Relationships between Inflation and Unemployment in the United States, Japan and Germany during the Economic Crisis Caused by the COVID–19 Pandemic
Bibliographic record
Abstract
The aim of the article is to clarify the controversies surrounding the relationship between inflation and unemployment in the three most economically significant countries in the world (apart from China), namely the United States, Japan, and Germany, during the coronavirus pandemic (from January 2020 to February 2022). The pandemic has had various adverse effects worldwide, including a severe economic crisis lasting from the first quarter of 2020 to the end of the first quarter of 2021. The primary causes of this crisis include declines in aggregate supply due to lockdowns in many sectors of the economy, particularly the service sector. A decrease in aggregate supply should cause not only an increase in unemployment but also an increase in inflation. The article, therefore, hypothesises that the relationships between unemployment and inflation in the countries studied during the above period were unidirectional. To verify this hypothesis, two basic research methods were used: analysis of correlation coefficients between the variables mentioned above and the shape of Phillips curves. Ultimately, the hypothesis was rejected because inflation during this period showed a decreasing tendency (mainly due to a significant drop in commodity prices). The article extends research presented in the literature before 2020, offering additional value by examining the period of the pandemic which precipitated an economic crisis. Future analysis should be expanded to include more variables (including the output gap) in line with the New Keynesian Phillips Curve.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 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".