population: US prime-age men (45-54).
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 171.239163577 (range 67.0594442632 to 298.286114222); child lifetime earnings lost $2,851,496,539.56 (range $740,648,451.83 to $4,814,214,936.92);
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.
### Scenario assumptions, sensitivity and source records
Outcome statistics use the displayed exposure. Baselines, coefficients, annual family schedules and component records retain their source normalization.
```json
{
"displayed_workers": 5954.337,
"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": 171.2391635773276,
"low": 67.05944426316485,
"high": 298.2861142215098,
"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": 171.2391635773276,
"low": 67.05944426316485,
"high": 298.2861142215098,
"evidence_status": "source_profile_with_transport_assumptions",
"citations": [
"sullivan2009"
]
},
"no_sustained_excess": {
"point": 122.20295187246879,
"low": 54.72550971787463,
"high": 210.55295227903684,
"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": 49.036211704858815,
"structural_point_range": {
"low": 122.20295187246879,
"high": 171.2391635773276,
"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": 2851496539.559238,
"low": 740648451.8335687,
"high": 4814214936.918193,
"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."
}
}
}
```
- 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.
Parameter set v1.54 · model v1.54+engine0.4.1+d84162591c94d06c8148c4834ae705ff3f9a015c29955d95d26749834cd136ed+national-reference1
## Filings by year
Calendar submission years 2007–2026. One row per filing.
| Year | Filings | Workers | Employers |
|---|---|---|---|
| 2007 | 8 | 9 | 7 |
| 2008 | 36 | 102 | 16 |
| 2009 | 23 | 75 | 14 |
| 2010 | 12 | 17 | 10 |
| 2011 | 56 | 185 | 18 |
| 2012 | 52 | 172 | 18 |
| 2013 | 58 | 294 | 27 |
| 2014 | 12 | 37 | 9 |
| 2015 | 108 | 673 | 35 |
| 2016 | 159 | 321 | 48 |
| 2017 | 155 | 441 | 52 |
| 2018 | 152 | 771 | 50 |
| 2019 | 209 | 416 | 57 |
| 2020 | 164 | 223 | 54 |
| 2021 | 145 | 279 | 48 |
| 2022 | 118 | 222 | 41 |
| 2023 | 108 | 153 | 43 |
| 2024 | 120 | 162 | 45 |
| 2025 | 124 | 175 | 43 |
| 2026 | 15 | 19 | 13 |
## Offered-wage percentiles (1,574 filings)
The distribution covers positive offers with wage unit Year, across all outcomes and visa classes. It differs from the certified H-1B summary cohort. Hourly and other wage units are outside this distribution.
| p10 | p25 | Median | p75 | p90 |
|---|---|---|---|---|
| $61K | $72K | $88K | $105K | $134K |
## Top employers (of 378)
| Employer | Filings | Workers | Avg wage | BLS gap |
|---|---|---|---|---|
| [Infosys Limited](https://www.xh1b.org/org/infosys-limited-08dee2e784) | 222 | 477 | $88K | - |
| [R Systems Inc.](https://www.xh1b.org/org/r-systems-inc-80fe4dd112) | 193 | 1,012 | $82K | - |
| [Cognizant Technology Solutions US Corp.](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | 190 | 190 | $94K | - |
| [Blue Shield of California](https://www.xh1b.org/org/blue-shield-of-california-55a42a9eed) | 100 | 100 | $107K | - |
| [Mphasis Corporation](https://www.xh1b.org/org/mphasis-corporation-7b048dfc43) | 65 | 831 | $75K | - |
| [Atos Syntel Inc.](https://www.xh1b.org/org/atos-syntel-inc-3894fd3e20) | 58 | 58 | $104K | - |
| [Nor-Cal Controls ES, Inc.](https://www.xh1b.org/org/nor-cal-controls-es-inc-b18c275dc5) | 41 | 41 | $98K | - |
| [Tech Mahindra (AMERICAS), Inc.](https://www.xh1b.org/org/tech-mahindra-americas-inc-1f17bf0f74) | 39 | 40 | $90K | - |
| [California Physicians' Service](https://www.xh1b.org/org/california-physicians-service-e40aafec2e) | 34 | 34 | $157K | - |
| [Satyam Computer Services Ltd.](https://www.xh1b.org/org/satyam-computer-services-ltd-b6b8948fdc) | 25 | 61 | $68K | - |
## SOC breakdown (of 59)
| SOC | Title | Filings | Workers | Avg wage | Gap | Wage theft |
|---|---|---|---|---|---|---|
| 13-1111 | Management Analysts | 42 | 237 | $68K | -33% | $7.9M |
| 15-1252 | Software Developers | 90 | 137 | $129K | -5% | $3.5M |
| 15-2031 | Operations Research Analysts | 6 | 124 | $76K | -15% | $3.0M |
| 15-1299 | Computer Occupations, All Other | 70 | 114 | $111K | -5% | $2.2M |
| 15-1253 | Software Quality Assurance Analysts and Testers | 67 | 114 | $103K | -1% | $1.6M |
| 17-2199 | Engineers, All Other | 25 | 25 | $95K | -23% | $733K |
| 15-2051 | Data Scientists | 17 | 23 | $97K | -19% | $633K |
| 11-3021 | Computer and Information Systems Managers | 18 | 18 | $159K | -9% | $544K |
## WARN mass-layoff notices
| Employer | Laid off | City | Date |
|---|---|---|---|
| [Blue Shield of California (Building C)](https://www.xh1b.org/org/blue-shield-of-california-building-c-5995997ed2) | 1 | Building C El Dorado Hills | 2026-10-20 |
| [Blue Shield of California (Building B)](https://www.xh1b.org/org/blue-shield-of-california-building-b-70c1c83d6b) | 12 | Building B El Dorado Hills | 2026-10-20 |
| Blue Shield of California (Building C) | 4 | Building C El Dorado Hills | 2026-07-28 |
| Blue Shield of California (Building B) | 5 | Building B El Dorado Hills | 2026-07-28 |
| John Adams Academy - El Dorado | 6 | El Dorado Hills | 2026-06-01 |
## Medical workforce: Local supply vs H-1B
AHRF county-level data cross-referenced with H-1B medical LCAs. Comparisons are against the median US county.
