All that Glitters: Precious Metals, Rent Seeking and the Decline of Spain
Bibliographic record
Abstract
I argue that Spain's long-term economic stagnation in the seventeenth century and beyond was the result of a process triggered by the windfall acquisition of precious metals from American mines, and driven by the consolidation of absolutist rule and the peculiar privilege structure of Spanish society in the sixteenth century. American treasure allowed the Spanish monarchs to command large amounts of credit and pursue an expansive imperial policy unlike that of any other Early Modern nation; when the cost of the Empire increased and mineral rents fell, the Crown increased the fiscal pressure while allowing skilled human capital to migrate into the tax-sheltered but largely unproductive nobility. I first provide evidence on the role of the silver windfall and the acquisition of nobility titles in the sixteenth century; of particular interest is a new data series of nobility lawsuits constructed from the population of cases housed at the Archive of the Royal Chancery Court in Valladolid. I then develop a unified theoretical framework that explains imperial policy as an optimal response given the existing institutions and the natural resource windfall, recreates the rent-seeking path followed by the Spanish Crown when mineral rents proved insufficient, and accounts for the long-term economic backwardness that Spain experienced in the following centuries as the result of an institutional lock-in.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".