Explaining Post-Pandemic Lumber Price Volatility and its Welfare Effects
Bibliographic record
Abstract
Abstract The COVID-19 pandemic led to an unprecedented increase in the U.S. price of softwood lumber by more than 300%. The price increase has been attributed to constraints on supply and increased demand for lumber caused by a pandemic-induced boom in domestic housing construction and, more so, home improvements. However, the volatility in lumber prices after the COVID-19 outbreak remains unexplained. In this paper, we employ a theoretical model to explain the cause of price volatility. We examine why demand and supply functions for lumber might be quite inelastic over the period from March 2020 to April 2022, despite very small shifts in demand. This implies that slight movements in interest rates or changes in the prices of substitutes, for example, can lead to large jumps in prices. Price volatility harms consumers while greatly benefitting lumber producers. Overall, as a result of the pandemic, U.S. producers gained some $5.3 billion, while U.S. consumers lost $7.3 billion per quarter.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".