Price Competition with Satisficing Consumers
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When Does Behavioural Economics Really Matter?
When does behavioural economics really matter? Ian McAuley, University of Canberra and Centre for Policy Development (www.cpd.org.au) Paper to accompany presentation to Behavioural Economics stream at Australian Economic Forum, August 2010. Summary Behavioural economics integrates the formal study of psychology, including social psychology, into economics. Its empirical base helps policy makers in understanding how economic actors behave in response to incentives in market transactions and in response to policy interventions. This paper commences with a short description of how behavioural economics fits into the general discipline of economics. The next section outlines the development of behavioural economics, including its development from considerations of individual psychology into the fields of neurology, social psychology and anthropology. It covers developments in general terms; there are excellent and by now well-known detailed descriptions of the specific findings of behavioural economics. The final section examines seven contemporary public policy issues with suggestions on how behavioural economics may help develop sound policy. In some cases Australian policy advisers are already using the findings of behavioural economics to advantage. It matters most of the time In public policy there is nothing novel about behavioural economics, but for a long time it has tended to be ignored in formal texts. Like Molière’s Monsieur Jourdain who was surprised to find he had been speaking prose all his life, economists have long been guided by implicit knowledge of behavioural economics, particularly in macroeconomics. Keynes, for example, understood perfectly the “money illusion” – people’s tendency to think of money in nominal rather than real terms – in his solution to unemployment. -
Industrial Organization 06 Market Structure and Market Power
Industrial Organization 06 Market structure and market power Marc Bourreau Telecom ParisTech Marc Bourreau (TPT) Lecture 06: Market structure and market power 1 / 39 Outline 1 Introduction: definition of market power 2 Definition of relevant market An example: market definition in the telecommunications sector Approach based on cross price-elasticities Other approaches 3 The relationship between concentration and market power An example with the Cournot oligopoly model Issues in measuring concentration and market power Other concentration measures Other market power measures 4 The SCP paradigm 5 Some elements about the control of concentration Marc Bourreau (TPT) Lecture 06: Market structure and market power 2 / 39 Introduction Introduction Remember, from the lecture on Monopoly: Definition of "market power" The ability of a firm to raise its price over marginal cost. Importance of measuring "market power": In competition policy, some practices (for instance, bundling) are illegal if a firm has market power. In order to estimate the market power of a firm, we need to define first the relevant market. Marc Bourreau (TPT) Lecture 06: Market structure and market power 3 / 39 Market definition Definition of the relevant market Two questions: Product market Which products should we include in the "market"? Geographical market Which geographical zone do we have to consider? An economic definition of the market A market can be defined as a group of products presenting strong demand- substitutability and supply-substitutability. Marc Bourreau (TPT) Lecture 06: Market structure and market power 4 / 39 Market definition Definition of the relevant market Definition of the European Commission (1997) The relevant market includes all the products and/or services considered as interchangeable or substitutable on the account of product characteristics, price, and regular use. -
ECON 1820: Behavioral Economics Spring 2015 Brown University Course Description Within Economics, the Standard Model of Be
