1 Economics 776: Experimental Economics First Semester 2007 Topic 2: Norms and Preferences: Evidence...

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Economics 776:Experimental Economics

First Semester 2007Topic 2: Norms and

Preferences: Evidence

Assoc. Prof. Ananish ChaudhuriDepartment of Economics

University of Auckland

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Other-regarding preferences

• The null hypothesis of game theory is the homo economicus assumption of self-interest

• But it turns out that in a large majority of cases people exhibit what might be called other-regarding preferences– Altruism– Fairness

• Emotions play a role in economic decisions

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Ultimatum Game

• Ultimatum Bargaining Game with a pie of $c• Player 1 offers an allocation (c-x,x) where x is

the amount offered to Player 2. • Player 2 is informed about the offer and can

accept or reject.• If player 2 rejects both players earn zero.• If player 2 accepts the proposed allocation (c-

x,x) is implemented.• Self-interest prediction: (c-,), with 0, is

proposed and accepted, as is every offer x > .

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Dictator Game

• Dictator Game

• Like the ultimatum game but player 2 has no choice so that the proposed allocation is always implemented.

• Self-interest prediction: (c -,) where 0 is proposed.

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The ultimatum game (Güth, Schmittberger and Schwarze, JEBO

1982)• Split DM 4 or DM 10 (multiples of DM 1)• inexperienced subjects• All offers above DM 1• Modal x = 50 percent of pie (7 of 21 cases)• Mean x = 37 percent of pie• A week later (experienced subjects)• All except one offer above DM 1• 2/21 offer an equal split• Mean offer 32 percent of pie• 5/21 of the offers are rejected• Systematic deviation from standard prediction

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Do higher stakes change the results?

• Hoffman, McCabe, Smith (1999): Ultimatum Game with 10$ and 100$– Offers are not dependent on the size of the cake.– Rejections up to 30$

• Cameron (1995): UG in Indonesia 2.5$, 20$, 100$ (GDP/Person = 670$)– The higher the stakes the more offers approach 50/50.– Responders more willing to accept a given percentage offer at

higher stakes.– Without payments we see differences: less generous offers and

more rejections.

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Does altruism explain the high first mover offers?

• Forsythe, Horowitz, Savin and Sefton (GEB 1994) compare simple ultimatum games with dictator games. In the latter, the proposer proposes a division (1-x,x) of the bargaining cake, which is then implemented.

• Result 1:– In the dictator game the distribution of x shifts

significantly towards x = 0 relative to the ultimatum game if real money is at stake (modal offer is x = 0).

– If only hypothetical questions are asked no such shift can be observed.

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Does altruism explain the high first mover offers

• Result 2:– Even with real pay there is a concentration of offers

around the equal split (see Fig. 4.4)

• Conclusion: Some of the subjects seem to be motivated by altruism but the higher concentration of offers around the equal split in the ultimatum game suggests that behavior cannot be fully attributed to altruism.

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Social distance

• Conjecture that experimenters exert a kind of social control merely by being able to observe subjects’ actions.

• Hoffman, McCabe and Smith (1999) report that if it is ensured that subjects know that the experimenter cannot observe individual decisions approximately 70 percent of the subjects in the dictator game give nothing and almost no offers above 0.3 can be observed.

• And: does such an environment itself lead to some sort of experimenter effect?

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Social distance

• Comparison of one-period ultimatum games with and without subject-experimenter anonymity (but always subject-subject anonymity).

• Comparison of one-period ultimatum game with the impunity game which has the same move structure as the ultimatum game but the same incentive structure as the dictator game (for first mover).

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Social distance

• Impunity Game: Player 1 proposes a division (1-x, x)

• Player 2 accepts or rejects. In case of rejection player 2 gets nothing while player 1 still gets 1-x.

• Punishment option is removed.

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Social distance (Bolton and Zwick 1995)

• Punishment hypothesis: First movers in the ultimatum game choose “high” offers because of the fear of rejection.

• Prediction: Lower offers in the impunity game compared to the ultimatum game.

• Anonymity hypothesis: First movers in the ultimatum game do not want to be judged by the experimenter to be greedy and selfish.

