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Record W2981041359 · doi:10.3390/admsci9040081

The Value of High School Graduation in the United States: Per-Person Shadow Price Estimates for Use in Cost–Benefit Analysis

2019· article· en· W2981041359 on OpenAlex
Aidan R. Vining, David L. Weimer

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdministrative Sciences · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsGraduation (instrument)Shadow (psychology)Shadow priceValue (mathematics)EconomicsGovernment (linguistics)Public economicsEngineeringPsychologyStatisticsMathematics

Abstract

fetched live from OpenAlex

One way for jurisdictions with limited analytic resources to increase their capability for doing cost–benefit analysis (CBA) is to use existing shadow prices, or “plug-ins”, for important social impacts. This article contributes to the further development of one important shadow price: the value of an additional high school graduation in the United States. Specifically, how valuable to a student, government, and the rest of society in aggregate is a high school graduation? The analysis builds on the method developed by the Washington State Institute for Public Policy and presents numerical updates and extensions to their analysis. For the U.S., the estimated net present value (the social value) using a 3 percent real discount rate of this shadow price is approximately $300,000 per each additional graduate. In appropriate circumstances, this value can be “plugged-in” to CBAs of policies that either directly or indirectly seeks to increase the number of students who graduate from high school.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.105
GPT teacher head0.307
Teacher spread0.202 · 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