# Litchfield, CT (FIPS 09005): H-1B profile
> County profile: https://www.xh1b.org/county/09005
Employers in Litchfield, CT submitted 616 certified H-1B applications requesting 1,388 workers across 161 companies. The median offered wage is $70,933/yr. The largest filers are Grandison Management, Inc., The Devereux Foundation and Amazon.com Services LLC. The displacement model assigns 1.66K worker-equivalent exposures to these filings.
| Metric | Value |
|---|---|
| H-1B filings | 616 |
| Workers requested | 1,388 |
| US workers displaced (modeled) | 1,661 |
| Distinct employers | 161 |
| Filings below prevailing wage (share of compared filings) | 0% |
| Wage gap vs BLS | 0% |
| Metric | Value |
|---|---|
| Average offered wage | $92K |
| Average prevailing wage | $76K |
| Average BLS market wage | $112K |
## US workers displaced (modeled)
| Metric | Value |
|---|---|
| Base rate (1.0 per certified worker) | 1,388 |
| Wage-gap multiplier | 273 |
| Matched WARN notice layoff counts | 0 |
| Certified filings | 616 |
| Filings below BLS median | 370 |
| **Total (modeled)** | **1,661** |
| 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 Litchfield County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.
[Explore Litchfield County’s scenario: Earnings · children · health · adjust the worker count](https://www.xh1b.org/downstream?workers=1661.281&entity=Litchfield%20County#downstream-explorer)
| Metric | Value |
|---|---|
| Exposure method version | Exposure method v1.2 (stored with this aggregate) · benchmarked to the latest BLS survey. |
| Consequence parameters v1.52; build | `v1.52+engine0.4.0+99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153+adapter605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e` |
| Outcome | Applicability | Estimate | Range |
|---|---|---|---|
| Excess deaths over 20 years | Illustrative reference calculation · population applicability is not established | 38.630808938 | 15.0957764318 to 67.4788648473 |
| Child lifetime earnings lost | Illustrative reference calculation · population applicability is not established | $813,614,892.92 | $210,568,811.25 to $1,378,573,793.89 |
20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.
Source-profile mortality point (deaths): 38.630808938
No sustained excess from year 7 (deaths): 27.5453433045
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 calculations per 1,000 displaced workers and a standard family. Not a prediction about an individual or evidence that an entity caused displacement. Only county rows use county place inputs, including their WONDER-derived all-cause mortality baseline; all other types use the national reference.
Input SHA-256: `59ddc1f050fc405e1e91e051340156383659a0139e6d3146ff768b7366f211a1`
Excess deaths over 20 years: `{"reason":"Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.","status":"illustrative_reference_calculation","decision":null,"target_population":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"],"eligible_for_public_headline":false}`
Child lifetime earnings lost: `{"reason":"Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.","status":"illustrative_reference_calculation","decision":null,"target_population":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"],"eligible_for_public_headline":false}`
Projection eligibility: `{"child":{"reason":"direct displacement-to-child adult earnings estimate; transport still requires an applicability decision","status":"direct_estimate","eligible_for_validated_direct_contrast":true},"grandchild":{"reason":"combines a direct child estimate with an intergenerational persistence assumption; not an identified displacement intervention effect","status":"structural_projection","eligible_for_validated_direct_contrast":false},"greatgrandchild":{"reason":"repeats an intergenerational persistence assumption beyond the identification core","status":"structural_projection","eligible_for_validated_direct_contrast":false}}`
Place baseline: `{"key":"09005","modifier_reason":null,"modifier_applied":true,"baselines_applied":true,"baseline_overrides":{"divorce_5y_cumulative":{"reason":"no county value in places.csv — national value used","applied":false},"all_cause_mortality_annual":{"events":265,"weight":0.97164,"applied":true,"outcome":"all_cause_mortality_annual","contract":"Gamma-Poisson posterior (strict events + person-years)","prior_rate":0.004944,"time_window":"2015-2019","person_years":68521,"population_scope":"county residents, male, ages 45-54 years; cause: All causes","national_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","prior_person_years":2000,"posterior_mean_rate":0.003897959,"observation_citation":"Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Sep 13, 2026 7:22:32 AM"}},"mobility_percentile":44.1721,"national_percentile":40.7679}`
Exposure source: `{"method":"scale the reference by the stored worker-equivalent exposure at read time","source":"normalized reference input; entity exposure remains on the warehouse aggregate"}`
Exact values: excess deaths over 20 years 38.630808938 (range 15.0957764318 to 67.4788648473); child lifetime earnings lost $813,614,892.92 (range $210,568,811.25 to $1,378,573,793.89);
The displayed range is a parameter-support envelope, not a confidence interval.
