The Impact of Economic Freedom on Economic Growth in Western Balkan Countries
Bibliographic record
Abstract
Although it is generally accepted that economic freedom stimulates economic growth, its effects in transitional economies are still up for debate. More empirical research is needed to examine the long-term effects of economic freedom on growth in the Western Balkans, a region characterised by uneven reform trajectories, fiscal pressures, and institutional fragility. This study examines the effects of seven fundamental factors on real GDP per capita growth (annual percentage change) in six Western Balkan nations between 2013 and 2023. These factors include property rights, government spending, government integrity, business freedom, monetary freedom, trade openness, and education spending. Importantly, in order to better capture macroeconomic constraints, it takes into account two fiscal burden indicators: the public debt and the government budget deficit. A triangulated analytical framework is used: Random Forest regression identifies non-linear patterns and ranks the importance of variables; Bayesian Vector Autoregression (VAR) models dynamic interactions and inertia; and the Generalised Method of Moments (GMM) handles endogeneity and reveals causal relationships. The GMM results show that while government integrity (β = −0.0820, p = 0.0206), government spending (β = −0.0066, p = 0.0312), and public debt (β = −0.0172, p = 0.0456) have negative effects on growth, property rights (β = 0.0367, p = 0.0208), monetary freedom (β = 0.0413, p = 0.0221), and the government budget deficit (β = 0.0498, p = 0.0371) have positive and significant effects on growth. Although the majority of economic freedom indicators are statistically insignificant, Bayesian VAR confirms strong growth persistence (GDP(−1) = 0.7169, SE = 0.0373). On the other hand, the Random Forest model identifies the most significant variables as property rights (3.72), public debt (5.88), business freedom (4.65), and government spending (IncNodePurity = 9.80). These results show that the growth effects of economic freedom depend on the context and are mediated by the state of the economy. Market liberalisation and legal certainty promote growth, but their advantages could be offset by inadequate budgetary restraint and difficulties with transitional governance. A hybrid policy approach, one that blends strategic market reforms with improved institutional quality, prudent debt management, and efficient public spending, is necessary for the region to achieve sustainable development.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".