ph.d. in political science - comparative and european politics academic year 2012-2013
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Ph.D. in Political Science - Comparative and European Politics Academic year 2012-2013 Part 3 Applied Experiments Experimental methods in political and social sciences Alessandro Innocenti [email protected] OUTLINE Part 1 Laboratory Methods - PowerPoint PPT PresentationTRANSCRIPT
Ph.D. in Political Science - Comparative and European PoliticsAcademic year 2012-2013Part 3 Applied Experiments
EXPERIMENTAL METHODS IN POLITICAL AND SOCIAL SCIENCES Alessandro Innocenti
OUTLINEPart 1 Laboratory Methods To provide a basic introduction to experimental methodology both from a theoretical and
an empirical point of view. Part 2 Experimental Design To learn how to design an experiment and to understand that experiments in political
sciences share many features from cognitive and experimental economics. Part 3 Applied ExperimentsTo understand the differences between different kinds of experimental designs by
discussing weaknesses and strengths of some experimental papers and the specificities of their designs.
1
Part 3 Applied Experiments
Informational cascades and overconfident behavior Innocenti, A., A. Rufa and J. Semmoloni (2010) "Overconfident behavior in informational cascades: An eye-tracking study", Journal of Neuroscience, Psychology, and Economics, 3, 74-82.
Voting by ballots and by feet Innocenti A. and C. Rapallini (2011) "Voting by Ballots and Feet in the Laboratory", Giornale degli Economisti e Annali di Economia, 70, 3-24.
Travel mode choice and transportation policyInnocenti, A., P. Lattarulo and M.G. Pazienza (2013) "Car Stickiness: Heuristics and Biases in Travel Choice", Transport Policy, 25, 153-168.
Eye tracker movements provide quantitative evidence on subjects’ visual attention and on the relation between attentional patterns and external stimulus.
Individuals perceive clearly what they look at only in the central area of their visual field and to observe wider areas they execute frequent and very fast eye-movements.
Informational cascades and overconfident behavior
Gaze direction alternates between eye fixations (longer than 200 ms), and saccades, which are fast transitions between two consecutive fixations.
Visual information is acquired during the fixations but the visual field looked at depend on saccades, which are so fast as not to be fully controlled.
First fixations are determined automatically and unconsciously.
A bit of introduction
For reading, it has been shown that, as text becomes conceptually more difficult, fixation duration increases and saccade length decreases ⇓
longer fixations imply more cognitive effort.
For scene screening, participants get the gist of a scene very early in the process of looking, even from a single brief exposure ⇓
first fixations gives the essence of the scene and the remainder is used to fill in details.
Findings on eye-movements
Attention as brain’s “allocation of limited processing resources to some stimuli or tasks at the expense of others” (Kowler, et al, 1995)
For this reason, the retina has evolved a fovea, which is a dense concentration of rod and cone cells collecting most of the information extracted from the visual scene.
This process is called foveation, the brain directs its attention to different objects in a visual field.
Attention allocation as foveation
Brain allocates its attentional resources toward a subset of the necessary information first, before reallocating them to another subset.
Mere exposure effect (Zajonc 1980) - subjects tend to like stimuli we are exposed to even when the presentation is entirely subliminal.
Advertising - Repeated exposure to the brand and its products is thought to increase viewer’s preference towards them.
Attention and preferences
When subjects allocate attention to decide what they prefer, they exhibit a gaze cascade effect, i.e. they look progressively more toward the item that they are about to choose. (Shimojo et al 2003)
This evidence is interpreted that as the brain is about to settle on a choice, it biases its gaze toward the item eventually to be chosen in order to “lock in” that preference.
Gaze direction would participate directly in the preference formation processes and could also be interpreted as preference at a subconscious level.
Gaze cascade effect
The rationality assumption implies that a player will look up all costlessly available information that might affect his beliefs and update consequently these beliefs.
Behavioral evidence contradicts this assumption (Costa Gomes-Crawford 2006, Johnson et al. 2002, Laibson et al 2006, Camerer et al. 2009, Chen et al 2009)
Subjects collect and process information by means of heuristic procedures and rules of thumb to limit cognitive effort.