Place reference: county inputs where the model has a validated county value.
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": 1661.281,
"source_normalization_workers": 1000,
"applicability": {
"excess_deaths": {
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.",
"status": "illustrative_reference_calculation",
"decision": null,
"target_population": 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"
],
"eligible_for_public_headline": false
},
"child_lifetime_earnings_lost_usd": {
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.",
"status": "illustrative_reference_calculation",
"decision": null,
"target_population": 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"
],
"eligible_for_public_headline": false
}
},
"target_population": null,
"projection_eligibility": {
"child": {
"reason": "direct displacement-to-child adult earnings estimate; transport still requires an applicability decision",
"status": "direct_estimate",
"eligible_for_validated_direct_contrast": true
},
"grandchild": {
"reason": "combines a direct child estimate with an intergenerational persistence assumption; not an identified displacement intervention effect",
"status": "structural_projection",
"eligible_for_validated_direct_contrast": false
},
"greatgrandchild": {
"reason": "repeats an intergenerational persistence assumption beyond the identification core",
"status": "structural_projection",
"eligible_for_validated_direct_contrast": false
}
},
"child_applicability": {
"source": "oreopoulos2008",
"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."
],
"source_event": "father firm closure 1980-1982",
"source_child_sex": "Male",
"source_geography": "Canada",
"unknown_children": [],
"source_parent_sex": "Male",
"incompatible_children": [],
"target_child_profiles": null,
"income_rank_definition": null,
"source_income_quartile": null,
"source_age_at_displacement": [
10,
14
],
"eligible_for_reviewed_transport": false
},
"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.",
"place_application": "initial_only",
"mortality_contract": {
"method": "odds_survival",
"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
}
],
"timing": "source_profile",
"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.",
"baseline_conversion": "1-exp(-annual_rate)"
},
"place_counterfactual": "county child-dollar contrasts use a same-place loss scaling and are exploratory; the national result is the reference"
},
"exposure_provenance": {
"method": "scale the reference by the stored worker-equivalent exposure at read time",
"source": "normalized reference input; entity exposure remains on the warehouse aggregate"
},
"place": {
"key": "09005",
"modifier_reason": null,
"modifier_applied": true,
"baselines_applied": true,
"baseline_overrides": {
"divorce_5y_cumulative": {
"reason": "no county value in places.csv — national value used",
"applied": false
},
"all_cause_mortality_annual": {
"events": 265,
"weight": 0.97164,
"applied": true,
"outcome": "all_cause_mortality_annual",
"contract": "Gamma-Poisson posterior (strict events + person-years)",
"prior_rate": 0.004944,
"time_window": "2015-2019",
"person_years": 68521,
"population_scope": "county residents, male, ages 45-54 years; cause: All causes",
"national_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",
"prior_person_years": 2000,
"posterior_mean_rate": 0.003897959,
"observation_citation": "Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Sep 13, 2026 7:22:32 AM"
}
},
"mobility_percentile": 44.1721,
"national_percentile": 40.7679
},
"place_mix": [],
"input_content_sha256": "59ddc1f050fc405e1e91e051340156383659a0139e6d3146ff768b7366f211a1",
"engine_version": "0.4.0",
"engine_sha256": "99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153",
"adapter_sha256": "605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e",
"outcomes": {
"excess_deaths": {
"low": 15.095776431779317,
"high": 67.47886484732084,
"unit": "deaths",
"point": 38.6308089379619,
"steps": [
{
"low": 2.1144,
"high": 3.3711,
"link": "earnings_shock->mortality_peak",
"tier": "canonical",
"phase": "displacement",
"point": 2.6698,
"citation": "sullivan2009",
"evidence_role": "conditional",
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"low": 1.3697,
"high": 2.8732,
"link": "earnings_shock->mortality_offset_1",
"tier": "canonical",
"phase": "offset_1",
"point": 1.9838,
"citation": "sullivan2009",
"evidence_role": "conditional",
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"low": 1.3238,
"high": 2.2649,
"link": "earnings_shock->mortality_offsets_2_3",
