Modeling International Trade of Forest Products: Application of PPML to a Gravity Model of Trade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To model international trade of forest products we use a gravity model of trade. In modeling trade, we estimate the impact of importer gross domestic product (GDP), exporter GDP, and distance between trading partners using Poisson pseudo-maximum likelihood (PPML). When estimating the log-linearized gravity model (ordinary least squares [OLS]), two issues arise. First, potential bias associated with truncation of all zero-trade observations due to the nonexistence of the natural log of zero. Second, heteroscedasticity can bias results from the log-linearized gravity model because of the multiplicative error term of the stochastic gravity model. To address these two issues, we propose avoiding the log-linearized gravity model and instead estimate the nonlinear gravity model via PPML. To estimate the model, trade data are compiled from the Food and Agriculture Organization of the United Nations. The observation window is from 1997 to 2014 and covers 13 product categories at a country-pair level. In our estimation, we find systematic differences in estimates from OLS in comparison with estimates from PPML. Using the estimated elasticities, in combination with estimates of future GDP from shared socioeconomic pathways, we project future US exports to the year 2030 for each item category in addition to total exports for Brazilian wood pulp, New Zealand industrial roundwood, and Canadian coniferous sawnwood. Using our approach, we provide a tool for policy makers and industry leaders alike to make informed decisions over prior estimates of forest product trade.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 it