Where does your institution stand?
U.S. four-year colleges and universities face compounding pressures — demographic decline, fiscal stress, and artificial intelligence — that will reshape the sector over the next decade. This project maps where 1,557 institutions are structurally positioned across two dimensions, using public data anyone can verify.
Can this institution absorb financial and enrollment shocks?
Endowment per student · Revenue diversification · Enrollment trend · Admissions selectivity
How well does this institution position graduates for the labor market ahead?
Completion rate · Earnings-to-debt ratio · AI exposure (inverted) · Demographic trajectory
Institutions above the median on both axes are High Capacity; below both, High Stress. The other two quadrants — Market Misaligned and Structurally Exposed — capture institutions with mixed structural positions. Institutions scoring higher are better-positioned on the indicators I track — but the indicators I track are analytical choices, and institutions serve missions that no set of quantitative measures can fully represent.
A few patterns from the data: most R1 research universities fall in High Capacity. Master's and RCU institutions distribute across all four quadrants — the tiers where institutional choices matter most. Carnegie classification alone explains less than a quarter of the variance in combined scores, which is why a framework like this adds information beyond what tier labels provide.
Data: All measures are derived from IPEDS, College Scorecard, O*NET, WICHE, and the Anthropic Economic Index. The map includes the 1,557 of 1,605 currently operating four-year institutions (2025 Carnegie classification) with enough data to score on both axes. For methodology, see the Methods tab or the working paper.
Position on the 2×2
Resilience Components (Percentile)
Each bar shows where this institution ranks among all currently operating institutions with that measure. E.g., "72nd" means it scores higher than about 72% of those institutions.
Market Position Components (Percentile)
Can this institution absorb financial and enrollment shocks?
Endowment per student · Revenue diversification · Enrollment trend · Admissions selectivity
How well does this institution position graduates for the labor market ahead?
Completion rate · Earnings-to-debt ratio · AI exposure (inverted) · Demographic trajectory
Institutions above the median on both axes are High Capacity; below both, High Stress. The other two quadrants — Market Misaligned and Structurally Exposed — capture institutions with mixed structural positions. Institutions scoring higher are better-positioned on the indicators I track — but the indicators I track are analytical choices, and institutions serve missions that no set of quantitative measures can fully represent.
A few patterns from the data: most R1 research universities fall in High Capacity. Master's and RCU institutions distribute across all four quadrants — the tiers where institutional choices matter most. Carnegie classification alone explains less than a quarter of the variance in combined scores, which is why a framework like this adds information beyond what tier labels provide.
Data: All measures are derived from IPEDS, College Scorecard, O*NET, WICHE, and the Anthropic Economic Index. The map includes the 1,557 of 1,605 currently operating four-year institutions (2025 Carnegie classification) with enough data to score on both axes. For methodology, see the Methods tab or the working paper.
The Institutional Landscape
1,557 institutions positioned by Institutional Resilience (x-axis) and Post-College Market Position (y-axis). Dashed lines mark the sample median on each axis. Institutions in the upper right (High Capacity) score above the median on both dimensions; those in the lower left (High Stress) score below both.
This is a strategic classification heuristic, not a ranking or a predictive model. The axes are equal-weight composites of publicly available indicators. Institutions scoring higher are better-positioned on the measures I track — but the measures are analytical choices, and institutions serve missions beyond what any set of quantitative indicators captures. Component-level scores tell a richer story than the quadrant label alone. Note: 48 of the 1,605 currently operating institutions lack enough data for one or both scores and are not shown. They are mostly small private institutions, branch or online campuses whose finances are reported by a parent institution, and graduate-only institutions without undergraduate outcome data.
How the Tiers Separate: Combined Score Distribution by Carnegie Classification
The chart below shows the distribution of combined scores (Resilience + Market Position) within each Carnegie tier. The combined score is used here for distributional comparison only — it is not a ranking, and the framework deliberately avoids collapsing two distinct dimensions into a single number for institutional assessment. The purpose is to show where tiers overlap and separate: a Master's L/M institution can score as well as an R2, or as poorly as a Baccalaureate. Where they separate, the tiers represent genuinely distinct structural positions. The new RCU (Research Colleges and Universities) tier captures 182 institutions (177 of them scored) with meaningful research activity that previously had no research recognition.
What Does This Tell You?
The tiers are real, but they're not destiny. R1 research universities cluster in the upper right: 81% are in the High Capacity quadrant. The Master's tiers show the opposite pattern: 51% of Master's L/M and 46% of Master's S institutions fall in High Stress, and so do 44% of Baccalaureate institutions. Carnegie classification alone explains about 23% of the variance in combined scores, and assigning every institution its tier's most common quadrant gets only 46% of placements right. Two institutions in the same tier can occupy opposite quadrants depending on their endowment, completion rate, program mix, enrollment trajectory, and regional context.
38% of mapped institutions are losing enrollment and sit below the median on resilience. These 588 institutions saw their 12-month headcount fall between 2019-20 and 2024-25, and they sit below the median on resilience. They are disproportionately in the Midwest and Northeast, and they make up roughly half of the Doctorate and Master's tiers. Many serve as anchor institutions in their communities, providing both educational access and local economic activity. The framework cannot predict which will close, but it identifies the population where compound pressures are most intense. About 32% (498) are both growing and above the median on resilience.
The quadrant labels are shorthand, not sentences. For any individual institution, the component-level scores — visible in the institution detail view — tell a richer story than the quadrant label alone. The Methods tab details how these assignments hold up under alternative specifications. Use the "Find Your Institution" tab to see the full breakdown.
AI Exposure: Two Timelines of Disruption
EXPLORATORY MEASURE — This is a first-generation attempt to quantify institutional AI exposure. External validation against actual labor market outcomes is not yet possible; I present it as a structured starting point for a conversation the sector needs to have.
