Determinants of export intensity and propensity among small and medium-sized enterprises: The case of the Philippines
Bibliographic record
Abstract
The small and medium enterprise (SME) sector in the Philippines is a significant group within the economy in terms of firm numbers and total employment. However, the SME sector's share of exports is disproportionately small, which raises considerable policy concerns. Prompted by the aforementioned policy issue, this study assesses the different factors that affect SME decisions (a) to export (propensity) and (b) on how much to export (intensity), i.e., export performance. The study utilizes data from the World Bank enterprise surveys, which contain subjective elements concerning the impediments to conducting business in general, e.g., concerns regarding labour regulations, shipping etc. Using a Heckman selection model, the study finds that firm size is a robust determinant, both of export propensity and intensity. It also suggests that while labour productivity is important in determining the value of firm exports, there are certain firm qualities that are important to the initial export decision, such as foreign ownership and the presence of informal competition. Finding such determinants of SME export intensity and propensity provides the direction for policy discussions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".