Commodity Price Volatility and World Market Integration since 1700
Bibliographic record
Abstract
Poor countries are more volatile than rich countries, and we know this volatility impedes their growth. We also know that commodity price volatility is a key source of those shocks. This paper explores commodity and manufactures price over the past three centuries to answer three questions: Has commodity price volatility increased over time? The answer is no: there is little evidence of trend since 1700. Have commodities always shown greater price volatility than manufactures? The answer is yes. Higher commodity price volatility is not the modern product of asymmetric industrial organizations -oligopolistic manufacturing versus competitive commodity markets -that only appeared with the industrial revolution. It was a fact of life deep into the 18th century. Does world market integration breed more or less commodity price volatility? The answer is less. Three centuries of history shows unambiguously that economic isolation caused by war or autarkic policy has been associated with much greater commodity price volatility, while world market integration associated with peace and pro-global policy has been associated with less commodity price volatility. Given specialization and comparative advantage, globalization has been good for growth in poor countries at least by diminishing price volatility. But comparative advantage has never been constant. Globalization increased poor country specialization in commodities when the world went open after the early 19th century; but it did not do so after the 1970s as the Third World shifted to labor-intensive manufactures. Whether price volatility or specialization dominates terms of trade and thus aggregate volatility in poor countries is thus conditional on the century.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".