Assessment of the Implications of Changes in Income Support Policies on Financial Health of Farms in Canada and the USA (at the Industry Aggregate Level)
Bibliographic record
Abstract
Agricultural policy is a key determinant of the condition of the entire farm sector and individual farms at the micro level. Previous publications focused on the impact of agricultural policy tools on farms and their market surroundings, the effects of which were quantified at the macroeconomic level utilising the Producer Support Estimate (PSE). Detailed studies of the balance sheet and profit and loss account of the companies in the sector, including in-depth analysis of the financial indicators were barely explored. This publication fills the gap. The aim of the publication is to analyse the extent to which alternations in the tools of agricultural policy affect the financial condition of farms. The main research method utilised is financial ratio analysis. The research covers the 2009-2014 period. Income assistance programmes in Canada and the United States have the greatest impact on the liquidity and profitability of the sector, while the impact on the management of net working capital and long-term assets is negligible. Similar phenomenon was observed by analysing the solvency ratios. Both in Canada and the US, the impact of direct aid programmes on the net profit exhibits a strong downward trend since the 2006-2009 financial crisis. Canadian direct payments accounted for more than 95% of agricultural entities’ net income in 2009. Therefore, they were the only safety buffer which allowed farms to break even and maintain profitability. Whereas American farms are significantly less dependent on state assistance, since in the post-crisis year 2009 direct payments accounted only for around 13% of net profit and had been falling gradually until 2014. Policy instruments in Canada and the US under review, quantification of their impact on the financial condition of the agricultural sector using the tools utilised by corporate finance, as well as thorough description of the adaptation of the solutions to the Polish agriculture are altogether the starting point for mid-term review of the Common Agricultural Policy (CAP) in 2017.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".