Bibliographic record
Abstract
I cover three topics in empirical microeconomics. In the first chapter, titled Investor Attention to Firm versus Market-wide Information Shocks: Evidence from North Korean Missile Tests, I study whether attention towards salient political events leads to underutilization of firm-specific information in the South Korean stock market. I find that companies with earnings surprises in the top quartile experience a 1.6% increase in the abnormal return on the announcement day, but a same-day missile test takes away 70% of the positive response. In the second chapter, titled Does Cultural Proximity Mitigate the Effect of Immigration on Electoral Outcomes? (with Gerard Domènech), we study the effect of immigration on electoral outcomes using individual-level administrative data in Spain. In a multiple instrumentations framework, we find that recent immigrants who arrived within two years are associated with an increase in the vote share of the extremist parties. Such an effect persists for additional two years but dissipates in the long-term. When split by regions of origin, African immigrants have the greatest impact, followed by Latin American immigrants. European immigrants do not affect the extremist vote shares. An analysis of the unemployment rate and the number of children suggests that immigrants tend to assimilate over time. The findings are consistent with the hypothesis that cultural proximity mitigates the political reaction to immigrants. In the third chapter, titled The Effect of Daddy Quota on Gender Labor Market Outcomes (with Petra Niedermeyerova), we study the impact of a father-specific parental leave policy on labor market outcomes in Quebec, Canada. Using a province-level difference-in-difference approach, we find that the so-called daddy quota increases the probability of employment for women and decreases the wage of younger men. The results suggest that the daddy quota promoted equal opportunities for women in the labor market. In a theoretical framework, we show that policy-driven changes in gender norms are consistent with our findings.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.009 |
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".