# Hudson County, NJ (FIPS 34017): H-1B profile
> County profile: https://www.xh1b.org/county/34017
Employers in Hudson, NJ filed 96,648 certified H-1B applications requesting 158,535 workers across 8,677 companies. The median offered wage is $100,000/yr. The largest filers are Ernst & Young U.S. LLP, Infosys Limited and Mphasis Corporation. The displacement model assigns 184.1K worker-equivalent exposures to these filings.
| Metric | Value |
|---|---|
| H-1B filings | 96,648 |
| H-1B filings with wage data | 89,470 |
| Workers requested | 158,535 |
| US workers displaced (modeled) | 184,053 |
| Distinct employers | 8,677 |
| Filings below prevailing wage (share of compared filings) | 0% |
| Wage gap vs BLS | 5% |
| Metric | Value |
|---|---|
| Average offered wage | $107K |
| Average prevailing wage | $93K |
| Average BLS market wage | $102K |
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) | 158,535 |
| Wage-gap multiplier | 25,518 |
| Matched WARN notice layoff counts | 0 |
| Certified filings | 96,648 |
| Filings below BLS median | 51,278 |
| **Total (modeled)** | **184,053** |
| 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 Hudson County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.
[Explore Hudson County’s scenario: Earnings · children · health · adjust the worker count](https://www.xh1b.org/downstream?workers=184053.357&entity=Hudson%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 | 5,293.14059756 | 2,072.86148486 to 9,220.26426602 |
| Child lifetime earnings lost | Illustrative reference calculation · population applicability is not established | $88,142,056,887.23 | $22,894,040,749.93 to $148,811,264,874.55 |
20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.
Source-profile mortality point (deaths): 5,293.14059756
No sustained excess from year 7 (deaths): 3,777.39176124
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 5,293.14059756 (range 2,072.86148486 to 9,220.26426602); child lifetime earnings lost $88,142,056,887.23 (range $22,894,040,749.93 to $148,811,264,874.55);
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": 184053.357,
"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": 5293.140597564308,
"low": 2072.8614848621905,
"high": 9220.264266022281,
"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": 5293.140597564308,
"low": 2072.8614848621905,
"high": 9220.264266022281,
"evidence_status": "source_profile_with_transport_assumptions",
"citations": [
"sullivan2009"
]
},
"no_sustained_excess": {
"point": 3777.3917612384575,
"low": 1691.6096245662527,
"high": 6508.361500737618,
"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": 1515.7488363258508,
"structural_point_range": {
"low": 3777.3917612384575,
"high": 5293.140597564308,
"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": 88142056887.2338,
"low": 22894040749.930866,
"high": 148811264874.5505,
"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 | 616 | 1,006 | 214 |
| 2008 | 4,899 | 7,236 | 756 |
| 2009 | 2,135 | 3,080 | 630 |
| 2010 | 982 | 2,309 | 355 |
| 2011 | 4,058 | 9,411 | 922 |
| 2012 | 4,323 | 10,016 | 901 |
| 2013 | 4,106 | 7,401 | 800 |
| 2014 | 1,279 | 2,426 | 386 |
| 2015 | 7,408 | 15,730 | 1,052 |
| 2016 | 7,054 | 13,193 | 1,081 |
| 2017 | 6,873 | 13,568 | 1,103 |
| 2018 | 7,185 | 14,428 | 1,101 |
| 2019 | 7,531 | 10,080 | 1,140 |
| 2020 | 6,110 | 7,542 | 1,051 |
| 2021 | 6,693 | 9,328 | 1,402 |
| 2022 | 7,456 | 9,764 | 1,600 |
| 2023 | 6,422 | 8,072 | 1,489 |
| 2024 | 6,437 | 7,495 | 1,585 |
| 2025 | 6,876 | 8,082 | 1,763 |
| 2026 | 1,253 | 2,243 | 383 |
## Offered-wage percentiles (87,784 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 |
|---|---|---|---|---|
| $60K | $75K | $100K | $132K | $165K |
## Top employers (of 8,918)
| Employer | Filings | Workers | Avg wage | BLS gap |
