# Cheshire County, NH (FIPS 33005): H-1B profile

> County profile: https://www.xh1b.org/county/33005

Employers in Cheshire, NH logged 1,274 certified H-1B applications requesting 3,585 workers across 241 companies. The median offered wage is $79,258/yr. The largest filers are Wipro Limited, Business Intelligence Systems, Inc. and XCELTECH, Inc.. The displacement model assigns 4.25K worker-equivalent exposures to these filings.

| Metric | Value |
|---|---|
| H-1B filings | 1,274 |
| H-1B filings with wage data | 1,164 |
| Workers requested | 3,585 |
| US workers displaced (modeled) | 4,245 |
| Distinct employers | 241 |
| Filings below prevailing wage (share of compared filings) | 0% |
| Wage gap vs BLS | -18% |

| Metric | Value |
|---|---|
| Average offered wage | $86K |
| Average prevailing wage | $77K |
| Average BLS market wage | $105K |

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) | 3,585 |
| Wage-gap multiplier | 660 |
| Matched WARN notice layoff counts | 0 |
| Certified filings | 1,274 |
| Filings below BLS median | 788 |
| **Total (modeled)** | **4,245** |
| 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 Cheshire County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.

[Explore Cheshire County’s scenario: Earnings · children · health · adjust the worker count](https://www.xh1b.org/downstream?workers=4245.31&entity=Cheshire%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 | 122.089719397 | 47.8118939732 to 212.671372743 |
| Child lifetime earnings lost | Illustrative reference calculation · population applicability is not established | $2,033,053,684.12 | $528,065,891.98 to $3,432,428,297.87 |

20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.

Source-profile mortality point (deaths): 122.089719397

No sustained excess from year 7 (deaths): 87.1279898356

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 122.089719397 (range 47.8118939732 to 212.671372743); child lifetime earnings lost $2,033,053,684.12 (range $528,065,891.98 to $3,432,428,297.87);

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": 4245.31,
  "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": 122.08971939721663,
      "low": 47.81189397322596,
      "high": 212.67137274321522,
      "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": 122.08971939721663,
            "low": 47.81189397322596,
            "high": 212.67137274321522,
            "evidence_status": "source_profile_with_transport_assumptions",
            "citations": [
              "sullivan2009"
            ]
          },
          "no_sustained_excess": {
            "point": 87.12798983559554,
            "low": 39.018072651982976,
            "high": 150.11957735004216,
            "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": 34.961729561621084,
        "structural_point_range": {
          "low": 87.12798983559554,
          "high": 122.08971939721663,
          "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": 2033053684.1223848,
      "low": 528065891.9798405,
      "high": 3432428297.868961,
      "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 | 14 | 14 | 7 |
| 2008 | 112 | 500 | 25 |
| 2009 | 74 | 1,025 | 32 |
| 2010 | 16 | 115 | 9 |
| 2011 | 52 | 383 | 23 |
| 2012 | 85 | 280 | 24 |
| 2013 | 110 | 167 | 42 |
| 2014 | 34 | 38 | 15 |
| 2015 | 125 | 143 | 50 |
| 2016 | 63 | 65 | 28 |
| 2017 | 64 | 74 | 21 |
| 2018 | 87 | 173 | 26 |
| 2019 | 100 | 109 | 39 |
| 2020 | 71 | 73 | 35 |
| 2021 | 92 | 92 | 21 |
| 2022 | 70 | 72 | 24 |
| 2023 | 28 | 29 | 16 |
| 2024 | 61 | 61 | 24 |
| 2025 | 52 | 208 | 16 |
| 2026 | 5 | 5 | 3 |

## Offered-wage percentiles (1,139 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 |
|---|---|---|---|---|
| $51K | $63K | $80K | $106K | $130K |

## Top employers (of 245)

| Employer | Filings | Workers | Avg wage | BLS gap |
|---|---|---|---|---|
| [C&S Wholesale Grocers, LLC](https://www.xh1b.org/org/cs-wholesale-grocers-llc-1b844201bb) | 191 | 191 | $117K | - |
| [C and S Wholesale GROCERS, Inc.](https://www.xh1b.org/org/c-and-s-wholesale-grocers-inc-87414a643e) | 74 | 74 | $121K | - |
| [Dice IT Solutions LLC.](https://www.xh1b.org/org/dice-it-solutions-llc-49422b6737) | 66 | 67 | $83K | - |
| [EMD Millipore Corporation](https://www.xh1b.org/org/emd-millipore-corporation-3424c65798) | 59 | 59 | $91K | - |
| [Dartmouth-Hitchcock Clinic](https://www.xh1b.org/org/dartmouth-hitchcock-clinic-378991a1c4) | 57 | 87 | $96K | - |
| [Markem-Imaje Corporation](https://www.xh1b.org/org/markem-imaje-corporation-f832840d6a) | 47 | 47 | $90K | - |
| [Infosys Limited](https://www.xh1b.org/org/infosys-limited-08dee2e784) | 46 | 110 | $76K | - |
| [Key  Business Solutions,Inc. (NH office)](https://www.xh1b.org/org/key-business-solutionsinc-nh-office-c238f71f04) | 43 | 72 | $56K | - |
| [Wipro Limited](https://www.xh1b.org/org/wipro-limited-de03ffe48e) | 34 | 715 | $55K | - |
| [Cognizant Technology Solutions US Corp.](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | 33 | 33 | $88K | - |

