Who is Picking Up the Child from Day Care?: Understanding the Intrahousehold Dynamics in Drop-Off and Pickup Allocation for Households with Dependent Children
Bibliographic record
Abstract
This paper presents a joint model for the allocation of drop-off and pickup responsibilities and the choice of the day care location for two-adult households with dependent children. This analysis aims to capture the trade-offs that occur at the household level in the selection of both day care location and drop-off and pickup responsibility allocation. The paper uses a stochastic frontier modeling approach to predict feasible locations for each possible allocation pair. The frontier model predicts the maximum distance an individual would be willing to travel within a time budget constraint. This travel time prediction is then applied to each individual for generating feasible location sets given two endogenous anchor points. The paper then presents a joint econometric-choice model of task allocation and day care location with heterogeneous sampling correction factors for each possible allocation. Captured within the model are the choice of who performs the drop-off and pickup activities and the eventual location that is selected for day care. The model structure provides key insights into both the choice to use day care and the allocation of drop-off and pickup responsibilities.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".