• Prediction: With subject-experimenter anonymity there are significantly lower offers than without subject-experimenter anonymity in the ultimatum game.

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Player 1

Player 2 Player 2

0.000.00

2.002.00

0.000.00

1

2

Top Bottom

Left Right Left Right

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• Choices for the payoffs:– 2.20, 1.80– 2.60, 1.40– 3.00, 1.00– 3.40, 0.60– 3.80, 0.20

• Three conditions: – Ultimatum– Double Blind– Impunity (which removes the punishment option for

Player 2)

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Trials

1 – 5

Trials

6-10

All Trials

Ultimatum 0.62

0.40

0.50

0.30

0.56

0.35

Double Blind

0.60

0.42

0.66

0.46

0.63

0.44

Impunity 0.96

0.96

1.0

1.0

0.98

0.98

Player A Equilibrium actions Equilibrium Outcomes (%)

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Rejections rates by second mover

1.80 1.40 1.00 0.60 0.20

Ultimatum 7.7 11.8 57.1 77.8 100

Double Blind

13.3 7.1 6.7 70 100

In the Impunity Treatment no offer was turned down by the second movers, not even one of $0.20

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Social distance

• Results: – Punishment confirmed – Anonymity rejected

• In the ultimatum game offers in the first five periods are slightly lower under anonymity, in the second five periods they are slightly higher. In general offers are similar to other non anonymous ultimatum games.

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The Trust GameBerg, Dickhaut and McCabe (1995)• Player 1 and 2 are endowed with $10.• Player 1 decides how much of her $10 to

transfer to player 2.• Experimenter triples any amount sent.• Player 2 is informed about 1’s transfer and

decides how much of the tripled transfer to send back.

• Standard prediction (using backward induction): – Player 2 sends back nothing.– Player 1 sends nothing.

25Treatment 1 (No history) 32 pairs

26Treatment 2 (Social History) 28 pairs

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Does the trust game really measure trust? (Gneezy, et al., 2000)

• Vary the upper bound on repayment amounts• If no repayment possible then this is the same

as a dictator game except any amount sent gets multiplied by three

• If more amount sent when the upper bound on repayments is higher then this would indicate that senders motivated by trust and expected reciprocation rather than a desire to “share” (or similar altruistic motives)

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• Initial endowment of 10 chips

• Any amount sent (a) is doubled to 20

• Receivers can send back (r)

• Three treatments which vary the upper bound on r– r = 2– r = 10– r = 18

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• Except for r = 2, resulting in the dictator game, higher contributions trigger higher repayments.

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Does the trust game really measure trust? (Chaudhuri and Gangadharan (2005))

• Compare behaviour in a trust game with that in a dictator game

• Within subjects treatment• 100 subjects• $10 initial endowment• Each play as sender and receiver• In the absence of trust there should be no

difference in the amount sent as the Proposer in the trust game and the Allocator in the dictator game

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Does the trust game really measure trust? (Chaudhuri and Gangdharan, 2005))

• Average amount sent in the trust game is $4.33

• Average amount sent in the dictator game is $1.345

• Significant difference using a t-test or a non-parametric Wilcoxon paired sign-rank test

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Histograms by Game

Dictator Game

0 1 2 3 4 5 6 7 8 9 10 0

.57

Trust Game

0 1 2 3 4 5 6 7 8 9 10

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Proposer’s decision and risk attitudes

• Suppose the proposer decides to send $X out of his initial endowment of $10.00 to the responder.

• The responder then gets $3X.

• With probability “p” he returns “” proportion of that amount and with probability “1-p” he returns nothing.

• So with probability “p” the proposer gets (10-X+3X) while with probability “1-p” he gets (10-X).

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• The expected payoff is E() = p(10-X+3X) + (1-p)(10-X)

= 10 – X + 3pX(1)

• Taking the derivative of expected payoff with respect to X we get

13)(

pdX

dE (2)

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• Thus the expected payoff is increasing in X if and only if 3p > 1.

• If 3p < 1 or p < 1/3, then from (2), the expected payoff is negative. In that case the proposer is better off simply holding on to the initial endowment.

