Where did British Foreign Capital Go? Fundamentals, Failures and the Lucas Paradox: 1870-1913
Bibliographic record
Abstract
A decade has passed since Robert Lucas asked why capital does not flow from rich to poor countries. Lucas used a contemporary example to illustrate his Paradox, the very modest flow of capital from the United States to India during the second great global capital market boom, after 1970. Had he paid more attention to the first great global capital market boom, after 1870, he might have been less surprised. Very little of British capital exports went to poor, labor-abundant countries. Indeed, about two-thirds of it went to the labor-scarce New World where only a tenth of the world's population lived, and only about a quarter of it went to labor-abundant Asia and Africa where almost two-thirds of the world's population lived. Why? Was it caused by some international market failure, or was it due to some shortfall in underlying economic, demographic or geographic fundamentals that made capital's productivity low in poor countries? This paper constructs a panel data set for 34 countries who as a group got 92 percent of British capital, and uses it to conclude that international capital market failure (including whether the country was on or off the Gold Standard) was not involved. It then ranks the three big fundamentals that mattered schooling, natural resources and demography.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".