Over 2 million records are packed into the H1B database, making it the largest public repository of visa petitions. It works by pulling directly from Department of Labor disclosures, listing every employer, job title, salary, and work location. This tool lets you quickly run salary comparisons with just a few searches.
What the H-1B Visa Data Repository Actually Contains
The H-1B Visa Data Repository, accessible through the “h1b database,” contains mandatory employer-submitted Labor Condition Applications (LCAs) and certified petition data. It specifically includes the employer’s legal name and primary address, the exact job title, the offered wage (annual salary or hourly rate), the full-time or part-time status, the worksite address, and the start/end dates of employment. A key practical element is the prevailing wage determination, which shows the minimum wage the Department of Labor assigned for that occupation and location.
The database reveals both the employer’s offered wage and the government-set wage floor, allowing you to spot if an employer is paying the bare minimum or a premium.
Each record also includes a case number linking back to the original filing, and whether the application was certified, denied, or withdrawn.
Fields You Will Find in Public Disclosure Records
Public disclosure records within the H-1B database include specific fields that identify the sponsoring employer name and address. You will also find the worker’s job title, the offered wage, and the period of intended employment. A list of key fields follows:
- Employer’s legal business name and primary location.
- Full-time or part-time hours per week.
- Prevailing wage rate and offered wage for the position.
- Job title and description under the Labor Condition Application (LCA).
- Start and end dates of the authorized work period.
Differences Between Employer-Filed and Government-Published Datasets
When exploring the H-1B database, a key distinction is how employer-filed datasets differ from government-published ones. Employer-filed data comes directly from Labor Condition Applications (LCAs), offering raw, unedited details like job titles and offered wages. In contrast, government-published datasets are cleaned and aggregated by agencies like USCIS, removing duplicates and correcting obvious errors for compliance reports. The government version often masks exact figures to protect privacy, while employer filings show precise but sometimes messy numbers. This difference matters if you’re tracking specific roles or salaries—raw filings give you granular, unvarnished views, while public data provides a polished, authoritative summary for broader analysis. Choose accordingly based on your research depth.
Time Periods Covered and Typical Update Cycles
The covered time periods in an H-1B visa data repository typically begin from fiscal year 2009, with most datasets now updated through the most recent fiscal year end. Typical update cycles follow the U.S. government’s annual release schedule, where finalized data for a completed fiscal year—such as petitions adjudicated between October 1 and September 30—becomes publicly accessible approximately six to nine months later. For instance, FY 2023 data would appear by mid-2024. Some repositories also incorporate quarterly supplemental updates, but core petition-level records only refresh once per fiscal year.
| Data Aspect | Typical Range |
|---|---|
| Earliest Year | 2009 |
| Latest Year | Most recently completed fiscal year |
| Update Cadence | Annual, 6–9 months after fiscal year end |
Key Insights Hidden in Wage and Occupation Records
Analyzing the h1b database reveals key insights hidden in wage and occupation records that empower job seekers and employers. By cross-referencing prevailing wage levels with specific occupation codes, you can identify which positions offer actual salary premiums versus those that appear high-earning due to geographic cost-of-living adjustments. A critical detail is that wage data often masks systematic underpayment in entry-level STEM roles, where companies list higher-tier occupations but pay the minimum allowed. This gap exposes employers who exploit visa classifications to depress compensation. Additionally, tracking wage progression across multiple petition cycles for the same occupation uncovers whether a firm consistently pays below market rates—flagging potential exploitation or budget-driven hiring. Directly comparing wage percentiles between similar occupations in the database also reveals which specialty roles command genuine negotiation leverage, allowing you to benchmark offers against actual paid wages, not just advertised ranges.
Top Paying Job Titles by Median Salary
Analyzing the H1B database reveals that highest median salary job titles are consistently dominated by specialized tech roles. Software Engineering Managers, Architects, and Principal Engineers routinely earn median annual wages above $180,000, while Physician and Surgeon roles command similar figures in medical fields. A quick scan shows that positions requiring deep expertise, like Data Scientists and Product Managers, also land in the top tier, often exceeding $150,000. What specific title showed the absolute highest median salary in the most recent H1B data? Typically, that distinction goes to “Chief Architect,” with median compensation frequently surpassing $200,000 annually.
Industries with the Highest Visa Approval Rates
When digging into the h1b database for high-visa-approval industries, you’ll find a clear pattern. Technology and engineering fields consistently top the list, with computer systems design and scientific research firms showing the most favorable outcomes. For a practical, user-focused breakdown:
- Tech companies (like software developers and IT consultancies) usually see approval rates above 90% due to structured job roles and clear wage levels.
