Transcript Slide 1

Experimental Economics: Short Course
Universidad del Desarrollo
Santiago, Chile
December 16, 2009
Dr. Jonathan E. Alevy
Department of Economics
University of Alaska Anchorage
[email protected]
Note on Hypothetical vs Salient Payments
• Hypothetical responses
– usually more noise in data
– Poor publication prospects
• Recent discussion on Economic Science
Association Listserv
Economic Science Association: Listserv
• Dear colleagues,
Is there a classical paper (or at least well-known)
paper that specifically compares people's behavior in
experiments where they are not paid for their
choices and when they are.
I googled keywords "hypothetical choice" and similar
but somehow all papers
that it shows seem to be, well, too applied.
Thank you in advance,
Dmitry
Partial Response to Dmitry
After doing (experimental economics) for several
decades, just don't waste time on this issue. I remain
astonished to see how many fine researchers still
decide to waste time on this, when the evidence is so
clear and has been for decades.
We really have much more important issues to
debate. If you or someone else insists on doing some
hypthetical choices, then at least run some checks
when you pay for real (and please do not do comical
things like pay 1-in-3000, which one recent study did as
an alleged check on hypothetical bias).
– Glenn Harrison
Holt & Laury, “Risk Aversion and Incentive Effects,” AER 2002
Decision
Option A
Option B
Your Choice
(Circle A or B)
1
$20.00 if throw of die is 1
$16.00 if throw of die is 2-10
$38.50 if throw of die is 1
$1.00 if throw of die is 2-10
A
B
2
$20.00 if throw of die is 1-2
$16.00 if throw of die is 3-10
$38.50 if throw of die is 1-2
$1.00 if throw of die is 3-10
A
B
3
$20.00 if throw of die is 1-3
$16.00 if throw of die is 4-10
$38.50 if throw of die is 1-3
$1.00 if throw of die is 4-10
A
B
4
$20.00 if throw of die is 1-4
$16.00 if throw of die is 5-10
$38.50 if throw of die is 1-4
$1.00 if throw of die is 5-10
A
B
5
$20.00 if throw of die is 1-5
$16.00 if throw of die is 6-10
$38.50 if throw of die is 1-5
$1.00 if throw of die is 6-10
A
B
6
$20.00 if throw of die is 1-6
$16.00 if throw of die is 7-10
$38.50 if throw of die is 1-6
$1.00 if throw of die is 7-10
A
B
7
$20.00 if throw of die is 1-7
$16.00 if throw of die is 8-10
$38.50 if throw of die is 1-7
$1.00 if throw of die is 8-10
A
B
8
$20.00 if throw of die is 1-8
$16.00 if throw of die is 9-10
$38.50 if throw of die is 1-8
$1.00 if throw of die is 9-10
A
B
9
$20.00 if throw of die is 1-9
$16.00 if throw of die is 10
$38.50 if throw of die is 1-9
$1.00 if throw of die is 10
A
B
10
$20.00 if throw of die is 1-10
$38.50 if throw of die is 1-10
A
B
Holt & Laury Elicitation Results
Hypothetical payments
Real payments
Visually: a treatment effect!
Statistically: How can we be more certain?
Statistical Analysis: Overview
• Experimental design drives the statistical analysis
– What type of data? Binary, ordinal, cardinal?
• HL Binary data (choose A or B)
– Within or between subjects?
• At what level are observations independent?
• HL: Dependent across Hypothetical and Real treatments
• HL: independent across subjects. (individual choice)
• Two approaches:
– Historically: Simple nonparametric tests provide insight on
treatment effects.
• Different tests used for within or between subjects designs
– Current practice: Supplement nonparametric tests with
conditional (regression) estimates of parameters.
• Use demographic or other data to explain results.
• Panel data techniques account for dependencies.
Statistical Analysis: HL Data
• Approach 1: nonparametric statistics
risk it
– If A choice = 1, B choice = 0. Define variable
as sum
of choices for individual i in treatment t
– Higher value implies more risk averse.
– Wilcoxon test for matched data (within subjects)
– Mann-Whitney test for between subjects design
• See appendix slides for details or Siegel & Castellan 1988
• Note: HL protocol is used to understand behavior in
other experiments (e.g. auction studies) .
– Use the risk variable on right side of estimation equation
is one way to do this.
Statistical Analysis HL Data
• Approach 2: Maximum likelihood techniques
– Maintain data in original binary form
– Estimate probability of A choice given treatment
dummy and other control variables.