• Using “U” to denote the expected utility (with U(0) = 0), we can express the expected utility of the proposer in this case as E(U) = p*U(10-X+3X) + (1-p)*U(10 – X)

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• Proposer chooses X to maximize expected payoff • FOC: (3-1)pU’(10-X+3X) = (1-p)U’(10 – X)• Let the utility function exhibit constant relative risk

aversion with the form

• U(W) =

• where = coefficient of relative risk aversion. A larger value of signifies a greater degree of risk aversion.

1

1W

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• Using this CRRA utility function and substituting in the first order condition above we get

)10)(1()310)(13( XpXXp

p

p

X

XX

1

)13(

10

310

1

1

)13(

10

310

p

p

X

XX

or

or

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• Taking the derivative of X w.r.t. the risk aversion parameter () we get

2

1

2

1)(log

)10(

30

KK

d

dX

X

p

pK

1

)13(

2

12 1)(log

30

)10(

KK

X

d

dX

where

or (3)

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• The sign of the derivative depends on the value of log K – negative if log K is positive – positive if log K is negative.

• If log K is negative that implies that

p

pK

1

)13( < 1 or 3p <1 or p <1/3

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• This would be true if and only if a subject sends money expecting to get back less than 1/3 of what the responder gets

• On the other hand, for those subjects who wish to maximize their payoff, log K must be positive, i.e. K > 1

11

)13(

p

p or p > 1/3 i.e.

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• For these subjects p > 1 and Log K > 0 and so the sign of the derivative in equation (3) is negative, i.e. the amount of money sent is decreasing in

• The higher the risk aversion parameter the smaller is the amount sent.

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• 44 people expect to get back less than 1/3 of what the receiver gets; on average sent $2.14 out of $10.00.

– The modal amount (18 out of 44) sent by these subjects is $0.00.

• 37 people expected to get back more than 1/3; average amount sent is $6.05.

• 17 people expected to get back exactly 1/3; on average sent $5.41.

• Average for those who expect to get back at least 1/3 or more is $6.05. – The modal amount sent is $10.00 (n = 17)

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Histograms by Gender

Men

0 1 2 3 4 5 6 7 8 9 10 0

.361702 Women

0 1 2 3 4 5 6 7 8 9 10

Amount Sent in the Trust Game Broken up by Gender

On average men (n = 47) send $5.30 while women (n = 53) send $3.47

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Chaudhuri and Gangadharan, 2005

• Amount of money received by the responder from the paired proposer and the percent of money sent back highly correlated. (Spearman’s Correlation Coefficient = 0.3203, p = 0.0033)

• Responder (second) stage of the trust game is analogous to a dictator game. – Average amount sent as the allocator in the dictator

game is 11.8% – Average amount returned as the responder in the

trust game is 17.4%. – (significant at the 5% level using non-parametric

Mann-Whitney U test /Wilcoxon ranksum test)

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• Responses regarding amount to be sent back using “strategy method” highly consistent with actual amounts sent back

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2 2 31 1

4 4

49

42 2 1 1

0

5

10

15

20

25

30

35

40

45

50

Number of Subjects

-10 -6 -5 -4 -2 -1.5 -1 0 1 2 3 4 13Deviation from stated amount to be returned

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• Those who trust do not necessarily reciprocate. • Define a subject as “trusting” if he or she sent exactly

50% or more of her initial endowment of $10.00; otherwise “non-trusting”

• Using the 50% cut-off we get 58 subjects who are non-trusting (sent less than 50%) and 42 trusting (sent exactly 50% or more).

• The non-trusting subjects returned on average 18% of the amount they received while the trusting subjects returned 16%. – This difference is not significant using either a t-test or a Mann

Whitney test and the result does not change when we try alternative definitions of “trusting”.

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• How about those who do reciprocate trust? Are they more trusting?

• The answer turns out to be an emphatic yes. • Define “trustworthy” those who return at least

1/3 or more of any amount offered to them; otherwise “non trustworthy”

• It turns out that the 27 trustworthy subjects send $5.33 on average which is higher than the $3.82 on average sent by the remaining 55 subjects. – (t = 1.79, p = 0.07 using a t-test and z = 1.84, p = 0.06

using a Mann Whitney test).

