Dissertation
DYNAMICS IN U.S. AGRICULTURE UNDER CLIMATE CHANGE, PRODUCTION UNCERTAINTY AND KNOWLEDGE ACCUMULATION
Doctor of Philosophy (PhD), Washington State University
01/2015
Handle:
https://hdl.handle.net/2376/6247
Abstract
This dissertation investigates dynamic adjustment in U.S. agriculture with climate change, production uncertainty, and knowledge accumulation. The dissertation is organized following the manuscript format approved by the graduate school, with separate self-contained chapters devoted to each of the three research questions.
Uncertainty about future climate change may influence a firm’s intertemporal investment decisions. A stochastic dynamic dual model is constructed to investigate the structural adjustment of two aggregate output and three aggregate input categories in U.S. agriculture under stochastic climatic change. More than a century of national annual data (1910-2011) is used in the empirical analysis. Results indicate that, with rational expectations, both output categories as well as all input categories exhibit quasi-fixity in response to market change and stochastic climate change. Failing to anticipate climate change increases overall adjustment costs. Failing to account for uncertainty in anticipated climate change has little impact on adjustment rates.
With production risk, producers need to make tradeoffs between adjustment costs and the potential benefits of investment on productive inputs to protect against unforeseen events. A general dynamic dual model is constructed for U.S. agriculture that allows asset fixity of inputs to be tested under production uncertainty as well as testing for functional form. The generalized Leontief is found to be significantly preferred to the translog and normalized quadratic functional forms for the dynamic model. With this functional form, family labor exhibits strict fixity, while land, capital, and hired labor exhibit quasi-fixity. Production uncertainty has limited impacts on investment decisions for quasi-fixed inputs.
The role of research investment (R&D) and learning by doing (LBD) in improving productivity is investigated through an empirical application to the U.S agriculture production sector. U.S. agriculture shows evidence (although statistically insignificant) of increasing returns to scale when LBD and R&D enter into the production process. LBD and R&D have a complementary relationship in reducing production costs. Both learning and research reduce short-run physical and human capital demand significantly, but these impacts become insignificant in the long run. LBD plays a more important role than R&D in improving the productivity of human capital.
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Details
- Title
- DYNAMICS IN U.S. AGRICULTURE UNDER CLIMATE CHANGE, PRODUCTION UNCERTAINTY AND KNOWLEDGE ACCUMULATION
- Creators
- Sansi Yang
- Contributors
- C. Richard Shumway (Advisor)Hayley H. Chouinard (Committee Member)Gregmar I Galinato (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Economic Sciences, School of
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 116
- Identifiers
- 99900581838301842
- Language
- English
- Resource Type
- Dissertation