Formal Finance Usage and Innovative SMEs: Evidence from ASEAN Countries
Bibliographic record
Abstract
This paper provides evidence on the likelihood of formal finance usage among innovative small and medium enterprises (SMEs) operating in ASEAN countries. To this end, the SMEs are classified into four categories, namely non-innovators and product, process, and product-and-process innovator SMEs. Subsequently, a propensity score weighting (PSW) analysis is performed to adjust for diversity existing across innovative SMEs. The resulting propensity scores are further used to perform the causal effect analysis based on the average treatment effect (ATE) approach, which measures the likelihood of formal finance usage among different types of innovative SMEs. Our ATE results reveal that SMEs simultaneously engaged in product and process innovation show a higher likelihood of using formal finance than non-innovators. However, formal finance usage of SMEs perusing only product/service or process innovation is not any different from non-innovators. Furthermore, our pairwise analysis shows that product and process innovators also exhibit a higher likelihood of formal finance usage than product/service or process innovators. Besides, younger and medium-size product and process innovating SMEs are more likely to use formal finance. These results are robust for different subsamples and firm- and country-level controls.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".