Opportunity has always depended on education. As artificial intelligence transforms the economy, it is reshaping the knowledge, skills, and credentials that create economic opportunity.
Opportunity Data is an independent public observatory built from public data. We develop statistical infrastructure, open tools, and transparent analysis that help researchers, policymakers, institutions, employers, and learners understand how technological change is reshaping education, work, and opportunity.
Building open infrastructure from public data.
Explore the toolsThe labor market is changing faster than the systems built to measure it. Administrative records, education data, and emerging A.I. indicators already contain signals about how opportunity is being reshaped, but those signals sit in separate systems designed for compliance, reporting, and institutional use, not for public understanding.
Opportunity Data turns those public resources into indexes, crosswalks, state trackers, and interactive tools that anyone can use and reuse. Rather than producing one-time reports, we build open infrastructure from public data.
Four interactive toolsets, each grounded in public data sources and reproducible end-to-end.
Which occupations are most exposed to A.I. automation, and which academic programs feed into them? Three-dimensional scoring across digital intensity, human interaction, and physical work. 772 occupations, 1,786 programs.
How A.I.-exposed is each state's mix of graduates? A completion-weighted map of all 50 states and D.C., built from 5.3 million IPEDS 2023 completions and the A.I. Exposure Index, with credential breakdowns and a findings brief.
From eligibility screening to state implementation. Approximate the federal earnings test program by program, track each governor's progress in standing up the approval pipeline, and compare the occupation lists states are approving.
What graduates actually earn at 1, 5, and 10 years, drawn from Census Bureau administrative wage records. Trajectories, within-program variation, and where suppression limits what we can see. 900+ institutions, 33 states, three credential levels.
How 73 bachelor's fields compare on underemployment, unemployment, early-career pay, and the number of graduates, mapped together so you can see both the odds and the scale. New York Fed outcomes paired with IPEDS completion counts.
Every article, dataset, and visualization contributes to a larger body of open statistical infrastructure for understanding education, work, and opportunity. Interactive analyses paired with published op-eds in national and trade press.
Nine entry-level credentials sort into four tracks with almost no bridges between them. BLS OEWS wage bands for every rung and Census PSEO graduate trajectories show which track stacks (nursing) and which stop where they start.
View analysis →Does a master's in teacher preparation pay back the cost? PSEO earnings for graduates of CIPs 13.10 (Special Ed), 13.12 (Subject Area/Secondary), and 13.13 (Levels & Methods/Elementary) at BA and MA, paired with NY Fed underemployment and BLS K-12 teacher wages.
View analysis →A bachelor's in child development earns about the same as a sub-associate certificate. PSEO earnings for Human Development and Family Studies graduates, paired with BLS OEWS wages and MIT Living Wage data across states.
View analysis →Colorado-only cut of the national ladder. PSEO earnings for graduates of 16 Colorado colleges in Human Development and Family Studies, paired with the Colorado Department of Early Childhood's published lead-teacher vs. kindergarten-teacher wage comparison and Child Care Aware of America's 2024 Colorado prices.
View analysis →Do bachelor's degrees from community colleges deliver comparable labor market value? Using Census Bureau earnings data at 1, 5, and 10 years post-graduation, CCB graduates match or exceed traditional BA earnings in most fields by Year 5.
View analysis →
"One federal statute, a nineteenfold difference in how wide the door opens."
"Colorado solved access first. It never solved the workforce behind it."
"A bachelor's degree in child development pays the same as a sub-associate certificate."
"Short-term credentials are now the most-cited emerging model in every sector surveyed."
"Community colleges aren't just sitting in a safe harbor. They're absorbing the students trying to get out of the weather."
"The value-added earnings test is not the obstacle most people think it is."
How the tools work. Data sources, formulas, scoring, and the limits of what the data can show. Neutral documentation, not commentary.
How the AI Exposure Index is constructed: variable selection from O*NET, dimension scoring for digital intensity, human interaction shield, and physical anchor, and the crosswalk from occupations to academic programs.
Read methodology →Data sources, formula, population filters, cohort pooling rationale, and the five key approximations behind the Value-Added Earnings Test tool.
Read methodology →What the Post-Secondary Employment Outcomes data can and can't tell us about program value. UI coverage gaps, small-cell suppression, and non-random missingness, layered out.
Read methodology →