- Educational services and hospitals also rank high for specialized professions like professors and medical researchers.
Geographic Hotspots for H-1B Labor Certifications
Analyzing the h1b database reveals that geographic hotspots for H-1B labor certifications cluster narrowly around major tech corridors. The San Francisco-San Jose metroplex consistently dominates, with Santa Clara County alone accounting for a dense concentration of certification filings tied to software and engineering roles. New York County follows, though its certifications lean more toward finance and consulting positions. A lesser-known hotspot is King County, Washington, where Amazon and Microsoft drive a high volume of applications. This data allows users to identify which cities have the highest employer demand for foreign talent, enabling targeted job searches by location.
Q: How do the geographic hotspots for H-1B labor certifications shift year-over-year in the database?
Answer: While the top three hotspots remain stable, secondary cities like Austin and Seattle show measurable growth in certification volume over recent filing periods.
How Companies Use This Information Strategically
Companies mine the H1B database to directly poach competitors’ specialized talent. They identify firms hiring for critical roles and undercompensating workers, then launch targeted recruitment offers. Strategic workforce planning relies on analyzing approval patterns to shape salary benchmarks and identify geographic talent clusters for satellite office placement. Q: How do firms exploit filing timelines? A: They map peak petition dates to align their own open roles, ensuring they can swiftly interview candidates whose current H-1B visas are nearing expiration, offering them immediate h1b data sponsorship transfers to avoid downtime.
Benchmarking Compensation Against Competitor Filings
When companies tap into the H1B database, a key strategic move is benchmarking compensation against competitor filings. You can directly compare salary offers for identical roles at rival firms, seeing exactly what they paid for a software engineer or data scientist. This turns the data into a practical tool for setting your own offers just above the median, ensuring you attract top talent without overspending. It’s a cheat sheet for market-rate alignment based on real, submitted numbers.
- Scan competitor LCA records for exact salary figures on similar job titles.
- Adjust your offers to sit in the top quartile of what rivals actually paid.
- Use historical filings to spot seasonal or location-based pay differences.
Identifying Visa Sponsorship Trends in Your Sector
To identify visa sponsorship trends in your sector, analyze the H1B database to pinpoint which employers in your niche are scaling their filings. Filter by Standard Occupational Classification (SOC) codes to see petition volumes for roles like software developer or data scientist. Compare year-over-year approval and denial rates for specific companies; a rising denial rate may signal stricter internal policies. **Q: How can I determine if a specific employer is increasing sector-specific sponsorship?** A: Query the database by employer name and SOC code across multiple fiscal years; a consistent upward trend in petition numbers indicates strategic growth in that role category.
Mapping Talent Pools by Citizenship Origin Data
Using the H1B database for talent sourcing, you can map pools by citizenship origin data to see exactly where specialized skills cluster. For instance, if you need design engineers, the database might reveal a high concentration from India, while German citizens dominate mechanical roles. This lets you target recruitment campaigns to specific countries or diaspora communities, slashing time wasted on unqualified leads. It’s like having a heatmap of global expertise.
Q: How does mapping by citizenship origin save hiring time?
A: You skip countries with low H-1B issuance for your role—focusing only on regions where your skill actually gets sponsored.
Common Pitfalls When Analyzing Public Visa Filings
The raw H1B database lures you into a false sense of precision. I once spent weeks charting employer growth from approved petitions, only to discover the data only counts approved petitions, not actual workers. A single filing could cover multiple years or go unused. You see a spike at Amazon in Q3? That’s likely a batch of extensions, not new hires. The real sting: many records lack end dates, so a “current” job might have ended last year. Q: What is the biggest mistake with H1B filings? A: Treating a petition count as headcount. Check the “status” field—many approved cases are later revoked, and the database never updates that.
Misinterpreting Prevailing Wage Calculations
A major pitfall in analyzing the H1B database is misinterpreting prevailing wage calculations, as the stated wage often reflects a composite of multiple job levels or geographic-specific surveys, not a single standardized rate. Users frequently assume the wage represents the worker’s actual salary, but it is actually the minimum legal threshold set by the Department of Labor for the intended position and location. This discrepancy can lead to false conclusions about compensation competitiveness or employer compliance. Additionally, failing to account for different wage tiers (Level I to IV) skews comparisons, as a Level I entry wage is not equivalent to a higher-level prevailing rate.