What this measure captures — and what it can't
Note: AI exposure (inverted) already feeds into the market position Y-axis as one of four equally-weighted components. What follows on this tab is a deeper, field-level analysis: an exploratory extension that compares the measure with real-world adoption data and examines its relationship to earnings. These are related but distinct uses of the same underlying measure.
The AI exposure component asks a specific question: to what degree do this institution's graduates enter career pathways where entry-level tasks could be performed or assisted by current AI systems? It is not measuring whether a school uses AI, how "techy" its programs are, or how vulnerable any individual graduate is. It is measuring task-level overlap between AI capabilities and the occupations that graduates of specific degree programs typically enter.
The pipeline works in five steps, each of which introduces assumptions:
2. Occupation scoring. For each of the 879 O*NET occupations with work-activity ratings, the score is the average importance of the AI-positive activities minus the average importance of the AI-negative ones, rescaled from 0 to 1. A task-level version built from O*NET's detailed work activities correlates with this one at ρ = 0.84; the published map uses the work-activity version, and the task-level version appears in the sensitivity analysis.
3. Entry-level weighting. I weight by O*NET Job Zone because AI disruption disproportionately affects entry-level work. Zones 2–3 (typical college-graduate entry points) receive full weight; Zone 4 receives half; Zones 1 and 5 receive minimal weight (0.2). This is consequential: Statisticians drop from 1.0 raw exposure to 0.2 entry-weighted. The map uses the entry-weighted score.
4. Degree-to-occupation crosswalk. I map degree fields to occupations via the NCES CIP 2020 to SOC 2018 crosswalk and average the linked occupations within each 2-digit degree field. This step assumes that the occupational distribution implied by a degree field reflects where graduates end up, a reasonable but imperfect assumption.
5. Institutional aggregation. Each institution's score is the average of its fields' scores, weighted by its share of degrees in each 2-digit field (College Scorecard program shares). This smooths out field-level variation: most institutions cluster in a narrow band (standard deviation = 0.026 on a 0.14–0.39 range) because program diversification washes out the signal. The measure discriminates most at the field level, not the institutional level, which is why I show the field-level chart alongside the institutional score.
The fundamental limitation is that this entire pipeline uses retrospective occupational data — O*NET describes jobs as they exist today — to make prospective claims about what AI might change. The approach uses the rearview mirror to look forward. A high exposure score means that AI is theoretically capable of performing tasks central to an occupation's entry-level work. It does not tell you whether the market response will be displacement (AI replacing workers), augmentation (AI enhancing productivity), or restructuring (AI reorganizing the occupation entirely). These are distinct labor market dynamics with very different implications for graduates, and I cannot distinguish between them with current data.
The gap between theory and reality
This is where the Anthropic Economic Index comes in. Anthropic recently published observed real-world AI adoption rates by occupation, based on actual conversations with Claude. This provides something genuinely new: a way to compare what AI could theoretically do (the O*NET-derived measure) with what AI is actually doing (Anthropic's observed usage data).
Across 37 degree fields, theoretical task exposure and observed AI adoption are weakly and negatively related (r = −0.12; rank correlation −0.35). Computer science ranks near the top on both, but beyond it the lists diverge: humanities fields (philosophy, area studies, languages, the arts) show heavy observed use with low entry-level exposure, while engineering technology, communications technology, and business show high exposure with modest observed use. The scatter plot below shows this gap for each degree field.
This divergence is consistent with one reading: what AI can theoretically automate and what AI is currently automating are mostly different things, implying two distinct exposure timelines. However, alternative interpretations (measurement artifacts, vendor-specific bias in single-platform adoption data, uneven regulatory constraints) are comparably plausible. The data cannot empirically distinguish among them. The measure belongs in the framework as a forward-looking signal, but its predictive validity is genuinely unknown.
Theoretical Exposure vs. Observed Adoption by Degree Field
AI Entry-Level Exposure by Degree Field (Ranked)
Institution-Level AI Exposure: Theoretical vs. Observed
The charts above show AI exposure by degree field. This chart asks a different question: where does your institution fall? Each dot is a single institution. Its horizontal position reflects the weighted average theoretical AI exposure of its graduates' career pathways (based on what programs it offers). Its vertical position reflects the weighted average observed AI adoption in those same career pathways (based on how much AI is actually being used).
The four corners tell different stories. Upper right: graduates enter fields where AI is both structurally capable of performing entry-level tasks and already actively in use — these institutions' program mix puts graduates in the thick of the AI transition right now. Lower right: graduates enter fields with high theoretical exposure but low current adoption — the "latent vulnerability" zone, where the task structure is ready for AI but the tools haven't arrived yet. Upper left: graduates are in fields with high AI adoption but lower theoretical task susceptibility, suggesting augmentation (AI as a tool) rather than substitution. Lower left: graduates enter physical, interpersonal, or hands-on fields where AI has limited relevance on either dimension.
Most institutions cluster in a narrow band because program diversification smooths out field-level differences. The institutions at the edges are the ones with concentrated program mixes — heavily STEM, heavily business, or heavily health/education — where the field-level AI signal comes through at the institutional level.
What Does This Tell You?
What AI can do and what AI is being used for are mostly different things, for now. The weak correlation (r = −0.12) between theoretical task exposure and observed adoption means that knowing how structurally susceptible a field's entry-level tasks are to AI tells you little about how much AI is actually being used in that field today. Outside computer science, the fields with the heaviest observed use and the fields with the highest entry-level exposure are mostly different lists.
Higher-earning fields tend to be more exposed. Fields with above-average graduate earnings, such as engineering technology, computer science, and business, involve exactly the kind of premium cognitive work (analyzing data, processing information, working with complex systems) that AI is structurally capable of performing. This creates a paradox for institutions: the programs that currently deliver the best return on investment for students are the same programs whose graduates face the most structural AI task exposure. Expanding these programs improves an institution's market position score today but may increase its exposure to disruption tomorrow.
