{"title":"基于权利的渔业结构行为模型","authors":"M. Reimer, J. Abbott","doi":"10.2139/ssrn.3582502","DOIUrl":null,"url":null,"abstract":"Rights-based management is prevalent in today's developed-world fisheries, yet spatiotemporal models of fishing behavior do not reflect such institutional settings. We develop a model of spatiotemporal fishing behavior that incorporates the dynamic and general equilibrium elements of catch-share fisheries. We propose an estimation strategy that is able to recover structural behavioral parameters through a nested fixed-point maximum likelihood procedure. We illustrate our modeling approach through a Monte Carlo analysis and demonstrate its importance for predicting out-of-sample counterfactual policies.","PeriodicalId":436211,"journal":{"name":"ERN: Natural Resource Economics (Topic)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Structural Behavioral Models for Rights-Based Fisheries\",\"authors\":\"M. Reimer, J. Abbott\",\"doi\":\"10.2139/ssrn.3582502\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Rights-based management is prevalent in today's developed-world fisheries, yet spatiotemporal models of fishing behavior do not reflect such institutional settings. We develop a model of spatiotemporal fishing behavior that incorporates the dynamic and general equilibrium elements of catch-share fisheries. We propose an estimation strategy that is able to recover structural behavioral parameters through a nested fixed-point maximum likelihood procedure. We illustrate our modeling approach through a Monte Carlo analysis and demonstrate its importance for predicting out-of-sample counterfactual policies.\",\"PeriodicalId\":436211,\"journal\":{\"name\":\"ERN: Natural Resource Economics (Topic)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-01-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ERN: Natural Resource Economics (Topic)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3582502\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ERN: Natural Resource Economics (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3582502","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Structural Behavioral Models for Rights-Based Fisheries
Rights-based management is prevalent in today's developed-world fisheries, yet spatiotemporal models of fishing behavior do not reflect such institutional settings. We develop a model of spatiotemporal fishing behavior that incorporates the dynamic and general equilibrium elements of catch-share fisheries. We propose an estimation strategy that is able to recover structural behavioral parameters through a nested fixed-point maximum likelihood procedure. We illustrate our modeling approach through a Monte Carlo analysis and demonstrate its importance for predicting out-of-sample counterfactual policies.