"tier": "canonical",
"phase": "offsets_2_3",
"point": 1.7315,
"citation": "sullivan2009",
"evidence_role": "conditional",
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"low": 0.97,
"high": 1.6661,
"link": "earnings_shock->mortality_offsets_4_5",
"tier": "canonical",
"phase": "offsets_4_5",
"point": 1.2712,
"citation": "sullivan2009",
"evidence_role": "conditional",
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"low": 1.0335,
"high": 1.2474,
"link": "earnings_shock->mortality_sustained",
"tier": "canonical",
"phase": "offset_6_plus",
"point": 1.1354,
"citation": "sullivan2009",
"evidence_role": "conditional",
"population_scope": "US high-seniority displaced men born 1930-1959"
}
],
"baseline": {
"age": "45-54 years",
"sex": "Male",
"unit": "deaths_per_person_year",
"cause": "All causes",
"notes": "place-resolved: county 09005 Gamma-Poisson posterior (265 deaths / 68521.0 person-years, county residents, male, ages 45-54 years; cause: All causes, 2015-2019) pooled toward the national 0.004944 with prior_person_years=2000.0, weight=0.97164; same unit and population as the national baseline — no conversion applied. 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",
"value": 0.003897959,
"years": "2015-2019",
"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",
"geography": "US",
"population": "US prime-age men (45-54)",
"annual_probability": 0.0038903718191917952
},
"components": {
"method": "odds_survival",
"timing": "source_profile",
"follow_up_years": 20
},
"sensitivity": {
"unit": "deaths",
"method": "one-input-at-a-time engine refits",
"schema": "downstream-result-sensitivity/1",
"limitations": [
"Removal experiments may lie outside source support and are hypothetical, not new study estimates.",
"Other inputs are fixed; individual removal changes need not sum to the total because effects interact.",
"Endpoint spans measure range sensitivity, not shares of uncertainty or output variance.",
"Baseline, exposure, demographic, timing and transport uncertainty are not quantified here."
],
"fixed_inputs": {
"follow_up_years": 20,
"mortality_method": "odds_survival",
"mortality_timing": "source_profile",
"displaced_workers": 1000,
"resolved_baseline": {
"age": "45-54 years",
"sex": "Male",
"unit": "deaths_per_person_year",
"cause": "All causes",
"notes": "place-resolved: county 09005 Gamma-Poisson posterior (265 deaths / 68521.0 person-years, county residents, male, ages 45-54 years; cause: All causes, 2015-2019) pooled toward the national 0.004944 with prior_person_years=2000.0, weight=0.97164; same unit and population as the national baseline — no conversion applied. 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",
"value": 0.003897959,
"years": "2015-2019",
"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",
"geography": "US",
"population": "US prime-age men (45-54)",
"annual_probability": 0.0038903718191917952
},
"target_population": null
},
"effect_drivers": [
{
"tier": "canonical",
"citation": "sullivan2009",
"input_id": "earnings_shock->mortality_sustained",
"input_unit": "odds_ratio",
"input_value": 1.1354,
"evidence_role": "conditional",
"effect_removed": {
"point": 27.545343304537596,
"input_value": 1,
"change_from_reference": -11.08546563342431,
"reference_minus_variant": 11.08546563342431
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.0335,
"input_high": 1.2474,
"outcome_low": 30.296212825576923,
"at_input_low": {
"point": 30.296212825576923,
"input_value": 1.0335,
"change_from_reference": -8.334596112384983,
"reference_minus_variant": 8.334596112384983
},
"outcome_high": 47.73454178551743,
"at_input_high": {
"point": 47.73454178551743,
"input_value": 1.2474,
"change_from_reference": 9.103732847555522,
"reference_minus_variant": -9.103732847555522
},
"endpoint_span": 17.438328959940506
},
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"tier": "canonical",
"citation": "sullivan2009",
"input_id": "earnings_shock->mortality_peak",
"input_unit": "odds_ratio",
"input_value": 2.6698,
"evidence_role": "conditional",
"effect_removed": {
"point": 28.899211809031552,
"input_value": 1,
"change_from_reference": -9.731597128930353,
"reference_minus_variant": 9.731597128930353
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 2.1144,
"input_high": 3.3711,
"outcome_low": 35.407909486394026,
"at_input_low": {
"point": 35.407909486394026,
"input_value": 2.1144,
"change_from_reference": -3.222899451567881,
"reference_minus_variant": 3.222899451567881
},
"outcome_high": 42.68062907768757,
"at_input_high": {