Alternate View: What the Data's Own Structure Reveals
The primary framework imposes two theoretically-motivated axes — Resilience and Post-College Market Position — with equal weighting across components. But what happens when the data speaks for itself?
A principal components analysis, which looks for components that rise and fall together across institutions, finds three dimensions. Credential Outcomes groups earnings-to-debt ratio and completion, with regional demographic trajectory loading in the opposite direction (institutions with strong graduate outcomes tend to sit in states with shrinking high school pipelines). Institutional Resources groups endowment per student, selectivity, a less AI-exposed program mix, and completion. Demand Environment groups enrollment trend, demographic trajectory, and revenue diversification. A standard test of how many dimensions the data support (parallel analysis) points to three. The two-axis equal-weight model compresses these three empirical dimensions into two composite axes.
The forward-looking indicators the framework elevates show up as their own dimension in the data. Even a two-factor solution separates an institutional hierarchy dimension (completion, endowment, earnings-to-debt, selectivity) from a demand dimension built on demographic trajectory, enrollment trend, and revenue diversification. In the three-factor solution, the factors are only weakly correlated with one another (|r| = 0.13 to 0.21). Demand conditions run largely independent of prestige and wealth.
The scatter plot below positions institutions on two of the three factors: Credential Outcomes (horizontal) and Institutional Resources (vertical). Dot color reflects the third factor, Demand Environment: warmer colors indicate stronger demand indicators (growing enrollment, favorable demographics, diversified revenue), cooler colors weaker ones.
Factor Loadings: How Components Group Together
The chart below shows which components tend to go together across institutions. Each row is one of the eight components in the framework. The colored bars show how strongly each component is associated with each factor — longer bars mean a stronger association. When two components have long bars on the same factor (same color), they tend to rise and fall together: institutions that score high on one tend to score high on the other.
Green (Factor 1: Credential Outcomes) groups earnings-to-debt ratio and completion rate, with demographic trajectory loading opposite. Blue (Factor 2: Institutional Resources) groups endowment, selectivity, a less AI-exposed program mix, and completion. Orange (Factor 3: Demand Environment) groups enrollment trend, demographic trajectory, and revenue diversification. Faded bars indicate weak associations (below 0.35).
Equal-Weight vs. Factor-Implied Component Weights
Weights normalized across all 8 components.
Robustness Across Specifications
The structure was checked across rotations (varimax, promax, oblimin) and two- and three-factor solutions. What is stable: enrollment trend, revenue diversification, and demographic trajectory load together on a demand dimension in every solution, and the factors are only weakly correlated with one another. What varies: in two-factor solutions, completion, endowment, earnings-to-debt, and selectivity form a single hierarchy dimension. In three-factor solutions, earnings-to-debt and completion split off as Credential Outcomes, demographic trajectory also loads on that factor in the opposite direction, and AI exposure joins Institutional Resources. Diagnostics (KMO, Bartlett's test, eigenvalues, and parallel-analysis thresholds) are in Methods section 4.2.
What Does This Tell You?
The factor analysis reveals something that matters for how you read every other tab on this site.
There is one dominant dimension in American higher education, and everyone already knows what it is. Call it institutional hierarchy: the gradient from well-resourced, selective institutions with strong graduate outcomes to tuition-dependent, open-access institutions with weaker outcomes. In the two-factor solution, completion rate, endowment, earnings-to-debt, and selectivity load together because they are, in practice, measuring overlapping aspects of the same underlying reality. This is what Carnegie classification, U.S. News rankings, and common intuition approximate.
The interesting question is what's independent of that hierarchy. Enrollment trend, demographic trajectory, and revenue diversification form their own dimension, only weakly correlated with the hierarchy. A prestigious R1 university in the Northeast and a struggling regional master's institution in the same state may score very differently on the hierarchy dimension but face similar demographic headwinds. AI exposure is not part of that separate dimension. Wealthier, more selective institutions tend to have program mixes with lower entry-level exposure, so AI exposure moves with institutional resources.
This is where the framework adds something new. Everyone already knows where institutions stand relative to each other on the hierarchy. What the framework surfaces are the contextual pressures that cut across it, pressures that can't be predicted from an institution's prestige or wealth alone. In the data, those pressures form a separate dimension; whether they predict future institutional outcomes is a question only time can answer.
Methods
1. Scope & Data
1.1 Institutional Universe
Institutions are included if they are four-year, degree-granting, public or private nonprofit, and currently operating. The list of 1,609 institutions is classified using the 2025 Carnegie Classification system (Institutional Classification and Research Activity Designation). The 9-tier grouping uses Research Activity Designation (R1/R2/RCU) as the primary axis for research institutions and Award Level Focus for non-research institutions. Under the 2025 system, some institutions previously classified as Baccalaureate are now classified as Special Focus based on academic program concentration; these remain in the analysis sample. Of the 1,609, 1,605 are active in the 2025-26 IPEDS directory (the site calls these currently operating), and 1,557 have sufficient data to compute both composite scores. Each composite requires at least two of its four components, and the resilience composite also requires at least one finance component (endowment or revenue diversification). The 48 active institutions without both scores are disproportionately small private institutions, branch or online campuses whose finances are reported by a parent institution, and graduate-only institutions without undergraduate outcome data. Results should be interpreted with this coverage gap in mind. Institution profiles flag any component that is missing.
1.2 Data Sources
IPEDS directory (2025-26), 12-month enrollment (2019-20 and 2024-25), finance (FY2023-24), and admissions (fall 2024) · College Scorecard institution-level data (June 10, 2026 release) · WICHE Knocking at the College Door (11th ed., 2024) · O*NET Database (v29.0) · NCES CIP 2020 to SOC 2018 Crosswalk · Anthropic Economic Index (August 2025 extract) · Census Post-Secondary Employment Outcomes (PSEO).