| Metric | Value |
|---|---|
| MDs per 100k residents | 245 (US county median 75) |
| Active MDs (non-federal) | 471 |
| Nurse Practitioners per 100k | 56 (US county median 96) |
| County population | 192,215 |
| Primary Care HPSA designations | 7 |
| H-1B medical workers certified | 45 |
| H-1B medical median vs BLS | 73% of BLS ($108K vs $149K) |
## Immigration law firms (of 174)
| Firm | LCAs filed | Below prevailing |
|---|---|---|
| [Chugh LLP](https://www.xh1b.org/firm/CHUGH%20LLP) | 72 | 0% |
| [Greenberg Traurig, LLP](https://www.xh1b.org/firm/Greenberg%20Traurig%2C%20LLP) | 24 | 0% |
| [Law Offices of Mary Kennedy, LLC](https://www.xh1b.org/firm/Law%20Offices%20of%20Mary%20Kennedy%2C%20LLC) | 22 | 0% |
| [Elliott and Davis PC](https://www.xh1b.org/firm/ELLIOTT%20AND%20DAVIS%20PC) | 18 | 0% |
| [Feldman Feldman & Associates, PC](https://www.xh1b.org/firm/Feldman%20Feldman%20%26%20Associates%2C%20PC) | 17 | 0% |
| [Kanter & Romo Immigration Law Office PC](https://www.xh1b.org/firm/Kanter%20%26%20Romo%20Immigration%20Law%20Office%20PC) | 14 | 0% |
| [Feldman Feldman & Associates](https://www.xh1b.org/firm/Feldman%20Feldman%20%26%20Associates) | 14 | 0% |
| [Greenberg TRAURIG, LLC](https://www.xh1b.org/firm/GREENBERG%20TRAURIG%2C%20LLC) | 13 | 0% |
## Federal contracts (of 929)
| Recipient | Agency | Action obligated | Date |
|---|---|---|---|
| [C.T.L. FOREST MANAGEMENT, INC.](https://www.xh1b.org/org/ctl-forest-management-inc-b69355cd7b) | Department of Defense | $6.1M | 2025-10-23 |
| [INFINITE TECHNOLOGIES, INC.](https://www.xh1b.org/org/infinite-technologies-inc-4f4c39992b) | Department of Defense | $6.0M | 2026-03-26 |
| [MACAULEY CONSTRUCTION INC](https://www.xh1b.org/org/macauley-construction-inc-df3b8c9c98) | Department of Agriculture | $2.3M | 2026-08-18 |
| [AEROMETALS, INC.](https://www.xh1b.org/org/aerometals-94ffb8be63) | Department of Defense | $2.1M | 2026-04-23 |
| [D F ZEE'S FIREFIGHTING CATERING, LLC](https://www.xh1b.org/org/d-f-zees-firefighting-catering-llc-9dc0221939) | Department of Agriculture | $1.6M | 2026-09-04 |
| [MOTHER LODE FIRECAMP SERVICES INC](https://www.xh1b.org/org/mother-lode-firecamp-services-inc-91337168cf) | Department of Agriculture | $1.6M | 2025-10-17 |
| [D F ZEE'S FIREFIGHTING CATERING, LLC](https://www.xh1b.org/org/d-f-zees-firefighting-catering-llc-9dc0221939) | Department of Agriculture | $1.6M | 2026-08-24 |
| [MOTHER LODE FIRECAMP SERVICES INC](https://www.xh1b.org/org/mother-lode-firecamp-services-inc-91337168cf) | Department of Agriculture | $1.4M | 2025-11-04 |
## Recent LCA filings (latest 10 of 1,863)
| Case number | Employer | Job title | Wage | Decision |
|---|---|---|---|---|
| [I-200-26083-725876](https://www.xh1b.org/lca/I-200-26083-725876) | [Mphasis Corporation](https://www.xh1b.org/org/mphasis-corporation-7b048dfc43) | SOFTWARE PROGRAMMER II | $95K | 2026-03-31 |
| [I-200-26077-711116](https://www.xh1b.org/lca/I-200-26077-711116) | [AuraWorx LLC](https://www.xh1b.org/org/auraworx-llc-d5d6a95e95) | Software Engineer | $140K | 2026-03-25 |
| [I-200-26076-708013](https://www.xh1b.org/lca/I-200-26076-708013) | [INFOSYS LIMITED](https://www.xh1b.org/org/infosys-limited-08dee2e784) | Infrastructure Project Lead 2 | $116K | 2026-03-24 |
| [I-200-26072-703357](https://www.xh1b.org/lca/I-200-26072-703357) | [El Dorado Multispecialty Medical Group, Inc.](https://www.xh1b.org/org/el-dorado-multispecialty-medical-group-i-61de27e990) | Pulmonologist/Intensivist | $401K | 2026-03-20 |
| [I-200-26070-697392](https://www.xh1b.org/lca/I-200-26070-697392) | [El Dorado Community Health Center](https://www.xh1b.org/org/el-dorado-community-health-center-7981c4a481) | Primary Care Physician | $260K | 2026-03-18 |