"point": 42.68062907768757,
"input_value": 3.3711,
"change_from_reference": 4.049820139725663,
"reference_minus_variant": -4.049820139725663
},
"endpoint_span": 7.272719591293543
},
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"tier": "canonical",
"citation": "sullivan2009",
"input_id": "earnings_shock->mortality_offsets_2_3",
"input_unit": "odds_ratio",
"input_value": 1.7315,
"evidence_role": "conditional",
"effect_removed": {
"point": 30.09231035048267,
"input_value": 1,
"change_from_reference": -8.538498587479236,
"reference_minus_variant": 8.538498587479236
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.3238,
"input_high": 2.2649,
"outcome_low": 33.88087594333565,
"at_input_low": {
"point": 33.88087594333565,
"input_value": 1.3238,
"change_from_reference": -4.749932994626251,
"reference_minus_variant": 4.749932994626251
},
"outcome_high": 44.811288108820186,
"at_input_high": {
"point": 44.811288108820186,
"input_value": 2.2649,
"change_from_reference": 6.180479170858284,
"reference_minus_variant": -6.180479170858284
},
"endpoint_span": 10.930412165484535
},
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"tier": "canonical",
"citation": "sullivan2009",
"input_id": "earnings_shock->mortality_offset_1",
"input_unit": "odds_ratio",
"input_value": 1.9838,
"evidence_role": "conditional",
"effect_removed": {
"point": 32.89722093015158,
"input_value": 1,
"change_from_reference": -5.733588007810323,
"reference_minus_variant": 5.733588007810323
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.3697,
"input_high": 2.8732,
"outcome_low": 35.05697327661319,
"at_input_low": {
"point": 35.05697327661319,
"input_value": 1.3697,
"change_from_reference": -3.5738356613487148,
"reference_minus_variant": 3.5738356613487148
},
"outcome_high": 43.7767329558567,
"at_input_high": {
"point": 43.7767329558567,
"input_value": 2.8732,
"change_from_reference": 5.145924017894792,
"reference_minus_variant": -5.145924017894792
},
"endpoint_span": 8.719759679243507
},
"population_scope": "US high-seniority displaced men born 1930-1959"
},
{
"tier": "canonical",
"citation": "sullivan2009",
"input_id": "earnings_shock->mortality_offsets_4_5",
"input_unit": "odds_ratio",
"input_value": 1.2712,
"evidence_role": "conditional",
"effect_removed": {
"point": 35.46803325820855,
"input_value": 1,
"change_from_reference": -3.162775679753357,
"reference_minus_variant": 3.162775679753357
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.97,
"input_high": 1.6661,
"outcome_low": 35.11755354160392,
"at_input_low": {
"point": 35.11755354160392,
"input_value": 0.97,
"change_from_reference": -3.5132553963579882,
"reference_minus_variant": 3.5132553963579882
},
"outcome_high": 43.21834994240877,
"at_input_high": {
"point": 43.21834994240877,
"input_value": 1.6661,
"change_from_reference": 4.587541004446866,
"reference_minus_variant": -4.587541004446866
},
"endpoint_span": 8.100796400804855
},
"population_scope": "US high-seniority displaced men born 1930-1959"
}
],
"interpretation": "Signed changes under declared input experiments; not causal shares or confidence intervals.",
"reference_point": 38.6308089379619,
"fixed_input_ranges": {
"reason": "No sensitivity ranges supplied for these inputs; no distributions inferred.",
"available": false
},
"variance_attribution": {
"reason": "Not requested.",
"request": "scenario --sensitivity-base N",
"available": false
}
},
"applicability": {
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.",
"status": "illustrative_reference_calculation",
"decision": null,
"target_population": 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"
],
"eligible_for_public_headline": false
},
"interpretation": "Conditional modeled expected difference; parameter bands are support envelopes, not a confidence interval.",
"persistence_sensitivity": {
"unit": "deaths",
"status": "declared_structural_sensitivity",
"variants": {
"source_profile": {
"low": 15.095776431779317,
"high": 67.47886484732084,
"point": 38.6308089379619,
"citations": [
"sullivan2009"
],
"evidence_status": "source_profile_with_transport_assumptions"
},
"no_sustained_excess": {
"low": 12.31714892495917,
"high": 47.557385888865085,
"point": 27.545343304537596,
"assumption": "Retain source odds through offset +5; set offset +6 onward OR=1.",
"evidence_status": "structural_assumption",
"motivating_citations": [
"eliasonstorrie2009"
],
"fixed_late_odds_ratio": 1,
"first_changed_follow_up_year": 7
}
},
"available": true,
"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."
