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Learning, irreversible investment, a...
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Lyon, Charles Christopher.
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Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology./
作者:
Lyon, Charles Christopher.
面頁冊數:
204 p.
附註:
Source: Dissertation Abstracts International, Volume: 54-05, Section: A, page: 1892.
Contained By:
Dissertation Abstracts International54-05A.
標題:
Economics, General. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9328363
Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology.
Lyon, Charles Christopher.
Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology.
- 204 p.
Source: Dissertation Abstracts International, Volume: 54-05, Section: A, page: 1892.
Thesis (Ph.D.)--University of Minnesota, 1993.
Responsible management of scarce water resources is a central issue in public debate about the future of U.S. agriculture. Growing nonagricultural demand for water and dwindling water reservoirs have resulted in increased pressure on agricultural producers and policymakers to be more efficient in water allocation. Targeting government policy to accelerate diffusion of new technologies such as drip irrigation may be one way to achieve this goal. A first step in designing effective technology policy is to establish and test theoretical relationships between key economic variables and the rate of technology diffusion.Subjects--Topical Terms:
1017424
Economics, General.
Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology.
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Learning, irreversible investment, and the intrafirm diffusion of a new agricultural technology.
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Source: Dissertation Abstracts International, Volume: 54-05, Section: A, page: 1892.
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Advisers: Yacov Tsur; Willis L. Peterson.
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Thesis (Ph.D.)--University of Minnesota, 1993.
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Responsible management of scarce water resources is a central issue in public debate about the future of U.S. agriculture. Growing nonagricultural demand for water and dwindling water reservoirs have resulted in increased pressure on agricultural producers and policymakers to be more efficient in water allocation. Targeting government policy to accelerate diffusion of new technologies such as drip irrigation may be one way to achieve this goal. A first step in designing effective technology policy is to establish and test theoretical relationships between key economic variables and the rate of technology diffusion.
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This thesis presents a framework for analyzing intra-firm diffusion of a new agrarian technology. The framework is applied to panel data describing diffusion of drip irrigation among kibbutz cotton farms in Israel. The conceptual analysis is notable in that it combines aspects of intra-firm diffusion not explicitly incorporated in previous models. In particular, the innovation is viewed as a "package" of components about which adopters learn by using. Learning benefits accrue according to learning ability and the irreversible or reversible nature of the component. The rate at which learning benefits accrue drives the rate of new technology adoption and diffusion. A second contribution stems from use of panel data in the empirical application. Unlike cross-sectional data used in previous analyses of innovation diffusion, panel data enables examination of dynamic elements of the intra-firm diffusion process.
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The theoretical model predicts behavior consistent with observed diffusion behavior for two broad classes of innovations. Additional qualitative results suggest empirically testable hypotheses about new technology adoption and diffusion. For example, the model suggests that innovation adoption will be earlier for larger farms and faster learners, but that impacts of these variables on diffusion speed depends on technology characteristics.
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Empirical investigation of model implications involves estimating new technology learning functions using panel data techniques. Learning function results are used to frame empirical analysis of drip technology adoption and diffusion behavior. Model implications are investigated using limited dependent variable and hazard rate models. Empirical results are encouraging and suggest there are elements of truth embodied in model implications.
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http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=9328363
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