1.3 Finance Data (GASB and FASB)
Public institutions report finances to IPEDS under GASB standards (form F1A) and private nonprofits under FASB standards (form F2). Net tuition and fees come from F1B01 (GASB) and F2D01 (FASB). Core revenues follow the IPEDS definition: total revenues, including investment return, minus auxiliary enterprises, hospitals, and independent operations (F1B25 − F1B05 − F1B06 − F1B07 for GASB filers; F2D16 − F2D12 − F2D13 − F2D14 for FASB filers). Endowment is the end-of-year value (F1H02, F2H02). Mean net tuition share of core revenues is 25% for public institutions and 54% for private nonprofits. Institutions that report zero net tuition (including the five federal service academies) receive no revenue diversification score. Before v1.5, revenue diversification for private nonprofit institutions (FASB filers) was computed from the wrong IPEDS finance fields.
2. Framework Construction
2.1 X-Axis: Institutional Resilience
A composite of four equally-weighted percentile-ranked components. Endowment per full-time-equivalent (FTE) student counts undergraduate and graduate students; FTE is derived from IPEDS instruction expenses and College Scorecard instructional spending per FTE student (or, where that is missing, from net tuition and Scorecard net tuition revenue per FTE student). Revenue diversification is 1 − net tuition share of core revenues. Enrollment trend is the percent change in 12-month unduplicated headcount, all students, from 2019-20 to 2024-25; for institutions formed by merger, predecessor institutions are added to the base year. Selectivity is 1 − admission rate, fall 2024.
Selectivity is the most contested component, and it admits a dual interpretation: a low admission rate can signal either a strong demand buffer (more applicants than seats) or a limited applicant pool that constrains growth. I include it as a demand signal — institutions with more applicants than seats have a structural advantage in enrollment management — while acknowledging the ambiguity. Removing it from the index (the no-selectivity specification in the sensitivity analysis below) produces 86.3% quadrant agreement with the baseline, meaning the vast majority of institutional placements are unaffected.
2.2 Y-Axis: Post-College Market Position
A composite of four equally-weighted percentile-ranked components: completion rate (share of first-time, full-time students completing within 150% of normal time, College Scorecard C150_4), earnings-to-debt ratio (10-year median earnings / median graduate debt), inverted AI entry-level exposure, and regional demographic trajectory (WICHE projected change in high school graduates, 2024–2030).
This axis is deliberately a hybrid: it blends realized outcomes (completion rate, earnings-to-debt ratio) with a modeled future-risk construct (AI exposure) and a regional exogenous condition (demographic trajectory). This heterogeneity is a design choice — the goal is to capture both what the institution delivers for its students and how exposed that position may be to emerging pressures — but it means the axis is better understood as a strategic index than as a single latent construct. Neither axis measures one coherent underlying reality in the psychometric sense; both are strategically bundled composites designed for institutional diagnosis, not latent variable measurement.
2.3 Weighting Rationale
All components within each axis receive equal weight. This is a deliberate simplicity choice for transparency and reproducibility, not an empirical claim that each component contributes equally to institutional outcomes. Weights implied by the factor analysis (each component's communality in the three-factor solution, the share of its variance the factors explain) would give earnings-to-debt, completion, demographic trajectory, and endowment more weight and revenue diversification, enrollment trend, selectivity, and AI exposure less. Equal-weight and factor-weighted composites place 85% of institutions in the same quadrant. I retain equal weighting because data-driven weights reflect historical relationships. Here they would cut the weight on two of the forward-looking components, enrollment trend and AI exposure, whose effects have not yet fully materialized.
2.4 Quadrant Assignment
Quadrant boundaries are set at the median of each composite among institutions with that score: High Capacity (above both medians), High Stress (below both), Structurally Exposed (below resilience, above market position), and Market Misaligned (above resilience, below market position). Institutions exactly at the resilience median count as below it; institutions exactly at the market-position median count as above it. These labels are descriptive shorthand, not definitive assessments. The median split imposes categorical distinctions on a continuous space.
3. AI Exposure Methodology
3.1 Theoretical Exposure Pipeline
The AI exposure component is the most exploratory element of the framework. It measures the degree to which graduates' typical entry-level career pathways involve tasks that current AI systems can perform or assist with. The pipeline: (1) classify the 41 O*NET generalized work activities as AI-positive (routine cognitive tasks), AI-negative (physical, interpersonal, embodied tasks), or neutral; (2) score each occupation as the average importance of AI-positive activities minus the average importance of AI-negative ones, rescaled to 0–1; (3) weight by O*NET Job Zone for entry-level relevance (Zones 2–3 full weight, Zone 4 half, Zones 1 and 5 at 0.2); (4) crosswalk SOC occupations to 2-digit CIP degree fields via the NCES CIP-SOC mapping, averaging linked occupations; (5) aggregate to institutions using College Scorecard program shares. The component is the percentile rank of the inverted entry-weighted score. Before v1.5, the Methods text described the score as a blend of two occupation-scoring approaches (work activities and detailed tasks) and of entry-weighted and raw exposure. In practice the published scores reflected the entry-weighted work-activity measure, which is what v1.5 uses and describes.
3.2 Exposure vs. Substitution vs. Augmentation
This measure captures task-level exposure — whether AI systems can perform the tasks central to an occupation's entry-level work. It does not determine whether the labor market response will be task substitution (AI replacing workers), task augmentation (AI enhancing productivity), or occupational restructuring (workflow reorganization). I use the term "exposure" rather than "vulnerability" or "risk" for this reason. The measure is presented as provisional and exploratory — external validation against actual labor market disruption outcomes is not yet possible given the recency of large-scale AI deployment.