],
"shared_inputs": [
"exposure",
"resolved mortality baseline",
"follow-up horizon",
"source odds through offset +5"
],
"structural_point_range": {
"low": 27.545343304537596,
"high": 38.6308089379619,
"interpretation": "Range of point estimates across these two assumptions; not a confidence interval or a bound on the true effect."
},
"eligible_for_public_headline": false,
"source_minus_no_sustained_point": 11.08546563342431
}
},
"child_lifetime_earnings_lost_usd": {
"low": 210568811.24857476,
"high": 1378573793.892836,
"unit": "usd_2024",
"point": 813614892.9217697,
"steps": [
{
"kind": "direct",
"link": "displacement->child_earnings",
"tier": "EXACT",
"citation": "oreopoulos2008",
"param_low": 0.844,
"value_low": 0.844,
"param_high": 0.976,
"value_high": 0.976,
"causal_role": "direct_displacement_estimate",
"param_point": 0.9076,
"value_point": 0.9076,
"evidence_role": "conditional",
"population_scope": "Canada 39k father-son pairs firm closures"
},
{
"kind": "gap_scale",
"link": "place:09005->child_outcomes_modifier",
"tier": "derived",
"citation": "chettyhendren2018",
"param_low": 1.018995436,
"value_low": 0.839889632752,
"param_high": 1.026348508,
"value_high": 0.975544109536,
"causal_role": "exploratory_place_effect_modifier",
"param_point": 1.022671972,
"value_point": 0.9055051097872,
"evidence_role": "structural",
"population_scope": "children of displaced workers"
}
],
"baseline": {
"unit": "usd_2024",
"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)",
"value": 2591418,
"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"
},
"sensitivity": {
"unit": "usd_2024",
"method": "one-input-at-a-time engine refits",
"schema": "downstream-result-sensitivity/1",
"limitations": [
"Removal experiments may lie outside source support and are hypothetical, not new study estimates.",
"Other inputs are fixed; individual removal changes need not sum to the total because effects interact.",
"Endpoint spans measure range sensitivity, not shares of uncertainty or output variance.",
"Baseline, exposure, demographic, timing and transport uncertainty are not quantified here."