ECON 1820: Behavioral Economics Spring 2015 Brown University Course Description Within economics, the standard model of behavior is that of a perfectly rational, self interested utility maximizer with unlimited cognitive resources. In many cases, this provides a good approximation to the types of behavior that economists are interested in. However, over the past 30 years, experimental and behavioral economists have documented ways in which the standard model is not just wrong, but is wrong in ways that are important for economic outcomes. Understanding these behaviors, and their implications, is one of the most exciting areas of current economic inquiry. The aim of this course is to provide a grounding in the main areas of study within behavioral economics, including temptation and self control, fairness and reciprocity, reference dependence, bounded rationality and choice under risk and uncertainty. For each area we will study three things: 1. The evidence that indicates that the standard economic model is missing some important behavior 2. The models that have been developed to capture these behaviors 3. Applications of these models to (for example) finance, labor and development economics As well as the standard lectures, homework assignments, exams and so on, you will be asked to participate in economic experiments, the data from which will be used to illustrate some of the principals in the course. There will also be a certain small degree of classroom ‘flipping’, with a portion of many lectures given over to group problem solving. Finally, an integral part of the course will be a research proposal that you must complete by the end of the course, outlining a novel piece of research that you would be interested in doing. -
Featured Topic Social & Behavioral Science Interventions at the Federal Level in This Issue Nudging People to Get Flu Vaccinations
a publication of the behavioral science & policy association volume 2 issue 2 2016 featured topic social & behavioral science interventions at the federal level in this issue Nudging people to get flu vaccinations behavioralpolicy.org founding co-editors disciplinary editors Craig R. Fox (UCLA) Behavioral Economics Sim B Sitkin (Duke University) Senior Disciplinary Editor Dean S. Karlan (Yale University) bspa executive director Associate Disciplinary Editors Oren Bar-Gill (Harvard University) Colin F. Camerer (California Institute ofTechnology) Kate B.B. Wessels M. Keith Chen (UCLA) advisory board Julian Jamison (World Bank) Paul Brest (Stanford University) Russell B. Korobkin (UCLA) David Brooks (New York Times) Devin G. Pope (University of Chicago) John Seely Brown (Deloitte) Jonathan Zinman (Dartmouth College) Robert B. Cialdini (Arizona State University) Adam M. Grant (University of Pennsylvania) Cognitive & Brain Science Daniel Kahneman (Princeton University) Senior Disciplinary Editor Henry L. Roediger III (Washington University) James G. March (Stanford University) Associate Disciplinary Editors Yadin Dudai (Weizmann Institute & NYU) Jeffrey Pfeffer (Stanford University) Roberta L. Klatzky (Carnegie Mellon University) Denise M. Rousseau (Carnegie Mellon University) Hal Pashler (UC San Diego) Paul Slovic (University of Oregon) Steven E. Petersen (Washington University) Cass R. Sunstein (Harvard University) Jeremy M. Wolfe (Harvard University) Richard H. Thaler (University of Chicago) Decision, Marketing, & Management Sciences executive committee Senior Disciplinary Editor Eric J. Johnson (Columbia University) Associate Disciplinary Editors Linda C. Babcock (Carnegie Mellon University) Morela Hernandez (University of Virginia) Max H. Bazerman (Harvard University) Katherine L. Milkman (University of Pennsylvania) Baruch Fischhoff (Carnegie Mellon University) Daniel Oppenheimer (UCLA) John G. Lynch (University of Colorado) Todd Rogers (Harvard University) John W. -
Behavioral Economics and Marketing in Aid of Decision Making Among the Poor
Behavioral Economics and Marketing in Aid of Decision Making Among the Poor The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Bertrand, Marianne, Sendhil Mullainathan, and Eldar Shafir. 