Stuctural transformation in general equilibrium:Policies and practices from canada, new zealand and the european union
Bibliographic record
Abstract
Models of structural change in general equilibrium are commonly used to address a number of questions regarding the behaviour of the macro-economy. In this paper, we first revise the main mechanisms at work in generating structural change in a multi-sector environment. These effects emerge due to both an interaction between consumers' preferences and technological change and to different income elasticities of the various goods and services entering the utility function. Next, we address the issue of measurement of these models when comparing them to the data. The typical assumption in multi-sector models is to define GDP as aggregate output in units of a numeraire good, often chosen to be the investment good. However, this procedure is equivalent to deriving nominal GDP in the data (i.e. total output of the economy in units of one particular good), and not to deriving a measure of real GDP. We then discuss how GDP in the model should be measured to provide a statistic that is comparable with the data in national accounts. The last part of the paper is devoted to show how structural transformation from manufacturing to services, when appropriately compared to the data, generates a decline in GDP growth and volatility along the growth path of an economy.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".