• Probit (or logit) specification
– Multiple choices by individuals accounted for in error
term (random effects model).
– Can impose structure on utility
• estimate Coefficient of Relative Risk Aversion and other
parameters
• See Harrison 2008 Maximum Likelihood in STATA on course
webpage
– For extensions (includes STATA code).
Inferring CRRA
• Assume U(y) = y1-r/(1-r) for r ≠ 1
• In this case r=0 is RN, r>0 is RA, and r<0 is RL
Summarizing Holt Laury
• Holt and Laury
– Important contribution to measuring risk attitudes
• Menu of choices (with real payments) provides incentive for
truthful response.
• Relatively easy to understand.
– Criticisms
• Original study confounds incentive effect by not varying
order
• Controlling for order, basic result holds
– Salient payments important, contra Kahneman & Tversky
conjecture.
– Large number of applications follow this protocol.
• Include extensions to non-expected utility, time preferences,
valuation of goods.
Alternative Elicitation: BDM
• Becker Degroot Marschak
– Handout
• A “single person auction”
• Comparison to HL
– Advantages
• Single decision
– Disadvantage
• Cognitively demanding?
Something Completely Different
Asset Market Experiments
• Yesterday we looked at induced value double
auction (commodity market)
– Smith 1962
– Quickly and reliably goes to competitive equilibrium
• Asset market experiment
– Smith, Suchanek, and Williams (1988)
– Prices diverge from fundamental values
• Price bubbles and crashes frequently observed
• Why the difference?
Why experiment with asset markets?
• Core methodological contribution: Able to induce value of
the asset
– Identification problem in field studies.
• What is the fundamental value?
– Solution: Create asset with specific payoff attributes and duration
• Able to control information
– Asset structure is common knowledge
– Endowments are private information
• Replication
– Test robustness of existing findings
– Systematically study new treatments
16
Core Experimental Design
• Smith, Suchanek and Williams, 1988
• Nine traders in a double auction market
– 15 trading periods - ‘days’
– Each trader is endowed with assets and cash
• Endowments are private information
• Endowments are of equal expected value for all traders
– The asset traded has
•
•
•
•
State contingent dividend = {0, 8, 28, 60}
Equal probability for each state.
Expected value of 24 cents
Dividends that pay at end of each trading day
– Traders can bid, offer, buy or sell or do nothing
17
Expected Price Dynamics
• Rational Expectations Equilibrium
– Price falls by value of expected dividend each period (-24).
Tirole (1982)
Declining Fundamental Value
Price
360
0
1
3
5
7
9
Period
11
13
15
18
Theory for lab experiment
• Rational expectations: Backward induction  no bubbles
– No trade if all are risk neutral
– Price path follows the red dashes
– Tirole (1982)
• Rational bubbles – relax rational expectations assumption
– Price rises due to:
• Lack of common knowledge of bubble
• Limits to arbitrage
– Risk of crash exists
– A coordinating device is needed to induce sales
– Abreu & Brunnemeier (2003)
19
Research Question: Bubbles & Experience
• Bubbles are observed in markets with new traders
– Robust to many alternative treatments
• Short-selling, futures markets, dividend certainty, price limits, initial
endowments, informed confederates.
• What works?  Experience
– “…trades fluctuate around fundamental values when the same group
returns for a third session.”
Porter and Smith (2003 JBF) (emphasis added)
• Two new results
– Alevy & Price 2008
• Convergence with inexperienced traders who have received advice
– Hussam Porter & Smith, 2008
• Convergence is not robust
• New fundamentals  bubbles resume.
20
Reduction of bubbles with “experience”
Alevy & Price: Experimental Design
• Control
– Single session of stage game - no advice.
– Do we get a bubble with our protocol?
• software, subject pool, instructions etc.
• Own-experience
– Same cohort repeats stage game three times
• Intergenerational advice
– Three generations - new traders in each
22
Experimental Design:Intergenerational Treatments
• Three “generations” of markets
– Second and third generation receives advice from
immediate predecessor.
