Effects of electricity outages on enterprise productivity in Egypt: Lessons learned
Bibliographic record
Abstract
This study investigates the impact of advanced electricity outage announcements on the operational efficiency of small and medium enterprises (SMEs), in Egypt, using profitability as a key performance indicator. Leveraging data from "Transition to Clean Energy Enterprise Survey" and applying the inverse probability-weighted regression adjustment (IPWRA) method to address selection bias, we estimate how outage predictability influences firm outcomes. We find that SMEs receiving advance notice of power disruptions are significantly more likely to achieve higher profitability compared to those without such information. The benefits are most evident among larger firms and sectors such as transportation, financial services, and accommodation, where operational planning is critical. While the policy partially offsets losses from outages, firms in areas with frequent blackouts still face substantial profitability challenges, highlighting the limits of transparency alone. Our findings emphasize that advance announcements enhance SME resilience by enabling adaptive measures, but long-term solutions require complementary infrastructure investments in high-risk regions. The study advocates for policy frameworks centered on transparency and rational expectations, demonstrating how proactive communication in public services can bolster economic resilience amid global uncertainties. These insights are particularly relevant for developing economies seeking to balance immediate crisis management with sustainable energy infrastructure development.
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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) | 0.000 | 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) | 0.000 | 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".