Sales Losses in the First Quarter of the COVID-19 Pandemic: Evidence from California Administrative Data
Bibliographic record
Abstract
COVID-19 led to a massive shutdown of businesses in the second quarter of 2020.Estimates from the CPS, for example, indicate that the number of active business owners dropped by 22 percent from February to April 2020.In this descriptive research note, we provide the first analysis of losses in sales and revenues among the universe of businesses in California using administrative data from the California Department of Tax and Fee Administration.The losses in sales average 17 percent in the second quarter of 2020 relative to the second quarter of 2019 even though year-over-year sales typically grow by 3-4 percent.We find that sales losses were largest in businesses affected by mandatory lockdowns such as Accommodations, which lost 91 percent, whereas online sales grew by 180 percent.Losses also differed substantially across counties with large losses in San Francisco (50 percent) and Los Angeles (24 percent) whereas some counties experienced small gains in sales.Placing business types into different categories based on whether they were essential or non-essential (and thus subject to early lockdowns) and whether they have a moderate or high level of person-to-person contact, we find interesting correlations between sales losses and COVID-19 cases per capita across counties in California.The results suggest that local implementation and enforcement of lockdown restrictions and voluntary behavioral responses as reactions to the perceived local COVID-19 spread both played a role, but enforcement of mandatory restrictions may have had a larger impact on sales losses.
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.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".