County intelligence
El Paso County
Colorado (CO) · FIPS 08041 · 914 employers · 3,852 filings with wage data
Data current as of Sep 2, 2026
What the local records contain / DOL & WARN
What XH1B calculates / Local wage comparisons
How to read the wage comparison
County wage averages combine occupations, locations, and years. They are a screening comparison, not a finding about any individual filing.
Definitions & methodology ↗Filings & wages
Annual filing records and a county-level wage comparison.Filing history
Annual DOL filing records. Workers requested are not confirmed hires.
Inspect filing outcomes All available years and DOL statuses
Certified, denied, and withdrawn counts describe application decisions. They do not measure visas issued or workers hired.
Offered-wage distribution
Percentiles across 3,884 positive year-unit LCA offers, all outcomes and visa classes.
Application outcomes all available years
- Certified3,989 · 95.2%
- Denied95 · 2.3%
- Withdrawn107 · 2.6%
How this distribution is built Year-unit offers, all outcomes and visa classes
The distribution covers positive offers with wage unit Year, across all outcomes and visa classes. It differs from the certified H-1B summary cohort. It does not adjust for occupation, employer, or worksite mix.
Downstream impact
Conditional effects of this county's modeled displacement exposure.Americans displaced (modeled estimate)
21,306
The math
2809 of 4071 filings below BLS median
Σ workers×(1+wage_gap) + Σ WARN layoffs. The 1.0 base rate per certified H-1B worker request is a site model assumption. Wage gap multiplier = (BLS median − offered wage) / BLS median, applied only to filings offering less than the BLS median. Related research provides context, not validation of these exact coefficients. This is a modeled estimate, not a count of actual displacements.
Exposure method v1.2 · This is a modeled worker-equivalent count.
Modeled consequence estimates
Conditional impacts associated with modeled displacement
These are conditional population estimates scaled from El Paso County's current modeled displacement count. They are not observed outcomes, a prediction about any person, or evidence that this entity caused displacement.
Exposure method v1.2 (stored with this aggregate) · benchmarked to the latest BLS survey. Consequence parameters v1.52; build v1.52+engine0.4.0+99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153+adapter605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e
Excess deaths over 20 years
Illustrative reference calculation · population applicability is not established
647.55
Range: 253.76 to 1,127.04
20-year reference scenario: high-seniority male displacement effect. This is not an expected count for an unknown population.
- Source-profile mortality point (deaths)
- 647.554702308
- No sustained excess from year 7 (deaths)
- 462.239247746
Declared structural comparison; not a confidence interval or a model probability.
Baseline population: US prime-age men (45-54).
Child lifetime earnings lost
Illustrative reference calculation · population applicability is not established
$10.2B
Range: $2.7B to $17.3B
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: 30b418f170eef50f3e76e94f3840cebef3d8b445c00c4680672ad6ed926b7301
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":"08041","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":1090,"weight":0.990425,"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":206879,"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":0.005265671,"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":41.0382,"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 647.554702308 (range 253.758284715 to 1,127.03541754); child lifetime earnings lost $10,221,675,907.04 (range $2,654,207,095.21 to $17,262,403,697.08);
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.
- 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:08041->child_outcomes_modifier; derived) Study population: children of displaced workers.
Scenario assumptions, sensitivity and source records
Outcome statistics use the displayed exposure. Baselines, coefficients, annual family schedules and component records retain their source normalization.