Starting hypotheses
Subjects collect only a limited portion of the available information.
Gaze direction often exhibit biases in scrutinizing information which depend on subjects’ cognitive attitude and past experience
Players’ types defined on actual choices and gaze direction are correlated.
Starting hypotheses
Can gaze bias predict the orienting behavior for decision processes that are not driven by individual preferences, but related to an uncertain event to be guessed on partial-information clues?
Cognitive reference theory: dual process theory of reasoning and rationality (System 1 vs. System 2)
Experimental setting: informational cascades - model of sequential decision for rational herding
Our inquiry
Since the 1970s a lot of experimental and theoretical work has been devoted to describe attention orienting as a dual processing activity (Schneider and Shiffrin 1977, Cohen 1993, Birnboim 2003)
Selective attention is defined as "control of information processing so that a sensory input is perceived or remembered better in one situation than another according to the desires of the subject" (Schneider and Shriffin 1977, p. 4)
This selection process operates according two different patterns: controlled search and automatic detection
Dual process theories
Controlled search is a serial process that uses short-term memory capacity, is flexible, modifiable and sequential
Automatic detection works in parallel, is independent of attention, difficult to modify and suppress once learned
Each subject adopts two types of cognitive processes, named System 1 and System 2 (Stanovich and West 1999, Kahneman and Frederick 2002)
Controlled vs. Automatic
System 1 collects all the properties of automaticity and heuristic processing as discussed by the literature on bounded rationality
System 1 is fast, automatic, effortless, largely unconscious, associative and difficult to control or modify
The perceptual system and the intuitive operations of System 1 generate non voluntary impressions of the attributes of objects and thought
System 1
System 2 encompasses the processes of analytic intelligence, which have traditionally been studied by information processing theorists
System 2 is slower, serial, effortful, deliberately controlled, relatively flexible and potentially rule-governed
In contrast with System 1, System 2 originates
judgments that are always explicit and intentional, whether or not they are overtly expressed
System 2
Both System 1 and System 2 are an evolutionary product. People heterogeneity as the result of individually specific patterns of interaction between the two systems
If eye movements and attention shifts are tightly tied, gaze direction could represent a signal of how automatic and immediate reactions (giving right or wrong information) to visual stimuli are modified or sustained by more conscious and rational processes of information collecting
Eye-movements and Systems 1/2
Informational cascade - model to describe and explain herding and imitative behavior focusing on the rational motivation for herding (Banerjee 1992, Bikhchandani et al. 1992)
Key assumptions
Other individuals’ action but not information is publicly observable
private information is bounded in quality agents have the same quality of private
information
Informational cascades
Consider two restaurants named "A" and "B" located next to one another
According to experts and food guides A is only slightly better than B (i.e. the prior probabilities are 51 percent for restaurant A being the better and 49 percent for restaurant B being better)
People arrive at the restaurants in sequence, observe the choices made by people before them and must decide where to eat
Apart from knowing the prior probabilities, each of these people also got a private signal which says either that A is better or that B is better (of course the signal could be wrong)
The restaurant example
Suppose that 99 of the 100 people have received private signals that B is better, but the one person whose signal favors A gets to choose first
Clearly, the first chooser will go to A. The second chooser will now know that the first chooser had a signal that favored A, while his or her own signal favors B
Since the private signals are assumed to be of equal quality, they cancel out, and the rational choice is to decide by the prior probabilities and go to A
The restaurant example
The second person thus chooses A regardless of her signal
Her choice therefore provides no new information to the next person in line: the third person's situation is thus exactly the same as that of the second person, and she should make the same choice and so on
Everyone ends up at restaurant A even if, given the aggregate information, it is practically certain that B is better (99 people over 100 have private signal that is the case)
This takes to develop a “wrong” information cascade, i.e. that is triggered by a small amount of original information followed by imitations
The restaurant example
A is chosen although almost all people receive private signal that B is better than A and there is no clear prior evidence that A is better than B (51% vs. 49%)
If the second person had been someone who always followed her own signal, the third person would have known that the second person's signal had favored B. The third person would then have chosen B, and so everybody else
The second person's decision to ignore her own information and imitate the first chooser inflicts a negative externality on the rest of the population
lf she had used her own information, her decision would have provided information to the rest of the population, which would have encouraged them to use their own information as well
What is wrong?
