Comments URL: https://news.ycombinator.com/item?id=49083314

Points: 568

# Comments: 241

sincerely.

sincerely.

You're correct that if staffing firms file significantly more total applications, their absolute number of affected Level I positions could exceed product companies despite lower concentration.

To properly claim "3x harder hit," I'd need to show either: 1. Total Level I applications by employer type, or 2. Total estimated selection losses by employer type

Without those absolute numbers, the "3x harder" claim overstates what the data shows. The accurate claim is that product companies have 3x higher Level I concentration - but that's not the same as 3x more impact.

This is a good catch. The proportions tell us about hiring patterns but not total system impact.

Quick summary of probability shifts under the weighted system:

  Level I ($80K median):  -57% selection probability
  Level II ($103K median): -14%
  Level III ($135K median): +29%
  Level IV ($158K median): +73%

Data source: DOL LCA disclosure data. Happy to discuss methodology.

Comments URL: https://news.ycombinator.com/item?id=46720032

Points: 10

# Comments: 8

Product companies (direct employers): 22.0% Level I concentration Staffing/consulting firms: 7.1% Level I concentration Ratio: 3.1× Tech companies hiring junior engineers get hit 3x harder than the outsourcing firms the rule supposedly targets.

Data: DOL H-1B LCA Disclosure Data FY2024, 517,874 certified applications. Stack: Python, pandas 2.1.0.

Comments URL: https://news.ycombinator.com/item?id=46705518

Points: 5

# Comments: 1

Your "gateway" hunch is likely spot on. Most rural docs start on J-1 waivers (mandatory 3 years). If they actually stayed after that, we'd see way more H-1B conversions filed by those rural hospitals to keep them. Since the volume is so low, it suggests once the 3 years are up, they bail for the city. The wage premium just isn't enough to anchor them.

Data: 20,225 H-1B LCA disclosures from DOL, FY2024, healthcare occupations only Analysis: Python (pandas), mapped ZIP → RUCC codes, median wage by volume quintile Key limitation: This is LCA data (intent to hire), not final USCIS approvals

Interesting rabbit holes:

Urban/rural split isn't binary—codes 4-6 show gradient effects

Wage level inversions strongest in codes 7-9 (most rural)

Happy to answer methodology questions.

Comments URL: https://news.ycombinator.com/item?id=46635185

Points: 3

# Comments: 3

Methodology: We joined DOL disclosure data with USDA Rural-Urban Continuum Codes (RUCC 2013) using a ZIP-to-County crosswalk.

The Signal: We found a clear inversion of the standard supply curve. Rural areas offered a 21.4% wage premium ($250k median vs $206k) yet achieved 10.2x lower placement volume.

Systemic Friction: The data suggests that for high-skill labor (physicians), geographic friction and regulatory overhead (including the new $100k fee) outweigh significant monetary incentives. The market is not clearing.

Comments URL: https://news.ycombinator.com/item?id=46616782

Points: 2

# Comments: 1