Econometrics of Stated Preferences (original) (raw)
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Contemporary Guidance for Stated Preference Studies
Journal of the Association of Environmental and Resource Economists, 2017
This article proposes contemporary best-practice recommendations for stated preference (SP) studies used to inform decision-making, grounded in the accumulated body of peer-reviewed literature. These recommendations consider the use of SP methods to estimate both use and non-use (passive-use) values, and cover the broad SP domain including contingent valuation and discrete choice experiments. We focus on applications to public goods in the context of the environment and human health, but also consider ways in which the proposed recommendations might apply to other common areas of application. The recommendations recognize that SP results may be used and reused (benefit transfers) by governmental agencies, non-governmental organizations, and that all such applications must be considered. The intended result is a set of guidelines for SP studies that is more comprehensive than that of the original National Oceanic and Atmospheric Administration (NOAA) Blue Ribbon Panel on contingent valuation, is more germane to contemporary applications, and reflects the two decades of research since that time. We also distinguish between practices for which accumulated research is sufficient to support recommendations and those for which greater uncertainty remains. The goal of this article is to raise the quality of SP studies used to support decision making and promote research that will further enhance the practice of these studies worldwide.
Scenario Adjustment in Stated Preference Research
2009
Stated preference (SP) survey methods have been used increasingly to assess willingness to pay for a wide variety of non-market goods and services, including reductions in risks to life and health. Poorly designed SP studies are subject to a number of well-known biases, but many of these biases can be minimized when they are anticipated ex ante and accommodated in the study's design or during data analysis. We identify another source of potential bias, which we call "scenario adjustment," where respondents assume that the substantive alternative(s) in an SP choice set, in their own particular case, will be different than the survey instrument describes. We use an existing survey, developed to ascertain willingness to pay for private health-risk reduction programs, to demonstrate a strategy to control and correct for scenario adjustment in the estimation of willingness to pay. This strategy involves data from carefully worded follow-up questions and ex post econometric controls for each respondent's subjective departures from the intended choice scenario. Our research has important implications for the design of future SP surveys.
Demand effects in stated preference surveys
Journal of Environmental Economics and Management, 2018
We argue that demand effects in stated preference studies are understudied. By demand effects, we mean anything in the survey that unintentionally influences respondents' beliefs about appropriate behavior, which in turn might affect their responses in the survey. We implement two methods for measuring and implicitly reducing the influence of demand effects. The first approach-random selection of good to be valued-does not have any effect on respondent behavior. The second approach-a demand script and a control question with feedback-has a sizable and statistically significant effect on respondent behavior. In particular, estimated marginal willingness to pay for improvements in water quality is substantially (around 50 percent) lower than a control treatment; we attribute this decrease to a reduced demand effect. Our results suggest that stated preference methods tend to lead to biased willingness-to-pay estimates due to demand effects, but that the bias can be reduced using simple measures.
Transportation Research Part B: Methodological, 1997
There are a number of studies on modelling with Revealed Preference (RP) data. It is a traditional technique and it is based on actual market data. The method has been extensively used in transportation as a tool for predicting travel demand. Although the method constitutes a relevant analysis on the process of modelling, it suffers from limitations, mainly associated with the lack of control over the experiment, that sometimes overwhelm the model results. This work proposes and tests a methodology for estimating a more efficient binary RP sample set. The objective is to develop and test a methodology that identifies and eliminates potentially irrational choices made. Responses are evaluated according to the set of trade-offs in values of time. Having identified these individuals they are eliminated from the original sample and a new sample is created, the selectively replicated (SR) sample. Original and SR samples are then re-estimated in a tree nested logit structure. 0 1997 Elsevier Science Ltd
Applied Choice Analysis: A Primer
Journal of the American Statistical Association, 2007
Almost without exception, everything human beings undertake involves a choice. In recent years, there has been a growing interest in the development and application of quantitative statistical methods to study choices made by individuals with the purpose of gaining a better understanding both of how choices are made and of forecasting future choice responses. In this primer, the authors provide an unintimidating introduction to the main techniques of choice analysis and include detail on themes such as data collection and preparation, model estimation and interpretation, and the design of choice experiments. A companion website to the book provides practice data sets and software to estimate the main discrete choice models such as multinomial logit, nested logit, and mixed logit. This primer will be an invaluable resource to students as well of immense value to consultants/professionals, researchers, and anyone else interested in choice analysis and modeling.
Statistical Analysis of Choice Experiments and Surveys
Marketing Letters, 2005
Measures of households' past behavior, their expectations with respect to future events and contingencies, and their intentions with respect to future behavior are frequently collected using household surveys. These questions are conceptually difficult. Answering them requires elaborate cognitive and social processes, and often respondents report only their "best" guesses and/or estimates, using more or less sophisticated heuristics. A large body of literature in psychology and survey research shows that as a result, responses to such questions may be severely biased. In this paper, (1) we describe some of the problems that are typically encountered, (2) provide some empirical illustrations of these biases, and (3) develop a framework for conceptualizing survey response behavior and for integrating structural models of response behavior into the statistical analysis of the underlying economic behavior.
Preference instability in stated choice surveys: more evidence (abridged version)
2019
Stated choice (SC) is a popular survey design for studying choice behaviour, especially in Transport research. This methodology has been used for several decades across many other areas of research, including marketing, health as well as environmental and resource economics (Carlsson, 2011; Hensher, 1994). A key application of SC data and the subsequent estimation of discrete choice models is the derivation of monetary valuations, commonly referred to as Value of Travel Time (VTT) or Willingness-To-Pay (WTP) measures. These look at the monetary value that respondents place on a unit change in the characteristics of products or alternatives.
Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software
PharmacoEconomics, 2017
We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post estimation. We also provide a review of standard software. In providing this guide we endeavor not only to provide guidance on choice modeling, but to do so in a way that provides researchers to the practicalities of data analysis. We argue that choice of modeling approach depends on: the research questions; study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful not only to researchers within but also beyond health economics.
The Use of Hypothetical Baselines in Stated Preference Surveys
2010
Researchers using stated preference (SP) techniques have increasingly come to rely on what we call -hypothetical baselines.‖ By this we mean that respondents are provided with a description of a current state, or baseline, but that this baseline is intentionally not the actual state of environmental quality, health, or other condition. The researcher then poses a valuation question or choice task that is contingent, not on the existing status quo, but rather on the state of the world described in this new hypothetical baseline. In this paper, we argue that researchers using SP techniques have often used hypothetical baselines without carefully considering the cognitive challenges this poses for respondents or the difficulties this practice creates for advising policymakers. We present a simple typology of four types of SP studies, two of which rely on hypothetical baselines, and give six examples of conditions that an SP researcher may change to create a hypothetical baseline. We discuss four main reasons why SP analysts use hypothetical baselines in their research designs, plus some of the risks associated with the use of hypothetical baselines. Finally, we offer guidance for the use of hypothetical baselines in future SP surveys.
Modeling methods for discrete choice analysis
1997
This paper introduces new forms, sampling and estimation approaches for discrete choice models. The new models include behavioral specifications of latent class choice models, multinomial probit, hybrid logit, and non-parametric methods. Recent contributions also include new specialized choice based sample designs that permit greater efficiency in data collection. Finally, the paper describes recent developments in the use of simulation methods for model estimation. These developments are designed to allow the applications of discrete choice models to a wider variety of discrete choice problems.