Market regulation and productivity: The case of the Canadian Wheat Board
Bibliographic record
Abstract
Abstract Changes to regulatory environments influence firm‐level incentives, which can move the productivity frontier or reposition firms within an existing frontier. Estimating causal effects of policy changes requires a credible counterfactual for productivity in the absence of policy change. We estimate causal effects of a major policy change in agricultural markets; deregulation of the Canadian Wheat Board. The Canadian Wheat Board was a state‐trading enterprise that controlled sale and distribution of wheat and barley produced in Western Canada through a single‐desk monopsony from 1943 to 2012. We investigate how deregulation affected farm‐level productivity for regulated crops. Field‐level production and input data for 13,000 farms over 17 years are used to generate a within‐farm difference‐in‐differences estimate of how relative productivity changed between regulated and unregulated crops when the single‐desk mandate was removed. Our within‐farm approach identifies the effects of regime change while controlling for factors that confound productivity estimation in other approaches, including unobserved time‐varying factors at the farm level. We identify significant positive effects of deregulation on farm‐level productivity of spring wheat. We also find heterogeneous effects across fields with different land productivity, and across farms with different levels of experience marketing their unregulated crops outside the single‐desk. Broadly, we find that deregulation of the Canadian Wheat Board's monopsonistic single desk incentivized farms to increase productivity of wheat. This research contributes to the understanding of how policy interventions can affect firm‐level productivity, and to the productivity literature with an estimation strategy for identifying productivity effects in the context of multi‐output firms.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".