|---|---|---|---|---|
| [Ernst & Young U.S. LLP](https://www.xh1b.org/org/ernst-young-us-llp-8dcf2d9a21) | 5,689 | 10,336 | $143K | - |
| [JPMorgan Chase & Co.](https://www.xh1b.org/org/jpmorgan-chase-co-09c43c6eb5) | 5,133 | 5,911 | $148K | - |
| [Infosys Limited](https://www.xh1b.org/org/infosys-limited-08dee2e784) | 4,321 | 9,145 | $86K | - |
| [Tata Consultancy Services Limited](https://www.xh1b.org/org/tata-consultancy-services-limited-25762f048c) | 3,134 | 4,223 | $77K | - |
| [Cognizant Technology Solutions US Corp.](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | 2,007 | 2,138 | $106K | - |
| [Larsen & Toubro Infotech Limited](https://www.xh1b.org/org/larsen-toubro-infotech-limited-6f7c20036a) | 1,966 | 2,046 | $75K | - |
| [ExlService.com, LLC](https://www.xh1b.org/org/exlservicecom-llc-cb8cda9f47) | 1,964 | 6,518 | $102K | - |
| [Goldman Sachs Services LLC](https://www.xh1b.org/org/goldman-sachs-services-llc-d169fa5f13) | 1,540 | 2,183 | $137K | - |
| [UBS Business Solutions US LLC](https://www.xh1b.org/org/ubs-business-solutions-us-llc-57c92d3ddb) | 1,364 | 2,433 | $133K | - |
| [Goldman Sachs & Co. LLC](https://www.xh1b.org/org/goldman-sachs-co-llc-9049ba61e6) | 1,335 | 1,666 | $109K | - |
## SOC breakdown (of 287)
| SOC | Title | Filings | Workers | Avg wage | Gap | Wage theft |
|---|---|---|---|---|---|---|
| 11-3021 | Computer and Information Systems Managers | 1,212 | 3,010 | $180K | 3% | $128.2M |
| 13-1111 | Management Analysts | 2,197 | 6,262 | $106K | 5% | $114.4M |
| 15-1252 | Software Developers | 8,250 | 10,152 | $148K | 9% | $101.9M |
| 15-1299 | Computer Occupations, All Other | 2,609 | 3,603 | $117K | 1% | $66.8M |
| 15-2041 | Statisticians | 1,581 | 3,545 | $113K | 7% | $52.4M |
| 13-2051 | Financial and Investment Analysts | 1,560 | 2,539 | $125K | 22% | $28.8M |
| 13-2011 | Accountants and Auditors | 2,163 | 3,602 | $105K | 26% | $27.4M |
| 15-2051 | Data Scientists | 1,937 | 2,541 | $133K | 10% | $23.7M |
## 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 | 213 (US county median 75) |
| Active MDs (non-federal) | 1,500 |
| Nurse Practitioners per 100k | 63 (US county median 96) |
| County population | 705,472 |
| Primary Care HPSA designations | 3 |
| H-1B medical workers certified | 630 |
| H-1B medical median vs BLS | 60% of BLS ($78K vs $130K) |
## Immigration law firms (of 1,959)
| Firm | LCAs filed | Below prevailing |
|---|---|---|
| [EY Law LLP](https://www.xh1b.org/firm/EY%20Law%20LLP) | 3,560 | 0% |
| [Ogletree, Deakins, Nash, Smoak & Stewart, P.C.](https://www.xh1b.org/firm/Ogletree%2C%20Deakins%2C%20Nash%2C%20Smoak%20%26%20Stewart%2C%20P.C.) | 3,416 | 0% |
| [FRAGOMEN, del REY, Bernsen & LOEWY, LLP](https://www.xh1b.org/firm/FRAGOMEN%2C%20DEL%20REY%2C%20BERNSEN%20%26%20LOEWY%2C%20LLP) | 3,082 | 0% |
| [Fragomen, Del Rey, Bernsen & Loewy, LLP](https://www.xh1b.org/firm/Fragomen%2C%20Del%20Rey%2C%20Bernsen%20%26%20Loewy%2C%20LLP) | 2,842 | 0% |
| [Seyfarth Shaw LLP](https://www.xh1b.org/firm/Seyfarth%20Shaw%20LLP) | 959 | 0% |
| [Quarles & Brady LLP](https://www.xh1b.org/firm/Quarles%20%26%20Brady%20LLP) | 839 | 0% |
| [Corporate Immigration Partners, P.C.](https://www.xh1b.org/firm/Corporate%20Immigration%20Partners%2C%20P.C.) | 771 | 0% |
| [Berry Appleman & Leiden LLP](https://www.xh1b.org/firm/Berry%20Appleman%20%26%20Leiden%20LLP) | 766 | 0% |
## Federal contracts (of 42,050)
| Recipient | Agency | Action obligated | Date |
|---|---|---|---|
| [J & J MECHANICAL AND CONSTRUCTION GROUP LLC](https://www.xh1b.org/org/j-j-mechanical-and-construction-group-ll-6c77df7ecf) | Department of Veterans Affairs | $19.2M | 2026-03-05 |
| [BETHEL INDUSTRIES INC](https://www.xh1b.org/org/bethel-industries-inc-c4eb704cbe) | Department of Defense | $18.5M | 2026-04-07 |
