This dissertation is centered on applying `nonlinearity' across various fields, a decision informed by the understanding that linear models frequently fail to capture the full extent of real-world complexities. This approach is driven by the distinct insights that nonlinearity offers, insights crucial for a more profound and precise understanding of diverse phenomena. In this regard, I have explored a variety of empirical methodologies and theoretical frameworks, each chosen for its effectiveness in unraveling and accurately depicting the complexity inherent in different subjects.
The first paper, "Warming Temperatures and Potential Adaptation through Breeding: Evidence from U.S. Soft Winter Wheat," examines the impact of climate change on wheat production. Given wheat's role as a major staple for much of the global population, its susceptibility to rising temperatures presents significant challenges to food security. Despite its importance, comprehensive production data for wheat considering extensive U.S. regions is relatively scarce. To address this, I compiled a unique dataset on wheat production comprising 35,000 observations over 50 years from USDA-ARS hard-copy publications. Findings obtained through the mixed-effects model reveal significant variations in the influence of nonlinearly specified temperature on wheat production. This analysis identifies a decline not only in wheat yield across various U.S. farming sites but also in wheat quality, an aspect often neglected in similar studies. The issue is further compounded by simulations I conducted, which predict worrying decreases in both yield and quality due to rising temperatures. Despite these challenges, my analysis of varietal improvements indicates modest yet significant progress in countering the effects of warming, offering viable strategies for agricultural adaptation.
In the second chapter, "Semiparametric Analysis of Out-farm Migration in China," I explore the nonlinear relationship between sectoral migration and the income gap within China. This work builds on my co-authored publication, "Intersectoral Labor Migration and Agriculture in the United States and Japan," published in Agricultural Economics. While the earlier study employed discrete thresholds and kink approaches to explore migration patterns in developed countries such as Japan and the United States, it did not reveal significant nonlinear relationships. This led me to investigate whether the results were influenced by the economic development status of the countries in question. Focusing on China, a developing country with distinct labor dynamics, I employ semiparametric methodologies to assess migration patterns, diverging from the linear assumptions common in existing literature. By using nationally representative data, it suggests a potential nonlinear relationship between farmers' sectoral migration and the income gap, providing new insights into labor migration in developing contexts.
The last chapter, "Enhanced Salience of Nonlinear Pricing and Energy Conservation," explores the energy consumption of residents of Hanoi in Vietnam, using a large-scale randomized control trial. I study whether enhancing salience of information with respect to the nonlinear pricing can help energy conservation. The novelty of the project lies in its experimental design and the utilization of digital tools such as smart meters and mobile apps, adopting technologies with the potential to alter consumer behavior. Currently, we are in the post-intervention data collection phase. Supported by the International Growth Centre (IGC), the project aims to bridge the research gap in energy consumption behavior in developing countries, thereby contributing to policymaking in energy management and development in these regions.
Through these diverse yet interconnected chapters, I attempt to use the varied applications of nonlinearity in studying economic and environmental issues. The main objective is to contribute to both academic knowledge and practical policymaking in these fields, addressing complexities that are often oversimplified. This approach aims to provide a more comprehensive understanding of the intricate dynamics in these areas. / Doctor of Philosophy / This dissertation focuses on the application of `nonlinearity' across various fields, acknowledging that linear models often fail to fully capture the complexities of real-world scenarios. This approach yields essential insights for a more precise understanding of diverse phenomena, incorporating a range of empirical methods and theoretical models, each selected for their efficacy in accurately depicting complexities in various subjects.
The first part of my dissertation examines the agricultural sector, specifically the impact of weather on crop yields. Instead of traditional methods that use basic temperature data like minimum, maximum, or average values, I use a nonlinear approach that aligns with the specific growth stages of crops.
In the area of labor economics, the dissertation explores migration patterns in China. It questions the traditional linear relationship between migration and income disparities, suggesting a more complex model that better represents the dynamics of a developing economy.
The final chapter addresses the field of energy policy, examining consumer responses to nonlinear electricity pricing models in practice. This section explores the challenges faced by individuals in understanding such policies and assesses the impact of providing real-time information about nonlinear pricing on promoting energy conservation.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/117219 |
| Date | 18 December 2023 |
| Creators | Ko, Minkyong |
| Contributors | Economics, Ta, Chi Lan, Ramsey, Ford, Moeltner, Klaus, Tack, Jesse |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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