- Publisher:
- Cambridge University Press
- Online publication date:
- December 2024
- Print publication year:
- 2025
- Online ISBN:
- 9781009263030
Advances in Efficient Design of Experiments in Economics
Book description
Amidst concerns about replicability but also thanks to the professionalisation of labs, the rise of pre-registration, the switch to online experiments, and enhanced computational power, experimental economics is undergoing rapid changes. They all call for efficient designs and data analysis, that is, they require that, given the constraints on participants' time, experiments provide as rich information as possible. In this Element the authors explore some ways in which this goal may be reached.
References
References
Abdellaoui, Mohammed. Parameter-free elicitation of utility and probability weighting functions. Management Science, 46(11):1497–1512, 2000.
Abdi, Hervé. Bonferroni and Šidák corrections for multiple comparisons. Encyclopedia of Measurement and Statistics, 3:103–107, 2007.
Agranov, Marina, Caplin, Andrew, and Tergiman, Chloe. Naive play and the process of choice in guessing games. Journal of the Economic Science Association, 1:146–157, 2015.
Agresti, Alan. Categorical data analysis, volume 792. John Wiley & Sons, 2012.
Akaichi, Faical, Costa-Font, Joan, and Frank, Richard. Uninsured by choice? A choice experiment on long term care insurance. Journal of Economic Behavior & Organization, 173:422–434, 2020.
Andreoni, James. Why free ride? Strategies and learning in public goods experiments. Journal of Public Economics, 37(3):291–304, 1988.
Bleichrodt, Han, and Luis Pinto, Jose. A parameter-free elicitation of the probability weighting function in medical decision analysis. Management Science, 46(11):1485–1496, 2000.
Box, E. P. George, Hunter, William Gordon, and Stuart Hunter, J.. Statistics for experimenters: Design, innovation, and discovery, 2nd edition. Wiley, 2005.
Brady, Henry E. Causation and explanation in social science. In Robert, E. Goodin, editor, The Oxford handbook of political science, Oxford University Press, 2008.
John, Bucknell, White, Justin S., and Shang, Ce. Can incentive-compatibility reduce hypothetical bias in smokers’ experimental choice behavior? A randomized discrete choice experiment. Journal of Choice Modelling, 37:100255, 2020.
Cavagnaro, Daniel R., Gonzalez, Richard, Myung, Jay I., and Pitt, Mark A.. Optimal decision stimuli for risky choice experiments: An adaptive approach. Management Science, 59(2):358–375, 2013.
Cavagnaro, Daniel R., Myung, Jay I., Pitt, Mark A., and Kujala, Janne V.. Adaptive design optimization: A mutual information-based approach to model discrimination in cognitive science. Neural Computation, 22(4):887–905, 2010.
Cetre, Sophie, Lobeck, Max, Senik, Claudia, and Verdier, Thierry. Preferences over income distribution: Evidence from a choice experiment. Journal of Economic Psychology, 74:102202, 2019.
Chapman, Jonathan, Snowberg, Erik, Wang, Stephanie, and Camerer, Colin. Loss attitudes in the U.S. population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE). Technical report, National Bureau of Economic Research, 2018.
Gary, Charness, Gneezy, Uri, and Kuhn, Michael A.. Experimental methods: Between-subject and within-subject design. Journal of Economic Behavior & Organization, 81(1):1–8, 2012.
Clark, Andrew E., Senik, Claudia, and Yamada, Katsunori. When experienced and decision utility concur: The case of income comparisons. Journal of Behavioral and Experimental Economics, 70:1–9, 2017.
Croissant, Yves. Estimation of random utility models in r: The mlogit package. Journal of Statistical Software, 95(11):1–41, 2020. https://doi.org/10.18637/jss.v095.i11.
Mikolaj, Czajkowski, Giergiczny, Marek, and Greene, William H.. Learning and fatigue effects revisited: Investigating the effects of accounting for unobservable preference and scale heterogeneity. Land Economics, 90(2):324–351, 2014.
Ding, Min. An incentive-aligned mechanism for conjoint analysis. Journal of Marketing Research, 44(2):214–223, 2007.
Fox, Armando, Gribble, Steven D., Chawathe, Yatin, and Brewer, Eric A.. Adapting to network and client variation using infrastructural proxies: Lessons and perspectives. IEEE Personal Communications, 5(4):10–19, 1998.
Frydman, Cary, Camerer, Colin, Bossaerts, Peter, and Rangel, Antonio. Maoa-l carriers are better at making optimal financial decisions under risk. Proceedings of the Royal Society B: Biological Sciences, 278(1714):2053–2059, 2011.