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• We next compare the behavior of the trustworthy receivers defined as those who send back 1/3 or more of the money received from the sender) and the less trustworthy ones (i.e. those who send back less than 1/3) in the dictator game.

• We find that on average trustworthy subjects send $1.89 as the allocator in the dictator game. The less trustworthy ones send $0.83. – This difference is highly significant using a t-test (t =

2.251, p = 0.03) and marginally significant using the non-parametric Mann-Whitney test (z = 1.756, p = 0.08).

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Fehr et al. (1997) Gift exchange game

• Worker provides effort “e”

• Output V = V(e) assumed linear – V = e – V = 10e

• Cost of Effort C = C(e) with C’(e)>0 and C’’(e) > 0

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Parameters of the Experiment

e 1 2 3 4 5 6 7 8 9 10

v(e) $1.00 $2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00 $9.00 $10.00

c(e) $0.01 $0.10 $0.20 $0.40 $0.60 $0.80 $1.00 $1.30 $1.60 $2.00

)10,...,1,0(w

)10,...,2,1(*, ee

3/1p3.10 f

1k

Value and Cost of Effort

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• Employer offers a wage (w) and suggest an effort level e* (and in some cases a penalty for shirking)

• Employee gets to see w and e* and then decides on actual effort level

• Payoffs depend on the actual treatment

• In the most general case

– Employer’s payoff: V(e) – w

– Employee’s payoff: w – C(e)

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• L identical risk neutral workers• N < L risk neutral firms• Three treatments

– No reciprocity (NRT)– Weak Reciprocity (WRT)– Strong Reciprocity (SRT)

• Competitive market with excess supply of workers

• Firms can impose contracts with zero rent

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Fehr et al. (1997)

• Differences in rent offered between NRT and WRT measures the extent to which firms wish to elicit reciprocal behaviour– WRT might be able to induce workers to exert

effort levels above an enforceable maximum by offering generous rents

• In SRT, firm’s reciprocity and anticipation of same by workers may lead to higher effort levels than WRT

56

Fehr et al. (1997)

• Once effort (e) is chosen a random mechanism determines with probability “p” whether shirking (i.e. e < e*) is verifiable by a third party or not

• If e e*, worker gets wage “w”

• If e < e*, then worker gets “w” with probability (1-p) and “w – f” with probability (p)

57

Fehr et al. (1997)

• Effort cost – cost c(e) with c(0) = 0, c’(e)> 0 and c’’(e) > 0

• Payoff to worker – Uns = w – c(e) if not shirking– Us = (1-p) (w - c(e)) + p(w – f - c(e))

• For any contract {w, e*, f} the worker will not choose e > e* since c’(e) > 0

• If worker shirks then set e = 0, since fine “f” is independent of the amount of shirking

58

Fehr et al. (1997)

• Perform e = e* – if Uns > Us – Uns = w – c(e*) – Us = (1-p) (w - c(0)) + p(w – f – c(0))

= (1-p)w + p(w-f) since c(0) = 0 given e = 0 p*f > c(e*)

Payoff to the employer from employing a non-shirking worker

= (q – w)e* where q is the “price” or “redemption value”

59

Fehr et al. (1997)

• Exogenous upper bound on f = F• Probability of detection is also

exogenous = p*• Thus in equilibrium p*F = c(ē)• w = c(ē)• e* = ē = c-1(p*F)• fine = F• Optimal contract {w = c(ē), ē , F}

60

Fehr et al. (1997)

• In the strong reciprocity treatment there is a third stage where the employer after observing the worker’s effort can choose to reward or punish the worker

• Implications for the third stage straightforward– Self-interested employers will never reward or

punish in stage 3– This in turn should not affect employee’s effort in

stage 2– Outcomes should be the same in WRT and SRT

63

Fehr et al. (1997)

• There exist high effort/high rent strategies that yield higher profits than low rent/low effort strategies

