Underinvestment in Producer‐Funded Agricultural R&D: The Role of the Horizon Problem
Bibliographic record
Abstract
Abstract Agricultural research and development (R&D) investment has become an increasingly important policy issue as food prices increased and food security problems emerged over the last decade. An important source of agricultural R&D funding is the producer check‐off, which is increasingly being used to fund applied agricultural research. Existing studies of producer‐funded agricultural R&D indicate there are high private rates of return to agricultural R&D investment by farmers, and thus farmers are underinvesting in R&D. Since a farmer's time horizon is typically less than the period of time over which the benefits of agricultural R&D take place, the horizon problem has been identified as a possible factor in this underinvestment. This paper shows that the horizon problem is unlikely to be the only cause of the underinvestment when the internal rate of return is large. Instead, shortened producer horizons only emerge as the main source of underinvestment when the internal rate of return is low. As a result, other factors, including behavioral determinants, need to be looked at as contributors to the underfunding of agricultural R&D. Les investissements en recherche et développement agricoles sont devenus un important enjeu politique étant donné l'augmentation des prix des aliments et les problèmes de sécurité alimentaire de la dernière décennie. Une importante source de financement pour la recherche et le développement dans le domaine agricole sont les programmes de contribution des producteurs, ces derniers étant de plus en plus sollicités pour financer la recherche agricole appliquée. Certaines études portant sur la recherche et le développement agricoles financés par les producteurs indiquent un haut taux de rendement privé des investissements en recherche et développement agricoles par les producteurs. Ces derniers y investissent donc moins. Puisque l'échéancier de l'agriculteur est typiquement moins long que celui pendant lequel les avantages liés à la recherche et au développement dans le domaine agricole s'échelonnent, le problème de l'horizon a été identifié comme facteur potentiel au sous‐investissement. Il est probable, selon cet article, que le problème de l'horizon ne soit pas la seule cause du sous‐investissement lorsque le taux interne de rendement s'avère grand. Plutôt, les échéanciers réduits des agriculteurs apparaissent seulement comme les sources principales de sous‐investissement lorsque le taux de rendement interne est bas. Il en résulte que d'autres facteurs, incluant les déterminants comportementaux, doivent être examinés à titre de contributeurs au sous‐investissement de la recherche et du développement en agriculture.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 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".