Bibliographic record
Abstract
This thesis is composed of three essays on labor and education in Iran and Canada.In the first chapter, I estimate the effect of having children on labor force participation of mothers in urban Iranian areas. I exploit sex composition of children as an exogenous source of variation in family size to account for endogeneity of fertility. Using information from the Iranian Household Income and Expenditure Survey (HIES) over three samples, namely households with one and more, two and more, and three and more children, I find no significant effect of fertility on female labor force participation in Iran.In the second chapter, I estimate family member’s resource shares and investigate gender bias in intra-household resource allocation. I follow Dunbar et al. (2013) in that I estimate the household member’s resource shares by observing how budget shares on private assignable goods vary with total expenditure and family size. I extend their methodology to analyze how sex composition of children influences resource shares. Using data from the 2005 Iranian Household Income and Expenditure Survey (HIES), I find that in Iranian rural areas parents assign 1.6 to 1.9% more resources toward their sons. Similarly, I find that mothers in all-boy families get 2.8 to 3.6% less resources than in all-girl families. These effects are more pronounced among farmer families. In contrast, I find no significant role of gender composition on intra-household resource allocation in Iranian urban areas.In the final chapter I, jointly with Dr. Friesen and Dr. Woodcock, investigate the question of whether schools that charge private tuition deliver higher quality education compared to their public counterparts has proven very challenging. This paper contributes new evidence regarding the quality of private schools relative to public schools. We use a longitudinal student-level data set from British Columbia, Canada that comprises the entire population of students in fourth through seventh grade who enrolled in public or private schools. We apply a procedure developed by Abowd et al. (2002), which allows us to exploit mobility between schools to estimate a full set of both school and student fixed effects.
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 | 0.011 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".