Economic complexity and employment in emerging countries: a comparative analysis
Bibliographic record
Abstract
Economic complexity is a measure of a country’s knowledge intensity by assessing the diversity and sophistication of its exports, productivity and development levels. However, the current literature does not comprehensively understand how economic complexity influences different employment segments in emerging economies. Employment is a significant component of sustainable development, particularly when considering the youthful population of emerging economies. Therefore, the study examined the role of economic complexity on employment in BRICS + and MINT countries, using data spanning 27 years, from 1995 to 2021. The study employs panel-corrected standard error and feasible generalized least squares estimators. The findings show that regardless of the employment indicators used, the results demonstrate that economic complexity significantly and positively impacts the employment rate in BRICS + and MINT countries. In addition, when the MINT and BRICS + countries are examined in separate regression models, the findings suggest that the effect of economic complexity on aggregate and youth employment is higher in MINT countries compared to BRICS + countries. In comparison, the effect of economic complexity on industrial employment is higher in BRICS + countries than in MINT countries. These results support BRICS + and MINT nations’ efforts to enhance the capacity of their economies to produce more complex goods.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".