Evidence for Economic Mobility

The earnings data that shapes workforce decisions

The visuals are designed for decision making by students, educators, and policymakers, translating longitudinal earnings data into clear views of trajectory and variation. The goal is not to produce a single metric, but to provide a more complete picture of program outcomes grounded in how earnings actually unfold.

Workforce Pell is expanding federal aid to short-term certificates with the goal of funding real impact and accountability. Opportunity Data uses earnings data from PSEO to examine what happens after completion: how earnings evolve over time, how outcomes vary across students, and where the data is missing. This analysis focuses on short-term credential programs reported in PSEO across participating states and institutions.

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states with short-term certificate earnings tracked across 500+ institutions

PSEO 2025Q4, degree level 01

10k+

program-institution records covering earnings at 1, 5, and 10 years after graduation

National PSEO, all cohorts combined

55%

of programs have earnings fully suppressed—too few graduates to clear Census privacy thresholds

Differential privacy, small cell suppression

1.6×

median earnings growth from Year 1 to Year 10 in programs with the strongest career ladders

PSEO, top-decile mobility ratio

Source: U.S. Census Bureau, Post-Secondary Employment Outcomes (PSEO), 2025Q4 release. Earnings in 2023 dollars (CPI-U adjusted). Short-term certificates (<1 year), all graduation cohorts, national geographic scope, all industries.

Amarillo College short-term certificate earnings over time

Career ladders: median earnings progression with variance bands

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Analysis

All articles

Data Suppression

The Programs We Can't See: Privacy, Small Cohorts, and Missing Evidence

Small cell suppression removes a non-random subset of workforce programs from earnings data. The analytical consequences for program evaluation and policy are structural.

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Framework

Why ROI Is the Wrong Question for Community Colleges and Workforce Programs

A single metric obsession strips away how careers actually develop. Two better lenses—career ladders and earnings variance—reveal what a snapshot ROI number hides.

Read on Substack

What this project is about

Most workforce program evaluation collapses to a single number: return on investment. That number tells you the average outcome. It does not tell you how earnings grow after graduation, how much outcomes vary between graduates, or which programs serve populations too small to appear in any dataset.

Opportunity Data exists to surface the structure that summary statistics hide. We use the career ladder (the trajectory of earnings over one, five, and ten years) and earnings variance (the spread between the 25th and 75th percentile) as the foundation for a more honest evaluation framework.

The goal is not to replace accountability. It is to make accountability honest. When more than half the programs in a state are invisible to the data, and the visible ones are reduced to a single figure, we are making policy on incomplete evidence. This project is an attempt to do better.

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Questions about the data, interested in collaborating, or have feedback? Send a message.