3.3 Theoretical vs. Observed Adoption
When compared with observed real-world AI adoption from the Anthropic Economic Index (August 2025 extract, usage share by occupational group, mapped to degree fields through the same crosswalk), the field-level correlation is weak and negative (r = −0.12; rank correlation −0.35 across 37 fields). Multiple interpretations are possible, including a lag between capability and adoption, measurement artifacts, vendor-specific bias in single-platform adoption data, and uneven regulatory and institutional constraints. I present both theoretical and observed dimensions separately rather than collapsing them into a single score.
4. Validation & Robustness
4.1 Sensitivity Analysis
I tested 20 alternative specifications plus a z-score scaling alternative against the baseline. The 20 are: dropping each of the eight components in turn; moving completion to the resilience axis; double-weighting completion; double-weighting AI exposure; half-weighting endowment; four alternative AI measures (raw rather than entry-weighted exposure, a blend of the two, a task-level version, and observed adoption from the Anthropic Economic Index); two adjustments that remove research expenditures (HERD) from revenues; the one-year change in endowment value in place of endowment level; and admission yield in place of admission rate. Resilience is robust to dropping any single component (Spearman ρ ≥ 0.86); the largest change comes from replacing endowment level with the one-year change in endowment value (ρ = 0.75). Market position is most sensitive to the AI measure (ρ from 0.73 with observed adoption to 0.99 with the task-level version). Across every specification in which they are scored, 379 institutions (24.3%) never change quadrant: 170 consistently High Capacity and 129 consistently High Stress. The other 1,178 change quadrant under at least one specification, though 50% of scored institutions keep their quadrant in at least 90% of them (mean stability 0.86). Each scored institution's stability score (the share of specifications that keep it in the same quadrant) is in the dataset.
The z-score specification produces 93.3% quadrant agreement with the baseline (ρ = 0.986 on resilience, ρ = 0.984 on market position), confirming that the main tier-level patterns are robust to the scaling choice. The 105 institutions that shift are distributed across all tiers; agreement is at least 92% in every tier except the small Assoc/Bacc tier (80%). I recommend examining component-level scores and stability alongside quadrant labels.
4.2 Factor Analysis
Principal components analysis of the eight percentile-ranked framework components (KMO = 0.56, Bartlett's χ²(28) = 1,684.64, p < .001, N = 1,273 complete cases) with promax rotation yields three factors: Credential Outcomes (earnings-to-debt and completion, with demographic trajectory opposite), Institutional Resources (endowment, selectivity, AI exposure, completion), and Demand Environment (enrollment trend, demographic trajectory, revenue diversification). Parallel analysis (Horn, 1965) supports three factors: the first three eigenvalues (2.29, 1.19, 1.16) exceed the 95th percentile of random data (1.16, 1.10, 1.06), and the fourth (1.01) does not (1.03). The KMO is low, as expected when the components are only weakly correlated with one another; the factor solution is best read as a description of how the components co-vary, not as evidence of underlying constructs. The factor-derived alternate view is on the Factor View tab.
4.3 PSEO Earnings Context
For 572 institutions with Census PSEO earnings data, I compared AI entry-level exposure scores to actual median earnings by field. The field-level correlation is positive (ρ = 0.257): higher AI exposure is associated with higher current earnings. This reflects the structural composition of AI-exposed fields: engineering technology, computer science, and business command above-average wages because they involve premium cognitive work that is also structurally susceptible to AI. (This comparison was run with the v1.4 field scores; the v1.5 field scores differ from them by less than 0.01.) The pattern does not confirm or disconfirm the AI exposure measure as a predictor of future disruption; it contextualizes what the measure currently captures.
5. Limitations & Caveats
The AI exposure measure captures structural task characteristics but not institutional curricular response — a school teaching students to work with AI receives the same exposure score as one ignoring AI entirely. College Scorecard earnings data covers only Title IV federal financial aid recipients, systematically understating outcomes at wealthier institutions. The CIP-SOC crosswalk applies typical career pathways regardless of institutional prestige — a psychology major from Harvard enters a different labor market than one from a regional institution, but both receive the same AI exposure score. The analysis is cross-sectional, providing a snapshot rather than a trajectory. Completion rate correlates moderately with both endowment and earnings-to-debt (about 0.5 to 0.6), raising a question about whether the framework partly rediscovers institutional hierarchy through a variable already known to correlate with selectivity and socioeconomic composition.
Note on structurally unusual institutions
The resilience index uses IPEDS and Scorecard data: endowment per student, revenue diversification, enrollment trends, selectivity. Military academies are structurally unusual in ways that cut against some of those components. Service academies report no net tuition, so they receive no revenue diversification score. West Point and the Merchant Marine Academy also report no endowment, so they have no resilience score and are not on the map; the Air Force, Naval, and Coast Guard academies are scored on the other three resilience components. None of this credits federal funding, which is arguably the most secure funding source in higher education. It's a real limitation of building composites from general-purpose institutional data. The framework works well for the roughly 1,500 institutions that operate in something like a common market. Institutions with fundamentally different funding and enrollment structures (military academies, tribal colleges, a few others) are cases where the component scores are technically accurate but strategically misleading.
This framework is a strategic classification heuristic, not a predictive model or a measure of latent social reality. The quadrant labels describe composite positions on the indicators I track. Institutions scoring higher are better-positioned on those measures, but the measures are analytical choices — institutions serve missions that no set of quantitative indicators fully represents.
6. Working Paper & Citation
Saunders, K. (2026). Mapping the Structural Divide: Institutional Resilience, Post-College Market Position, and Artificial Intelligence Exposure Across U.S. Higher Education. Working Paper v1.5, October 2026. Colorado State University. https://kylesaunders.com/university-map
This version, based on the v1.5 data, replaces the April 2026 working paper (v1.4), whose numbers should not be cited. The paper's first page summarizes what changed, and Supplementary Appendix B documents the correction.
