Fair points. Title should specify "employer type" - it's staffing vs product companies, not all H-1B.

And you're right that I didn't prove concentration causes wage gaps, just that both exist. Would need to analyze if top 100 employers actually pay less than smaller sponsors. Geography section had tighter logic.

Lemme appreciate the feedback, this is my first time posting here on HN, and getting such great feedback will definitely pave my way forward. Thank you.

It's already relatively simple for a H-1B holder to transfer jobs under AC21.

The reason backlogged Green Card applicants stay in their sponsoring positions is because they don't want to restart the PERM process which is not transferable to another employer.

When a H-1B transfers to a new employer the approved I140 should be transferred too, assuming it's older than 180 days and the job meets the other requirements set out by AC21.

That would unlock a wave of job portability.

Also, it's not only Indians backlogged now. Almost all EB categories are backlogged across all nationalities although nowhere near as severe as India's.

The mobility → wages connection is clear in the data. Interesting point about green card backlogs adding another mobility restriction layer.

I focused on what's measurable: the wage gap and its correlation with job-switching constraints. Policy intentions are beyond my scope - I'm just showing what the numbers reveal.

Thank you! For policy/academic contexts, please use:

[Aditya Z]. (2025). "Analysis of H-1B Wage Disparities by Employer Type: Evidence from 2.4 Million Department of Labor Records (2020-2024)." Available at: [https://open.substack.com/pub/theh1brecords/p/analysis-of-h1...]

Key points for policymakers: • Dataset: DOL OFLC LCA Disclosure Data (2,404,784 certified applications) • Finding: $50,950 median annual wage gap between employer types • Mechanism: Mobility restrictions create monopsony conditions • Geographic: 52% concentration in 5 states amplifies effects

We can provide: - Full methodology documentation - Cleaned dataset (anonymized, aggregated) - Detailed methodology documentation - Raw source file list

Email for detailed briefing materials: theh1brecords@gmail.com

Note: This is empirical analysis of public data, not policy advocacy. The data patterns are reproducible and verifiable.

It's really shocking how hard it is for a skilled foreigner to get an EAD-like work permit that allows them to freely change employers. It takes 3+ years for most, and I think up to 15+ if you're from India. Of course the ability to suppress wages explains it.

This is excellent feedback. You are absolutely right that a multivariate regression controlling for location, experience, and job family is the rigorous way to isolate the staffing firm coefficient from the raw data. We stuck to descriptive statistics (medians/distributions) for this initial post to keep it accessible to a general audience, but the 'Cap Exempt' comparison you suggested is a brilliant idea for a validity test. I’ll definitely look into and will try to add a 'Cap Exempt' binary variable to our roadmap for Part 2. Thanks for the 2 cents, it’s worth a lot more than that!

yeah, the job title data is pretty wild. 62.8% of these apps are just 'Analyst' or 'Developer.' From a data perspective, using those generic SOC codes lets high-volume firms standardize everything. It meets the 'specialty' degree requirement on paper, but avoids the higher wage floors that a more specific title like 'Machine Learning Engineer' would trigger. Essentially, the system is being used for scale rather than niche talent scarcity, which shows up clearly in that $50k wage gap.