Education and Experience Categories Track Higher Occupational Pay

CHARTS AND GRAPHICS

The evidence in view

Each figure identifies the source, methodology record, and finding ID. Downloadable publication and social formats are available below.

Across 825 detailed U.S. occupations, one coded category higher in BLS typical-entry education was associated with 22.59% higher occupational median pay in MyTopMatch's primary employment-weighted model. A one-category increase in related work experience was associated with 21.50% higher pay, and one category higher in typical on-the-job training was associated with 4.51% higher pay.

The model included all three preparation measures together with broad occupation-family fixed effects and explained 85.5% of weighted cross-occupation variation in log median wages. These figures compare occupations. They do not estimate what an individual would earn after completing a degree or accumulating more experience.

One category is not one year

BLS classifies typical education needed for entry in eight categories: no formal educational credential, high school diploma or equivalent, some college without a degree, postsecondary nondegree award, associate degree, bachelor's degree, master's degree, and doctoral or professional degree.

Related work experience has three categories: none, less than five years, and five years or more. Typical on-the-job training has six categories: none, short-term, moderate-term, long-term, apprenticeship, and internship or residency.

The measures are ordered occupational classifications. Their steps are unequal in time, cost, difficulty, and selectivity. Moving from high school to some college differs from moving from a bachelor's to a master's degree. Apprenticeship and internship or residency describe structured pathways rather than a shared duration.

The coefficients should therefore be read as conditional associations with a one-step change in the frozen coding scheme. They should not be multiplied by years or treated as universal credential premiums.

Education and experience carry the largest primary associations

The education and related-experience estimates were close in magnitude and appeared in the same model with on-the-job training and occupation-family controls. Education may represent technical knowledge, professional pathways, screening, or selection. Experience may represent judgment, responsibility, scarcity, employer trust, or advancement structures.

The regression identifies conditional relationships after accounting for the included variables. It does not distinguish among the mechanisms that produced them.

The 85.5% model fit has a narrow meaning

The weighted R-squared indicates that the fitted model accounted for a large share of in-sample, employment-weighted variation in occupational log median wages. Larger occupations received more weight.

The measure is not an individual wage-prediction accuracy score. Hours, industry, geography, benefits, unionization, working conditions, responsibility, scarcity, and worker selection remain outside the primary specification.

Job Zone changes the education estimate

Adding O*NET Job Zone reduced the estimated education-category association from 22.6% to 15.0%. Job Zone summarizes education, experience, and training and overlaps with the BLS inputs. The reduction shows that broader preparation measures share information, so the sensitivity estimate belongs beside the primary coefficient.

Both values define the public result. The 22.6% figure represents the primary frozen specification. The 15.0% figure shows how the estimate changes after adding an overlapping preparation measure.

The training estimate is less stable

The on-the-job-training estimate ranged from about 1.8% to 9.7% across tested specifications. The direction was positive in the primary model, while the magnitude was materially sensitive to model design. MyTopMatch treats it as secondary evidence.

Why residual occupation rankings remain withheld

An occupation's residual is the difference between observed and model-predicted log pay. Large positive or negative residuals can identify cases for deeper research. They do not establish that an occupation is overpaid, underpaid, unfairly paid, undervalued, or a good bargain.

Residuals can include measurement error and omitted factors such as hours, industry mix, location, working conditions, risk, unionization, benefits, responsibility, firm productivity, scarcity, and selection. MyTopMatch has withheld the occupation names and residual ranks.

What workers can do with the result

The analysis supports a structured comparison of preparation and opportunity costs. Workers should compare the national entry classification with current programs, employers, and local requirements, then calculate tuition, fees, foregone earnings, completion probability, debt, and time to credential.

• Review occupation-specific wage distributions instead of applying the 22.6% category estimate to a personal salary.
• Evaluate experience-based pathways, apprenticeships, paid employer training, and progression routes alongside degrees.
• Compare schedules, benefits, geography, legal requirements, working conditions, and job stability.
• Treat national occupational medians as reference points rather than promised offers.

Questions for employers and workforce organizations

Preparation requirements and compensation should be reviewed together. Public occupational data can identify national patterns. Employer records and audited job-posting data are required to test actual screening, applicant flow, hiring decisions, performance, and promotion.

• Which degree or experience requirements are legally necessary?
• Which requirements predict performance, safety, retention, or advancement?
• Can a skills demonstration, apprenticeship, or structured training program replace a broad credential screen?
• Do pay bands reflect the time and opportunity cost imposed by entry requirements?
• Where do actual employer requirements differ from the BLS typical-entry classification?

How MyTopMatch estimated the associations

The unit is a detailed occupation aligned to the 2018 SOC occupational spine. The dependent variable is the natural logarithm of the May 2025 OEWS median annual wage. Each occupation is weighted by its May 2025 OEWS employment estimate.

RTP-1.0 models education, related experience, and on-the-job training together with broad occupation-family fixed effects. MyTopMatch converted log coefficients to approximate percentage associations using 100 × (exp(beta) − 1). Sensitivity tests added O*NET Job Zone and examined alternate preparation specifications.

What this research cannot establish

• An individual's return to completing a degree or training program.
• A causal effect of education, experience, or training on pay.
• The education or experience actually held by every worker in an occupation.
• A specific employer's hiring requirements or screening decisions.
• Complete legal access across states and occupations.
• Total compensation, benefits, schedules, working conditions, or job quality.
• A fair-pay ranking derived from model residuals.
• Future occupational wages or individual employment outcomes.