2006. Behavioral economics and marketing in aid of decision making among the poor. Journal of Public Policy and Marketing 25(1): 8-23. Published Version http://dx.doi.org/10.1509/jppm.25.1.8 Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:2962609 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA Behavioral Economics and Marketing in Aid of Decision Making Among the Poor Marianne Bertrand, Sendhil Mullainathan, and Eldar Shafir This article considers several aspects of the economic decision making of the poor from the perspective of behavioral economics, and it focuses on potential contributions from marketing. Among other things, the authors consider some relevant facets of the social and institutional environments in which the poor interact, and they review some behavioral patterns that are likely to arise in these contexts. A behaviorally more informed perspective can help make sense of what might otherwise be considered “puzzles” in the economic comportment of the poor. A behavioral analysis suggests that substantial welfare changes could result from relatively minor policy interventions, and insightful marketing may provide much needed help in the design of such interventions. -
Esther Duflo Wins Clark Medal
Esther Duflo wins Clark medal http://web.mit.edu/newsoffice/2010/duflo-clark-0423.html?tmpl=compon... MIT’s influential poverty researcher heralded as best economist under age 40. Peter Dizikes, MIT News Office April 23, 2010 MIT economist Esther Duflo PhD ‘99, whose influential research has prompted new ways of fighting poverty around the globe, was named winner today of the John Bates Clark medal. Duflo is the second woman to receive the award, which ranks below only the Nobel Prize in prestige within the economics profession and is considered a reliable indicator of future Nobel consideration (about 40 percent of past recipients have won a Nobel). Duflo, a 37-year-old native of France, is the Abdul Esther Duflo, the Abdul Latif Jameel Professor of Poverty Alleviation Latif Jameel Professor of Poverty Alleviation and and Development Economics at MIT, was named the winner of the Development Economics at MIT and a director of 2010 John Bates Clark medal. MIT’s Abdul Latif Jameel Poverty Action Lab Photo - Photo: L. Barry Hetherington (J-PAL). Her work uses randomized field experiments to identify highly specific programs that can alleviate poverty, ranging from low-cost medical treatments to innovative education programs. Duflo, who officially found out about the medal via a phone call earlier today, says she regards the medal as “one for the team,” meaning the many researchers who have contributed to the renewal of development economics. “This is a great honor,” Duflo told MIT News. “Not only for me, but my colleagues and MIT. Development economics has changed radically over the last 10 years, and this is recognition of the work many people are doing.” The American Economic Association, which gives the Clark medal to the top economist under age 40, said Duflo had distinguished herself through “definitive contributions” in the field of development economics. -
Sendhil Mullainathan [email protected]
Sendhil Mullainathan [email protected] _____________________________________________________________________________________ Education HARVARD UNIVERSITY, CAMBRIDGE, MA, 1993-1998 PhD in Economics Dissertation Topic: Essays in Applied Microeconomics Advisors: Drew Fudenberg, Lawrence Katz, and Andrei Shleifer CORNELL UNIVERSITY, ITHACA, NY, 1990-1993 B.A. in Computer Science, Economics, and Mathematics, magna cum laude Fields of Interest Behavioral Economics, Poverty, Applied Econometrics, Machine Learning Professional Affiliations UNIVERSITY OF CHICAGO Roman Family University Professor of Computation and Behavioral Science, January 1, 2019 to present. University Professor, Professor of Computational and Behavioral Science, and George C. Tiao Faculty Fellow, Booth School of Business, July 1, 2018 to December 31, 2018. HARVARD UNIVERSITY Robert C Waggoner Professor of Economics, 2015 to 2018. Affiliate in Computer Science, Harvard John A. Paulson School of Engineering and Applied Sciences, July 1, 2016 to 2018. Professor of Economics, 2004 (September) to 2015. UNIVERSITY OF CHICAGO Visiting Professor, Booth School of Business, 2016-17. MASSACHUSETTS INSTITUTE OF TECHNOLOGY Mark Hyman Jr. Career Development Associate Professor, 2002-2004 Mark Hyman Jr. Career Development Assistant Professor, 2000-2002 Assistant Professor, 1998- 2000 SELECTED AFFILIATIONS Co - Founder and Senior Scientific Director, ideas42 Research Associate, National Bureau of Economic Research Founding Member, Poverty Action Lab Member, American Academy of Arts -
Connections in Transportation Department of Urban Studies and Planning, MIT, Spring 2015