## SOC breakdown (of 66)

| SOC | Title | Filings | Workers | Avg wage | Gap | Wage theft |
|---|---|---|---|---|---|---|
| 29-1122 | Occupational Therapists | 2 | 80 | $60K | -40% | $3.2M |
| 29-1123 | Physical Therapists | 2 | 80 | $76K | -26% | $2.2M |
| 29-1229 | Physicians, All Other | 13 | 13 | $103K | -61% | $2.1M |
| 11-3021 | Computer and Information Systems Managers | 37 | 37 | $147K | -16% | $1.2M |
| 15-1252 | Software Developers | 34 | 34 | $114K | -16% | $890K |
| 17-2199 | Engineers, All Other | 19 | 19 | $77K | -38% | $880K |
| 19-1042 | Medical Scientists, Except Epidemiologists | 10 | 16 | $91K | -12% | $728K |
| 29-9099 | Healthcare Practitioners and Technical Workers, All Other | 7 | 21 | $42K | -36% | $495K |

## 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 | 196 (US county median 75) |
| Active MDs (non-federal) | 152 |
| Nurse Practitioners per 100k | 154 (US county median 96) |
| County population | 77,703 |
| Primary Care HPSA designations | 0 |
| H-1B medical workers certified | 65 |
| H-1B medical median vs BLS | 50% of BLS ($91K vs $183K) |

## Immigration law firms (of 78)

| Firm | LCAs filed | Below prevailing |
|---|---|---|
| [GoffWilson, P.A.](https://www.xh1b.org/firm/GoffWilson%2C%20P.A.) | 56 | 0% |
| [Clark Lau LLC](https://www.xh1b.org/firm/Clark%20Lau%20LLC) | 44 | 0% |
| [GOFFWILSON, P.A.](https://www.xh1b.org/firm/GOFFWILSON%2C%20P.A.) | 42 | 0% |
| [Harter Secrest Emery LLP](https://www.xh1b.org/firm/Harter%20Secrest%20Emery%20LLP) | 29 | 0% |
| [LAW Office of Thomas V. Allen PLLC.](https://www.xh1b.org/firm/LAW%20OFFICE%20OF%20THOMAS%20V.%20ALLEN%20PLLC.) | 27 | 0% |
| [O'Donoghue LAW, LLC](https://www.xh1b.org/firm/O'DONOGHUE%20LAW%2C%20LLC) | 13 | 0% |
| [EY Law LLP](https://www.xh1b.org/firm/EY%20Law%20LLP) | 12 | 0% |
| [LAW Offices of Susheela Verma](https://www.xh1b.org/firm/LAW%20OFFICES%20OF%20SUSHEELA%20VERMA) | 10 | 0% |

## Federal contracts (of 82)

| Recipient | Agency | Action obligated | Date |
|---|---|---|---|
| [ENVIRONMENTAL ALTERNATIVES INC](https://www.xh1b.org/org/environmental-alternatives-llc-a6c012a6a5) | Department of Defense | $848K | 2026-01-13 |
| [SOLDIER SYSTEMS D-MIL LLC](https://www.xh1b.org/org/soldier-systems-d-mil-llc-8ca914cb2d) | Department of Defense | $418K | 2026-05-14 |
| [PEAK HEALTHCARE AT KEENE LLC](https://www.xh1b.org/org/peak-healthcare-at-keene-llc-b7da075851) | Department of Veterans Affairs | $330K | 2026-01-02 |
| [PEAK HEALTHCARE AT KEENE LLC](https://www.xh1b.org/org/peak-healthcare-at-keene-llc-b7da075851) | Department of Veterans Affairs | $248K | 2026-01-08 |
| [ENVIRONMENTAL ALTERNATIVES INC](https://www.xh1b.org/org/environmental-alternatives-llc-a6c012a6a5) | Department of Defense | $216K | 2026-03-04 |
| [GLOBAL LEADERSHIP EXCHANGE, INC.](https://www.xh1b.org/org/global-leadership-exchange-inc-bb0a83fc16) | Department of Health and Human Services | $209K | 2026-08-05 |
| [PEAK HEALTHCARE AT KEENE LLC](https://www.xh1b.org/org/peak-healthcare-at-keene-llc-b7da075851) | Department of Veterans Affairs | $190K | 2026-08-04 |
| [JAFFREY REHABILITATION & NURSING CENTER LLC](https://www.xh1b.org/org/jaffrey-rehabilitation-nursing-center-ll-2087cad445) | Department of Veterans Affairs | $135K | 2026-08-04 |

