A critical question facing experimental economists is whether behavior inside the laboratory is a good indicator of behavior outside the laboratory. To address that question, we build a model in which the choices that individuals make depend not just on financial implications, but also on the nature and extent of scrutiny by others, the particular context in which a decision is embedded, and the manner in which participants and tasks are selected. We present empirical evidence demonstrating the importance of these various factors. To the extent that lab and naturally occurring environments systematically differ on any of these dimensions, the results obtained inside and outside the lab need not correspond. Focusing on experiments designed to measure social preferences, we discuss the extent to which the existing laboratory results generalize to naturally-occurring markets. We summarize cases where the lab may understate the importance of social preferences as well as instances in which the lab might exaggerate their importance. We conclude by emphasizing the importance of interpreting laboratory and field data through the lens of theory.

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What Do Laboratory Experiments Measuring Social Preferences Reveal About the Real World

Discrete choice experiments (DCEs) have become a commonly used instrument in health economics. This paper updates a review of published papers between 1990 and 2000 for the years 2001-2008. Based on this previous review, and a number of other key review papers, focus is given to three issues: experimental design; estimation procedures; and validity of responses. Consideration is also given to how DCEs are applied and reported. We identified 114 DCEs, covering a wide range of policy questions. Applications took place in a broader range of health-care systems, and there has been a move to incorporating fewer attributes, more choices and interview-based surveys. There has also been a shift towards statistically more efficient designs and flexible econometric models. The reporting of monetary values continues to be popular, the use of utility scores has not gained popularity, and there has been an increasing use of odds ratios and probabilities. The latter are likely to be useful at the policy level to investigate take-up and acceptability of new interventions. Incorporation of interactions terms in the design and analysis of DCEs, explanations of risk, tests of external validity and incorporation of DCE results into a decision-making framework remain important areas for future research.

Discrete choice experiments in health economics: A review of the literature

Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns. Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001–2008, we report whether earlier trends continued during 2009–2012. This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms. A total of 179 health-related DCEs for 2009–2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project. The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.

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Discrete Choice Experiments in Health Economics: A Review of the Literature

Design and Marketing of New Products

Consumers' willingness to pay for organic chicken breast: Evidence from choice experiment

We compare the ability of three preference elicitation methods (hypothetical choices, nonhypothetical choices, and nonhypothetical rankings) and three discrete-choice econometric models (the multinomial logit [MNL], the independent availability logit [IAL], and the random parameter logit [RPL]) to predict actual retail shopping behavior in three different product categories (ground beef, wheat flour, and dishwashing liquid). Overall, we find a high level of external validity. Our specific results suggest that the nonhypothetical elicitation approaches, especially the nonhypothetical ranking method, outperformed the hypothetical choice experiment in predicting retail sales. We also find that the RPL can have superior predictive performance, but that the MNL predicts equally well in some circumstances. experiment.

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How Closely Do Hypothetical Surveys and Laboratory Experiments Predict Field Behavior

This paper analyzes price differentials among conventional, cage-free, organic, and Omega-3 eggs using retail scanner data from two regional markets and the United States as a whole. Results reveal significant premiums attributable to cage-free (a 57% premium on average) and organic (an 85% premium on average). However, significant variation exists among geographic locations; price premiums for organic over conventional eggs in Dallas are almost twice as high as those in San Francisco. Estimates indicate that about 42% of the typically observed premium for cage-free eggs over conventional eggs (and 36% of the premium for organic eggs) can be attributed to egg color rather than differ ences in hens' living conditions. Despite the large implicit price premiums for cage-free and organic, our data reveal that most shoppers are not willing to pay such high prices for cage-free and organic attributes.

Jae Bong Chang

Papers

How Closely Do Hypothetical Surveys and Laboratory Experiments Predict Field Behavior

The Price of Happy Hens: A Hedonic Analysis of Retail Egg Prices

Fairness and food choice

Mixed logit models: accuracy and software choice

Consumer valuation of health attributes for soy-based food: A choice modeling approach