Harrison, Glenn W., Lau, Morten I., and Elisabet Rutström, E.. Risk attitudes, randomization to treatment, and self-selection into experiments. Journal of Economic Behavior & Organization, 70(3):498–507, 2009.
Hess, Stephane, and Palma, David. Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application. Journal of Choice Modelling, 32:100170, 2019.
Holland, Paul W. Causation and race. ETS Research Report Series, 2003(1):i–21, 2003.
Holland, Paul W. Statistics and causal inference. Journal of the American Statistical Association, 81(396):945–960, 1986.
Horiuchi, Yusaku, Markovich, Zachary, and Yamamoto, Teppei. Does conjoint analysis mitigate social desirability bias? Political Analysis, 30(4):535–549, 2022.
Horowitz, John K. The Becker–DeGroot–Marschak mechanism is not necessarily incentive compatible, even for non-random goods. Economics Letters, 93(1):6–11, 2006.
Jacquemet, Nicolas, and l’Haridon, Olivier. Experimental economics. Cambridge University Press, 2018.
Cathleen, Johnson, Baillon, Aurélien, Bleichrodt, Han, Zhihua, Li, Van Dolder, Dennie, and Wakker, Peter P. PRINCE: An improved method for measuring incentivized preferences. Journal of Risk and Uncertainty, 62(1):1–28, 2021.
Kachurka, Raman, Krawczyk, Michał, and Rachubik, Joanna. State lottery in the lab: an experiment in external validity. Experimental Economics, 24: 1242–1266, 2021.
Krawczyk, Michał. What should be regarded as deception in experimental economics? Evidence from a survey of researchers and subjects. Journal of Behavioral and Experimental Economics, 79:110–118, 2019.
Krawczyk, Michał, Blasco, Andrea, Gajderowicz, Tomasz, and Giergiczny, Marek. Europeans’ attitudes towards displaced populations: Evidence from a conjoint experiment on support for temporary protection, 2023. Available at https://ssrn.com/abstract=4564737 or http://dx.doi.org/10.2139/ssrn.4564737.
Krawczyk, Michał, and Sylwestrzak, Marta. Exploring the role of deliberation time in non-selfish behavior: The double response method. Journal of Behavioral and Experimental Economics, 72:121–134, 2018.
Levitt, Steven D., and List, John A.. Field experiments in economics: The past, the present, and the future. European Economic Review, 53(1):1–18, 2009.
Lindley, Dennis V. On a measure of the information provided by an experiment. The Annals of Mathematical Statistics, 27(4):986–1005, 1956.
List, John. Sometimes winning means knowing when to quit. Wall Street Journal, 30 December 2021.
Mahoney, James, and Acosta, Laura. A regularity theory of causality for the social sciences. Quality & Quantity, 56:1889–1911, 2021.
Mariel, Petr, Hoyos, David, Meyerhoff, Jürgen, Czajkowski, Mikolaj, Dekker, Thijs, Glenk, Klaus, Bredahl Jacobsen, Jette, Liebe, Ulf, Bøye Olsen, Søren, Sagebiel, Julian, and Thiene, Mara. Environmental valuation with discrete choice experiments: Guidance on design, implementation and data analysis. Springer Nature, 2021.
Marks, David F., and Colwell, John. The psychic staring effect. Skeptical Inquirer, 24(5):41–49, 2000.
McFadden, Daniel. The measurement of urban travel demand. Journal of Public Economics, 3(4):303–328, 1974.
McFadden, Daniel. Modeling the choice of residential location. In Karlqvist, Anders, Snickars, Folke, and Weibull, Jürgen, editors, Spatial interaction theory and planning models, pp. 75–96. North Holland, 1978.
Menapace, Luisa, and Raffaelli, Roberta. Unraveling hypothetical bias in discrete choice experiments. Journal of Economic Behavior & Organization, 176:416–430, 2020.
Meyerhoff, Jürgen, Oehlmann, Malte, and Weller, Priska. The influence of design dimensions on stated choices in an environmental context. Environmental and Resource Economics, 61(3):385–407, 2015.
Moffatt, Peter G. Experimetrics: Econometrics for experimental economics. Macmillan International Higher Education, 2015.
Overstall, Antony M., and Woods, David C.. Bayesian design of experiments using approximate coordinate exchange. Technometrics, 59(4):458–470, 2017.