People have private information ("signals") and can also observe public information
Public information is a history of all the actions (not information) of predecessors
People are rational because they are assumed to update their prior probabilities by using Bayes’ rule to process the public and private information they possess
An individual herds on the public belief when his action is independent of his private signal
If all agents herd there is an informational cascade that may be both “wrong” or “right”
Model’s key features
The theory of informational cascades assumes that decision makers behave rationally in processing all the available information
Experimental evidence points out how subjects exhibit in the laboratory various cognitive biases in deciding if entering or not a cascade:
One third of the subjects exhibit a tendency to rely on the mere counting of signals (Anderson-Holt 1997)
Subjects’ overconfidence consistently explains the deviations from Bayes’ rule (Huck-Oechssler 2000, Nöth-Weber 2003, Spiwoks et al. 2008)
Heuristics and biases in cascades
Experimental setting
Two events - Square and Circle - may occur with equal probability.
For each session, 9 students were arranged in a pre-specified order and asked to predict the state with a monetary reward for a correct prediction
Each subject observes: an independent and private signal (Private Draw)
which has a 2/3 chance of indicating the correct event
the predictions (Previous Choices) made by the subjects choosing previously
Experimental Design
Private draw
?2/3
1/3
2/3
1/3
HP: rational subjects process information according to Bayes’ rule and predict the event indicated as more probable by the combination of private signals and publicly known predictions
This implies that the choice of the first decision maker reveals the private signal he has drawn
For example, if he chooses A, later decision makers
will infer that he has observed the signal a[Pr(a|A)=2/3 > Pr(a|B)=1/3]
Bayesian learning
If the second decision maker observes the same private signal a he will predict accordingly.
If she receives the other signal b, he will assign a 50% probability to the two events and both predictions will be equally rational.
If the second decision maker chooses A, the third decision maker will observe two previous choices of A. If her private signal is b, it will be rational to ignore this private information and to predict A as the previous choosers (information cascade).
Bayesian learning
If (a,b) indicates the numbers of signals a and b received or inferred, Bayes’ rule imposes:
[Pr(a,b|A) Pr(A)] Pr (A|a,b) = ______________________________________________
[Pr(a,b|A) Pr(A) + Pr(a,b|B) Pr(B)]
In the example, the third decision maker observes two signals a inferred and receives one signal b received and the expression above gives:
(2/3)2(1/3)(1/2) Pr (A|a,b) = ______________________________________________________= 2/3 (2/3)2(1/3)(1/2) + (1/3)2(2/3)(1/2)
Bayesian learning
Being signals balanced [Pr(A|a) = Pr(B|b) = 2/3], the difference between the number of signals a and b inferred or observed determines the more probable event.
In this simplified case, Bayes’ rule corresponds to a very simple and intuitive counting heuristic, which is easily computable by all subjects.
In the example above, the third decision maker has to
count two previous choices over his/her only one private signal to determine her choice of A as rational
Bayesian learning
Experiment 1
Session Treatment Participants (women + men) 1 (PD left - PC right) 9 (4 + 5) 2 (PD left - PC right) 9 (5 + 4) 3 (PD left - PC right) 9 (6 + 3) 4 (PC right - PD left) 9 (4 + 5) 5 (PC right - PD left) 9 (5 + 4) 6 (PC right - PD left) 9 (5 + 4) 7 (PD left - PC right) 9 (3 + 6) 8 (PD left - PC right) 9 (5 + 4) 9 (PD left - PC right) 9 (4 + 5)
Total 81 (41+40)
Participants: 81 Mean age: 22,4 Years
First screen (5 seconds)
2 sec
Private draw- PD (right)
Previous choice-PC (left)
Initial screen (2 seconds)
First screen (5 seconds)
Second screen (5 seconds)
First Fixations
Total number of fixations (Fixations = gazing at region of interest –ROI- for at least 200 milliseconds)
Relative time spent fixating ROI (relative time = time in a ROI divided by the total time spent on a task)
Sequence of last fixations
Experimental variables
BAYESIAN - the equal probability of the two states implies that the optimal Bayesian decision rule is to predict the state which obtains the greatest number of observed (Private draw) and inferred signal (Previous choices).
