Bibliographic record
Abstract
This thesis consists of four self-contained empirical studies, with three underlying themes: Migration, Education and Culture. First, using annual data over the period 1820-2010, Chapter 2 examines the productivity effects of immigrants’ traits on growth in Argentina, Australia, Brazil, Canada, New Zealand and the United States. Immigrants’ traits such as wealth, culture, institutions, R&D knowledge, and education are traced back to their country of origin. Culture is found to be consistently the most important productivity-enhancing trait of immigrants, followed by education. Second, using annual data over the period 1850-2010, Chapter 3 examines the impact of immigration as well as the immigrants’ educational and cultural background on unemployment in Argentina, Australia, Brazil, Canada, New Zealand and the United States. The results show that immigrants lowered unemployment before WWII but not thereafter and that immigrants from Protestant countries have lowered unemployment throughout the entire period, 1850-2010. Third, using panel data on the eight Australian states and territories, Chapter 4 examines the effects of migration on house prices in Australia from 1971-2013, accounting for both international and internal migratory movements. The results show that migration driven population growth has a significant effect on house prices in the short-run and that inter-state migration needs to be account for due to the large inter-state movements. Overall, the other results are relatively consistent with existing literature. In the short-run, housing prices are significantly driven by inertia in house prices, interest rates, the unemployment rate and income. In the long run however, house prices are driven by their replacement costs, measured by construction costs. Last, using panel data of 21 OECD countries from 1820 to 2009, Chapter 5 seeks to explain the mass rise in education we have witnessed in the last few centuries. Specifically, it examines the impact of government regulation in the form of compulsory schooling laws and child labour laws, culture, life returns to education, structural changes in the economy and the sequential nature of schooling on school enrolment rates – primary, secondary and tertiary. Results suggest that primary and secondary schooling is significantly influenced by government regulation on schooling and that more liberal cultural values, higher life expectancy and an expansion of the knowledge intensive sector have a positive effect on enrolment across the three levels of schooling.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".