THE CURSE OF GEOGRAPHY: A VIEW ABOUT THE PROCESS OF WEALTH CREATION AND DISTRIBUTION
Bibliographic record
Abstract
This paper presents an alternative view of why geography is a key determinant of the process of wealth creation and distribution of the countries. A new set of supporting evidence is also provided. The core ideas explored in the paper are: a) the exporting sector offers a picture (an x-ray) of a country’s underlying process of wealth creation and distribution. Efficient producers and therefore exporters of manufactures, for example, have high incomes and low levels of inequality while exporters of crops and raw materials have low incomes and unequal income distributions; b) the export mix of a country is largely determined by three fundamentals: resources, remoteness, and climate. Manufacturing, for example, likes cool climates, educated workforces, and locations close to high-wage marketplaces. Two are the suggested mechanisms linking geography to growth and inequality that are not present in the existing literature. First, because of high fixed costs, manufacturing requires operating the equipment at high pace for long hours, creating a distinct disadvantage for the tropics. Second, because the exchange of complex uncodifiable messages can only be done on a face-to-face basis, with the participants within a handshake of each other, the production of ideas and new products is firmly rooted where it has always been, in the economic centers of the globe. As a result, toys, apparel, and footwear are footloose. Machinery and pharmaceuticals are not. Links between physical geography and economic development have been proposed at least since Machiavelli (1519). More recently Gallup, Sachs, and Mellinger (1998) indicate four major areas where it has been suggested that physical geography may have a direct impact on economic productivity: transport cost, human health, agricultural productivity, and proximity and ownership of natural resources. In that paper, as well as in Sachs (2001), empirical evidence is provided supporting that geography indeed has direct, as well as indirect, effects on economic development. Hall and Jones (1999) and Engerman and Sokoloff (1997) argue that physical geography may affect economic development by shaping the countries’ institutions. Acemoglu, Johnson, and Robinson (2001), Rodrik, Subramanian, and Trebbi (2002) and Easterly and Levine (2002) go one step further Cuadernos de Economia, Ano 40, No 121, pp. 423-433 (diciembre 2003)
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.044 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".