If subjects choose differently from what implied by Bayesian update:
OVERCONFIDENT - if subject’s choice is equal to his Private draw
IRRATIONAL - if subject’s choice is not equal to his Private draw
Subjects’ types
Subjects’ types
Order of choice Bayesian Overconfident Irrational
1st 6 0 3 2nd 9 0 0 3rd 5 2 2 4th 6 2 1 5th 7 1 1 6th 6 2 1 7th 6 3 0 8th 6 3 0 9th 6 3 0
Total 57 16 8 Total (first chooser excluded) 51 16 5
Total allocation of attention
TABLE 5. TOTAL ALLOCATION OF ATTENTION (PERCENTAGE OF TOTAL TIME) PRIVATE
DRAW (PD) FORMER
CHOICES (FC) NO FIXATION TOTAL FORMER CHOICES/
N. OF FORMER CHOICES
BAYESIAN 26.9 63.0 10.1 100 22.4 OVERCONFIDENT 10.4 86.4 3.2 100 19.5 IRRATIONAL 47.1 39.9 13.0 100 22.6 TOTAL 25.6 65.3 9.1 100 21.8
TABLE 6. TOTAL ALLOCATION OF ATTENTION BY SCREEN SIDE (PERCENTAGE OF TOTAL TIME) PRIVATE DRAW FORMER CHOICES / N. OF FORMER
CHOICES LEFT SIDE RIGHT SIDE TOTAL LEFT SIDE RIGHT SIDE TOTAL
BAYESIAN 19.5 29.5 26.9 25.5 21.2 22.4 OVERCONFIDENT 9.2 10.9 10.4 16.8 20.7 19.5 IRRATIONAL 52.0 12.7 47.1 21.4 27.5 22.6 TOTAL 25.6 21.8
Only irrational subjects were significantly more inclined to look at private draw (47.1%) than at former choices (22.6%).
First fixationsPrivate Draw Previous Choices
Latency offirst fixations
N. of first fixations
% N. of first fixations
% Averageduration
Bayesian 0.306 sec27
(13L+14R)
52.924
(13L+11R)
47.1 0.838 sec
Overconfident 0.412 sec 13 (6L+7R) 81.2 3 (1L+2R) 18.8 0.523 sec
Irrational 0.191 sec 3 (2L+1R) 60.0 2 (0L+2R) 40.0 0.835 sec
Total 0.321 sec43
(21L+22R)
46.825
(14L+15R)
53.2 0.775 sec• Overconfident subjects allocated their initial attention to private draw in 81% of the cases, and exhibited a longer average reaction time (0.412 sec.) and a shorter average duration of first fixation (0.523)
First fixations by side
TABLE 4. FIRST FIXATION BY SCREEN SIDES (FIRST CHOOSERS EXCLUDED) PRIVATE DRAW (PD) FORMER CHOICES (FC)
LEFT RIGHT LEFT RIGHT
N. TOT. % N. TOT. % N. TOT. % N. TOT. %
BAYESIAN 8 14 57.1 20 30 66.6 16 38 42.1 6 16 37.5
OVERCONFIDENT 5 9 55.6 9 15 60.0 2 6 33.3 1 3 33.3
IRRATIONAL 1 1 100 2 3 66.6 2 4 50.0 0 3 0
TOTAL 14 24 58.3 31 48 64.6 21 48 43.7 8 24 33.3
No statistically significant difference between left and right orientation of the screen
was detected and the pattern of first fixations across subjects’ types
Likelihood to look at the chosen item
2.00
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No gaze cascade effect: observers gaze was not increasingly directed towards the chosen signal
Likelihood by types
Fig.2 Likelihood that subjects look at the chosen signal as a function of time until decision (by subjects' types)
0,62
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Overconf
Overconfident subjects allocate the first fixation (initial attention) toward private draw and take more time than others to decide if the private signal is on the right or the left of the screen.