Confusing Certified Positions with Actual Hires
A critical mistake in analyzing the H1B database is treating each certified Labor Condition Application (LCA) as a confirmed hire. A certification only proves the employer could hire someone for that role, not that they did. This gap occurs because companies often oversubscribe positions to secure flexibility, and many certified visas are never used due to budget freezes, candidate withdrawal, or internal reshuffling. To avoid confusion:
- Cross-reference certified positions with actual visa issuance data from the USCIS, which indicates true hires.
- Prioritize entries where the employer has a known history of utilizing certified positions at similar rates.
- Treat extremely high certification volumes relative to company size as potential indicators of inflated headcount plans.
Overlooking Data Lags and Verification Gaps
When analyzing the H1B database, verification gaps emerge from lagging USCIS data updates that fail to reflect real-time petition status. Users often treat filing dates as definitive employment start points, ignoring that approvals can take months, while denied or withdrawn cases linger in outdated records. This oversight skews applicant counts and employer trends, as base records lack final adjudication outcomes. Without cross-referencing current case status or employer validation, any conclusion drawn risks being built on historical snapshots rather than actionable truths.
Overlooking data lags and verification gaps means trusting stale filings as current reality, producing insights disconnected from actual petition outcomes.
Legal and Privacy Considerations for Researchers
When using an h1b database for research, legal and privacy considerations hinge on data minimization and anonymization. Researchers must ensure they do not re-identify individuals from public records, as even aggregated, salary and employer data can be traced back to specific petitioners.
Key insight: You must not combine the database with other datasets to infer personal characteristics, as this violates standard research ethics and may breach data protection principles under applicable laws.
Accessing the database for non-commercial, statistical analysis is generally permissible, but publishing any personally identifiable information—even if technically public—requires careful redaction and a clear justification under fair use or academic exemption doctrines. Always document your data handling protocol regarding retention and secondary use.
Redaction Rules for Personally Identifiable Information
Researchers using the h1b database must apply strict redaction rules for Personally Identifiable Information before any publication or sharing of findings. This involves systematically obscuring direct identifiers like full names, passport numbers, and precise home addresses from visa records, replacing them with generic placeholders or aggregating data to a regional level rather than an individual employer. Failure to redact indirect identifiers such as salary data combined with a unique job title can still enable re-identification of a visa holder. Adhering to these rules protects research subjects from potential discrimination or privacy violations, ensuring the database remains a viable tool for analysis without compromising individual rights.
Fair Use Guidelines for Publishing Aggregate Statistics
When publishing aggregate statistics from the h1b database, researchers must adhere to fair use guidelines for aggregate data to avoid revealing personally identifiable information. A clear sequence applies: first, ensure the minimum cell size in any aggregated group exceeds five records to prevent re-identification. Second, use rounding or suppression for any cell count below that threshold. Third, compute averages and medians only from datasets with sufficient sample sizes to mask outliers. Even aggregated salary ranges can inadvertently expose an individual’s earnings if the grouping is too narrow. Valid statistical disclosure control, such as noise infusion, must be documented in your methodology to maintain reproducibility without compromising privacy.
State-Level Variations in Disclosure Exemptions
State-level variations in disclosure exemptions within the H1B database create a fragmented compliance landscape. Researchers accessing employer-specific labor condition applications (LCAs) must verify whether a state’s open-records law exempts proprietary wage data or trade secrets, as exemptions differ. For instance, California’s Public Records Act may withhold specific salary ranges if deemed confidential, while Texas’s statute might release those figures. This forces a sequential check: state-level exemptions analysis must precede data use. To navigate this:
- Identify the employer’s primary state jurisdiction.
- Consult that state’s specific public records exemption criteria for LCA documents.
- Cross-reference the exemption against the H1B database fields needed (e.g., wages, job titles).
Only after this can a researcher determine which database entries are usable without legal exposure.
Alternative Data Sources That Complement These Records
To move beyond the static snapshot of an official H1B database, which only lists approved petitions, you must layer in alternative data sources like LinkedIn and professional networking archives. These platforms reveal the actual career trajectory—showing if an H1B holder transitioned to a different visa, departed the U.S., or remains in the role listed.
Cross-referencing an H1B record with visa holder’s GitHub commit history or academic publications provides concrete proof of ongoing employment in a specialized field, which the government filing alone cannot verify.
Company employee directories on sites like Apollo or ZoomInfo further complement the data by tying a specific H1B petition number to a current, active job title and department, offering a real-time validation of status.
