Three essays on Applied Economics
Bibliographic record
Abstract
The purpose of this thesis is to contribute to the economics and financial literature by providing novel evidence on the impact culture and society social structure have on economic performance. In doing so, I produced three chapters in which I explore the relationship between them and business performance using advanced empirical techniques and high quality data. In the first chapter (which is a joint work with I. Hasan from Fordham University and F. Noth from IWH), I investigates the critical role of culture in the economic recovery after highimpact natural disasters. Using Hurricane Katrina as our laboratory, we find a significant adverse treatment effect for plant-level productivity. However, local religious adherence and larger shares of ancestors with disaster experiences mutually mitigate this detrimental effect from the disaster. Religious adherence further dampens anxiety after Hurricane Katrina, which potentially spur economic recovery. We also detect this effect on the aggregate county level. More religious counties recover faster in terms of population, new establishments, and GDP. The second chapter (which is a joint work with I. Hasan from Fordham University) investigates whether managers’ personal connections help corporations to escape the productivity trap. Leveraging the heterogeneity in the severity of the Great Recession across different sectors, the paper reports that (i) the Great Recession had a negative effect on corporate productivity, (ii) the effect was long-lasting and persistent, supporting a productivity-hysteresis hypothesis, (iii) managers’ personal connections are counter-cyclical and indeed allowed corporations to escape the productivity trap primarily via favorable credit conditions, in periods of high information asymmetries and tight credit constraints. The third chapter (which is a joint work with E. Florio from HEC Montreal) analyses the effect of the attitudes of CEOs’ ancestors on firm performance. We collect information on Italian emigrants during the Age of Mass Migration from Ellis Island ships lists and use emigration as a proxy for ancestors’ risk propensity. Using an IV approach, we find that Italian firms managed by a CEO that belongs to a family with past emigration experience tend to perform better and to be more productive. In line with an inter-generational transmission of attitudes hypothesis, we show a positive relationship between the emigration of CEOs’ ancestors and alternative measures of corporate risk-taking and cost of capital.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".