The Income Elasticity of Demand and Firm Performance of US Restaurant Companies by Restaurant Type during Recessions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
During the economic downturns of 2008 and 2009, many US restaurant companies struggled to avoid heavy losses. However, some still managed to outperform the market and even made large profits in the midst of widespread economic difficulties. McDonald's was one such company and, in light of its example, many industry magazines and newspapers featured articles suggesting that a quick-service restaurant, with a lower income elasticity of demand, might be better able to survive during constrained economic conditions than upper-level restaurants. This paper empirically examines whether US restaurants' income elasticity of demand and actual financial performances during economic downturns are affected by the restaurant type. The findings suggest that restaurant type showed no significant effects on the income elasticity of demand for US restaurant companies, while fast-food restaurants showed significantly greater accounting performances than those of non-fast-food restaurants during recession. The insignificant differences in the income elasticity of demand and significant differences in accounting performances during the recession may suggest that fast-food restaurants implemented cost control more effectively than non-fast-food restaurants, and the authors' additional analysis confirms this.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 it