Bayesian subjects allocate their initial attention to both kinds of information without exhibiting any particular bias
No evidence of the gaze cascade effect
Findings
In terms of the Dual Process theory, our findings support the hypothesis that automatic detection, as inferred from gaze direction, depends on cognitive biases.
The heuristic and automatic functioning of System 1 orients attention so as to confirm rather than to eventually correct these biases.
The controlled search attributable to System 2 does not significantly differ across subject types.
Interpretation
“Highly accessible impressions produced by System 1 control judgments and preferences, unless modified or overridden by the deliberate operations of System 2.” (Kahneman and Frederick 2002, p. 53)
Gaze participates actively in the process of choice under uncertainty
first fixation effect ⇒ orienting choice gaze cascade effect ⇒ reinforcing choice
Conclusions (1)
Heuristic processes of System 1 select the aspect of the task on which gaze direction is immediately focused
Analytic processes of System 2 derive inferences from the heuristically-formed representation through subsequent visual inspection
This dual account of visual attention orienting may explain the emergence of cognitive biases whenever relevant information is neglected at the heuristic stage.
Conclusions (2)
Voting by ballots and by feet
48
• Aim- is to provide experimental evidence on the Tiebout efficiency-enhancing property in a decentralized system of public goods provision
• Object - a test of the Tiebout model in order to assess if
decentralization and local sorting produces efficiency gains
• Key finding – we show that the model fails because residents are different, not only in their preferences on local public goods, but also because voting and moving are not equally shared.
The Tiebout Model
Tiebout’s (1956) model shows that if a sufficient number of local communities exists to accommodate different types of preferences, individuals could move to that community whose local governments best satisfies his set of preferences (pattern of expenditures and taxes).
Quoting Oates (1969) “Tiebout’s world is one in which the consumer “shops” among different communities offering varying packages of local public services and selects as a residence the community which offers the tax-expenditure program best suited to his tastes”.
The Tiebout Model
This is the so called voting by feet
if households are free to move, people effectively sort themselves into groups that are homogeneous with respect to their demands for local services.
More recently Since 1956, there is a wealth of literature surrounding
the Tiebout hypothesis, both from an empirical and a theoretical point of view.
Rodhe and Strumpf (American Economic Review, 2003) argue that long–run trends in geographical segregation are inconsistent with choice model where residential choices depends solely on local public goods
More precisely they demonstrate that the secular decline in the mobility costs is not matched with an increase in the heterogeneity in local public policies
Rhode and Stumpf (AER, 2003)
Some example of mobility costs historical data - CAR: in 1903 to drive a car cost 143,8 cents/mile in
1998 dollars, while in 1998 54,9 cents;- TRAIN: a passenger mile cost 37,4 cents in 1895
and 13,4 cents in 1995;- AIR: a passenger mile cost 108 cents in 1929 vs.
13,7 cents in 1995;- 3 minute of transcontinental call in January 1915
cost 20,70$ in current dollars, which was almost 314$ in 1998 dollars.
Rhode and Stumpf (AER, 2003)
Data on a sample of US municipalities (1870-1990), all Boston-area municipalities (1870-1990) and all US counties (1850-1990) show that municipal per capita taxes, school district per capita taxes, total spending, protection spending etc. incomes and racial composition are now less differentiate across local communities than in the past.
Rhode and Stumpf (AER, 2003)They explain this result as follows
1. growing federal role in providing public services
2. zoning policies are more and more popular after the war, avoiding the poor-chasing-the-rich phenomenon
3. growing local government competition (to have the same category of residents: the richer !)
Our explanationIn our paper we test a fourth cause of the phenomenon observed.
We show that the Tiebout model may fail not only because residents have heterogeneous preferences on local public goods, but also because voting and moving are not equally shared.
The experimental design• 2 treatments x 3 sessions• 10 rounds for each session• In each session 15 subjects (undergraduate
students) are randomly allocated in 5 communities • Each community is allowed to provide only one
public good• The four suits of cards (clubs, diamonds, hearts,
spades) represent different types of public goods• Playing cards randomly assigned to each subject
for the whole experiment• The number over the cards determines subject’s
preferences over public goods
The experimental designThe individual welfare given by public good provision is the difference between individual benefit and individual cost.