],
"fixed_inputs": {
"place": {
"key": "09005",
"modifier_reason": null,
"modifier_applied": true,
"baselines_applied": true,
"baseline_overrides": {
"divorce_5y_cumulative": {
"reason": "no county value in places.csv — national value used",
"applied": false
},
"all_cause_mortality_annual": {
"events": 265,
"weight": 0.97164,
"applied": true,
"outcome": "all_cause_mortality_annual",
"contract": "Gamma-Poisson posterior (strict events + person-years)",
"prior_rate": 0.004944,
"time_window": "2015-2019",
"person_years": 68521,
"population_scope": "county residents, male, ages 45-54 years; cause: All causes",
"national_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",
"prior_person_years": 2000,
"posterior_mean_rate": 0.003897959,
"observation_citation": "Centers for Disease Control and Prevention, National Center for Health Statistics. National Vital Statistics System, Mortality 1999-2020 on CDC WONDER Online Database, released in 2021. Accessed at http://wonder.cdc.gov/ucd-icd10.html on Sep 13, 2026 7:22:32 AM"
}
},
"mobility_percentile": 44.1721,
"national_percentile": 40.7679
},
"child_profiles": null,
"displaced_workers": 1000,
"lifetime_baseline": {
"unit": "usd_2024",
"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)",
"value": 2591418,
"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"
},
"children_per_worker": 2,
"child_income_quartile": null,
"income_rank_definition": null
},
"effect_drivers": [
{
"tier": "EXACT",
"citation": "oreopoulos2008",
"input_id": "displacement->child_earnings",
"input_unit": "earnings_ratio",
"input_value": 0.9076,
"evidence_role": "conditional",
"effect_removed": {
"point": 0,
"input_value": 1,
"change_from_reference": -813614892.9217697,
"reference_minus_variant": 813614892.9217697
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.844,
"input_high": 0.976,
"outcome_low": 211328543.61604446,
"at_input_low": {
"point": 1373635533.504287,
"input_value": 0.844,
"change_from_reference": 560020640.5825173,
"reference_minus_variant": -560020640.5825173
},
"outcome_high": 1373635533.504287,
"at_input_high": {
"point": 211328543.61604446,
"input_value": 0.976,
"change_from_reference": -602286349.3057252,
"reference_minus_variant": 602286349.3057252
},
"endpoint_span": 1162306989.8882425
},
"population_scope": "Canada 39k father-son pairs firm closures"
},
{
"tier": "EXACT",
"citation": "chettyhendren2018",
"input_id": "neighborhood_exposure->child_outcomes_modifier",
"input_unit": "annual place exposure coefficient",
"input_value": 0.037,
"evidence_role": "structural",
"effect_removed": {
"point": 795577580.2974386,
"input_value": 0,
"change_from_reference": -18037312.62433108,
"reference_minus_variant": 18037312.62433108
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.031,
"input_high": 0.043,
"outcome_low": 810689923.3070139,
"at_input_low": {
"point": 810689923.3070139,
"input_value": 0.031,
"change_from_reference": -2924969.6147558987,
"reference_minus_variant": 2924969.6147558987
},
"outcome_high": 816539862.5365263,
"at_input_high": {
"point": 816539862.5365263,
"input_value": 0.043,
"change_from_reference": 2924969.6147565916,
"reference_minus_variant": -2924969.6147565916
},
"endpoint_span": 5849939.22951249
},
"population_scope": "children of families moving across counties (US tax data, births 1980-88); county-level predictions of permanent residents' income ranks at age 24"
}
],
"interpretation": "Signed changes under declared input experiments; not causal shares or confidence intervals.",
"reference_point": 813614892.9217697,
"fixed_input_ranges": {
"reason": "No sensitivity ranges supplied for these inputs; no distributions inferred.",
"available": false
},
"variance_attribution": {
"reason": "Not requested.",
"request": "scenario --sensitivity-base N",
"available": false
}
},
"applicability": {
"reason": "Target cohort applicability and transport have not been established for this outcome; display as an illustrative reference calculation.",
"status": "illustrative_reference_calculation",
"decision": null,
"target_population": 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"
],
"eligible_for_public_headline": false
},
"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.
- Child lifetime earnings lost: chettyhendren2018 (place:09005->child_outcomes_modifier; derived) Study population: children of displaced workers.