{
"displayed_workers": 21305.982,
"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": "08041",
"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": 1090,
"weight": 0.990425,
"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": 206879,
"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": 0.005265671,
"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": 41.0382,
"national_percentile": 40.7679
},
"place_mix": [],
"input_content_sha256": "30b418f170eef50f3e76e94f3840cebef3d8b445c00c4680672ad6ed926b7301",
"engine_version": "0.4.0",
"engine_sha256": "99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153",
"adapter_sha256": "605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e",
"outcomes": {
"excess_deaths": {
"low": 253.7582847146229,
"high": 1127.0354175408556,
"unit": "deaths",
"point": 647.5547023084107,
"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 08041 Gamma-Poisson posterior (1090 deaths / 206879.0 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=0.990425; 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": 0.005265671,
"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": 0.0052518316562589
},
"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 08041 Gamma-Poisson posterior (1090 deaths / 206879.0 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=0.990425; 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": 0.005265671,
"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": 0.0052518316562589
},
"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": {
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"input_value": 1,
"change_from_reference": -185.31545456229028,
"reference_minus_variant": 185.31545456229028
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.0335,
"input_high": 1.2474,
"outcome_low": 508.2732217901054,
"at_input_low": {
"point": 508.2732217901054,
"input_value": 1.0335,
"change_from_reference": -139.2814805183053,
"reference_minus_variant": 139.2814805183053
},
"outcome_high": 799.3579554332135,
"at_input_high": {
"point": 799.3579554332135,
"input_value": 1.2474,
"change_from_reference": 151.80325312480284,
"reference_minus_variant": -151.80325312480284
},
"endpoint_span": 291.0847336431081
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"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": {
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"change_from_reference": -162.48787448240688,
"reference_minus_variant": 162.48787448240688
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 2.1144,
"input_high": 3.3711,
"outcome_low": 593.8233197230306,
"at_input_low": {
"point": 593.8233197230306,
"input_value": 2.1144,
"change_from_reference": -53.731382585380025,
"reference_minus_variant": 53.731382585380025
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"outcome_high": 714.9586964411884,
"at_input_high": {
"point": 714.9586964411884,
"input_value": 3.3711,
"change_from_reference": 67.40399413277767,
"reference_minus_variant": -67.40399413277767
},
"endpoint_span": 121.1353767181577
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"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": {
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"change_from_reference": -142.63767285621748,
"reference_minus_variant": 142.63767285621748
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.3238,
"input_high": 2.2649,
"outcome_low": 568.2583497655007,
"at_input_low": {
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"input_value": 1.3238,
"change_from_reference": -79.29635254290994,
"reference_minus_variant": 79.29635254290994
},
"outcome_high": 750.5362240641471,
"at_input_high": {
"point": 750.5362240641471,
"input_value": 2.2649,
"change_from_reference": 102.98152175573631,
"reference_minus_variant": -102.98152175573631
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"endpoint_span": 182.27787429864625
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"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": 551.8213384829276,
"input_value": 1,
"change_from_reference": -95.73336382548321,
"reference_minus_variant": 95.73336382548321
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 1.3697,
"input_high": 2.8732,
"outcome_low": 587.9125662990659,
"at_input_low": {
"point": 587.9125662990659,
"input_value": 1.3697,
"change_from_reference": -59.6421360093448,
"reference_minus_variant": 59.6421360093448
},
"outcome_high": 733.2588879559064,
"at_input_high": {
"point": 733.2588879559064,
"input_value": 2.8732,
"change_from_reference": 85.70418564749569,
"reference_minus_variant": -85.70418564749569
},
"endpoint_span": 145.3463216568405
},
"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": 594.7362886103016,
"input_value": 1,
"change_from_reference": -52.8184136981091,
"reference_minus_variant": 52.8184136981091
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.97,
"input_high": 1.6661,
"outcome_low": 588.8796739957696,
"at_input_low": {
"point": 588.8796739957696,
"input_value": 0.97,
"change_from_reference": -58.67502831264108,
"reference_minus_variant": 58.67502831264108
},
"outcome_high": 724.0629048284115,
"at_input_high": {
"point": 724.0629048284115,
"input_value": 1.6661,
"change_from_reference": 76.50820252000084,
"reference_minus_variant": -76.50820252000084
},
"endpoint_span": 135.18323083264193
},
"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": 647.5547023084107,
"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": 253.7582847146229,
"high": 1127.0354175408556,
"point": 647.5547023084107,
"citations": [
"sullivan2009"
],
"evidence_status": "source_profile_with_transport_assumptions"
},
"no_sustained_excess": {
"low": 207.09669520977585,
"high": 795.9278449517795,
"point": 462.23924774612044,
"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": 462.23924774612044,
"high": 647.5547023084107,
"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": 185.31545456229028
}
},
"child_lifetime_earnings_lost_usd": {
"low": 2654207095.2140703,
"high": 17262403697.08456,
"unit": "usd_2024",
"point": 10221675907.039045,
"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:08041->child_outcomes_modifier",
"tier": "derived",
"citation": "chettyhendren2018",
"param_low": 1.001508274,
"value_low": 0.843673628968,
"param_high": 1.002092122,
"value_high": 0.975963801424,
"causal_role": "exploratory_place_effect_modifier",
"param_point": 1.001800198,
"value_point": 0.9074336617048,
"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": "08041",
"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": 1090,
"weight": 0.990425,
"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": 206879,
"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": 0.005265671,
"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": 41.0382,
"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": -10221675907.039045,
"reference_minus_variant": 10221675907.039045
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.844,
"input_high": 0.976,
"outcome_low": 2654980755.0750833,
"at_input_low": {
"point": 17257374907.988007,
"input_value": 0.844,
"change_from_reference": 7035699000.94896,
"reference_minus_variant": -7035699000.94896
},
"outcome_high": 17257374907.988007,
"at_input_high": {
"point": 2654980755.0750833,
"input_value": 0.976,
"change_from_reference": -7566695151.963961,
"reference_minus_variant": 7566695151.963961
},
"endpoint_span": 14602394152.912922
},
"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": 10203307932.505568,
"input_value": 0,
"change_from_reference": -18367974.533476744,
"reference_minus_variant": 18367974.533476744
},
"supported_range": {
"scope": "One input at its source endpoints; all others fixed.",
"input_low": 0.031,
"input_high": 0.043,
"outcome_low": 10218697316.57416,
"at_input_low": {
"point": 10218697316.57416,
"input_value": 0.031,
"change_from_reference": -2978590.464884517,
"reference_minus_variant": 2978590.464884517
},
"outcome_high": 10224654497.503944,
"at_input_high": {
"point": 10224654497.503944,
"input_value": 0.043,
"change_from_reference": 2978590.464899756,
"reference_minus_variant": -2978590.464899756
},
"endpoint_span": 5957180.929784273
},
"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": 10221675907.039045,
"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."
