The Multifunctional Farm Household Enterprise: Using Farm Microdata to Assess the Rural Economy Impacts Generated by Farmer-Operated Off-Farm Businesses
Bibliographic record
Abstract
Rural development specialists working with agricultural statistics confront the tension between collecting data for the purposes of measuring farm sector performance versus that of assessing farm household well-being. While it is recognized that the activities of the farm enterprise and farm household generate a broad spectrum of market relationships in their local economies, most agricultural data collection systems focus primarily on commodity production and just the basics of farm household structure. The survey instrument that embraces the dual mission of collecting data on the farming enterprise and on farm households can allow specialists to study a broader complement of farm-rural economy linkages. In this case study, we exploit microdata on farm household activities drawn from U.S. and Canadian national agricultural surveys to shed light on the impact of farmers who simultaneously operate off-farm businesses on their local communities – a farm/rural interface often overlooked by agricultural economists and rural development specialists alike. Given the sufficiently detailed data on these farmer-operated off-farm businesses, we are able to use the input/output modeling toolkit to recover estimates of nonfarm value added, sales, and employment generated by them. With respect to the rural economy, we find that the share of a rural county’s employed nonfarm labor force linked to these off-farm businesses increases the further they are located from the urban core. Thus, the business acumen of these farm portfolio entrepreneurs is an even more valued intangible asset for communities in more remote rural areas. Hence, instead of depending on the local communities’ resilience for their household well-being, they contribute to it.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".