Bibliographic record
Abstract
This thesis collects three papers studying topics in development and cultural economics. In Chapter 1, I study the role of culture in driving entrepreneurship. I present robust evidence that cultural groups which emphasize obedience as a moral value produce fewer entrepreneurs. First, I document this relationship at the language group level, exploiting within-country variation in group-level values. Second, I show that these group-level values continue to predict entrepreneurship rates and related occupational choices among first- and second-generation immigrants to Canada. Third, I present evidence that obedience values form as a function of values received both from parents and from the wider community during childhood. In Chapter 2, I present joint work with Limin Fang and Tongtong Hao on the effects of China's One Child Policy on the education and early labour market outcomes of those born in its wake. We present evidence from a difference-in-difference design that individuals who grew up as only children as a result of the policy obtained significantly more education than their counterparts with siblings, while early labour market outcomes were less affected. We place our results in context of the large literature on quantity-quality tradeoffs and argue that they are consistent with existing theoretical models, which allow for non-linearity in the effect of child quantity on quality. In Chapter 3, I study the effect of natural resource abundance on human development in Sub-Saharan Africa between 1970 and 2013. I use cross-section and panel IV analysis to identify the long- and short-run impacts of resource wealth on human development, measured primarily by infant mortality. I find that natural resource revenue has had zero average impact on infant mortality rates in the short run and a potentially negative impact in the long run, despite increasing budget resources and reported health spending. I further find that while democratic transitions are generally followed by a drop in infant mortality, this is not true in resource rich nations with weak constraints on power. I conclude that the pass-through of natural resource wealth to broad-based development in Africa has been weak and that electoral competition without deeper political reform is unlikely to turn things around.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".