– Incentive to leave quality advice
• Predecessors receive payment tied to successors performance
23
Experimental Design:
Intergenerational Treatments
• Full advice
• All traders receive unique advice from predecessors
• Partial advice
• Three or six traders receive advice
24
Result:
Bubble attenuated with advice
800
750
700
650
600
550
500
450
400
350
300
250
200
150
800
100
750
50
700
0
1
2
3
4
5
650
6
7
8
9
10
11
12
13
14
15
Third Generation – 9 Advised
P1G3A9a
600
550
500
450
800
400
750
350
700
300
650
600
250
550
200
500
150
450
100
400
350
50
300
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
800
700
650
250
200
Second Generation – 9 Advised
P1G2A9b
750
150
100
50
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
600
Third Generation – 9 Advised
P1G3A9b
550
500
450
400
800
750
350
700
300
650
600
250
550
200
500
150
450
400
100
350
300
50
250
0
1
2
3
4
5
6
7
8
9
Progenitor 1
P1G1A0
10
11
12
13
14
200
15
800
150
750
100
50
700
0
650
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
13
14
15
Third Generation – 3 Advised
P1G3A3
600
550
500
450
400
800
350
750
300
700
650
250
600
200
550
150
500
450
100
400
50
350
0
1
2
3
4
5
6
7
8
9
10
11
12
Second Generation – 9 Advised
P1G2A9a
13
14
15
300
250
200
150
100
50
0
1
2
3
4
5
6
7
8
9
10
11
12
Third Generation – 6 Advised
P1G3A6
25
Result:
Bubble Size
• Bubble size declining by generation p<.05
• No significant difference between advice and
experience
26
Testing the rational expectations model
• Dynamic model: price depends on history
Pit
: average price in session i on day t
Oit
: number of offers in session i on day t
Bit
: number of bids in session i on day t
Pit  Pit 1     Bit 1  Oit 1 

Excess Demand
• Prediction under rational expectations
  24   0
27
Result: Price Dynamics
Table A.1. Random Effects –Advice Only
Change in Mean Price
Constant
(Bid-Offer)
2Gen*(Bid-Offer)
3Gen*(Bid-Offer)
Obs
R2
Model A
(SSW)
-16.12*
3.02*
210
0.21
Model B
Pit  Pit 1     Bit 1  Oit 1 

Excess Demand
-20.77*
5.35*
-2.44
-4.87*
210
0.26
** Denotes statistical significance at the p < 0.05 level
* Denotes statistical significance at the p < 0.10 level


(Models A and B) Fail to reject Ho: alpha = -24
(Model B) Fail to reject Ho: betaBO+ beta3Gen*BO= 0  rational expectations
28
Extension: Trading Styles
• Fundamentalist
– If price > fundamentals, active as a seller
• Definition: # offers > # bids when prices are above
fundamental value
• Momentum Trader
– If price > fundamentals, active as a buyer
• Definition: # bids > # offers when prices are above
fundamental value
29
Advice and Trading Strategy
• 75% of advised and 48% of unadvised are fundamentalists.
• Qualitative analysis of advice shows
– Little stress on fundamentals
– Heuristics adopted due to advice move prices towards fundamentals
– Advice is ‘sticky’
• In 2nd generation those receiving advice leave advice like their
predecessor
• Those without advice differ…slightly greater emphasis on fundamentals.
30
Conclusions
• Prices converge rapidly to rational
expectations equilibrium
– A novel finding in the literature
• Advice is unsophisticated but effective in
changing behavior
• Benefits of advice accrue at market level
– Reduces variance in earnings
– Advised do not earn more
31
Hussam Porter and Smith, 2008
• Achieve convergence in usual manner
– Experienced group of traders
• After convergence
– Change fundamentals, wider distribution of dividends
– Bubbles rekindle.
• Would advised be more robust?
– Think more deeply about the problem when giving or receiving
advice.
– Perhaps less brittle type of learning
Social Preferences
• The Dictator “game”
– An individual decision task on splitting a surplus
with another
• Stylized fact across many replications
– Give none or give some (often half) two “types”
• Selfish & Altruistic
Origin of Dictator Game
• Dictator game run to better understand
ultimatum game results
• Ultimatum game (two person)
– Player 1: Offers a division of surplus
– Player 2: Accept or reject offer
– If reject both players receive zero.
• Dictator game
– Decompose ultimatum game offers
• Is a component of ultimatum offer altruistic?
Forsythe et al. 1994
Dictator game
Ultimatum game
Examining Robustness of Dictator
giving
• Innovation: The “Bully” game
– Extend the action space to allow giving & taking
– List 2007, Bardsley 2008
Give
Take 1
Take 5
Take 5 Earn
Bully Game
• Behavior inconsistent with “preference based”
explanation
• Emphasizes importance of institutions in
shaping behavior.
– Including experimenter demand effects in the
laboratory.
– Property rights (earned endowment treatment)
Appendix: Nonparametric Statistics
• From Andreas Lange University of Maryland