Information age to genetic revolution: Embodied technology transfer and assimilation - A tale of two technologies
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Das, Gouranga Gopal | - |
dc.date.accessioned | 2021-06-23T19:38:07Z | - |
dc.date.available | 2021-06-23T19:38:07Z | - |
dc.date.created | 2021-01-21 | - |
dc.date.issued | 2007-07 | - |
dc.identifier.issn | 0040-1625 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/43564 | - |
dc.description.abstract | In this paper, a fifteen regions-fifteen sectors global Computable General Equilibrium (CGE) model is calibrated. It offers quantitative enumeration of 5% exogenous biotechnological invention in USA in genetically modified crops namely, maize grains and soybean. Consequently, it results in endogenously transmitted productivity gains via traded intermediates in user sectors in donor and recipient regions. Sustained absorption and domestic usability of transgenic varieties depend on constellation of: human capital-induced absorptive capacity, governance, and structural congruence between source and recipients contingent on technology infrastructure and socio-institutional parameters. Such innovations result in higher production, welfare and global trade. Also, concomitant 4% exogenous productivity shock in information technology along with 5% productivity growth in the agro-biotech sectors further enhances such simulated impacts on global production and welfare. Regions with larger extent of technology capture aided by higher human capital, better governance, conducive institutional-structural features, and superior technological expertise perform better. (c) 2006 Elsevier Inc. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Elsevier BV | - |
dc.title | Information age to genetic revolution: Embodied technology transfer and assimilation - A tale of two technologies | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Das, Gouranga Gopal | - |
dc.identifier.doi | 10.1016/j.techfore.2006.07.001 | - |
dc.identifier.scopusid | 2-s2.0-34249727165 | - |
dc.identifier.wosid | 000247924200007 | - |
dc.identifier.bibliographicCitation | Technological Forecasting and Social Change, v.74, no.6, pp.819 - 842 | - |
dc.relation.isPartOf | Technological Forecasting and Social Change | - |
dc.citation.title | Technological Forecasting and Social Change | - |
dc.citation.volume | 74 | - |
dc.citation.number | 6 | - |
dc.citation.startPage | 819 | - |
dc.citation.endPage | 842 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Business & Economics | - |
dc.relation.journalResearchArea | Public Administration | - |
dc.relation.journalWebOfScienceCategory | Business | - |
dc.relation.journalWebOfScienceCategory | Regional & Urban Planning | - |
dc.subject.keywordPlus | RESEARCH-AND-DEVELOPMENT | - |
dc.subject.keywordPlus | MAIZE | - |
dc.subject.keywordPlus | ADOPTION | - |
dc.subject.keywordPlus | TRADE | - |
dc.subject.keywordPlus | PERSPECTIVE | - |
dc.subject.keywordAuthor | biotechnology | - |
dc.subject.keywordAuthor | information technology | - |
dc.subject.keywordAuthor | spillover | - |
dc.subject.keywordAuthor | computable general equilibrium | - |
dc.subject.keywordAuthor | institutional factors | - |
dc.subject.keywordAuthor | absorption | - |
dc.subject.keywordAuthor | intermediate-input augmenting technical change | - |
dc.identifier.url | https://www.sciencedirect.com/science/article/pii/S0040162506001351?via%3Dihub | - |
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