The individual benefit given by public good provision is the minimum between the community provision level and the sum of cards of that public good (suit) possessed.
The individual cost is equal to the community production cost, that is the quantity produced (Hp. constant and unitary costs) divided by the number of community members (Hp. community total cost is shared equally among the members of the community).
i typeof cards of sum i, typeof levelprovision minB
Two treatments
In each community there is a subject (the dictator) who decides before each of the 10 rounds the type and the amount of the public good to produce
The other community members can only move or not move and are informed of the type and the amount of goods produced by all the communities before deciding
In each of the 10 rounds each community decides by majority vote the type and the amount of the public good to produce
All subjects can move or not move and are informed of the good produced by all the communities in the previous period
Dictator treatmentDemocracy treatment
1 2 3 4 5 6 7 8 9 100
10
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Session 1
1 2 3 4 5 6 7 8 9 100.00
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1 2 3 4 5 6 7 8 9 100.0010.0020.0030.0040.0050.0060.00
Session 3
Democracy treatment Dictator treatment
Total welfare
Average per capita cost
1 2 3 4 5 6 7 8 9 100123456
Session 1
1 2 3 4 5 6 7 8 9 100.000.501.001.502.002.503.003.504.004.50
Session 2
1 2 3 4 5 6 7 8 9 100.000.501.001.502.002.503.003.504.00
Session 3
Democracy treatment Dictator treatment
Average produced quantity
1 2 3 4 5 6 7 8 9 10012345678
Session 1
1 2 3 4 5 6 7 8 9 100.001.002.003.004.005.006.007.008.00
Session 2
1 2 3 4 5 6 7 8 9 100.001.002.003.004.005.006.007.008.00
Session 3
Democracy treatment Dictator treatment
Data analysis Total welfare is higher in the democracy treatment i.e.
voting by feet increases efficiency if voting by ballot is equally shared
Average cost per capita is higher in the dictator treatment
In the democracy treatment cost per capita tends to increase in the first part of the session, but decreases after the 5th-6th rounds
Quantity produced does not statistically differentiate across treatments
Statistical analysisDemocracy Dictator Democracy Dictator Democracy Dictator
VARIABLES deltaW deltaW deltaW deltaW deltaW deltaW
Nr of moving in 10 rounds 1.019*** 0.295** 0.278*** 0.380*** 0.325*** -0.480***(0.098) (0.142) (0.103) (0.088) (0.070) (0.090)
Nr. of Rounds in wich a person with an opposite SC is meet -0.439*** -0.168 -3.320*** 2.440*** -0.813*** 0.447***
(0.063) (0.120) (0.593) (0.275) (0.242) (0.084)Nr. of Rounds in wich at least two people with an opposite SC are meet -0.576*** -0.091 -0.096 -0.419* -0.729*** 0.511***
(0.045) (0.119) (0.113) (0.229) (0.099) (0.127)
Moving/non moving decisions taken rightly 1.106*** -1.003*** 4.795*** -0.481*** -0.544*** 0.076(0.147) (0.289) (0.275) (0.148) (0.172) (0.364)
Community in which the individual move is chosen rightly -0.291*** 1.625*** -5.218*** 0.577*** 0.783*** 0.060
(0.102) (0.241) (0.317) (0.158) (0.115) (0.165)Constant -4.460*** -3.230*** 4.991*** -1.552 2.729** 0.615
(0.765) (1.223) (0.806) (1.023) (1.254) (1.960)
Observations 150 100 150 100 150 100R-squared 0.764 0.685 0.787 0.667 0.762 0.444Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Session 1 Session 2 Session 3OLS -Welfare difference between 10th-1th round
Statistical results Positive correlation between the number of moving individuals
and the increase of individual welfare in all the sessions of the democracy treatment, and in two out of three session in the dictator treatment
In the democratic treatment, the welfare increase is negatively related with the number of rounds in which each individual met a person with a SRCC* of the opposite sign, while in the dictator treatment this variable is not significantly correlated with welfare.
