A Multivariate Analysis of the Impact of COVID-19 on the Consumer Price Index by Category in Selected Major Developed Countries
Bibliographic record
Abstract
The global COVID-19 pandemic significantly impacted economies worldwide, leading to varied inflationary trends across different countries and consumer sectors. This study investigates the changes in the Consumer Price Index (CPI) across 12 expenditure categories including food, beverages, clothing, housing, furnishing, health, transport, communication, recreation, education, restaurants, and miscellaneous in six countries: Canada, the United States, Germany, Italy, the United Kingdom, and France. Using secondary data from the International Monetary Fund (IMF) International Financial Statistics Database, a two-way mixed design was employed to explore both within-subjects effects of time period (Pre-COVID-19 vs. Post-COVID-19) and between-subjects effects of country on CPI changes. A two-way mixed MANOVA with a general linear model (GLM) framework was conducted to examine differences in CPI between the pre- and post-COVID-19 periods. Significant effects were found for both country (Wilks’ Lambda = .038, F(60, 2593.26) = 43.75, p < .001, partial η² = .48) and time period (Wilks’ Lambda = .559, F(12, 553) = 36.42, p < .001, partial η² = .44), with substantial interaction effects between country and time period (Wilks’ Lambda = .400, F(60, 2593.26) = 9.34, p < .001, partial η² = .17). Post-hoc analyses using Scheffe tests revealed that Germany and Italy experienced the most pronounced CPI increases in categories such as food and housing, while Canada and France showed more moderate inflationary trends. In contrast, communication CPI declined significantly in Canada while increasing in the United Kingdom
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".