please direct correspondence to
Bibliographic record
Abstract
Unique new data from a college with a mandatory work-study program are used to examine the relationship between working during school and academic performance. Particular attention is paid to the importance of biases that are potentially present because the number of hours that are worked is endogenously chosen by the individual. A “naive ” OLS regression, which indicates that a positive and statistically significant relationship exists between hours-worked and grade performance, highlights the potential importance of endogeneity bias in this context. Although a fixed effects estimator suggests that working an additional hour has an effect on grades which is quantitatively very close to zero, we suggest that there are likely to exist causes of endogeneity which are not addressed by the fixed effects estimator. Indeed, an instrumental variables approach, which takes advantage of unique institutional details of the work-study program at this school, indicates that working an additional hour has a negative and quantitatively large effect on grade performance at this school. The results suggest that, even if results appear “reasonable, ” a researcher should be cautious when drawing policy conclusions about the relationship between hours-worked and a particular outcome of interest unless he/she is confident that potential problems associated with the endogeneity of hours have been adequately addressed. 1 The University of Western Ontario and Berea College. We would like to thank John Bound and
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.863 | 0.719 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".