The nonprofit advantage: Producing quality in thick and thin child care markets
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.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".