The Income Elasticity of Demand and Firm Performance of US Restaurant Companies by Restaurant Type during Recessions
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.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".