COSTLY INTERMEDIATION AND THE POVERTY OF NATIONS*
Bibliographic record
Abstract
This article has two goals: (i) to reduce the 7‐fold productivity differential required to explain the observed 33‐fold income difference between the richest and poorest countries of the world; and (ii) to explain cross‐country differences in the capital‐output ratio. To achieve the first goal we modify the production function of the standard neoclassical growth model to include public capital whose provision is subject to intermediation costs. For the second goal we distort private investment by introducing credit frictions. The model, quantified using cross‐country data, generates an income gap of 33 with productivity differences of only 3 under the measured variations in public and private capital. The required productivity gap declines even further, to 2.1, when we introduce a home‐production sector. On the second goal, however, credit frictions do a poor job of explaining cross‐country variations in the capital‐output ratio.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".