Research question

Across the 1,200+ SOC occupations tracked in PlainVisa's H-1B database, which occupations account for the largest absolute volume of certified petitions, and how does petition concentration align with median certified wages by occupation?

Methodology

We ranked SOC occupations by total certified petitions, along with total workers, median wage, and total sponsoring employers, then took the top 10. Every number on this page reflects the current data; no figure is hardcoded, and the ranking updates whenever the underlying U.S. Department of Labor dataset is refreshed.

Field definitions: identifier fields carry the entity slug or code used elsewhere in PlainVisa; quantitative fields store values as exported by the U.S. Department of Labor(preserving the original measurement unit). Where the source publishes values in thousands of dollars, we render them via the standard PlainVisa money formatter that converts to billions or millions depending on magnitude. Where the source publishes raw integer counts, we render with thousand-separators preserved.

This ranking reflects the most recently published data. See the methodology page for the source vintage and how the figures are derived.

Coverage and exclusions: rows with null or zero values on the ranking metric are excluded. U.S. Department of Labor occasionally suppresses values for reasons of confidentiality, sample size, or quality control; suppressed rows are excluded from this ranking by design rather than displayed as zeros. If the underlying source revises a value in a later release, the revised value appears here automatically.

Data provenance and update cadence: the U.S. Department of Labor releases the DOL LCA Disclosure Data quarterly. We pull each new release as it becomes publicly available and update this site accordingly, so readers see the current data rather than a stale copy.

PlainVisa organizes upstream records using the natural identifiers published by U.S. Department of Labor (entity codes, geographic identifiers, fiscal-year markers, program slugs). Where a figure combines several upstream fields, the consolidation rule is documented on the methodology page.

Edge-case handling: when a record appears in the source with a null value on the ranking column, we exclude it from this ranking page rather than treat null as zero, treating nulls as zeros would create misleading rankings that surface low-information records ahead of higher-information records. When a record appears with a negative or implausibly large value relative to its peer distribution, we surface the outlier in the table without applying any silent clipping or transformation; readers can see the raw value as published and follow the source link for context. The methodology page explains the agency-specific quirks for the dataset behind this ranking.

Comparability across vintages: the source agency periodically revises its release schedule, column definitions, or coverage scope. When such revisions occur, the affected vintages are noted on the methodology page and consumers are advised to compare like-with-like rather than join across schema-changed vintages. Where this page references a particular fiscal year, that year corresponds to the agency-defined reporting period, calendar year for most economic statistics, federal fiscal year (October through September) for federal program disbursements, school year (July through June) for education statistics. Readers comparing values across multiple agencies should map each agency's reporting period back to a common calendar window.

Detail pages reachable from each row in the ranking show additional metrics and, where the upstream source publishes it, time-series history for that entity.

A separate aggregate query summarizes the full population for context. The aggregate runs against the same occupations table without the LIMIT clause and computes a population count plus optional sum and mean. These aggregates anchor the top-10 ranking against the full distribution so readers can gauge how concentrated the top of the distribution is. The aggregate uses the same WHERE filter as the ranking query, ensuring apples-to-apples comparison between the top and the full population. Where the population is unevenly distributed, the gap between the mean and the median is a useful concentration measure; where the distribution approximates uniform spread, the ranking and the aggregate converge.

A secondary cut renders an adjacent dimension from the same dataset: a related ranking complements the primary table by surfacing a different metric. This pairing lets the reader compare two related rankings derived from the same source without juxtaposing data from heterogeneous agencies. The secondary chart below the limitations panel visualizes this related ranking, while the primary chart above the ranking table visualizes the headline metric. Readers seeking the full multi-dimensional cut should explore the underlying detail pages reachable through entity links in the table.

Reproducibility: this analysis is fully derived from the current dataset with no hardcoded values. A researcher can reproduce the exact ranking from the same public source data. We treat this transparency as part of the editorial contract, every claim is auditable to the row level. Researchers and journalists are welcome to cite this page as the analytical surface and the upstream agency as the underlying source; the methodology page documents the recommended citation format and the URL of the most recent dataset release.

Editorial governance: PlainVisa maintains an editorial standards document that codifies how rankings are constructed, how outliers are surfaced, how privacy-protected records are handled, and how corrections are processed when an entity disputes a value attributed to it. Subject-submitted corrections route through a defined intake process and are reconciled against the upstream record before the page next rebuilds; cosmetic corrections are recorded as overlay metadata while substantive corrections wait for the next official source release. Corrections, takedowns, and clarifications can be requested through the contact channels documented in the portal footer.

Transparency commitment: PlainVisa publishes its full methodology and source registry through dedicated pages reachable from the footer navigation. Visitors can trace any number on this page back to the underlying source record by following the entity link and comparing against the most recent data published by U.S. Department of Labor. Where the agency itself publishes online lookup tools, we link to those so independent verification only requires the original public source.

See the methodology page for the source vintage and how the figures are derived.