Introduction, Definition, and Methodology

1. Introduction, Definition, and Methodology David Laibson June 30, 2014 Note: Powerpoint deck includes many “hidden slides,” which were not used in actual presentation.

3. Outline • Very quick introductions: Emily, Leana, Matthew, David • Very quick introductions: you • Name • School • Fields of interest • Who you started rooting for in the world cup • Definition of Behavioral Economics • Methodology • Seven properties • Thumbnail history (for more details look at slides)

4. If you ask questions that are too aggressive, we’ll use the following system to let you know. =

5. Semantics • Behavioral economics • name irritates people • are there any economists who aren’t studying behavior? • Other names you’ll hear: • Psychology and economics • Psychological economics • Subfields: • Behavioral Finance • Behavioral Game Theory • Behavioral Public Finance • Behavioral IO • etc…

6. Definition: Behavioral Economics • Behavioral economics is just like the rest of economics, but also includes psychological factors. • Adds psychology to economics, particularly cognitive psychology, social psychology, and neuroscience. • Buy texts in these fields to learn the psychology • Schacter, Gilbert, and Wegner, Psychology • Ross and Nisbett, The Person and the Situation • Glimcher et al eds, Neuroeconomics • Consider taking a couple of intro psych courses (tastes good and good for you)

8. An obnoxious definition • The Guardian: The study of “how people actually make decisions rather than how the classic economic models say they make them.” • We don’t apply ideological litmus tests (like rationality or dynamic consistency). Nothing is ruled out or ruled-in ex-ante.

9. Definition • Pay special attention to these psychological factors: • Imperfect rationality • Imperfect self-control • Imperfect selfishnss (social preferences) • But this list is only a start (e.g. psychological conceptions of personality) • Emphasize the importance of microfoundations • Preferences • Beliefs • Cognition • Take experimental evidence seriously • but don’t rely exclusively on it • Vote for Obama

10. Naïve quasi-hyperbolic agent

11. (ex-)Regulator-in-chief Cass Sunstein Administrator of the White House Office of Information and Regulatory Affairs

12. But we also vote for David Cameron(the conservative Prime Minister of the UK) The Behavioural Insights Team • “Set up in July 2010 with a remit to find innovative ways of encouraging, enabling and supporting people to make better choices for themselves.” It turns out that behavioral economics has supporters on both sides of the political aisle – e.g., the (US) Pension Protection Act was bipartisan. This legislation championed the use of defaults and auto-escalation.

13. Distinct from... • Experimental economics • Psychology • Behavioralism (we are not Behavioralists) • Evolutionary psychology • Evolutionary economics (BE takes preferences and cognition as primitives) • Sociology and economics • Radical economics • ‘Economics sucks’ economics • Lazy economics • Sloppy economics • Ad hoc economics

14. No: Few “pure” jobs Difficult job market No journal Why ghettoize? Applied theory is not a field, so why should applied psychology be a field? Yes: Some courses You can take behavioral orals Some seminars Many conferences Some “methodological” fields do exist: econometrics, theory, experimental economics Is behavioral economics a field? Future field status uncertain.

15. Our expectation/wish • All economists will eventually incorporate behavioral stuff where appropriate. • Psychology is to “normal economics” as game theory is to “normal economics.” • Everyone uses it as a matter of course.

17. Methodology • Experimental science • What makes a good model? • [Beware of multiple-testing bias (and p-hacking)]

18. Lab empirics (experiments) • If experiments are run well, they will have high internal validity • I understand the specific causal mechanism that is driving my result • I can turn the result on and off by manipulating the experimental treatment • My result is robust and replicable (not “fragile”) • But even a well-run experiment may have low external validity • The mechanism that I am studying is important for particular real-world behaviors • Experiments complement (do not substitute for) field research

19. Internal validity experimental artifacts demand effects (are the subjects trying to respond to the perceived expectations of the experimenter?) External validity unrepresentative subjects under-experienced subjects missing decision aids under-incentivized tasks non-naturalistic problems Thousands of other ways that lab decisions differ from field decisions Problems with internal and external validity in lab experiments.

20. “The Rules” Adapted from George Loewenstein

21. Experimental Debriefing(especially for pilots) Aggressively use debriefing surveys. • “Was the experiment confusing?” • “What strategies did you use?” • “How did you come up with your answer?” • “What was the experiment about?” • “What were the other subjects thinking?” • What would your payoff have been if you had gone UP instead of DOWN?”