## Recent LCA filings (latest 10 of 1,324)

| Case number | Employer | Job title | Wage | Decision |
|---|---|---|---|---|
| [I-200-24278-385894](https://www.xh1b.org/lca/I-200-24278-385894) | [42 North Dental Care, PLLC](https://www.xh1b.org/org/42-north-dental-care-pllc-39a7892fad) | General Dentist | $102K | 2026-03-27 |
| [I-200-26068-687900](https://www.xh1b.org/lca/I-200-26068-687900) | [COGNIZANT TECHNOLOGY SOLUTIONS US CORP](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | SA JC60 - Software Quality Assurance Analysts and Testers | $85K | 2026-03-16 |
| [I-200-26068-687891](https://www.xh1b.org/lca/I-200-26068-687891) | [COGNIZANT TECHNOLOGY SOLUTIONS US CORP](https://www.xh1b.org/org/cognizant-technology-solutions-us-corp-5ba3af5514) | SA JC60 - Software Quality Assurance Analysts and Testers | $85K | 2026-03-16 |
| [I-200-25240-273271](https://www.xh1b.org/lca/I-200-25240-273271) | [C&S Wholesale Grocers LLC](https://www.xh1b.org/org/cs-wholesale-grocers-llc-1b844201bb) | Manager, Web Application | $173K | 2026-03-02 |
| [I-200-26020-571102](https://www.xh1b.org/lca/I-200-26020-571102) | [Cheshire Medical Center](https://www.xh1b.org/org/cheshire-medical-center-4db7856224) | Clinical Laboratory Scientist III | $47K | 2026-01-27 |
| [I-200-26020-570942](https://www.xh1b.org/lca/I-200-26020-570942) | [Dartmouth-Hitchcock Clinic](https://www.xh1b.org/org/dartmouth-hitchcock-clinic-378991a1c4) | Surgical Pathologist/Assistant Professor | $127K | 2026-01-27 |
| [I-200-26016-565110](https://www.xh1b.org/lca/I-200-26016-565110) | [Dartmouth-Hitchcock Clinic](https://www.xh1b.org/org/dartmouth-hitchcock-clinic-378991a1c4) | Gastroenterology Physician | $172K | 2026-01-26 |
| [I-200-25352-494982](https://www.xh1b.org/lca/I-200-25352-494982) | [Dartmouth-Hitchcock Clinic](https://www.xh1b.org/org/dartmouth-hitchcock-clinic-378991a1c4) | Physician (Nephrology) | $81K | 2025-12-26 |
| [I-200-25097-838495](https://www.xh1b.org/lca/I-200-25097-838495) | [Franklin Pierce University](https://www.xh1b.org/org/franklin-pierce-university-6ae207f2f4) | Assistant Professor of Math | $55K | 2025-12-22 |
| [I-200-25339-462450](https://www.xh1b.org/lca/I-200-25339-462450) | [Grandison Management, Inc.](https://www.xh1b.org/org/grandison-management-inc-6afe352d91) | Occupational Therapist | $59K | 2025-12-12 |

## Community well-being (Cheshire 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 | 9.9% | 5.3 points below U.S. |
| Adults receiving SNAP | 2023 | 7.2% | 4.6 points below U.S. |
| Frequent mental distress | 2023 | 15.8% | 0.2 points below U.S. |
| Adult smoking | 2023 | 11.9% | 0.2 points below U.S. |
| Drug overdose deaths | 2021 | 35.7 | 3.6 above U.S. |

## More

- Full page (charts, tables, interactive): https://www.xh1b.org/county/33005
- JSON API: https://www.xh1b.org/api/county-lcas?fips=33005
- JSON API: https://www.xh1b.org/api/county-employers?fips=33005
- JSON API: https://www.xh1b.org/api/county-soc?fips=33005


## References

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-lca-data"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-bls-data"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-dataset-warn"></a>
<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-calculation-method"></a>
<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-indicators-method"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-dataset-federal_contracts"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-displacement-method"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-downstream-method"></a>

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.

<a id="reference-data-lca-data-bls-data-dataset-warn-dataset-federal_contracts-calculation-method-indicators-method-displacement-method-downstream-method-ahrf-data-ahrf-data"></a>

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.