Effects of the COVID-19 pandemic on the restaurant industry: Comparisons between immigrants and US-born workers
Bibliographic record
Abstract
The COVID-19 pandemic reduced employment in the U.S., across many industries. The restaurant industry was particularly hard hit, losing 2.5 million jobs in 2020 alone. Now in the recovery from the pandemic, the restaurant industry is experiencing an unprecedented shortage of workers, forcing many restaurants to raise their offered wages, reduce their hours of service, or close altogether. How have workers been affected by these demand shocks? Understanding the demand for restaurant workers during the pandemic and the recovery has important policy implications because restaurant workers make up the third largest occupation group and have the lowest wages of any occupation group. Foreign-born workers in particular are overrepresented in restaurants and in the back-of-house jobs that are lower paying and more dangerous. Using results from our nationally-representative survey of restaurant owners and hiring managers and our analysis of Community Population Survey data, we found that foreign-born workers fared worse than native-born workers as the restaurant industry shed jobs during the height of the pandemic. Our findings are consistent with previous studies suggesting that foreign-born workers are more vulnerable to negative business cycles than their native-born counterparts. But during the various stages of the recovery, when the restaurant industry experienced worker shortages, we also found that there was very little shifting toward foreign-born workers. These results are at odds with previous studies suggesting that foreign-born workers would be more attractive in these circumstances because they are more adaptable to changing labor demands (including dangerous working conditions like a pandemic) and do not have access to social safety net benefits like unemployment compensation. Part of the explanation for this surprising result may be found in recent, more restrictive immigration policies that have decreased the pool of available foreign-born workers.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".