Download Working Paper (PDF) Download Supplementary Materials (PDF)
Data & Downloads
All data used in this analysis come from publicly available sources: federal data (IPEDS, College Scorecard, O*NET, Census PSEO) plus WICHE projections and the Anthropic Economic Index. I provide the complete institutional dataset for transparency and replication.
Download v1.4 to v1.5 Changes (CSV) Download Working Paper (PDF) Download Supplementary Materials (PDF)What's in the dataset
1,605 currently operating institutions (1,557 with both composite scores) with: composite resilience and market position scores, all eight component percentile ranks, raw values for key variables (endowment, FTE enrollment, net tuition and core revenues, 12-month headcount in both years, admission rate, completion rate, earnings-to-debt ratio, theoretical and observed AI exposure), quadrant assignment, a stability score, notes on missing components and data flags, 2025 Carnegie classification (IC2025, Research Activity Designation, and derived 9-tier grouping), legacy 2021 Basic Classification, and state.
Data Sources
IPEDS (nces.ed.gov/ipeds) · College Scorecard (collegescorecard.ed.gov) · WICHE (wiche.edu/knocking) · O*NET (onetcenter.org) · NCES CIP-SOC Crosswalk · Anthropic Economic Index (huggingface.co/datasets/Anthropic/EconomicIndex) · Census PSEO (lehd.ces.census.gov/data/pseo)
License
The dataset is released under CC BY 4.0. You are free to use, share, and adapt it with attribution.
Replication Materials
The dataset can be rebuilt from public raw data files. The replication package includes:
replicate_v15.zip: the Python scripts that build the dataset from the raw IPEDS, College Scorecard, WICHE, O*NET, CIP-SOC crosswalk, and Anthropic Economic Index files (finance and enrollment components, the AI exposure pipeline, percentile ranking, composite scoring, quadrant assignment, and the sensitivity and stability analysis).
README.md — Download URLs for all required raw data files, directory structure, output field descriptions, and critical methodological notes.
Requirements: Python 3.10+, pandas, numpy, scipy, openpyxl, factor_analyzer.
About This Project
Origin
I first encountered Scott Galloway's 2020 "USS University" 2×2 not long after he published it. It stuck with me — because the exercise itself was clarifying in a way that most writing about higher education isn't. Mapping institutions on two dimensions forced a conversation about which schools were structurally positioned for what was coming and which weren't. It made the landscape legible. Galloway deserves credit for that: he was, as far as I know, the first person to attempt this kind of strategic positioning exercise for the full sector, and the fact that it resonated so widely tells you something about the demand for this kind of analysis.
The project came back to mind a few months ago as I started thinking more seriously about the compounding pressures facing higher education — demographic decline, enrollment contraction, fiscal stress, and now artificial intelligence. Galloway had done his version through a COVID lens: international student dependence, brand strength, pandemic resilience. The pandemic turned out to be a shock that most institutions survived. The pressures accumulating now feel more structural and more permanent, and the measures available to capture them have improved substantially since 2020.
When I went back to the literature, I found that Galloway's work sat inside a larger scholarly conversation I hadn't fully appreciated. Kelchen, Ritter, and Webber (2025) had built comprehensive institutional closure prediction models. Zemsky, Shaman, and Baldridge (2020) had developed the "College Stress Test." Grawe (2018, 2021) had mapped the demographic cliff in granular detail. Third Way (2023) had documented persistent outcome variation across the sector. What Galloway added to this was the strategic visualization — the idea that you could position institutions on a map and let the quadrants tell the story. What I wanted to add was methodological rigor: outcome-based measures instead of brand proxies, forward-looking indicators including AI exposure, and full transparency about the analytical choices involved.
One data source that made this project possible in a way it wouldn't have been even a year ago is the Anthropic Economic Index — a recently released dataset that provides observed real-world AI adoption rates by occupation, based on actual conversations with Claude. This let me do something genuinely new: compare theoretical AI task exposure (derived from O*NET occupational analysis) with observed AI adoption (what's actually happening in the workplace). The weak, negative relationship between the two is one of the project's most distinctive findings, and it's only measurable because Anthropic made this data publicly available.
This project is not a prediction and not a judgment about which institutions deserve to survive. It is evaluative — institutions scoring higher are better-positioned on the indicators I track — but the indicators are analytical choices, not comprehensive measures of institutional value or educational mission. An institution serving a vital access mission in a declining-enrollment region may score low on these measures precisely because it is doing important work in a structurally challenging context. The framework makes structural positions visible; it does not determine which positions are worth occupying.
The companion essay, "On the Adaptations Before the Exigencies: Higher Education and the Structural Pressures Ahead", provides the broader argument for why this mapping exercise matters now — and why the signals the labor market is sending back to higher education may be more consequential than the sector has yet recognized. A related piece, "Why Higher Education's AI Backlash Reveals Some of Its Deepest Cracks", examines how institutional responses to AI — from outright bans to cautious avoidance — may themselves be a source of structural vulnerability.
Human–AI Collaboration
I built this project in collaboration with Claude (Anthropic). The AI contributed substantially to data processing, pipeline development, code generation, statistical analysis, visualization, and drafting across many iterative sessions. It would be dishonest to minimize that contribution — the scope and speed of this work would not have been possible without it.
That said, the intellectual direction is mine. The decision to replace Galloway's brand proxies with outcome measures. The choice to include AI exposure as an exploratory component despite knowing it would complicate the factor structure. The insistence on presenting limitations prominently and owning the framework as a strategic heuristic rather than a discovered latent structure. The interpretation of what the patterns mean for institutions, students, and the sector. These are human decisions, informed by years of thinking about research methodology and, more recently, about what's happening to the institutions where that methodology gets practiced.
The convention for crediting AI contributions in academic work is not yet settled. I've chosen transparency: describe what the tool did, be honest about it, and take responsibility for the output. That seems like the right standard for now.
