Substitutes versus Complements among Canadian Business Risk Management Programs
Bibliographic record
Abstract
Business risk management (BRM) continues to be the central objective of agricultural policy in many countries, including Canada and the US. The unprecedented volatility that has characterized the farming sector in recent years is only expected to rise. Thus, governments continue to implement comprehensive suites of BRM programs to assist farmers in coping with these gyrations. This paper aims to examine 1) the relationships between the main Canadian BRM programs (AgriInsurance, AgriStability, and AgriInvest), and 2) if and how those relationships differ across different farms. Understanding the interlinkages between government BRM programs is central for policy makers in order to achieve the desired objectives. The analysis uses data from the Ontario Farm Income Database (OFID), which is a longitudinal farm-level dataset compiled from Ontario farm tax-file records from 2003 to 2011. The dataset contains detailed financial, production and program payment (except for Production Insurance/AgriInsurance payments) data for all Ontario tax-filling farm operations. Additional operator-level Production Insurance/AgriInsurance payment data is linked to farm-level OFID records to complement the program payment data. The paper uses a two-stage approach to examine the relationships between AgriInsurance, AgriStability, and AgriInvest. First, a multinomial probit model is estimated in which the dependent variables are dummies for all eight possible combinations of participation in the three programs and the independent variables include program participation in previous year, operating profit margin, operating expense ratio, leverage, diversification index, size, and sector. In the second stage, the predicted probabilities of the eight participation states from the first stage multinomial function is used as regressors against percentage falls in gross margin (operating revenue minus operating expense), controlled for size and sector. We find that participation in the previous year has a strong and positive effect on participation in the current year, and the three programs are generally treated as compliments. We also find that in general, farms that participate in some combination programs have smaller drops in gross margin compared to those that participate in no programs. Despite this effect, operators continue to drop out of BRM programs.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".