Digital Fiscal Stimulus and SMEs: Insights from Thailand's Half and Half Program
Bibliographic record
Abstract
This study investigates the impact of Thailand's 'Half and Half' program on SMEs. Designed to boost spending on small food vendors, the program distributes subsidies via a digital platform, granting consumers a fixed-percentage copay on every eligible purchase. I employ a difference-in-difference approach using weekly province-level data from LINE MAN Wongnai. I find that the program significantly elevates sales among participating vendors relative to non-participants. Regarding the underlying mechanism, the findings indicate that the increase in sales is primarily driven by an expansion in the unique customer base, rather than an increase in individual order sizes. Crucially, these positive effects persist even after the program's conclusion, with smaller vendors experiencing more pronounced sustained benefits. This investigation enriches the emerging literature on digital fiscal stimulus, underlining their potential for both immediate and sustained economic impact. The findings bear crucial implications for policymakers navigating fiscal strategies in an increasingly digital economic landscape.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".