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Record W2127475749

Do Dropouts Drop Out Too Soon? Evidence from Changes in School-Leaving Laws

2003· preprint· en· W2127475749 on OpenAlex
Philip Oreopoulos

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

VenueRePEc: Research Papers in Economics · 2003
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Literacy, Pension, Retirement Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEarningsDrop outEconomicsDropout (neural networks)Norm (philosophy)School dropoutDemographic economicsActuarial scienceLabour economicsLawPolitical scienceAccounting
DOInot available

Abstract

fetched live from OpenAlex

Abstract: This paper investigates if decisions to leave school early are sub-optimal, and whether would-be-dropouts benefit from policies, such as a minimum school leaving age, that oblige them to continue. I use changes in minimum school-leaving laws in Great Britain and Ireland, which were remarkably influential, to measure pecuniary and nonpecuniary gains from education. I find, similar to previous tudies, students compelled to take an extra year of school experienced an average increase of 12 percent in annual earnings. I also find significant gains from education to health, leisure and labor activities, and subjective measures of well-being, which hold up against a wide array of specification checks. Comparing these estimates with intertemporal models of educational choice, the main conclusion of this paper is that it is very difficult to explain early school leaving decisions without the presence of time inconsistent preferences, misguided expectations, or disutility from identifying with a social group that considers dropping out the norm. To prefer dropping out early, the one-year cost from attending school would likely have to exceed a dropout’s maximum lifetime annual earnings by a factor of at least five.

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)0.004
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.211
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.000

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 head0.057
GPT teacher head0.318
Teacher spread0.261 · 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