| [I-200-26068-690557](https://www.xh1b.org/lca/I-200-26068-690557) | [R SYSTEMS INC.](https://www.xh1b.org/org/r-systems-inc-80fe4dd112) | SOFTWARE ENGINEER | $135K | 2026-03-16 |
| [I-200-26065-684532](https://www.xh1b.org/lca/I-200-26065-684532) | [INFOSYS LIMITED](https://www.xh1b.org/org/infosys-limited-08dee2e784) | Technology Consultant 2 | $88K | 2026-03-13 |
| [I-200-26060-671730](https://www.xh1b.org/lca/I-200-26060-671730) | [COGNIZANT TECHNOLOGY SOLUTIONS US CORP](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | Senior Manager JC45 - Computer Systems Engineers/Architects | $144K | 2026-03-06 |
| [I-200-26056-663742](https://www.xh1b.org/lca/I-200-26056-663742) | [California Physicians' Service](https://www.xh1b.org/org/california-physicians-service-e40aafec2e) | Product Manager, Principal | $179K | 2026-03-04 |
| [I-200-26054-659071](https://www.xh1b.org/lca/I-200-26054-659071) | [Guardant Health, Inc.](https://www.xh1b.org/org/guardant-health-inc-3a4d5379f7) | Senior Software Quality Assurance Engineer | $134K | 2026-03-02 |
## Community well-being (El Dorado County)
CDC PLACES model-based small-area estimates (BRFSS survey year 2023); overdose mortality from NCHS. Values are local estimates, not survey counts. The gap column compares against the population-weighted average of all reporting areas for the same data year.
| Indicator | Data year | Local value | Gap vs benchmark |
|---|---|---|---|
| Food insecurity | 2023 | 10.3% | 4.9 points below U.S. |
| Adults receiving SNAP | 2023 | 9.3% | 2.5 points below U.S. |
| Frequent mental distress | 2023 | 15.5% | 0.5 points below U.S. |
| Adult smoking | 2023 | 10.4% | 1.7 points below U.S. |
| Drug overdose deaths | 2021 | 38.5 | 6.4 above U.S. |
## More
- Full page (charts, tables, interactive): https://www.xh1b.org/county/06017
- JSON API: https://www.xh1b.org/api/county-lcas?fips=06017
- JSON API: https://www.xh1b.org/api/county-employers?fips=06017
- JSON API: https://www.xh1b.org/api/county-soc?fips=06017
## References
1. Dataset documentation · DOL OFLC. [LCA public disclosure record layout (FY2026 Q3)](https://www.dol.gov/sites/dolgov/files/ETA/oflc/pdfs/FY26Q3/LCA_Record_Layout_FY2026_Q3.pdf). Pages 1–4: application, DOL outcome and dates, requested positions, employer, representatives, worksites and pay units. This 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)](https://www.bls.gov/oes/2025/may/oes_tec.htm). Scope of the survey; occupational coding; concepts and estimation methodology. Documents 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](https://www.xh1b.org/data-sources). Source registry entry: warn; Filing units, annualization, weighting, wage comparisons, and inference limits; Reading records: units, source coverage, wage gaps, entity links, and inference limits. XH1B 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](https://www.usaspending.gov/data/Federal-Spending-Guide.pdf#page=9). Pages 3, 5, 9 and 17: transactions, amount fields and recipient identifiers. Contract 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](https://www.xh1b.org/displacement). Eligibility, requested-position weighting, scopes, WARN matching, and counting limits. Model 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](https://www.xh1b.org/downstream#downstream-methods). Study populations, parameter bounds, baselines, and expected outcomes; linked model card.
7. Dataset documentation · HRSA. [Area Health Resources Files: 2024–2025 technical documentation](https://data.hrsa.gov/DataDownload/AHRF/AHRF_USER_TECH_2024-2025.zip). County User Guide: physicians, Table 10 NPI professions, population estimates and missing values; Technical Documentation: variable years and sources. Clinician 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.