Author
Kyle Saunders
Professor, Department of Political Science
Colorado State University
kyle.saunders@colostate.edu
kylesaunders.com · Substack · Google Scholar
How to Cite
Saunders, K. (2026). Mapping the Structural Divide: Institutional Resilience, Post-College Market Position, and Artificial Intelligence Exposure Across U.S. Higher Education. Dataset and interactive tool, v1.5. Colorado State University. https://kylesaunders.com/university-map
Acknowledgments
I will update this list moving forward, but already owe thanks to a lot of people who have influenced my thinking on this and helped out with initial drafts of this whole thing. I appreciate you.
Version History
Last updated: October 1, 2026
v1.5, October 1, 2026: data and methods update
Refreshed with the latest IPEDS and College Scorecard releases; the enrollment trend now covers 2019-20 to 2024-25. Revenue diversification and endowment per student were recomputed directly from the IPEDS and College Scorecard files so they match the definitions in the Methods tab, which corrects the private-nonprofit revenue figures in earlier versions. Quadrant changes are listed in the v1.4 to v1.5 changes file (CSV) on the Data & Downloads tab.
Finance: Revenue diversification uses net tuition over IPEDS core revenues for both public and private institutions. Endowment is divided by full-time-equivalent (FTE) enrollment, undergraduate and graduate, derived from IPEDS instruction expenses and College Scorecard instructional spending per FTE student. Institutions reporting zero net tuition receive no revenue diversification score.
Data refresh: Selectivity (fall 2024 admissions), completion, and earnings-to-debt (College Scorecard, June 2026) were refreshed. Institutions formed by merger have their predecessors' enrollment added to the base year.
AI exposure: Rebuilt from O*NET 29.0 and current College Scorecard program shares. The map uses the entry-weighted measure, and the Methods tab now describes the measure the map actually uses. Field-level observed adoption is now built from the Anthropic Economic Index file through the CIP-SOC crosswalk.
Coverage: The resilience composite now requires at least one finance component. Institution profiles flag missing components and unusual data.
Sensitivity and statistics: The sensitivity analysis now covers 20 specifications, including four alternative AI measures, and every scored institution has a stability score. All statistics on this site were recomputed except the Census PSEO earnings comparison, which uses the v1.4 field scores. The Factor View reflects the recomputed factor structure, and Factor 2 is now called Institutional Resources (it was Institutional Character).
Working paper: The working paper and supplementary materials have been removed from this site while a revised version is prepared.
Working paper (October 2026): The working paper and supplementary materials were reissued on the v1.5 data, with a new section on how the Carnegie tiers separate on each axis and by institutional control. They replace the April 2026 versions at the same links.
v1.4 — April 15, 2026
Z-score robustness check: Added a z-score (probit transform) scaling alternative to the sensitivity analysis. The z-score specification produces 92.1% quadrant agreement with the baseline (ρ = 0.987 on resilience, ρ = 0.985 on market position), confirming that the main tier-level patterns are robust to the choice between percentile ranking and standardization. The 123 institutions that shift are distributed across all tiers, with no tier showing agreement below 90%; R1 institutions show the highest agreement (96.0%).
Manuscript framing: Revised AI exposure interpretation on the AI tab to present all interpretations with equal weight, matching the manuscript's treatment. Strengthened the two-timelines framing. Added Master's tier High Stress percentages (46% L/M, 43% S) and Carnegie-modal baseline accuracy (45%) to the Overview tab. Updated sensitivity section with stable-institution quadrant breakdown (216 HC, 167 HS) and axis-level robustness statistics.
Static figures: Regenerated all four manuscript figures with website-matched D3.js color palette for visual parallelism between the journal submission and the interactive tool.
v1.3 — March 29, 2026
Carnegie 2025 integration: Replaced Carnegie Basic 2021 (C21BASIC) with the 2025 Carnegie Classification system as reported in IPEDS HD2024. Tier groupings now use a 9-tier scheme derived from the Research Activity Designation (R1/R2/RCU for research institutions) and Award Level Focus (Doctorate/Master's/Baccalaureate/Associate for non-research institutions). All visualizations — ridge plots, scatter plot color-coding, filter dropdowns — updated to the new tier definitions. The new RCU (Research Colleges and Universities) tier captures 182 institutions with meaningful research activity that previously had no research tier recognition. Raw 2025 classification codes (IC2025, RESEARCH2025) added to the downloadable dataset. Composite scores unchanged (Carnegie tier is a grouping variable, not a model input).
Expanded sensitivity analysis: Added 5 new specifications testing revenue diversification (sponsored research discount via ICR and full HERD removal), endowment encumbrance (yield vs. level), half-weight endowment, and admission yield as a selectivity alternative. Total sensitivity specifications: 18 (up from 13). Stability score now reflects proportion of all 18 specifications producing the same quadrant assignment.
Quadrant label correction: Fixed a temporary label transposition in the sensitivity analysis function where High Stress and Structurally Exposed assignments were swapped. All 780 affected institutions now carry correct quadrant labels. Composite scores and component values were unaffected — this was purely a labeling fix in the quadrant assignment logic.
Data integrity audit: Forensic diff of v1.2 → v1.3 dataset confirmed zero changes to any composite score or component value across all 1,609 institutions. JSON and CSV verified in sync (1,609 records, zero empty strings, all numeric fields in [0,1] range). Cross-referenced all quantitative claims across working paper, supplementary appendix, website, and README. Cache buster updated to force browsers to load current data file.
v1.2.1-geo — March 25, 2026
GEO/AEO metadata (back-of-house): Added JSON-LD structured data (Dataset and WebApplication schemas) to page head for AI search engine and generative engine discoverability. Added sameAs entity linking to author profiles. Created llms.txt providing a machine-readable project summary. No changes to visible content, data, or methodology.
v1.2.1 — March 23, 2026
Carnegie 2025 notice: Added preliminary notice about the 2025 Carnegie reclassification. (Superseded by v1.3 full Carnegie 2025 integration.)
