Asymmetric impact of microfinance on economic growth: Evidence from Bosnia and Herzegovina
Bibliographic record
Abstract
This study explores the correlation between microfinance loans (MFL) and economic growth in Bosnia and Herzegovina (Bosnia). It utilizes the non-linear Autoregressive Distributed Lag (NARDL) method to examine cointegration and short-run dynamics by analyzing quarterly data spanning from 2010 to 2022. The findings underscore the link between MFL shocks and long-term economic growth. The study unveils the unique effects of both positive and negative MFL shocks on growth, suggesting a non-linear relationship between microfinance loans and economic growth in Bosnia. However, the study concludes that the impact of MFL on Bosnia's GDP is adverse. Short-term fluctuations in MFL show no substantial influence on Bosnian economic growth. The coefficient of the error correction model is both negative and significant indicating the stability of the long-term relationship. This implies a rapid correction, with 46.4 % of the previous quarter's imbalance rectified within the current quarter. While our results are based on a single country, they align with recent criticisms of microfinance practices. Furthermore, our study offers a novel approach as it represents the first examination of the asymmetric relationship between MFL and GDP in Bosnia, providing valuable policy recommendations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".