Browsing by Subject "uncertainty"

Now showing 1 - 6 of 6
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    Application of Project Analysis to Natural Resource Decisions
    (Water Resources Research Center, University of Minnesota, 1980-06) Easter, K. William; Waelti, John J.
    This publication is intended to serve as a guide for the application of water project planning and analysis. Included are a perspective from which to review economic decisions; a brief history of evaluation procedures for U.S. water projects; a description of the Water Resources Council's procedures; the basic economics of project evaluation; problems in cost allocation; and individual applications to irrigation, flood control, navigation and related transportation, and recreation and environmental resources. Emphasis is placed on social benefits measured in terms of additions to real product and savings in terms of real resources.
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    The effects of lexical and discourse-based hedging in news stories of cancer screening and treatment on cancer-related behavioral beliefs and trust towards cancer scientists
    (2021-07) Wang, Le
    Hedging, a way to convey scientific uncertainty, could manifest in two different ways: lexical hedging (expression of uncertainty through linguistic elements such as “might,” “may,” and “likely”) and discourse-based hedging (expression of uncertainty through disclosing experimental weaknesses, lack of generalizability of study results, and so forth). Previous studies in cancer communication documented some positive effects of hedging on variables pertaining to cancer prevention and control, but they focused on discourse-based hedging. To assess and compare the effects of the two different types of hedging on people’s cancer-related behavioral beliefs and trust towards cancer scientists, an online survey experiment was conducted. No significant effects of hedging on beliefs or trust were found. The associations among variables of interests, including behavioral beliefs, trust, attitude, and behavioral intention, were examined, and the potential moderating role of research literacy was explored. Implications of the study’s results are discussed.
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    Essays on Trade under Uncertainty
    (2022-05) Carreras Valle, Maria Jose
    This dissertation consists of three chapters focused on the role of uncertainty in trade. The first chapter studies trade policy uncertainty in an environment with multiple stages of production. The recent increase in trade policy uncertainty affects a variety of industries, and in particular, uncertainty is important for industries whose final good is produced in multiple stages that are located across different countries. These industries are the most concerned about trade policy and reduce investment during uncertainty periods. This paper analyzes trade policy uncertainty in a two-country dynamic, stochastic, general equilibrium model with multistage production where a firm?s decisions today depends on the future tariff path. Studies with one stage of production that measure the effect of trade policy find that uncertainty, as a second moment shock, does not play a big role in explaining the changes observed in the economy. Introducing multistage production, which generates a magnified response of trade to tariff changes, provides a better mechanism to analyze the role uncertainty in future tariffs plays in the economy. In the second chapter, I revisit the trend of U.S. manufacturing inventories and the role of delivery times. U.S. manufacturing inventories have been increasing since 2005, reversing a declining trend that lasted for decades. The rise is observed across U.S. manufacturing industries and types of inventories. While the long term decline is well-understood as a consequence of improvements in transportation and information technology, the reversal of the trend has not yet been studied. This paper explores the role of increasing delivery times due to the creation of global supply chains. As foreign inputs become cheaper, firms choose to source more inputs from abroad, and in particular inputs from China which face long delivery times and frequent delays. This increases the firms' exposure to volatility in demand leading to a greater incentive to hold inventories. I build a dynamic trade model that features stochastic delivery times for different inputs in the presence of idiosyncratic demand risk. In this framework, firms face a tradeoff when sourcing inputs from different locations between their relative price and delivery times. I find that the initial decrease in delivery times explains $61\%$ of the decline in inventories from 1992 to 2004, and the increase in reliance on inputs from China, which face longer and more volatile delivery times, explains $34\%$ of the increase in inventories from 2005 to 2018. In the third chapter, I explore the cost of supply chain disruptions and examine the role of inventories as insurance. Supply chain disruptions have become increasingly common in today's integrated world. As the risk of disruptions increases, so does the value of inventories, as firms rely on their inventories to smooth and insure their production process. In this paper, I build a model to study supply chain disruption in an environment with demand risk and inventory storage. Then, I use the model to study the recent supply chain disruptions cause by COVID-19. I allow for specially long delivery delays of inputs, and sudden rise of sales, and track a firm's choices from 2020 onwards. With this model, I am able to quantify the effect on prices, analyze the use of inventories to insure and smooth out production during the period, and study its effect on sourcing choices and imported inputs substitutability during this period of long delivery times. Finally, I study the effects of the increasing and more volatile delivery times in firms sourcing choices. The rise in delivery times increases the cost of sourcing from farther countries, and could force firms to change or diversify their current supply chain networks.