*we evaluate heterogeneity in preferences within communities by calculating Spearman's Rank Correlation Coefficients between each one experimental subject and all the others
Interpretations In the democratic treatment the welfare increase is
positively related with the right decision of moving/non moving
In the dictator treatment the sign of the correlation between welfare increase and the decision of moving/non moving depends on the chosen community
Voting by feet enhances welfare, by reducing heterogeneity among members of a community, only if all that people has the right to vote.
Analysis of moving/non movingDemocracy Dictator Democracy Dictator Democracy Dictator
VARIABLES move move move move move move
Individual loss in the previous round(dummy) 1.972*** 2.160*** 1.174*** 2.004*** 1.642***(0.374) (0.550) (0.268) (0.422) (0.378)
Decision taken in the first f ive rounds(dummy) 0.564** 0.889*** 0.529* 1.398*** -0.229(0.263) (0.313) (0.276) (0.388) (0.310)
Presence of at least one person w ith a SC w ith the opposite sign 1.137*** -0.540 0.425 1.063***
(0.308) (0.418) (0.388) (0.352)Presence of at least tw o people w ith a SC w ith the opposite sign 0.744** 0.033 -0.596 0.010
(0.336) (0.398) (0.651) (0.483)Different individual vote on the type of good produced(dummy) 1.278***
(0.487)Different individual vote on the quantity of good produced(dummy) 0.623**
(0.243)Constant -1.700*** -1.184*** -1.277*** -0.698*** -2.277*** -0.639***
(0.265) (0.251) (0.205) (0.197) (0.368) (0.219)
Observations 150 100 150 100 150 100Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Session 1 Session 2 Session 3Probit Moving-Non Moving
Moving decisions1. Individual loss in previous rounds is always
significantly correlated with the decision to move in both treatments
2. Moving decisions are taken more often in the first five rounds
3. In the democracy treatment the decision to move is mainly influenced by community heterogeneity or by the discrepancy between vote and public good produced
ConclusionsTotal welfare is always higher in democracy, i.e. voting by feet increases efficiency if voting by ballot is equally shared among residents
Under democracy the increase of individual welfare is positively related to the number of moving decisions
The better performance of the democratic process over the dictatorship depends on the decreaseof the average public good costs per capita
Conclusions
In democracy, the welfare increase is negatively related with the heterogeneity of people in each community, while in the dictator design this variable is not significant
In the dictator design the increase in welfare is positively related with a right decision on moving/non moving only if the community is correctly chosen, while in the democracy design there is a positive coefficient if the moving/non moving decision is rightly taken.
Aim: to extend previous experimental evidence on travel mode choice by providing subject not only with information acquired through personal experience, but also with actual travel times of the alternative non chosen travel modes
Key Findings: subjects exhibit a marked preference for cars are inclined to confirm their first choices update imperfectly expectations on travel times
Travel mode choice and transportation policy
Experimental literature on travel mode choice relies on studies on route choice
Common object: coordination games, i.e. the payoff each traveler can achieve is conditional on her/his ability to diverge from or to converge with other travelers’ choices
Selten et al. (2007), Ziegelmeyer et al. (2008), Razzolini-Dutta (2009) provide laboratory evidence that choices between route A and route B generate Nash equilibria
Background literature
Evidence from the field that these learning processes are affected by cognitive biases (Kareev et al. 1997, Verplanken–Aarts 1999)
To provide travelers with more accurate information on actual travel times does not necessarily increase their propensity to minimize travel costs (Avineri-Prashker 2006)
Information is better processed when travelers lack long-term experience on travel time distribution (Ben Elia–Erev-Shiftan 2008)
Background literature
Cars are generally perceived as travel means giving people the sensation of freedom and independence
The costs associated to car use are undervalued because they not paid contextually with car use
Pollution or social costs due to car accidents are often neglected and not easily computable
These factors explain the presence of a general propensity to use private cars and of a psychological resistance to reduce it (Van Vugt et al. 1995, Tertoolen et al. 1998, Bamberg et al. 2003)
Background literature
62 undergraduate students (31 women and 31 men) from the University of Firenze
Computerized experiment
Between subject
Each session lasted approximately an hour
Average earnings 18.4 euro
The design
1) Choice between car or metroMetro travel costs are fixed, while car costs are uncertain and determined by the joint effect of casual events and traffic congestion
2) Choice between car or busCar and bus are both uncertain and determined by the combination of casual events and traffic congestion.
