Estimating the effect of time‐invariant characteristics in panel data: wheat adoption in <scp>Western Canada</scp>
Bibliographic record
Abstract
Abstract A growing global population and the challenges of climate change have made the need to develop new and improved wheat varieties increasingly important. Creating varieties that are more disease resistant and tolerant to changing environmental conditions such that they will be widely adopted requires breeders to understand the needs of producers. Existing literature suggests that time‐invariant traits such as disease resistance and brand matter in crop adoption decisions; however, studies using common panel data approaches are unable to identify the individual effects of time‐invariant variables, as they are captured by fixed effects dummies. This study uses a recently developed econometric approach—the fixed effects filter model—that can estimate the effects of time‐variant, slowly changing, and time‐invariant traits. Using wheat variety adoption in the Canadian Prairies as our empirical setting, we find that both time‐variant traits, such as varietal adaptability, and time‐invariant traits, such as resistance to stripe rust infection, are positively correlated with adoption. We also find that seed brand has a statistically significant effect on adoption. Prior panel data studies on crop adoption have not given much consideration to time‐invariant variety characteristics like disease tolerance and seed brand, but a better understanding of their impact on adoption can assist breeders in responding to emerging problems, particularly new diseases, more quickly and more efficiently. This will also enhance the work by crop research institutions, prevent considerable economic loss to farmers, and improve crop production.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".