The Causal Impact of Covid-19 Government-backed Loans on MSMEs Liquidity and Earnings
Bibliographic record
Abstract
We conducted two randomized controlled trials (RCTs) to evaluate the impact of government-guaranteed loans offered by the Chilean and Colombian governments. The public funds of these programs greatly expanded following the start of the Covid-19 pandemic and offered loans to Micro, Small and Medium Enterprises to mitigate the negative impact of the shock. Through a collaboration with private banks, we launched two experiments which offered loans to a sub-set of the 10,072 Chilean and 3,079 Colombian small businesses that took part in our experiments. Most of these firms had previously applied for a loan during the pandemic--but prior to the RCTs--but were rejected by banks due to their risk analysis of the firms. With take-up rates of 27% and 29%, respectively, we find that Covid-19 loans had a significant positive impact on the total liquidity that treated MSMEs could access: total liquidity with the formal banking system increased by 15.7% (statistically significant at the 1% level). The results of our RCTs will inform Latin American governments concerning their strategies to support MSMEs via government-backed loan programs and will shape similar public policies in the future.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".