Travelers’ utility only depends on travel times, which are converted in monetary costs. After each choice, subjects are informed of actual times of both available modes, but not of the probability distributions determining casual events
The design
The designTable 2 Experimental parameters made known to subjects
Treatment Car Expected Time Travel (in minutes)
Car Fixed Cost
Metro / Bus Expected Time
Travel
Metro / Bus Fixed Cost
Metro vs. Car 25 1.5 30 1.0
Bus 1.0 vs. Car 27 1.5 32 1.0
Bus 0.8 vs. Car 27 1.5 32 0.8
Metro Car treatment- the expected total costs of car and metro were equivalent if the share of car users was not greater than 55%;
Bus 1.0 Car treatment - the expected total costs of car and bus were equivalent if the share of car users was not greater than 55%;
Bus 0.8 Car treatment- the expected total cost of the bus was 20% lower than car expected total costs if the share of car users was not greater than 55%.
The design
Results – Preference for CarsTable 8 Proportion of car choices by treatment (each five periods)
Period Metro Bus 1.0 Bus 0.8 1 0.70 0.60 0.59 5 0.67 0.67 0.35
10 0.60 0.47 0.35 15 0.57 0.67 0.47 20 0.57 0.53 0.53 25 0.77 0.53 0.41 30 0.67 0.73 0.71 35 0.70 0.60 0.71 40 0.60 0.53 0.53 45 0.67 0.60 0.53 50 0.73 0.53 0.53
Total 0.68 0.58 0.50
Results– Preference for cars
Results – Preference for cars
Results – Preference for cars
Results – Preference for cars
Results – First Choice Effect
Results – First Choice Effect
Results– Travel Mode Stickiness
The first choice effect decreases the propensity to change travel mode
Only 28.6% of the subjects in the metro treatment and 39% of the subjects in the bus treatments change more than 20 times over 50 periods.
On average, subjects change mode 17.7 times in the metro treatment and 18.0 times in the bus treatments
Travel mode choice is significantly affected by heuristics and biases that lead to robust deviations from rational behaviour
Travelers choose modes using behavioural rules that do not necessarily involve the minimization of total travel costs.
Subjects show a marked preference for cars, are inclined to confirm their first choice and exhibit a low propensity to change travel mode.
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Conclusions
In repeated travel mode choice, available information is not properly processed, cognitive efforts are generally low and rational calculation play a limited role
The habit of using cars should be assumed to be relatively resistant, to the effect of economic incentives
Little progress can be expected by asking travelers to voluntarily reduce the use of a car or even by subsidizing public transport costs
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Conclusions
One of the basic tenets of laboratory methodology is that the use of non-professional subjects and monetary incentives allows making subjects’ innate characteristics largely irrelevant
In our experiment, it is as if subjects take into the lab the preferences applied to real choices and stick to them with high probability
This inclination to prefer cars tends to override the incentives effect
Labels give subjects clues to become less and not more rational
Back to Methodology
In our experiment, subjects’ behavior depends more on prior learning outside the laboratory than on expected gains in the laboratory
Labels have the power to increase external validity with a minimal sacrifice of the internal validity
To test learning and cognitive models, it is necessary to remind and to evoke contexts which may activate emotions, association, similarities in the laboratory
The power of labels
Tramhttp://www.youtube.com/watch?v=H1XrL_U38-c
Metrohttp://www.youtube.com/watch?v=dYmsmWqCV0A
Carhttp://www.youtube.com/watch?NR=1&v=Sk-WlKTtExA
Progetto ALBOhttp://www.progettoalbo.it/images/video/trailer.mp4
LAVREB Laboratory of Virtual reality and Economic Behaviorhttp://www.lavreb.unisi.it/
Some examples