Perm Disclosure Data: Count Employers by Job City or Address?

The short answer is yes: the U.S. Department of Labor’s Permanent Labor Certification (PERM) disclosure data includes the employer’s worksite city and street address for each certified application. You can filter or aggregate the public dataset by either field, though the address is typically the employer’s headquarters or the specific worksite listed on the application.

Background: What Is PERM Disclosure Data?

The PERM program is the first step for most employment-based green cards. Employers must test the U.S. labor market and prove no qualified American worker is available. Once the Department of Labor approves a PERM application, it becomes a public record. The disclosure data—often called the “PERM disclosure file�—is a downloadable spreadsheet containing details about every certified application, including the employer name, job title, wage, and worksite location.

The data is released quarterly and annually under the Freedom of Information Act. It is widely used by immigration lawyers, economists, and job seekers to understand hiring patterns, wage levels, and employer behavior. The worksite address is one of the core fields, so you can count employers by city or by full street address.

How the Data Is Structured

Each row in the disclosure file represents one certified PERM application. The relevant location fields are:

• Employer City – the city of the worksite where the job will be performed.
• Employer State – the two-letter state abbreviation.
• Employer Postal Code – the ZIP code.
• Employer Address 1 – the street address, often including a building name or suite number.

These fields are separate, so you can count by city, state, ZIP, or full address. The address is usually the location where the foreign worker will actually work, not necessarily the company’s corporate headquarters. For example, a large tech firm may list its main campus in Redmond, Washington, even if its legal office is in Delaware.

Why Count by City vs. Address?

Counting by city gives a broader regional view. You might want to know how many PERM certifications were approved in Austin, Texas, in 2023. Counting by address gives a more granular view, useful for identifying specific offices or campuses. For instance, you could compare two addresses of the same company to see which location hires more foreign workers.

City counts are more common in analysis because they are easier to aggregate and less noisy. Addresses can be messy: one company might use “1 Main St� and “1 Main Street� in different filings, so cleaning the data is necessary for accurate address-level counts.

How to Count Employers from the Data

If you have the disclosure file (usually a CSV or Excel file), you can use a spreadsheet or a tool like Python or R to count. Here’s a simple approach:

1. Download the latest PERM disclosure file from the DOL’s website.
2. Open it in Excel or Google Sheets.
3. Create a pivot table with “Employer City� (or “Employer Address 1�) as the row field.
4. Count the number of rows for each city/address.

If you want to count unique employers (not just applications), you’d need to count distinct employer names per city. The data includes a field for employer name, so you can use that to deduplicate.

Common Problems and Limitations

Several issues can trip up someone trying to count by location:

• Inconsistent formatting – “New Yorkâ€� vs. “NYCâ€� vs. “New York Cityâ€� appear in the same field.
• Missing addresses – Some older records may have blank address fields.
• Multiple worksites – If a PERM covers multiple locations, the employer may list a primary address.
• Data updates – The DOL sometimes corrects records, so the latest file may differ from older versions.

For address-level counts, you’ll likely need to standardize the strings (e.g., convert “St.� to “Street�) before pivoting.

Comparison with Other Immigration Data

PERM data is often compared with H-1B disclosure data. The H-1B data also includes worksite city and address, but it is reported by fiscal year and includes both initial and continuing employment. PERM data is specifically for permanent residence, so it’s a better indicator of long-term hiring. The two datasets have different field names and formats, so you can’t merge them without mapping.

Costs and Access

The PERM disclosure data is free to download from the DOL’s website. There is no cost to access the raw files. However, if you want a cleaned, analysis-ready version, some commercial services sell processed data. For most individual researchers, the free file is sufficient.

Safety and Privacy Considerations

The data is public, but it contains employer names and addresses, which are not personal information. However, the data may include the foreign worker’s name in some versions, but that is typically redacted in the public disclosure. Always respect the terms of use and do not use the data for marketing or harassment.

Frequently Asked Questions

Can I filter by ZIP code instead of city?

Yes, the data includes a separate ZIP code field. You can count by ZIP code, which is often more precise than city, especially in large metro areas.

Does the address field include the full street address?

Yes, the “Employer Address 1� field typically contains the full street address, including building number and street name. Sometimes it also includes a suite or floor number.

How often is the data updated?

The DOL releases quarterly updates and an annual cumulative file. The quarterly files are usually posted a few months after the quarter ends.

Can I count employers by address if they have multiple offices?

Yes, each PERM application lists the specific worksite address. If an employer has multiple offices, each application will show the relevant address, so you can count per office.

Is the address always the actual worksite?

Generally, yes, but there are exceptions. Some employers list a legal address or a headquarters if the worksite is not yet determined. Always check the “Worksite� fields if present.

What if the address is missing?

If the address is blank, you can still count by city or state. In the raw data, missing addresses are rare but can occur. You may need to exclude those rows from address-level counts.

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