Global Macroeconomic Performance: A Comparative Study Based on Composite Scores
Bibliographic record
Abstract
This paper proposes a composite indicator designed to summarise in a single statistic a variety of different facets of macroeconomic performance and assesses relative performances of countries with respect to six macroeconomic variables, viz., the growth rate of real GDP, real per capita GDP, unemployment rate, fiscal balance, rate of inflation, and current account balance. An appropriate mathematical model to aggregate these variables to form composite scores has been implemented by adopting the MCDM (Multiple Criteria Decision Making) technique of TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). This allows a parsimonious representation of a variety of different facets of macroeconomic performance and its inter-temporal comparison across countries. The distinctive features of the indicator relate to the domains covered, the normalisation methodology and the weights used for aggregation. Some existing indices like the Okun index and the Calmfors index turn out to be special cases of our proposed index. The data comprising a wide spectrum of countries and spanning the pre- and post- crisis years allow us to capture the effect of the recent global financial and economic crisis on the overall macroeconomic performance of countries relative to others. Not only do the relative performance scores show tremendous variability during the post-crisis years, but the measures of disarray are also at their highest, despite there being overall stability in the country rankings in terms of indicators, which are traditionally relied on, like GDP growth or per-capita GDP. A single graphical plot easily identifies countries that have performed consistently over time, and those whose overall macroeconomic performances have deteriorated sharply relative to others during the post-crisis years.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".