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Record W4288074143 · doi:10.1257/aer.20200224

The Impact of Childhood Social Skills and Self-Control Training on Economic and Noneconomic Outcomes: Evidence from a Randomized Experiment Using Administrative Data

2022· article· en· W4288074143 on OpenAlexaff
Yann Algan, Elizabeth Beasley, Sylvana M. Côté, Jungwee Park, Richard E. Tremblay, Frank Vitaro

Bibliographic record

VenueAmerican Economic Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSchool Choice and Performance
Canadian institutionsStatistics CanadaUniversité de Montréal
FundersAgence Nationale de la Recherche
KeywordsIntervention (counseling)Control (management)Treatment and control groupsPsychologyRandomized experimentDemographic economicsEconomicsDemographyMedicineSociology

Abstract

fetched live from OpenAlex

A childhood intervention to improve the social skills and self-control of at-risk kindergarten boys in the 1980s had positive impacts over the life course: higher trust and self-control as adolescents; increased social group membership, education, and reduced criminality as young adults; and increased marriage and employment as adults. Using administrative data, we find this intervention increased average yearly employment income by about 20 percent and decreased average yearly social transfers by almost 40 percent. We estimate that $1 invested in this program around age 8 yields about $11 in benefits by age 39, with an internal rate of return of around 17 percent. (JEL I21, I26, I28, J13, J24, J31, Z13)

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.045

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.082
GPT teacher head0.417
Teacher spread0.335 · 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 designRandomized trial
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

Citations78
Published2022
Admission routes1
Has abstractyes

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