David F. Layton

Context Dependence and Aggregation in Disaggregate Choice Analysis

There is an emerging consensus among disciplines dealing with human decision making that the context in which a decision is made is an important determinant of outcomes. This consensus has been slow in the making because much of what is known about context effects has evolved from a desire to demonstrate the untenability of certain common assumptions upon which tractable models of behavior have generally been built. This paper seeks to␣bring disparate disciplinary perspectives to bear on the relation between context and choice, to formulate (1) recommendations for improvements to the state-of-the-practice of Random Utility Models (RUMs) of choice behavior, and (2) a future research agenda to guide the further incorporation of context into these models of choice behavior.

Potential Cost-Effectiveness of Incentive Payment Programs for the Protection of Non-Industrial Private Forests

This study assesses the potential cost-effectiveness of incentive payment programs relative to traditional, top-down regulatory programs for biological conservation. We develop site-level estimates of the opportunity cost and non-monetized biological benefits of protecting biodiversity hotspots in Finnish non-industrial private forests. We then use these estimates to contrast and compare the cost-effectiveness of alternative conservation programs. Our results suggest that incentive payment programs, which tacitly capitalize on landowners’ private knowledge about the opportunity costs of conservation, may be considerably more cost-effective than traditional, top-down regulatory programs. (JEL Q23)

Revealing Preferences for Leisure Time from Stated Preference Data

When consumer choice is constrained by time as well as money, willingness to pay can be defined with respect to either numeraire. The two measures can be related formally within a utility-consistent model of choice subject to two constraints. Furthermore, when information is collected on both, the respondents marginal value of time can be identified. A system of willingness to pay time and money and the marginal value of leisure time is estimated jointly in an application to California whalewatching and whale stock enhancement. The empirical approach can be applied with only minor additions to survey techniques for nonmarket valuation. Copyright 2004, Oxford University Press.

Bayesian Approaches to Modeling Stated Preference Data

Bayesian econometric approaches to modeling non-market valuation data have not often been applied, but they offer a number of potential advantages. Bayesian models incorporate prior information often available in the form of past studies or pre-tests in Stated Preference (SP) based valuation studies; model computations are easily and efficiently performed within an intuitively constructed Markov chain Monte Carlo framework; and asymptotic approximations, unreasonable for the relatively small sample sizes seen in some SP data sets, need not be invoked to draw (posterior) inferences. With these issues in mind, we illustrate computationally feasible approaches for fitting a series of surveys in a sequential manner, and for comparing a variety of models within the Bayesian paradigm. We apply these approaches to a series of SP surveys that examined policies to conserve old growth forests, northern spotted owls, and salmon in the U.S. Pacific Northwest.

The Cost of Power Outages to Heterogeneous Households

We use a repeated dichotomous choice contingent valuation survey to elicit households’ willingness to pay to a void unannounced interruptions in electricity service. The data pose multiple econometric challenges including: correlated responses for a given household, heteroskedastic errors, and a willingness to pay distribution with large mass near zero. We address these issues by combining a gamma distribution for outage costs with a lognormally distributed scale parameter defined as a function of household characteristics, outage attributes, outage history, and random coefficients. The model is estimated through simulated maximum likelihood. We demonstrate that cost estimates are sensitive to the interaction of attributes of previously experienced and hypothetical interruptions.