| [J & J MECHANICAL AND CONSTRUCTION GROUP LLC](https://www.xh1b.org/org/j-j-mechanical-and-construction-group-ll-6c77df7ecf) | Department of Veterans Affairs | $14.7M | 2026-04-13 |
| [INSURANCE SERVICES OFFICE, INC.](https://www.xh1b.org/org/insurance-services-office-inc-e737dd80fe) | Department of Homeland Security | $11.2M | 2026-06-11 |
| [QUEST DIAGNOSTICS INCORPORATED](https://www.xh1b.org/org/quest-diagnostics-incorporated-45b164d68e) | Department of Veterans Affairs | $8.3M | 2025-10-01 |
| [SPECTRASERV INC](https://www.xh1b.org/org/spectraserv-inc-31578aa2f6) | Department of Defense | $7.4M | 2026-01-16 |
| [QUEST DIAGNOSTICS INCORPORATED](https://www.xh1b.org/org/quest-diagnostics-incorporated-45b164d68e) | Department of Veterans Affairs | $4.7M | 2026-03-24 |
| [THE TRUSTEES OF THE STEVENS INSTITUTE OF TECHNOLOGY](https://www.xh1b.org/org/the-trustees-of-the-stevens-institute-of-563b234111) | Department of Defense | $3.7M | 2026-05-27 |
## Recent LCA filings (latest 10 of 100,734)
| Case number | Employer | Job title | Wage | Decision |
|---|---|---|---|---|
| [I-200-26083-724259](https://www.xh1b.org/lca/I-200-26083-724259) | [JRI America, Inc.](https://www.xh1b.org/org/jri-america-inc-f00f7f8fce) | Vice President, Senior Java Developer | $156K | 2026-03-31 |
| [I-200-26083-725381](https://www.xh1b.org/lca/I-200-26083-725381) | [WAL-MART ASSOCIATES, INC.](https://www.xh1b.org/org/wal-mart-associates-inc-c741f8d1ba) | Senior Manager, Data Analytics | $191K | 2026-03-31 |
| [I-200-26083-726233](https://www.xh1b.org/lca/I-200-26083-726233) | [RANDSTAD DIGITAL, LLC](https://www.xh1b.org/org/randstad-digital-llc-9afedb250c) | Software Developer | $136K | 2026-03-31 |
| [I-200-26083-726367](https://www.xh1b.org/lca/I-200-26083-726367) | [RANDSTAD DIGITAL, LLC](https://www.xh1b.org/org/randstad-digital-llc-9afedb250c) | Software Developer | $132K | 2026-03-31 |
| [I-200-26084-727290](https://www.xh1b.org/lca/I-200-26084-727290) | [CELTIC BANK CORPORATION](https://www.xh1b.org/org/celtic-bank-corporation-aad384df66) | VP, Fintech Business Development | $183K | 2026-03-31 |
| [I-200-26083-726058](https://www.xh1b.org/lca/I-200-26083-726058) | [GP TECHNOLOGIES LLC](https://www.xh1b.org/org/gp-technologies-llc-2bb4f3eeef) | Computer Programmer | $75K | 2026-03-31 |
| [I-200-26083-726068](https://www.xh1b.org/lca/I-200-26083-726068) | [GP TECHNOLOGIES LLC](https://www.xh1b.org/org/gp-technologies-llc-2bb4f3eeef) | COMPUTER PROGRAMMER | $98K | 2026-03-31 |
| [I-200-26083-725183](https://www.xh1b.org/lca/I-200-26083-725183) | [AIR Worldwide Corporation](https://www.xh1b.org/org/air-worldwide-corporation-2473fdd9e0) | Team Lead Data Science | $115K | 2026-03-31 |
| [I-200-26083-725928](https://www.xh1b.org/lca/I-200-26083-725928) | [Jefferies LLC](https://www.xh1b.org/org/jefferies-llc-74e1c6aa95) | Vice President | $235K | 2026-03-31 |
| [I-200-26084-727473](https://www.xh1b.org/lca/I-200-26084-727473) | [BANK OF AMERICA N.A.](https://www.xh1b.org/org/bank-of-america-na-9d0b252eae) | Assistant Vice President; Feature Lead - Technology | $171K | 2026-03-31 |
## Community well-being (Hudson 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 | 18.4% | 3.2 points above U.S. |
| Adults receiving SNAP | 2023 | 10.4% | 1.4 points below U.S. |
| Frequent mental distress | 2023 | 14.7% | 1.3 points below U.S. |
| Adult smoking | 2023 | 10.6% | 1.5 points below U.S. |
| Drug overdose deaths | 2021 | 25.1 | 7.1 below U.S. |
## More
- Full page (charts, tables, interactive): https://www.xh1b.org/county/34017
- JSON API: https://www.xh1b.org/api/county-lcas?fips=34017
- JSON API: https://www.xh1b.org/api/county-employers?fips=34017
- JSON API: https://www.xh1b.org/api/county-soc?fips=34017
## 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.