Three Essays in Macroeconomics
Bibliographic record
Abstract
This thesis consists of three papers in macroeconomics that investigate the following questions: (1) How do changes in global demand for fossil fuels affect welfare across households in small resource-rich economies? (2) How did the expansion of the oil sector in Canada affect measured aggregate productivity? (3) How sensitive are cross-country comparisons to measurement errors introduced by nominal-to-real conversions? Chapter 1 develops a quantitative model of a fossil fuel exporting economy to show that the oil price boom between 1997 and 2020 increased welfare among young, low-income households between 11\% and 16\%. I then simulate the transition to a Net Zero world between 2020 and 2050 and show that while the fall in global demand for fossil fuels reduces lifetime consumption by 0.56\% (between 0.49\% and 0.77\% for the youngest low-income households), the growth of the clean energy sector can dampen these losses by 15\% to 54\% depending on the speed of the expansion. Chapter 2, co-authored with Pau Pujolas, studies the observed stagnation of Canadian Total Factor Productivity (TFP) between 2000 and 2018. We find that the entirety of the slowdown can be accounted for by the expansion of the oil sector, due to the massive capital investments that occurred. Comparing TFP growth in the rest of the economy to the United States, we find that Canadian TFP grew at comparable rates over the same period. Chapter 3, also co-authored with Pau Pujolas, explores how conclusions drawn from comparing GDP per capita of developed economies relative to the United States differ significantly depending on if current- or constant-Purchasing power parity (PPP) metrics are used. Using data from the Organization for Economic Co-operation and Development (OECD), we first document the differences in the evolution of GDP per capita relative to the US in current-PPPs and constant-PPPs before demonstrating in a numerical example how the choices made in constructing real metrics of GDP to make cross-country comparison can lead to contradictory interpretations.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 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".