Behavior and Policy 11.478 Behavior and Policy: Connections in Transportation Department of Urban Studies and Planning, MIT, Spring 2015 Full Reading List Part I: Behavior and Policy in a Nutshell Class 1. Cafeteria Trays and Multiple Frameworks • Etheredge (1976) The case of the unreturned cafeteria trays: An Investigation based upon theories of motivation and human behavior Class 2. Ten Instruments for Behavioral Change • Miller and Prentice (2013) Psychological Levers of Behavior Change, Chapter 17 in Eldar Shafir, The Behavioral Foundations of Public Policy • Richard Thaler, Cass R. Sunstein: Nudge: Improving Decisions About Health, Wealth, and Happiness, Introduction • Daniel Kahneman: Thinking, Fast and Slow, Introduction Class 3. Measurement, Tools and Technology (Emile Bruneau) • Emile Bruneau 2015 "Putting Neuroscience to Work for Peace”, Working Paper • Duflo, E., Glennerster, R., & Kremer, M. (2007). Using randomization in development economics research: A toolkit. Handbook of development economics, 4, 3895-3962. • Greenwald, A. G., Nosek, B. A., & Banaji, M. R. (2003). Understanding and using the implicit association test: I. An improved scoring algorithm. Journal of personality and social psychology, 85(2), 197. You may try the Implicit Association Test here https://implicit.harvard.edu/ • Winter Mason and Siddharth Suri (2012) Conducting Behavioural Research on Amazon’s Mech Turk, Behavior Research Method 44(1) Class 4. My Brain at the Bus Stop: EEG & Waiting • Dan Ariely and Gregory S. Berns (2010), “Neuromarketing: The Hope and Hype of Neuroimaging in Business.” Nature Reviews Neuroscience. • Li, Zelin, F. Duarte, J. Zhao, Z. Zhao (2015) My brain at the bus stop: an exploratory framework for applying EEG-based emotion detection techniques in transportation study, working paper Class 5. -
Chapter 4: Conceptual Illusions
CHAPTER 4: CONCEPTUAL ILLUSIONS In this chapter we will consider some fallacies and peculiarities of the way we reason. We’ll briefly consider the fallacy of denying the evidence and then confirmation bias, the paradox of choice, the conjunction fallacy and implicit bias. 1 DENYING THE EVIDENCE My scale is broken. It shows a higher figure every day so I have to keep adjusting it down. 2 CONFIRMATION BIAS "It is the peculiar and perpetual error of the human understanding to be more moved and excited by affirmatives than by negatives." --Francis Bacon Confirmation bias refers to a type of selective thinking whereby one tends to notice and to look for what confirms one's beliefs, and to ignore, not look for, or undervalue the relevance of what contradicts one's beliefs. For example, if you believe that during a full moon there is an increase in admissions to the emergency room where you work, you will take notice of admissions during a full moon, but be inattentive to the moon when admissions occur during other nights of the month. A tendency to do this over time unjustifiably strengthens your belief in the relationship between the full moon and accidents and other lunar effects. 52 This tendency to give more attention and weight to data that support our beliefs than we do to contrary data is especially pernicious when our beliefs are little more than prejudices. If our beliefs are firmly established on solid evidence and valid confirmatory experiments, the tendency to give more attention and weight to data that fit with our beliefs should not lead us astray as a rule. -
The United States Has a Market Concentration Problem Reviewing Concentration Estimates in Antitrust Markets, 2000-Present
THE UNITED STATES HAS A MARKET CONCENTRATION PROBLEM REVIEWING CONCENTRATION ESTIMATES IN ANTITRUST MARKETS, 2000-PRESENT ISSUE BRIEF BY ADIL ABDELA AND MARSHALL STEINBAUM1 | SEPTEMBER 2018 Since the 1970s, America’s antitrust policy regime has been weakening and market power has been on the rise. High market concentration—in which few firms compete in a given market—is one indicator of market power. From 1985 to 2017, the number of mergers completed annually rose from 2,308 to 15,361 (IMAA 2017). Recently, policymakers, academics, and journalists have questioned whether the ongoing merger wave, and lax antitrust enforcement more generally, is indeed contributing to rising concentration, and in turn, whether concentration really portends a market power crisis in the economy. In this issue brief, we review the estimates of market concentration that have been