Parameter set v1.52 · model v1.52+engine0.4.0+99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153+adapter605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e
## Filings by year
Calendar submission years 2007–2026. One row per filing.
| Year | Filings | Workers | Employers |
|---|---|---|---|
| 2007 | 1 | 1 | 1 |
| 2008 | 46 | 46 | 22 |
| 2009 | 33 | 35 | 23 |
| 2010 | 3 | 3 | 3 |
| 2011 | 45 | 45 | 24 |
| 2012 | 57 | 67 | 28 |
| 2013 | 38 | 38 | 19 |
| 2014 | 6 | 6 | 5 |
| 2015 | 71 | 122 | 41 |
| 2016 | 40 | 58 | 25 |
| 2017 | 42 | 67 | 21 |
| 2018 | 32 | 41 | 16 |
| 2019 | 46 | 61 | 23 |
| 2020 | 38 | 194 | 20 |
| 2021 | 34 | 151 | 18 |
| 2022 | 33 | 111 | 18 |
| 2023 | 33 | 140 | 17 |
| 2024 | 25 | 103 | 18 |
| 2025 | 34 | 153 | 21 |
| 2026 | 7 | 11 | 7 |
## Offered-wage percentiles (504 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 |
|---|---|---|---|---|
| $41K | $55K | $72K | $100K | $189K |
## Top employers (of 174)
| Employer | Filings | Workers | Avg wage | BLS gap |
|---|---|---|---|---|
| [The Devereux Foundation](https://www.xh1b.org/org/the-devereux-foundation-c9004d7574) | 50 | 52 | $37K | - |
| [Neeltran Inc](https://www.xh1b.org/org/neeltran-inc-5e4f9b04f0) | 28 | 28 | $82K | - |
| [Compuweigh Corporation](https://www.xh1b.org/org/compuweigh-corporation-7bb70a01a1) | 26 | 26 | $77K | - |
| [Grandison Management, Inc.](https://www.xh1b.org/org/grandison-management-inc-6afe352d91) | 24 | 720 | $79K | - |
| [The Charlotte Hungerford Hospital](https://www.xh1b.org/org/the-charlotte-hungerford-hospital-b17cf89de5) | 23 | 26 | $194K | - |
| [Infosys Limited](https://www.xh1b.org/org/infosys-limited-08dee2e784) | 22 | 32 | $87K | - |
| [Global Steering Systems LLC](https://www.xh1b.org/org/global-steering-systems-llc-516cb4983b) | 20 | 20 | $118K | - |
| [Fairfax Data SYSTEMS, Inc.](https://www.xh1b.org/org/fairfax-data-systems-inc-42ecdee84f) | 19 | 19 | $72K | - |
| [Hartford HealthCare Medical Group, Inc.](https://www.xh1b.org/org/hartford-healthcare-medical-group-inc-d4201f8204) | 16 | 16 | $291K | - |
| [Bentley Systems, Incorporated](https://www.xh1b.org/org/bentley-systems-incorporated-7af0a035e8) | 15 | 15 | $63K | - |
## SOC breakdown (of 62)
| SOC | Title | Filings | Workers | Avg wage | Gap | Wage theft |
|---|---|---|---|---|---|---|
| 29-1123 | Physical Therapists | 27 | 657 | $78K | -24% | $14.6M |
| 29-1122 | Occupational Therapists | 1 | 40 | $66K | -34% | $1.4M |
| 17-2141 | Mechanical Engineers | 25 | 25 | $81K | -22% | $680K |
| 17-2071 | Electrical Engineers | 19 | 19 | $93K | -23% | $676K |
| 29-1021 | Dentists, General | 24 | 27 | $157K | -8% | $631K |
| 25-2031 | Secondary School Teachers, Except Special and Career/Technical Education | 26 | 26 | $50K | -31% | $576K |
| 21-1015 | Rehabilitation Counselors | 38 | 40 | $35K | -24% | $444K |
| 17-2112 | Industrial Engineers | 17 | 17 | $80K | -21% | $374K |
## 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 | 151 (US county median 75) |
| Active MDs (non-federal) | 282 |
| County population | 186,551 |
| Primary Care HPSA designations | 0 |
| H-1B medical workers certified | 137 |
| H-1B medical median vs BLS | 62% of BLS ($99K vs $160K) |
## Immigration law firms (of 77)
| Firm | LCAs filed | Below prevailing |
|---|---|---|
| [Duane Morris LLP](https://www.xh1b.org/firm/Duane%20Morris%20LLP) | 29 | 0% |
