The nonprofit advantage: Producing quality in thick and thin child care markets
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Nonprofit child care centers are frequently observed to produce child care which is, on average, of higher quality than care provided in commercial child care centers. In part, this nonprofit advantage is due to different input choices made by nonprofit centers—lower child‐staff ratios, better‐educated staff and directors, higher rates of professional development for staff. Nonprofit centers may have an additional productivity advantage, due to unmeasured staff motivation and abilities or to better management of the production of good‐quality child care. However, where nonprofit and for‐profit child care firms compete in the same local markets, we speculate that this extra advantage should only appear where demand is sufficiently “thick” to permit a quality differentiation strategy to be financially viable for nonprofits. We estimate the effect of nonprofit status on quality, controlling for differences in financial resources available to the center, differences in the clientele served, and differences in staff and center inputs. In this conventional examination, nonprofit status has a moderately positive impact on quality. However, when we account for the unobserved heterogeneity and separate markets into “thick” and “thin,” a particularly strong nonprofit advantage is found in thick markets, but no productivity advantage for nonprofits is found in thin markets. This finding suggests a clear role for nonprofit organizations in improving the cost‐quality trade‐off faced by parents, but also identifies the market conditions that affect the ability of nonprofit managers to employ this advantage. © 2009 by the Association for Public Policy Analysis and Management.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it