}
}
}Parameter set v1.52 · model v1.52+engine0.4.0+99173fbf23e75037da3fb9b4a9017a1e56909260ccb112f9902bc5ac86242153+adapter605700852536cef19bdae955f055979ab182f352c107882e7b46fb6cccd3e68e
Local connections
County context, public notices, and linked public records.Community well-being
Food insecurity, SNAP receipt, mental distress, smoking, and overdose mortality in this area. CDC models these from survey data: treat them as local estimates, not headcounts. Bars compare each estimate against population-weighted averages of all reporting areas for the same data year.
Frequent mental distress
202317.8%: 1.8 points above U.S.
Adult smoking
202310.7%: 1.4 points below U.S.
Drug overdose deaths
202140.8: 8.6 above U.S.
Source: CDC PLACES 2025 release (BRFSS survey year 2023) and NCHS drug poisoning mortality. PLACES values are model-based small-area estimates, not survey counts. Benchmarks are population-weighted averages across all reporting areas.
Medical workforce context
El Paso County in context
Read the county map County-level H-1B intensity
The map gives geographic context. A county average does not classify every filing, employer, or worker within it.
Employers & occupations
The sponsors and job categories recorded in this county.Employers (914)
Job categories (147)
- Computer and Information Systems Managers$81.9M · 54.4%
- Electronics Engineers, Except Computer$44M · 29.2%
- Software Developers$8.5M · 5.6%
- Electrical Engineers$7.5M · 5.0%
- Sales Engineers$4.1M · 2.7%
- Mechanical Engineers$1.7M · 1.1%
- Computer Occupations, All Other$1.5M · 1.0%
- Industrial Engineers$1.4M · 1.0%
1,156 workers · 57 filings · $81.9M wage theft
1,903 workers · 260 filings · $44.0M wage theft
553 workers · 209 filings · $8.5M wage theft
269 workers · 86 filings · $7.5M wage theft
54 workers · 35 filings · $1.7M wage theft
183 workers · 89 filings · $1.5M wage theft
Labor condition applications (4,415)
RK Infotech LLC(1 position requested)
Infrastructure engineer · Colorado Springs 80907 · 2026-03-30
Keysight TECHNOLOGIES, Inc.(1 position requested)
IT Engineer, Software, Advanced · Colorado Springs 80907 · 2026-03-26
Tata Consultancy Services Limited(1 position requested)
Database Administrator · Colorado Springs 80907 · 2026-03-23
Accenture LLP(1 position requested)
Business Architecture Senior Manager · Colorado Springs 80908 · 2026-03-23
Harrison School District Two(1 position requested)
Language Arts Teacher (High School) · Colorado Springs 80906 · 2026-03-20
Advanced Micro Devices, Inc.(1 position requested)
SMTS Software Development Engineer · Colorado Springs 80924 · 2026-03-20
Colorado College(1 position requested)
Assistant Professor of Economics and Business · Colorado Springs 80903 · 2026-03-19
Colorado College(1 position requested)
Assistant Professor of Spanish and Portuguese · Colorado Springs 80903 · 2026-03-19
A negative % sits below the BLS market median; a positive % sits above.
What the filing rows show One application per row
Each row links to the disclosure record. A wage comparison is a screening calculation, not a finding of a wage violation.
WARN notices (0)Public layoff notices: open to inspect
No WARN notices on record.
Records to inspect
Open the linked public records behind this county's filings.Immigration law firms (320)
Federal contracts (9,636)
Evidence & sources
Scope, methods, and source coverage for this county record.Public source records and site analysis. We do not attest to accuracy. Read the full disclaimer.
