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| 1 |
ID:
119204
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| Publication |
2013.
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| Summary/Abstract |
In this paper we develop a simple model with anchoring and loss aversion to explain house price dynamics. The model has two testable implications: 1) when both cognitive biases are present, price dispersion and trade volume are pro-cyclical; 2) if anchoring decreases with time, then price dispersion and trade volume are higher for transactions with a previous purchase that is more recent. Using a data set that contains most real estate transactions in Hong Kong from 1992 to 2006, we find anchoring and loss aversion to be important, and the results are robust to type of housing and sample period. The finding is consistent with the strong correlations among house price, price dispersion, and volume found in the data. Moreover, anchoring, price dispersion and volume decrease with time since previous transaction. Our results suggest that anchoring and loss aversion contribute to the cyclicality of the housing market.
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| 2 |
ID:
171404
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| Summary/Abstract |
This paper reports evidence from a field experiment investigating households’ electricity saving behavior. We motivated households’ efforts to save electricity via pro-environmental incentives that did not affect people’s monetary utility but targeted their environmental preferences. The results show that such pro-environmental incentives can be effective, especially when framed as potential losses to the environment. Our loss-framed pro-environmental incentive led households to save 5% on their monthly electricity consumption compared to a control group.
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| 3 |
ID:
128374
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| Publication |
2014.
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| Summary/Abstract |
In this paper, we extend previous reference-dependence newsvendor research by incorporating both consumer trade-offs and government subsidies to evaluate the relevant influences on the optimal electric vehicle (EV) production decisions. We present the properties of the model, derive the closed-form solutions for the model given the relevant constraints, and use numerical experiments to illustrate the results. We find that subsidies, loss aversion, the performance of both EVs and internal combustion engine-powered vehicles (ICEVs), and the coefficient of variation of demand are significant factors influencing the optimal production quantity and the expected utilities of EV production. The high selling price and other high costs of ICEVs help offset the influence of loss aversion, whereas the high costs of EV enhance loss aversion. Our study enriches the literature on subsidies for EVs by establishing a behavioral model to incorporate the decision bias in terms of loss aversion at the firm level. These findings provide guiding principles for both policymakers and EV managers for making better strategies to promote EVs in the early immature market.
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| 4 |
ID:
125683
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| Publication |
2013.
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| Summary/Abstract |
Prospect theory holds that human beings faced with a risky bet will tend to value potential losses about twice as much as potential gains. Previous research has demonstrated that prospect theory could be sufficient to explain an energy paradox in the market for automotive fuel economy. This paper analyzes data from questions added to four commercial, multi-client surveys of 1000 U.S. households each in 2004, 2011, 2012 and 2013. Households were asked about willingness to pay for future fuel savings as well as the annual fuel savings necessary to justify a given upfront payment. Payback periods inferred from household responses are generally consistent over time and across different formulations of questions. Mean calculated payback periods are about 3 years, but there is substantial dispersion among individual responses. The calculated payback periods do not appear to be correlated with the attributes of respondents. Respondents were able to quantitatively describe their uncertainty about both vehicle fuel economy and future fuel prices. Simulation of loss averse behavior based on respondents' stated uncertainty illustrates how loss aversion could lead consumers to substantially undervalue future fuel savings relative to their expected value.
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