The title of the original post is "Analysis of 2.4M H-1B records shows $50k annual wage gap between employer types". That's not at all what the HN title conveys. The specific wage discrepancy is by "employer type", not some general discrepancy.
The section on geography is better.
The section about specific employers doesn't seem to go anywhere toward an analysis of wage discrepancy. It's just a statement about concentration and a hand-wavy extrapolation to wage discrepancies.
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.
There has always been this argument that allowing H1bs to switch jobs easily and clearing the green card backlog for India will increase labor liquidity in the market which may in turn result in higher wages for local and foreign population.
Employers don't want this. Policymakers also don't want this because higher wages (for everyone) may put inflation pressure. Instead, the middle ground is to have employers have their way by having hostage labor, while at the same time, keep spreading hate for H1bs so that local population doesn't feels alienated by policymakers.
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.
Part of this is that employers stubbornly refuse to start PERM until you've started on the job. There's no legal reason to do this and it means adding even more months to an already long and uncertain process, compared to doing it when you've accepted the job offer.
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.
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.
The mechanism appears to be mobility restriction creating monopsony conditions.
When workers can't easily change jobs (employer-specific visa, 60-day rule),
employers can offer below-market wages.
Used OFLC disclosure data, filtered to computer occupations (SOC 15-xxxx).
Happy to discuss methodology. Built an interactive calculator for people to
check their specific situation.
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.
Spot on. That 15+ year backlog turns a 'temporary' visa into a long-term economic trap. Our data actually showed the wage gap widens the longer a worker stays on H-1B (rather than converging with citizen wages), precisely because they are locked out of the free market (EAD) for the prime earning years of their career.
It's kind of an aside, but it also prevents the individual from doing the most economically efficient thing. I mean, suppose an H1B worker wanted to start a startup and hire some Americans to work for their company. They're legally not allowed to do that and create those jobs. Technically they need a business visa, but how would they get one if they can't start the business in the first place?
Exactly. We call this the 'Innovation Tax.' By legally tying workers to a single employer, the system prevents them from becoming employers themselves.
Our data showed 62% of H-1B filings use generic job titles like 'Analyst'—suggesting that even highly specialized founders are forced into 'cogs in the machine' roles. This isn't just a wage loss for the worker; it's a job-creation loss for the U.S. economy. No taking any side, but based on real data analysis quoting this.
The data shows it’s a structural feedback loop. Even comparing identical job titles in the same city, the $50k gap persists. The gap widens because citizen workers can leave staffing firms for raises at any time, while H-1B holders face 'mobility friction' (60-day rule/backlogs) that keeps them locked into lower-paying tiers longer.
I am guessing you have detailed data as you took 3 months to put it together. I suggest regressing wages on various employer-, employee-, and job-specific characteristics. That analysis will be more insightful. Also, comparing this wage delta across employers who are subject to the H1B cap and those who are cap exempt (e.g., non-profit universities) would make your story more compelling. Your analysis suggests that the wage delta would be small or 0 for cap exempt employers. Btw, you could consider this as a characteristic of employers and include it as a binary (1/0) variable in the regression model. Just my 2 cents.
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!
I think the job titles (Tax #3) are particularly damming. As the post mentions, H1B Specialty Occupations require Theoretical and practical application of a body of highly specialized knowledge.
The jobs titles many WITCH firms are submitting for are just not specialized at all -- Developer, analyst, whatever. I personally fail to see how this could possibly qualify for a H1B visa when there are 10s of millions of employees in the US matching these vague descriptions.
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.
The title is a little misleading.
The title of the original post is "Analysis of 2.4M H-1B records shows $50k annual wage gap between employer types". That's not at all what the HN title conveys. The specific wage discrepancy is by "employer type", not some general discrepancy.
The section on geography is better.
The section about specific employers doesn't seem to go anywhere toward an analysis of wage discrepancy. It's just a statement about concentration and a hand-wavy extrapolation to wage discrepancies.
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.
There has always been this argument that allowing H1bs to switch jobs easily and clearing the green card backlog for India will increase labor liquidity in the market which may in turn result in higher wages for local and foreign population.
Employers don't want this. Policymakers also don't want this because higher wages (for everyone) may put inflation pressure. Instead, the middle ground is to have employers have their way by having hostage labor, while at the same time, keep spreading hate for H1bs so that local population doesn't feels alienated by policymakers.
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.
Part of this is that employers stubbornly refuse to start PERM until you've started on the job. There's no legal reason to do this and it means adding even more months to an already long and uncertain process, compared to doing it when you've accepted the job offer.
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.
Great work, thanks for sharing. Is there any specific citation you'd like used when this is shared with policymakers?
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.
We analyzed 2.4 million H-1B Labor Condition Applications from DOL (2020-2024).
Key findings: - Product companies median: $150k - Staffing companies median: $99k - Annual gap: $50,950 (compounds to $305,700 over 6 years)
The mechanism appears to be mobility restriction creating monopsony conditions. When workers can't easily change jobs (employer-specific visa, 60-day rule), employers can offer below-market wages.
Used OFLC disclosure data, filtered to computer occupations (SOC 15-xxxx).
Happy to discuss methodology. Built an interactive calculator for people to check their specific situation.
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.
Spot on. That 15+ year backlog turns a 'temporary' visa into a long-term economic trap. Our data actually showed the wage gap widens the longer a worker stays on H-1B (rather than converging with citizen wages), precisely because they are locked out of the free market (EAD) for the prime earning years of their career.
It's kind of an aside, but it also prevents the individual from doing the most economically efficient thing. I mean, suppose an H1B worker wanted to start a startup and hire some Americans to work for their company. They're legally not allowed to do that and create those jobs. Technically they need a business visa, but how would they get one if they can't start the business in the first place?
Exactly. We call this the 'Innovation Tax.' By legally tying workers to a single employer, the system prevents them from becoming employers themselves.
Our data showed 62% of H-1B filings use generic job titles like 'Analyst'—suggesting that even highly specialized founders are forced into 'cogs in the machine' roles. This isn't just a wage loss for the worker; it's a job-creation loss for the U.S. economy. No taking any side, but based on real data analysis quoting this.
> Our data actually showed the wage gap widens the longer a worker stays on H-1B
Isn't this because the majority of Indians on H-1Bs work for staffing companies?
The data shows it’s a structural feedback loop. Even comparing identical job titles in the same city, the $50k gap persists. The gap widens because citizen workers can leave staffing firms for raises at any time, while H-1B holders face 'mobility friction' (60-day rule/backlogs) that keeps them locked into lower-paying tiers longer.
I am guessing you have detailed data as you took 3 months to put it together. I suggest regressing wages on various employer-, employee-, and job-specific characteristics. That analysis will be more insightful. Also, comparing this wage delta across employers who are subject to the H1B cap and those who are cap exempt (e.g., non-profit universities) would make your story more compelling. Your analysis suggests that the wage delta would be small or 0 for cap exempt employers. Btw, you could consider this as a characteristic of employers and include it as a binary (1/0) variable in the regression model. Just my 2 cents.
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!
I think the job titles (Tax #3) are particularly damming. As the post mentions, H1B Specialty Occupations require Theoretical and practical application of a body of highly specialized knowledge.
The jobs titles many WITCH firms are submitting for are just not specialized at all -- Developer, analyst, whatever. I personally fail to see how this could possibly qualify for a H1B visa when there are 10s of millions of employees in the US matching these vague descriptions.
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.