The validity of Rodrik’s conclusion on real exchange rate and economic growth: factor priority evidence from feature selection approach
Bibliographic record
Abstract
Abstract The undesirable effect of poor exchange rate policy on economic growth has been firmly established in the literature using various parametric methods of econometric techniques. However, less is known about the prioritization of the exchange rate as a determinant of economic growth using a nonparametric approach. Thus, this study introduced machining learning approach (feature selection, particle swarm optimization—PSO, and genetic algorithm—GA techniques) to evaluate the relative primacy of the exchange rate for sustainable economic growth in Germany, South Africa, and Slovakia using Rodrik model with time series data from 1990 to 2016. The study reveals that GDP per capita is the most crucial variable for economic growth in Germany and South Africa whereas, in Slovakia, the real exchange rate takes precedence over all other determinants of economic growth. That is, exchange rate takes precedence over other factors as a determinant of economic growth in an economy (Slovakia) with the high rate of trade openness while income per capita is the most important determinant of economic growth in economies (Germany and South Africa) with a relatively lower rate of trade openness. This partly supports Rodrik’s conclusion. We, therefore, recommend that highly opened economies should focus on viable exchange rate policies, such as undervaluation of currency to enhance sustained economic growth. On the other hand, relatively less open economies should focus on policies that improve income per capita rather than exchange rate policies.
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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 | 0.013 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".