Structurally unusual institutions: Added limitation note in Methods tab Section 5 acknowledging that military academies, tribal colleges, and other institutions with fundamentally different funding and enrollment structures may produce technically accurate but strategically misleading component scores under the framework's general-purpose indicators.
Ordinal suffix fix: Corrected percentile display across institution detail panel and scatter plot tooltips — previously all values displayed with "th" suffix (e.g., "63th," "2th"); now renders correctly (e.g., "63rd," "2nd," "1st").
Ridge plot label fix: Increased left margin on combined score distribution chart to prevent Carnegie tier labels from clipping outside the visible area.
Prose clarifications: Added note on AI Exposure tab clarifying the dual role of AI exposure as both a market position component and the subject of a separate field-level analysis. Added combined-score rationale to distribution chart description. Expanded selectivity component description to flag dual interpretation (demand buffer vs. limited pool). Added parenthetical gloss to demographic trajectory label on institution detail.
v1.2 — March 20, 2026
Factor analysis diagnostics: Added KMO measure of sampling adequacy (0.63), Bartlett's test of sphericity (χ²(28) = 1900.58, p < .001), parallel analysis (Horn, 1965; 1,000 replications confirming 3-factor solution), communality tables for all loading specifications, and per-variable MSA values. Documented missingness: 294 of 1,556 mapped institutions excluded from factor analysis due to incomplete component data, with excluded institutions disproportionately in High Stress (42.5%). Clarified PCA used for data reduction, not latent construct identification. Updated Methods tab and supplementary materials throughout.
Framework framing: Revised quadrant label language across all pages to acknowledge the evaluative gradient while contextualizing what the indicators do and don't capture. Replaced "descriptive labels, not predictions or rankings" with language that owns the directional content of the axes while noting that the indicators are analytical choices and institutions serve missions beyond what quantitative measures represent.
PSEO section: Reframed from "External Benchmarking" to "Earnings Context" across working paper, supplementary materials, and website. The PSEO finding contextualizes what the AI exposure measure captures but does not validate it as a predictor of future disruption.
AI exposure interpretation: Softened "preferred interpretation" language around the ρ ≈ −0.09 finding. All interpretations now presented with equal weight; the substantive interpretation (temporal gap between capability and adoption) is one of several plausible readings.
Methodology citations: Added references to composite index methodology literature (Saisana & Saltelli, 2011; NORC, 2024), parallel analysis (Horn, 1965), and Kelchen et al. (2025) as external evidence supporting enrollment trend as a forward-looking indicator.
AI tab: Added institutional-level standard deviation (0.030) to clarify that AI exposure has low institutional-level discriminating power; field-level analysis is where the measure provides meaningful differentiation.
v1.1 — March 10, 2026
Framework restructure: Restructured component axes based on pre-factor correlation diagnostics. Dropped raw median earnings (redundant with earnings-to-debt ratio at r = 0.80). Moved completion rate from Resilience to Market Position axis to resolve cross-axis loading (completion–earnings r = 0.734). Framework now uses 8 components (4 per axis). Recomputed all composites, quadrant assignments, factor loadings, and sensitivity analysis. 1,556 institutions mapped (vs. 1,550 previously).
Factor analysis: Recomputed all factor results using proper 8-component specification. Updated promax loadings, factor correlations, and uniqueness values across website, working paper, and supplementary materials. Renamed Factor 2 from “Institutional Resources” to “Institutional Character.” Flipped Factor 1 sign on the factor scatter so higher credential outcomes appear to the right, matching the axis arrow.
Front page and navigation: Added contextual onboarding around the search box — axis definitions, quadrant labels, key patterns, data sources, and explore cards for each visualization tab. Added same context to institution detail view. Fixed mobile tab scrolling. Renamed “The 2×2” tab to “The Whole 2×2.”
AI Exposure tab: Added comprehensive explainer walking through the 5-step AI exposure pipeline, the retrospective-vs-prospective problem, and the Anthropic Economic Index comparison. Reframed the exploratory badge from warning to informational.
Parallelism and documentation: Full consistency pass across all deliverables. Fixed abstract sensitivity wording (31% stable, not 31% sensitive). Clarified 1,262 complete-case vs. 1,556 mapped institution counts. Updated README with correct v1.1 component structure and field descriptions. Updated supplementary Appendix C to reflect current axis composition. Regenerated all PDFs with embedded figures.
SSRN: Working paper posted at SSRN.
Website readability: Increased font sizes and added bold weight to axis labels, quadrant markers, and tick text across all chart tabs (Whole 2×2, AI Exposure, Factor View). Fixed y-axis arrow direction on main scatter plot. Adjusted chart legend positioning to prevent overlap with data. Corrected equal-weight normalization in component weights comparison chart (now normalized across all 8 components). Added caption clarifying normalization basis.
v1.0 — March 8, 2026
Initial public release. Website, working paper (v1.0), and supplementary materials published simultaneously.
Framework: 1,556 institutions, 8 components, equal-weight composite scoring, median-split quadrant assignment. AI exposure derived from O*NET work activities pipeline with entry-level Job Zone weighting. Factor analysis across varimax, promax, and oblimin rotations (2 and 3 factors). Sensitivity analysis across 13 alternative specifications. PSEO external benchmarking for 572 institutions.
Planned Updates
Annual data refresh: IPEDS, College Scorecard, and WICHE data update annually. I intend to refresh the dataset each fall when new data releases are available, enabling longitudinal tracking of institutional positions.
Replication code: Replication package (replicate_v15.zip) and README available on the Data & Downloads tab. The scripts rebuild the dataset from raw public files plus the institution list.
Correspondence
Working paper version: v1.5 (October 2026)
Website version: v1.5
Dataset version: v1.5
Correspondence: kyle.saunders@colostate.edu
The website, dataset, and working paper versions are synchronized.