conducted in a number of industries since 2000 as part of merger retrospectives and other empirical investigations. The result of that survey is clear: market concentration in the U.S. economy is high, according to the thresholds adopted by the antitrust agencies themselves in the Horizontal Merger Guidelines. By way of background, recent studies of industry concentration conclude that it is both high and rising over time. For example, Grullon, Larkin, and Michaely conclude that concentration increased in 75% of industries from 1997 to 2012. In response to these and similar studies, the antitrust enforcement agencies recently declared that their findings are not relevant to the question of whether market concentration has increased because they study industrial sectors, not antitrust markets. Specifically, they wrote, “The U.S. -
Harvard Law Review 132, No. 2, December 2018, Pp
SURESH NAIDU November 12, 2019 BUSINESS ADDRESS: 1405 IAB MC 3328 [email protected] 420 W. 118th St. www.santafe.edu/~snaidu New York, NY. 10027 APPOINTMENTS: Professor, Department of Economics/SIPA, Columbia University. September-December 2016 Visiting Researcher Princeton University Industrial Relations Section. July 2010-July 2016 - Assistant Professor, Department of Economics/SIPA, Columbia University. August 2013-May 2014 – Visiting Assistant Professor, MIT Department of Economics. 2008-2010 - Harvard Academy Junior Scholar Postdoctoral Fellow, Harvard University. OTHER AFFILIATIONS: Santa Fe Institute External Faculty Roosevelt Institute Fellow NBER Faculty Research Fellow (DEV, POL, and, DAE) BREAD faculty affiliate Microsoft Research New York: Visiting Researcher (2016-2017), Consulting Researcher (2017-2018). Faculty Affiliate with Columbia University Population Research Center and Data Sciences Institute. Social Science Research Council Working Group on Big Data in the Historical Social Sciences. EDUCATION: DEGREE DATE FIELD University of California, Berkeley Ph.D. December 2008 Economics University of Massachusetts, Amherst M.A. August 2004 Economics University of Waterloo B.Math.(With Distinction) May 2001 Pure Mathematics (minor in Peace and Conflict Studies) PUBLISHED PAPERS: “Text-based Ideal Points” (with David Blei and Keyon Vafa) -Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics 2020. “Do Americans Want to Tax Capital? Evidence from Online Surveys” (with Ray Fisman, Ilyana Kuziemko, and Keith Gladstone). -Forthcoming in Journal of Public Economics.. “Monopsony in Online Labor Markets” (with Arindrajit Dube, Jeff Jacobs, and Siddarth Suri) -American Economic Review- Insights, 2(1) March 2020. 33-46. “American Slavery and Labor Market Power” -Economic History of Developing Regions, 35(1), January 2020 3-22. -
Failures of Utility Maximization Failures Of
2/1/2017 Failures of Utility Maximization • Choice difficulty • Too much choice • Asymmetric dominance/compromise effects Failures of Utility Maximization • Leaving money on the table • Endowment effect Behavioral Economics • Status quo bias • Faming effects Columbia University • Preference reversals Mark Dean • Random Choice 1 2 Failures of Utility Maximization Choice Difficulty • Choice difficulty • Basic Idea: People may dislike making difficult • Too much choice comparisons • Asymmetric dominance/compromise effects • • Leaving money on the table May behave in such a way as to avoid having • Endowment effect to make such comparisons • Status quo bias • Faming effects • Preference reversals • Random Choice 3 4 Example: Tversky and Shafir (1992) Example: Tversky and Shafir (1992) • 80 Subjects • 80 Subjects • Each subject filled out a questionnaire • Each subject filled out a questionnaire • Paid $1.50 for doing so • Paid $1.50 for doing so • Two treatments: • Two treatments: 25% 75% 5 6 1 2/1/2017 Example: Tversky and Shafir (1992) Example: Tversky and Shafir (1992) • 80 Subjects • Clear violation of IIA • Each subject filled out a questionnaire – If money was chosen in the ‘big’ choice set, should also should have been chosen in the • Paid $1.50 for doing so smaller choice set • Two treatments: • Interpretation: Stay with the money in order to avoid the ‘difficult choice’ between the different types of pen • Taken as an example of ‘decision avoidance’ 25% 75% 53% 47% 7 8 Failures of Utility Maximization Too Much Choice • Choice difficulty