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Record W3080302856 · doi:10.3386/w27717

The EITC and Maternal Time Use: More Time Working and Less Time with Kids?

2020· report· en· W3080302856 on OpenAlexaff
Jacob Bastian, Lance Lochner

Bibliographic record

VenueNational Bureau of Economic Research · 2020
Typereport
Languageen
FieldSocial Sciences
TopicGender, Labor, and Family Dynamics
Canadian institutionsWestern University
FundersW.E. Upjohn Institute for Employment ResearchSid W. Richardson FoundationSmith Richardson Foundation
KeywordsEarned income tax creditInvestment (military)Time allocationEconomicsLabour economicsWork timeTime-use surveyWork (physics)Production (economics)Demographic economicsTax creditPublic economicsMicroeconomicsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Parents spend considerable sums investing in their children's development, with their own time among the most important forms of investment. Given well-documented effects of the Earned Income Tax Credit (EITC) on maternal labor supply, it is natural to ask how the EITC affects other time allocation decisions, especially time with children. We use the American Time Use Surveys to study the effects of EITC expansions since 2003 on time devoted to a broad array of activities, with considerable attention to the amount and nature of time spent with children. Our results confirm prior evidence that the EITC increases maternal work and reduces time devoted to home production and leisure, especially among unmarried women. More novel, we show that the EITC also reduces time spent with children; however, almost none of this reduction comes from time devoted to active investment-related activities that are most likely to foster child development.

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 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)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.554
Threshold uncertainty score0.599

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.235
GPT teacher head0.453
Teacher spread0.219 · 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 teacher head, 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

Citations18
Published2020
Admission routes1
Has abstractyes

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