Life expectancy and economic outcomes in a small open economy
Bibliographic record
Abstract
Chapter 1 applies the Ramsey-Cass-Koopmans (RCK) growth model to an open economy so that, when calibrated with standard parameter values that are commonly used in the small open economy macroeconomic literature, the time paths of the model variables and the speeds of convergence implied by the model conform with empirical evidence. Open-economy versions of the RCK growth model lead to several counterfactual conclusions including: infinite speeds of convergence for physical capital and output; and unbalanced consumption and asset growth. We avoid these undesired results by extending the baseline model with human capital, international credit constraints, and finite horizons. Given its finite-horizons feature, our model allows us to study the growth implications of changes in life expectancy from the perspective of an open economy. We find that increased life expectancy has positive but diminishing marginal effect on long-run output per effective labor. Using our model, we quantify the contribution of life expectancy to the long-run economic performance of Canada, sub-Saharan Africa, and the OECD member countries over the past six decades. Our quantitative results from Chapter 1 suggest that life expectancy has a substantial impact on the steady state values of the key macroeconomic variables. The second chapter builds on the first by further exploring the business cycle implications of life expectancy in a small open economy setting. Using our framework, we quantify how changes in life expectancy have impacted Canadian business cycle fluctuations over the past forty years. Further, we evaluate how the cyclical volatility of the main aggregate variables may change if the average life expectancy at birth in sub-Saharan African region catches up with its OECD counterparts. Using panel data of 71 countries for the period 1991-2015, Chapter 3 examines how population aging affects the marginal effects of the factors that determine growth. Sub-sample comparisons between the OECD member countries and low and lower-middle income countries are also performed. The analysis is based on a fixed effects panel data varying coefficient model. I estimate the model by a consistent estimator, proposed in the literature, that removes fixed effects using kernel-based weights.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".