Analysis of impacts of inflation on the distribution of household consumption expenditures
Bibliographic record
Abstract
Abstract As a consequence of the COVID‐19 pandemic of early 2020, production in the United States, as in much of the world, largely came to a standstill. Unemployment in the United States quickly rose from 3.5% in February to 13.2% in May, quarter‐to‐quarter GDP fell 7.8%, and substantial transfers were enacted to maintain household income. The resulting mismatch between aggregate supply and demand not surprisingly ignited an inflation that by early 2022 had reached a year‐over‐year 40‐year high. The purpose of the present communication is to utilize a framework developed from data embodied in surveys of households’ consumer expenditures to analyze impacts of this inflation on separate categories of expenditure. The engine for the analysis, whose construction is described in detail by Taylor (2013, is a matrix of “intra‐budget” coefficients that represent the direct relationships amongst different categories of expenditure in households’ budgets. The elements of this matrix are constructed from the information in 58 quarters of data (2006 through 2019) from the ongoing BLS Survey of Consumer Expenditure to analyze effects and impacts on 16 categories of US household consumption expenditure of the 2021–2022 inflation. Principal findings include: expenditures for housing, transportation, gasoline and oil, and personal insurance consistently endure the largest impacts from inflation; real‐ income effects from inflation differ from those arising from a like cut in nominal income; not surprisingly, food expenditures are most impacted at low income.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".