Papoutsi, Georgia Rodolfo, S. Nayga Jr, M., Lazaridis, Panagiotis, and Andreas, C. Drichoutis. Fat tax, subsidy or both? The role of information and children’s pester power in food choice. Journal of Economic Behavior & Organization, 117:196–208, 2015.
Pearl, Judea. Causality. Cambridge University Press, 2009.
Perny, Patrice, Viappiani, Paolo, and Boukhatem, Abdellah. Incremental preference elicitation for decision making under risk with the rank-dependent utility model. In Ihler, Alexander and Janzing, Dominik, editors, Uncertainty in artificial intelligence, pp. 597–606. AUAI Press for Association for Uncertainty in Artificial Intelligence, 2016.
Shannon, Claude Elwood. A mathematical theory of communication. The Bell System Technical Journal, 27(3):379–423, 1948.
Shigeoka, Hitoshi, and Yamada, Katsunori. Income-comparison attitudes in the United States and the United Kingdom: Evidence from discrete-choice experiments. Journal of Economic Behavior & Organization, 164:414–438, 2019.
Smith, Mike D. Biased coin randomization. In Balakrishnan, Narayanaswamy, editor, Methods and applications of statistics in clinical trials. Volume 1: Concepts, principles, trials, and design, pp. 90–105. Wiley, 2014.
Sokol-Hessner, Peter, Hsu, Ming, Curley, Nina G., Delgado, Mauricio R., Camerer, Colin F., and Phelps, Elizabeth A.. Thinking like a trader selectively reduces individuals’ loss aversion. Proceedings of the National Academy of Sciences, 106(13):5035–5040, 2009.
Spirtes, Peter, Glymour, Clark, and Scheines, Richard, with additional material by David Heckerman. Causation, prediction, and search. MIT Press, 2001.
Steimle, Lauren N., Sun, Yuming, Johnson, Lauren, Besedeš, Tibor, Mokhtarian, Patricia, and Nazzal, Dima. Students’ preferences for returning to colleges and universities during the COVID-19 pandemic: A discrete choice experiment. Socio-economic Planning Sciences, page 101266, 2022.
, Student. Appendix to Mercer and Hall’s paper ‘The experimental error of field trials’. Journal of Agricultural Science, 4, 128–131.
Vandana, Subroy, Rogers, Abbie A., and Kragt, Marit E.. To bait or not to bait: A discrete choice experiment on public preferences for native wildlife and conservation management in Western Australia. Ecological Economics, 147:114–122, 2018.
Thye, Shane. Logical and philosophical foundations of experimental research in the social sciences. In Webster, Murray and Sell, Jane, editors, Laboratory experiments in the social sciences, pp. 53–82. Elsevier, 2014.
Titchener, Edward Bradford. Experimental psychology: A retrospect. American Journal of Psychology, 36(3):313–323, 1925.
Toubia, Olivier, Hauser, John, and Garcia, Rosanna. Probabilistic polyhedral methods for adaptive choice-based conjoint analysis: Theory and application. Marketing Science, 26(5):596–610, 2007.
Toubia, Olivier, Johnson, Eric, Evgeniou, Theodoros, and Delquié, Philippe. Dynamic experiments for estimating preferences: An adaptive method of eliciting time and risk parameters. Management Science, 59(3):613–640, 2013.
Train, Kenneth E. Recreation demand models with taste differences over people. Land economics, University of Wisconsin Press, 74(2), 230–239, 1998.
Tversky, Amos, and Kahneman, Daniel. Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4):297–323, 1992.
Amos, Tversky, and Thaler, Richard H.. Anomalies: Preference reversals. Journal of Economic Perspectives, 4(2):201–211, 1990.
Vossler, Christian A., Doyon, Maurice, and Rondeau, Daniel. Truth in consequentiality: Theory and field evidence on discrete choice experiments. American Economic Journal: Microeconomics, 4(4):145–171, 2012.
Vossler, Christian A., and Evans, Mary F.. Bridging the gap between the field and the lab: Environmental goods, policy maker input, and consequentiality. Journal of Environmental Economics and Management, 58(3):338–345, 2009.
Wakker, Peter, and Deneffe, Daniel. Eliciting von Neumann–Morgenstern utilities when probabilities are distorted or unknown. Management Science, 42(8):1131–1150, 1996.
Woodward, James. Causation and manipulability. In Edward, N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Metaphysics Research Lab, Stanford University, Winter edition, 2016.
Ziliak, Stephen T. Field balanced versus randomized field experiments in economics: Why W. S. Gosset aka ‘Student’ matters. Review of Behavioral Economics, 1(1–2):167–208.
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