The Influence of Gender Disparities on the Unemployment Rate in the United States amid the Covid-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic, which emerged in late 2019 and quickly spread globally in 2020, hit economic activity tough in most countries. In the United States, the socially enforced shutdown and economic stagnation caused by the pandemic led to a rapid rise in unemployment from the first quarter of 2020 onwards. Although the rise in unemployment rates in the US during the pandemic involves individuals of every gender, the COVID-19 pandemic's effects on the rise in unemployment rates were different for men and women, according to examination of statistics on unemployment for both genders from 2010 to 2023, with women experiencing a greater increase in their unemployment rate compared to men during the pandemic. Moreover, by constructing an ARIMA model based on the unemployment rates of different genders from 2010 to 2019, this paper predicts the unemployment rate of the United States from 2020 to 2023 under the assumption of no pandemic effects. It can be observed that the pace of decline in female unemployment rates during the pandemic was larger than that for male unemployment rates by comparing the gap between the prediction model and the actual data, and by January 2023, whether overall unemployment rates or unemployment rates across different genders were significantly higher than those predicted by the ARIMA model. The data compilation and analysis presented within this paper are useful in understanding the impact of public health events on employment status based on gender differences, and in providing a reference for post-COVID-19 mitigation policies.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".