THE MEASUREMENT OF INEQUALITY IN CANADIAN AND U.S. AGRICULTURAL INCOME BY COMPONENTS OF NET VALUE ADDED
Bibliographic record
Abstract
This paper examines changes in net value added generated through Canadian and U.S. farm production, 1970-2000. We consider how the structural changes in Canadian and U.S. agriculture have affected the size and distribution of net value added and its components: rent, capital, labor, and to net farm income. We use the Theil Measure of Inequality (TMI) to compare and explain changes in 1) the between and within-region distribution of net value added, and 2) changes in the distribution of factor shares of net value added in Canada and in the U.S. Results show that in Canada (1960-2000), net value added has become somewhat more equally distributed relative to the number of farms per province, but has varied widely from 1972-1988. Between-region inequality in net value added accounted for from 0.5 to 85.5 percent of this inequality from 1960-2000. In the U.S. (1949-2000), net value added has become more unequally distributed. About half of the variation in net value added in the U.S. is due to between-region variation and about half to within-region variation in net value added. We find that most of the variation in the components of net value added (returns to capital, labor, nonoperator landlords, and to farm operators) in Canada and the United States is due to variations across regions, rather than to variations in the components of net value added themselves. These variations have generally been due to macroeconomic differences in regions, such as shifts in enterprise specialization, urbanization, changes in government programs, and to other structural changes in agriculture.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".