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    Experimental Evidence on the Effect of Insurance on Producer Behavior in the Face of Price Risk
    (2019-08) Kadam, Aditi
    Production risks can be caused by indirect factors such as weather, and direct factors such as price. Failure of constructing resilient financial markets to mitigate these price risks, can cause damaging and lasting impacts to the economy. This study contributes to the literature on risk and uncertainty by testing the effect of insurance, both full and partial, on producer behavior in the face of price risk. I use an experimental setting to address the relationship between behavior under price risk uncertainty, and how that behavior is shaped in the presence of insurance. I find that participants do not adjust their production choices in situations of price risk. When provided with insurance, they do increase production significantly, and reduce it when it is unavailable. The positive effect of full insurance, is higher than that of partial insurance. By comparing the effect of partial, and full insurance, I find evidence for moral hazard.
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    Toward a Structurally-Sound Model of Uncertainty-Related Personality Traits
    (2018-08) Rautu, Alex
    Numerous personality constructs have been proposed over the last 70 years to describe individual differences in attitudes toward uncertainty, including Intolerance of Ambiguity (IA), Intolerance of Uncertainty (IU), Uncertainty Orientation (UO), Need for Closure (NFC), and Personal Need for Structure (PNS). Despite differences in their theoretical foundations and applications, these constructs share considerable similarities in conceptualization and measurement, which have confounded researchers about how they can be meaningfully distinguished. Study 1 examined the facet- and construct-level overlap among multiscale measures of IA, IU, and NFC—constructs that have received the lion’s share of research attention—also including the Uncertainty Response Scale (URS; Greco & Roger, 2001). Confirmatory factor analysis revealed that the general factors underlying each measure were identical or near-identical with one another (in two US samples but not an Italian sample), and exploratory factor analyses of both the items and facets of each measure found that only four factors replicated across US and Italian samples. Moreover, the four factors uncovered showed strong concurrent validity with trait measures of neuroticism, orderliness, curiosity/exploration, and naïve epistemic beliefs, which suggested that they reflect distinct types of responses to uncertainty, consistent with the model assumptions of the URS. Another two studies examined the discriminant predictive validity of the four factors with respect to evidence gathering (Study 2) and avoidance of ambiguity under conditions of threat and high cognitive demand (Study 3). Their results confirmed that these factors predict different behavioral outcomes and have more predictive power than NFC, IA, and IU scales. Overall, this work demonstrates that a four-factor model has psychometric and conceptual advantages over current measures of uncertainty-related constructs, which are lacking in discriminant validity.
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    Uncertainty in Cropland Data layer derived land-use change estimates: puting corn and soy expansion estimates in context
    (2015-01) Noe, Ryan
    Increased demand for corn for ethanol and the subsequent record high commodity prices has resulted in rapid expansion of corn and soy in the Midwestern U.S. Whether or not this expansion is replacing existing agricultural production or is expanding onto previously uncultivated grass or pasture land has profound implications for ecosystem services such as soil carbon storage, soil erosion prevention, and water quality. Several studies have used the Cropland Data Layer (CDL) to track fine scale land-use changes driven by corn and soy. However, these studies rarely account for the variability in data quality throughout the CDL's history. Here I compare established techniques as well as the application of USDA's Common Land Unit (CLU) data for removing `noise' from change rasters and quantifying the land covers lost to corn and soy expansion. I compare these estimates to equivalent measures from the National Agricultural Statistics Services (NASS) and use the discrepancy between them to identify spatial and temporal variability in CDL data that could influence land-use change study results. The CLU results differed little from established techniques, but both improved over direct comparisons of CDLs. Comparison to NASS data revealed pre-2010 versions of the CDL underestimate corn and soy area much more than later versions, leading to the detection of illusory land-use change when they are compared to post-2010 versions. Spatial and temporal variability resulted in errors that were several times larger than the trends the data are being used to detect. According to the CDL, approximately five million hectares of corn and soy expanded onto the grass, pasture, hay, and wheat between 2007 and 2012. However, over the same time period the CDL overestimated the amount of corn and soy expansion by 3.5 million hectares in the unmodified treatment and 1.5 million with cleaning methods applied. This work suggests that studies that use the CDL should test for and report variability and uncertainty in their results.