Department of Labor PERM and LCA Archives
The Department of Labor’s PERM and LCA archives unlock the hiring process behind every H1B petition. These records show the actual employer-sponsored labor certifications and prevailing wage determinations that prove a job couldn’t be filled by a U.S. worker. For users combing an H1B database, cross-referencing LCA filings reveals salary levels, work locations, and employer attestations not always visible in visa approval data. PERM archives, in turn, expose the permanent residency pipeline that often follows an H1B stint, letting you track a company’s true long-term hiring patterns and job market impact.
USCIS Case Status Tracker and FOIA Requests
For augmenting the core USCIS Case Status Tracker and FOIA Requests data, the Case Status Tracker provides real-time, per-case updates on petition adjudication phases (e.g., “Received,” “Approved”). Conversely, the FOIA process yields complete PDF copies of an H-1B approval notice (Form I-797) and the underlying petition, revealing employer details, job duties, and validity dates not shown in the tracker. The table below contrasts their utility:
| Aspect | Case Status Tracker | FOIA Request |
| Information Depth | Status only (e.g., “Case Was Approved”) | Full petition text, employer, LCA details |
| Timeliness | Immediate, real-time updates | Delayed (weeks to months) |
| Data Format | Structured, queryable via API | PDF scan, unstructured text |
Use the tracker for bulk status monitoring; use FOIA to verify specific employer-sponsorship claims or obtain field-specific job titles absent from the database.
Private Aggregators Offering Enhanced Search Tools
Private aggregators offering enhanced search tools supplement the official H1B database by providing refined query capabilities. These platforms allow users to filter results by employer, job title, or geographic region beyond the government’s basic name and year search. Specialized data normalization corrects inconsistencies in employer names across records, enabling more accurate searches. The aggregated tools often present a clear sequence:
- Users input specific criteria like occupation code or salary range.
- The engine cross-references multiple USCIS fiscal year datasets.
- Results display aggregated counts and detailed case statuses in a streamlined interface.
This functionality reduces manual data sifting for researchers tracking visa outcomes.
Practical Steps to Query the Dataset Efficiently
You start by filtering the dataset on the employer name and fiscal year to isolate relevant records. Then, you use a LIKE query on the job title column to narrow down specific roles, like “software engineer,” avoiding full-text scans by indexing those fields first. Adding a status filter for “Certified” on a recently updated partition cuts your scan time from minutes to seconds. Finally, you join the worksite table using the case number to map geographic density, running your aggregation after the join to minimize memory load.
Filtering by Fiscal Year and Employer Name
To isolate a specific employer’s petition history, begin by setting a precise fiscal year range within the H1B database query interface. This prevents results from including outdated data or future projections. Next, enter the full legal employer name into the designated filter field, ensuring exact spelling to avoid mismatches. Execute the query to return only records matching both criteria. For a detailed audit, follow this sequence:
- Select the desired start and end fiscal years in the date filter.
- Input the employer’s exact legal name in the employer filter field.
- Apply both filters simultaneously before running the search.
Using SOC Codes to Isolate Specific Job Roles
To zero in on precise roles like “Software Developers” without wading through thousands of mismatched job titles, you must use SOC Codes. These six-digit numbers act as a universal filter in the H1B database, instantly slicing away noise. For example, filtering by 15-1251 pulls every Data Scientist record, regardless of how employers phrase the job. This eliminates manual scanning and accelerates queries. Using SOC Codes to isolate specific job roles also reveals true demand for a position, not just creative job titles. Q: How do I find the correct SOC Code for a niche role? A: Use the DOL’s O*NET Online site to search keywords, then apply that exact code to your H1B query.
Exporting and Visualizing Trends in Spreadsheet Software
After running your query, export the H1B dataset as a CSV. Open it in spreadsheet software like Excel or Google Sheets. Use PivotTables to group by job title or employer, then insert a line chart to show salary trends over years. Filter by year to compare H1B approval volumes—a line chart makes clusters like sudden demand for software developers obvious. If comparing two variables, like employer vs. wage, a clustered bar chart visualizes differences at a glance. Always remove empty rows before plotting to keep visuals clean.
What Exactly Is an H1B Database and How Does It Work
Core Data Fields You Can Expect in a Typical H1B Record
How the Database Pulls Information from Public Sources
Understanding the Difference Between Raw Data and Parsed Results
Key Features to Look for When Choosing a Search Tool
Advanced Filtering Options Beyond Basic Employer and Job Title
Export Capabilities for Spreadsheets and Reports
Real-Time Updates Versus Static Snapshots of Data
Practical Ways to Use This Resource for Job Seekers
Identifying Companies That Sponsor Visas in Your Field
Comparing Wage Levels Across Different Geographic Regions
Tracking Approval Rates for Specific Employer Petitions
Common Pitfalls When Interpreting H1B Records
