ENHANCING EFFICIENCY AT NONPROFITS WITH ANALYSIS AND DISCLOSURE
Notice bibliographique
Résumé
The U.S. nonprofit sector spends $2.54 trillion each year. If the sector were a country, it would have the eighth largest economy in the world, ahead of Brazil, Italy, Canada, and Russia. The government provides nonprofits with billions in tax subsidies, but instead of evaluating the quality of their work, it leaves this responsibility to nonprofit managers, boards, and donors. The best nonprofits are laboratories of innovation, but unfortunately some are stagnant backwaters, which waste money on out-of-date missions and inefficient programs. To promote more innovation and less stagnation, this Article makes two contributions to the literature. First, this Article breaks new ground in identifying sources of inefficiency at nonprofits. The literature focuses on incentives, arguing that managers and board members are less motivated to run a nonprofit efficiently because they cannot keep its profits. In response, this Article emphasizes that the problem is not just motivation, but also information. Measuring success is harder at nonprofits. Instead of tracking profitability, they use metrics that are less reliable and harder to measure. These measurement challenges complicate the efforts even of dedicated and competent managers to operate efficiently. While this information problem is familiar, another has been largely overlooked in the literature: When success is hard to measure, incompetence and self-interested practices are less visible, and thus are harder to stop. For example, if managers regularly overpay vendors, the consequence at a for-profit firm (lower profits) is easier to observe than at a nonprofit (less effective service for beneficiaries). Second, this Article recommends a response to this underappreciated source of inefficiency: better analysis and disclosure as a strategy for organizational change. In principle, nonprofits are supposed to maximize social return, but how can they operationalize this abstract principle? To help them do so, this Article recommends three questions that nonprofits should answer every year: first, how important are the challenges the nonprofit is trying to address?; second, how effective are the nonprofit’s responses to these challenges?; and third, is the nonprofit the right organization to respond to these challenges? These questions press nonprofit managers and boards to be more explicit about priorities, monitor progress, improve and expand high-value programs, and fix or shut down ineffective ones. This Article also recommends that nonprofits should disclose this analysis to the public, even though current law does not require them to do so. This disclosure would empower donors and rating agencies to be more effective monitors. It also would help donors make better informed philanthropic choices and would enable charities to borrow innovative ideas from each other more easily.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,055 | 0,185 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,004 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,016 | 0,014 |
| Science ouverte | 0,002 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».