Dataset guides and site methods
- [1] Dataset documentation · DOL OFLC. LCA public disclosure record layout (FY2026 Q3). Pages 1–4: application, DOL outcome and dates, requested positions, employer, representatives, worksites and pay unitsThis layout covers determinations issued October 1, 2025 through June 30, 2026. It documents employer-provided application fields and DOL decisions, not USCIS decisions, payroll verification, legal bills or worker-linked moves. Older layouts can differ.Checked 2026-10-17.
- [2] Dataset documentation · BLS. OEWS technical notes (May 2025). Scope of the survey; occupational coding; concepts and estimation methodologyDocuments occupation and geographic wage estimates, not an individual worker’s pay. Use the estimate year and geography stated by the site; older releases can differ.Checked 2026-10-17.
- [3] XH1B method or registry · XH1B. WARN Notices (mass layoffs): source registry; Calculation and Matching Methods; Reading Public Records and Derived Indicators. Source registry entry: warn; Filing units, annualization, weighting, wage comparisons, and inference limits; Reading records: units, source coverage, wage gaps, entity links, and inference limitsXH1B dataset description: One row per source_notice_id. The definition of a mass layoff and the layoff threshold vary by state. The threshold is typically 50 or more employees.; These are the site's review methods, not validated predictions of wrongdoing. Inspect underlying records and alternative explanations.
- [4] Dataset documentation · USAspending / U.S. Treasury. Federal Spending Guide: contract transactions. Pages 3, 5, 9 and 17: transactions, amount fields and recipient identifiersContract downloads contain prime award transactions. XH1B’s use of recipient identifiers for employer links is its own processing step.Checked 2026-10-18.
- [5] XH1B method or registry · XH1B. Displacement Model and Assumptions. Eligibility, requested-position weighting, scopes, WARN matching, and counting limitsModel outputs are estimates. A matched layoff notice does not prove that an H-1B worker replaced a laid-off worker.
- [6] XH1B method or registry · XH1B. Downstream Research and Methods. Study populations, parameter bounds, baselines, and expected outcomes; linked model card
- [7] Dataset documentation · HRSA. Area Health Resources Files: 2024–2025 technical documentation. County User Guide: physicians, Table 10 NPI professions, population estimates and missing values; Technical Documentation: variable years and sourcesClinician categories and variable years differ. County APRN counts use NPI taxonomy; physician active and federal status matter. Release year is not the year of every measure.Checked 2026-10-18.
Questions about the data & methods
How many H-1B filings are in El Paso County, CO?
4,071 H-1B labor condition applications requesting 17,654 workers have been filed in El Paso County, CO, per Department of Labor disclosure records.
How many employers have H-1B filings in El Paso County, CO?
855 distinct employers have filed H-1B labor condition applications in El Paso County, CO. The employer list on this page is ranked by filings and wage impact.
What worker-equivalent exposure does the displacement model estimate for El Paso County, CO?
The model estimates 21,306 worker-equivalents of displacement exposure in El Paso County, CO: one per requested worker on a certified H-1B filing, raised where the offered wage sits below the occupation's BLS median, plus government-reported WARN layoffs matched to filings. This is a modeled exposure estimate, not a count of documented job losses; the formula is on the About page.
Who files the most H-1B filings in El Paso County, CO?
Mphasis Corporation leads with 307 filings, about 8% of all El Paso County, CO filings. Its dossier page carries the full filing, wage, and layoff history.
What share of El Paso County, CO H-1B filings does DOL deny?
DOL disclosure records show 3,989 certified, 95 denied, 107 withdrawn of 4,191 labor condition applications with reported outcomes in El Paso County, CO. The denial share is 2% across the reported outcomes.
When did H-1B use peak in El Paso County, CO?
Filings peaked in 2019 at 345 labor condition applications. The filings-by-year chart on this page shows the full trend since records began.
Which immigration law firms file the most H-1B filings in El Paso County, CO?
320 distinct law firms and agents have filed H-1B applications for employers in El Paso County, CO. The law-firm table on this page ranks them by filings and by the wage gap their filings carry.
Do El Paso County, CO H-1B employers hold federal contracts?
9,636 federal contract actions in El Paso County, CO name employers that also file H-1B labor condition applications. The contracts table links each award to the employer dossier. A shared employer name does not show how award funds were spent.
Where does the El Paso County, CO H-1B data come from?
Every figure on this page traces to a public government record: DOL OFLC disclosure files (LCA filings, wages, outcomes), BLS OES wage data (occupation medians), state WARN notices, and federal contract awards. The archive is read-only evidence; nothing is crowdsourced or anonymous.