Cost of research and education activities in US colleges - scalability, complementarity, and heterogeneous efficiency
Bibliographic record
Abstract
Abstract Universities in the United States are remarkably diverse in their efficiency, both in terms of research output and educational achievement. Recent work has highlighted how important this heterogeneity is by showing that different broad categories of institutions demonstrate different scaling relationships for various features as a function of the number of students. These differences in scaling relationships reflect differences in organizational goals, constraints, and strategies. In the existing literature, this heterogeneity is under-explored and often ignored due to the lack of appropriate data and methodological limitations. In this paper, we address this problem by exploiting a newly consolidated dataset and adopting a neural-network based method to infer cost functions for universities. Our analyses reveal (1) the specific economy of scale in two distinct output types (education and research), (2) the nature of the trade-off between research and education efficiency, and (3) significant efficiency differences across universities. Particularly, we show that while both research and education outputs generally exhibit an economy of scale, their scalability largely depends on their size and other institutional characteristics. Similarly, research and education activities are complementary to each other (economy of scope) only in some situations, particularly when the scale of production is small to medium. Consequently, the cost isoclines of universities are highly non-convex, implying the possibility of multiple optima that may explain the diverse strategies universities adopt, and potential efficiency gains from specialization. It also suggests that some basic assumptions of microeconomic models may not be empirically supported.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".