Applied Economics and Policy Research in Health, Dairy, and Fishery Industries
Bibliographic record
Abstract
Chapter 1: With aging populations, policymakers are encouraging people to work longer to sustain the financial stability of social security systems. However, the effect of postponing retirement on mental health remains uncertain. The US Social Security Amendments of 1983 raised the social benefit age by two months each year for those cohorts born after 1937. These amendments provide a unique opportunity to assess the causal effect of retirement age on mental health. Using data from the 1994 to 2012 waves of the US Health and Retirement Survey, this paper instrumented retirement age with the amendments to control for the biases caused by the endogeneity between retirement age and mental health. This instrumental variable estimation showed that a slight gradual rise in retirement age was beneficial to retirees’ mental health. Chapter 2: There has been increasing awareness by policymakers of the need to identify the key factors that affect dairy farmers’ management decisions. This study utilizes a theoretic model to illustrate why Canadian farmers might be hesitant about disease control under the supply management system and how peer pressure can induce farmers to improve their efforts. An experiment based on a Johne’s disease scenario was implemented among Canadian dairy farmers to test the theoretic model conclusions. Both the theoretic model and the pilot experiment suggest that without a strong external monitoring policy, optimal effort level cannot be achieved. Chapter 3: The Norwegian purse seine fishery has employed multiple rights-based regulatory instruments to limit the fishing effort. Following the management reforms, this fleet shrunk substantially while the capital investment in this fleet expanded substantially. The comprehensive efficiency change in an era of revolutions are worth exploring, yet ambiguous. This paper evaluates the production and cost efficiency changes among the Norwegian purse seiners using data on Norwegian purse seiners for the period of 1994-2013. The stochastic frontier analysis shows that the purse seiners could reduce production cost significantly by eliminating inefficiencies. Meanwhile, both technical and allocative efficiencies are improving over time. A further investigation suggests that the transferable quota policies had limited impact on efficiencies.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.001 |
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".