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Record W3045922015 · doi:10.7916/cjtl.v11i2.6841

ENHANCING EFFICIENCY AT NONPROFITS WITH ANALYSIS AND DISCLOSURE

2020· article· en· W3045922015 on OpenAlexaboutno aff
David M. Schizer

Bibliographic record

VenueColumbia Journal of Tax Law · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBusiness Law and Ethics
Canadian institutionsnot available
Fundersnot available
KeywordsInefficiencySubsidyProfitability indexIncentiveBusinessGovernment (linguistics)Profit (economics)MarketingEconomicsPublic economicsPublic relationsFinanceMarket economyMicroeconomicsPolitical science

Abstract

fetched live from OpenAlex

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best nonprofits are laboratories of innovation, but unfortunately some are stagnant backwaters, which waste money on out-of-date missions and inefficient programs.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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.055
metaresearch head score (Gemma)0.185
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.292

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0550.185
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.004
Science and technology studies0.0040.004
Scholarly communication0.0160.014
Open science0.0020.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0150.004

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.

Opus teacher head0.013
GPT teacher head0.201
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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