Reassessing the Path to 2075: A Long-Term Economic Projection Excluding Exchange-Rate Effects
Bibliographic record
Abstract
This paper re-examines Goldman Sachs’ The Path to 2075 – Slower Global Growth, but Convergence Remains Intact (2022) by isolating the long-term projections of global gross domestic product (GDP) from the effects of projected real-exchange-rate adjustments. Using the original econometric framework – comprising demographic, productivity, and investment dynamics – this study reconstructs 2075 GDP estimates under a constant real-exchange-rate scenario, effectively removing the Balassa–Samuelson convergence mechanism that underpins emerging-market currency appreciation. The recalculated outcomes demonstrate significant ranking shifts: the United States becomes the world’s largest economy, China follows in second place, and India remains third, while the rapid ascent of Nigeria, Pakistan, and Egypt is notably curtailed. The analysis reveals that currency appreciation functions as a powerful amplifier of nominal convergence, accounting for between 10 and 20 per cent of the emerging-market growth premium in Goldman Sachs’ baseline. By comparing exchange-rate-neutral projections with the institution’s published forecasts, the research shows how valuation effects can distort perceptions of long-run global balance. A comparative table and ranking-shift figure illustrate how removing exchange-rate assumptions rebalances the projected hierarchy of the world’s largest economies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".