| [Shipman & Goodwin LLP](https://www.xh1b.org/firm/Shipman%20%26%20Goodwin%20LLP) | 14 | 0% |
| [Duane Morris LLP](https://www.xh1b.org/firm/DUANE%20MORRIS%20LLP) | 13 | 0% |
| [Robinson & Cole LLP](https://www.xh1b.org/firm/Robinson%20%26%20Cole%20LLP) | 10 | 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.) | 10 | 0% |
| [Hall, Render, Killian, Heath & Lyman, P.C.](https://www.xh1b.org/firm/Hall%2C%20Render%2C%20Killian%2C%20Heath%20%26%20Lyman%2C%20P.C.) | 8 | 0% |
| [CONSTANGY, BROOKS, Smith & PROPHETE, LLP](https://www.xh1b.org/firm/CONSTANGY%2C%20BROOKS%2C%20SMITH%20%26%20PROPHETE%2C%20LLP) | 8 | 0% |
| [Lamboley Law FIrm LLC](https://www.xh1b.org/firm/Lamboley%20Law%20FIrm%20LLC) | 7 | 0% |
## Recent LCA filings (latest 10 of 690)
| Case number | Employer | Job title | Wage | Decision |
|---|---|---|---|---|
| [I-200-26083-724990](https://www.xh1b.org/lca/I-200-26083-724990) | [Torrington Board of Education](https://www.xh1b.org/org/torrington-board-of-education-f21bc35033) | Teacher | $65K | 2026-03-31 |
| [I-200-26083-724811](https://www.xh1b.org/lca/I-200-26083-724811) | [The Taft School](https://www.xh1b.org/org/the-taft-school-18027fb546) | Mathematics Instructor | $62K | 2026-03-31 |
| [I-200-26058-670232](https://www.xh1b.org/lca/I-200-26058-670232) | [Neeltran Inc](https://www.xh1b.org/org/neeltran-inc-5e4f9b04f0) | Transformer Production Manager | $148K | 2026-03-06 |
| [I-200-26036-622704](https://www.xh1b.org/lca/I-200-26036-622704) | [Hartford HealthCare Medical Group, Inc.](https://www.xh1b.org/org/hartford-healthcare-medical-group-inc-d4201f8204) | Primary Care Physician | $235K | 2026-02-12 |
| [I-200-26029-601162](https://www.xh1b.org/lca/I-200-26029-601162) | [Matrix Strategies LLC](https://www.xh1b.org/org/matrix-strategies-llc-953d5fab83) | Carpenters/Framer | $48K | 2026-01-30 |
| [I-200-26009-549445](https://www.xh1b.org/lca/I-200-26009-549445) | [The Devereux Foundation](https://www.xh1b.org/org/the-devereux-foundation-c9004d7574) | Program Supervisor | $50K | 2026-01-16 |
| [I-200-26006-537242](https://www.xh1b.org/lca/I-200-26006-537242) | [Compuweigh Corporation](https://www.xh1b.org/org/compuweigh-corporation-7bb70a01a1) | Software QA Lead Test Engineer | $100K | 2026-01-13 |
| [I-200-25352-495476](https://www.xh1b.org/lca/I-200-25352-495476) | [The Devereux Foundation](https://www.xh1b.org/org/the-devereux-foundation-c9004d7574) | Habilitation Counselor | $41K | 2025-12-26 |
| [I-200-25310-384779](https://www.xh1b.org/lca/I-200-25310-384779) | [Connecticut Department of Transportation](https://www.xh1b.org/org/connecticut-department-of-transportation-2d50198fcd) | Transportation Engineer Trainee (Construction Engineering) | $73K | 2025-11-14 |
| [I-200-25262-322888](https://www.xh1b.org/lca/I-200-25262-322888) | [Compuweigh Corporation](https://www.xh1b.org/org/compuweigh-corporation-7bb70a01a1) | System Integration Engineer | $150K | 2025-09-26 |
## Community well-being (Litchfield 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 |
|---|---|---|---|
| Drug overdose deaths | 2021 | 43.4 | 11.2 above U.S. |
## More
- Full page (charts, tables, interactive): https://www.xh1b.org/county/09005
- JSON API: https://www.xh1b.org/api/county-lcas?fips=09005
- JSON API: https://www.xh1b.org/api/county-employers?fips=09005
- JSON API: https://www.xh1b.org/api/county-soc?fips=09005
## 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.