Bibliographic record
Abstract
This dissertation contains three essays in applied microeconomics, with a focus on household decision-making.In the first chapter, I study the effect of asymmetric information about income on household decisions, resource sharing, and welfare. I proceed in four steps. In the first step, I develop a theoretical model that accounts for the possible existence of asymmetric information. The model predicts that households will partly mitigate the welfare cost of asymmetric information by incentivizing the wage earner to provide information about his or her true income. These incentives are provided by making the consumption share increase with reported income: the wage earner’s consumption share is high when reporting a high income and low when reporting a low income. Second, I derive a new non-parametric identification result for this model. Third, I estimate the model using a survey of Bangladeshi day laborers. The estimation confirms the predictions of the model, providing evidence that the households in the data are affected by asymmetric information. Finally, I conduct three counterfactual analyses to document how asymmetric information interacts with policies and compute the willingness to pay in each case.In the second chapter, which is co-authored with Maria Casanova and Maurizio Mazzocco, we show that the intratemporal and intertemporal preferences of each decision-maker in the household can be identified even if individual consumption is not observed. This identification result is used jointly with the Consumer Expenditure Survey (CEX) to estimate the intratemporal and intertemporal features of individual preferences. The empirical findings indicate that there is heterogeneity in intertemporal preferences between wife and husband.In the third chapter, I use a major reform of the parental leave system in Quebec in 2006 to analyze how households make decisions related to parental leave. I show that the introduction of a father’s quota - a policy designed to incentivize fathers to take parental leave - was successful in more than doubling the proportion of fathers taking some parental leave. However, the impact on the intensive margin was limited: in 80% of households, mothers take all the leave that is available to both parents. I also use an administrative dataset to analyze the relationship between parental leave decisions and income. In general, households with higher labor income take more parental leave overall (summing the mother’s and the father’s weeks). However, fathers with higher labor income take less parental leave.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.007 |
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".