23. Field experiments and lab experiments are complementary • Neither is the gold standard • They feed off (and stimulate) each other in useful ways • Avoid making the mistake of thinking that just because you’ve run a well-designed lab experiment you know how the phenomenon will generalize • Avoid making the mistake of thinking that just because you’ve run a well-designed field experiment you know how the phenomenon will generalize

24. Seven PropertiesGabaix and Laibson (2008) These properties typically need to be traded off against each other. No social science model achieves all of these goals. • Parsimony • Tractability • Conceptual insightfulness • Generalizability (portability) • Falsifiability • Empirical accuracy • Predictive precision: the model makes sharp predictions.

25. Figure 1: The value of parsimony. Sample for estimation of a 5th order polynomial The data (squares) is generated by sin(x/10) + ε, where ε is distributed uniformly between -½ and ½. The sold line fits the first 50 data points to a fifth-order polynomial – a non-parsimonious model. The polynomial has good fit in sample.

26. Figure 1: The value of parsimony. Sample for estimation of a 5th order polynomial The data (squares) is generated by sin(x/10) + ε, where ε is distributed uniformly between -½ and ½. The sold line fits the first 50 data points to a fifth-order polynomial – a non-parsimonious model. The polynomial has good fit in sample and poor fit out of sample (dashed line).

27. Figure 2: Falsifiability, Empirical Consistency, and Predictive Precision Model = “(X,Y) = (1,5)” = Data = Model = “X+Y > 1” = Data = Y Y 5 1 1 1 1 X X Panel A: Model is falsifiable, empirically consistent, and does not have predictive precision. Panel B: Model is falsifiable, empirically inconsistent, and has predictive precision.

28. If physicists wrote theorems like economists: Theorem (existence and uniqueness): Given any initial conditions for a set of mass-points in a vacuum, there exists a unique continuation path that obeys the laws of gravity. This is falsifiable (is it interesting or useful?).

29. Useful classical physics: Theory: At the surface of the earth gravity causes a constant acceleration of g = 9.8 m/s². Predictive precision: An object projected from the surface of the earth will follow a parabolic path, attaining a height of h = v2/(2g) before falling back to the surface (where v is the vertical velocity of the object at t = 0).

30. Predictive Precision in Economics Black-Scholes Option Pricing Formula Auction Theory Solow model with the Kaldor facts Quantity theory of money These theories are not exactly right, but they do make precise quantitative predictions that are almost right.

31. The Role of Assumptions • Models use assumptions – including axioms – to make predictions. • Scientific models do not have inviolate axioms. • Scientific axioms – even seemingly sacrosanct axioms – are usually modified with time. • Earth is flat • Planets and stars rotate around earth • Ptolemaeus vs. Copernicus • Space is three dimensional and Euclidean • Newton vs. Einstein

32. Economic Assumptions • Classical economic assumptions are also useful approximations. • Perfect rationality • Dynamic consistency • Revealed preference • These assumptions should be continuously judged on their ability to enhance the seven modeling properties enumerated a few slides back.

33. Outline • Quick introductions • Definition of Behavioral Economics • Methodology • Seven properties • Thumbnail history

34. Thumbnail history... • Bounded rationality of Simon succeeded more as rhetoric than as something for economists to do • Satisficing wasn’t a precise theory that could be an alternative to mainstream economics • Anomalies of the 1950’s and 1960’s did not stop the rational expectations revolution of the 1970’s • “the rational model is a good approximation” • 1970’s: heyday of “as-if” economics

35. 1970’s • 1974: Heuristics and Biases (K&T) • representativeness (similarity heuristic) • availability • anchoring • 1979: Prospect Theory • probability weighting function • risk-seeking in the loss domain • risk-avoidance in the gain domain • loss aversion • framing

36. 1980’s • Endowment effect (Thaler) • “Mugs,” markets, and the passage to economics. • Experiments • Anomalies Column (Thaler) • Behavioral finance • Not much formal modeling

37. 1990’s • Formalization • Fairness, reciprocity, and social preferences • Intertemporal choice • Learning • Behavioral Game Theory • JDM biases-Quasi Bayesian approaches • Self serving bias, Confirmatory bias, Overconfidence • Field evidence • Acceptance of behavioral economics in the profession

38. 2000+ • Clark Medal: Matthew Rabin • Nobel Prizes: • George Akerlof (2001) • Daniel Kahneman (2002) • Robert Shiller (2013) • Interventions, policy, “nudges” • Behavioral IO, development, public finance • Behavioral economics starts to feel like normal science (maybe it’s time to sell?)

39. What will probably be the key growth areas in the coming decades? • Theory • Field experiments/natural experiments • Structural estimation of behavioral models • Policy • Biosocial science

40. Outline • Introductions • Definition of Behavioral Economics • Methodology • Seven properties • Thumbnail history (for more details look at slides)