• Firms demand and enforce significantly higher effort levels in SRT than WRT

65

McCabe, Rassenti and Smith (1998)

• Evolutionary psychologists have argued that the human mind is evolutionarily adapted to help make social exchange work in family and tribal groups,

• Reasoning experiments showing that subjects have a pronounced tendency to punish cheaters in social exchange contexts (Cosmides, 1985; Cosmides and Tooby, 1992)

• Behavior in ultimatum games can be interpreted as an expression of this tendency

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• An alternative hypothesis for explaining these anomalous results is the concept of reciprocal “altruism” or simply “reciprocity” developed in the evolutionary biology and psychology literatures

• In this literature, people are assumed to possess a specialized mental algorithm in which their long-term self interest is best served by an unconscious willingness to punish cheating on cooperative social exchanges (negative reciprocity).

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• They also postulate a willingness to reward the initiation of cooperative social exchanges – “positive reciprocity”

• This idea is that subjects are culturally, if not biologically, disposed to reputation building across games– i.e., the game of life is a repeated sequence

of constituent games that may differ markedly.

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• Hence, if you want to have a reputation for reciprocity, you do not engage in noncooperative or defectionist strategies just because the experimenter thinks he has presented you with a one-shot game.

• If you exploit situations in that you think you cannot be ‘‘found out,’’ a condition which is uncertain, then how can you be sure that you will not slip into exploitative tactics at other times, and compromise your reputation?

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SPNE

SJPM

Game 1

70

SPNESJPM

Game 2

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• The treatments also vary what the subjects know about their counterpart’s payoffs.

• Under private information, subjects know only their own payoffs. This precludes a subject’s ability to make inferences about her counterpart’s intentions to determine why a choice might have been made, and to engage in reciprocity.

• Thus, in Same 1, a left move at x2 under complete payoff information can be read by 2 as a signal to player 1, saying that player 2 wants to reach 50, 50 which dominates 40, 40 , and that if player 1 defects, 2 has the option of punishing him. With private information, such ‘‘mind reading’’ is not possible.

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Hypotheses

• H1: Right game play will predominate in Single 1 and Single 2

• H2: The {50, 50} SJPM outcomes in Single 1 will exceed those in Single 2.

– Expect more Player 1s to go for (60, 30) in Game 2 compared to Game 1 because in Game 2 player 2 is more likely to take (50, 50) rather than move down to (20, 20) to punish Player 1

– Reciprocity will be more difficult in Single 2, without the punishment option, compared to Single 1.

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Hypotheses

• H3: Based on the Folk Theorem, the repeated games Same 1 and Same 2 will result in more cooperation than Single 1 and Single 2

• H4: Private 1 will yield a higher proportion of right game play, and of the SP outcome, than any other treatment condition.

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Treatment Left

Branch

50

50

60

30

20

20

Right

Branch

30

60

40

40

15

30

Single 1 0.43 0.85 0.08 0.08 0.57 0.00 0.94 0.06

Single 2 0.46 0.50 0.50 0.00 0.54 0.00 1.00 0.00

Same 1 0.82 0.88 0.04 0.06 0.18 0.038 0.85 0.05

Same 2 0.62 0.84 0.16 0.00 0.38 0.17 0.70 0.13

Private 1 0.16 0.43 0.56 0.015 0.84 0.14 0.84 0.02

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Findings

• Right game play does not predominate in either Single 1 or Single 2. Using a binomial test, we cannot reject the null hypothesis that left and right game play are equally likely under either treatment

• Contingent on right game play, however, we observe strong support for SP in both Single 1 and Single 2

• The SJM outcome is significantly more frequent in Single 1 than in Single 2 p < 0.001, in accordance with the punishment hypothesis.

• The outcomes in Same 1 and Same 2 show more cooperative play than in the corresponding Single 1 and Single 2 experiments.

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• Same 1 shows more left plays and more cooperative outcomes than Same 2, p < 0.001 .

• Comparing Private 1 and Same 1, non-cooperative right game play is very much larger in the former p < 0.001

• Across all treatments, Private 1 yields by far the highest incidence of non-cooperative right game play.

• On average across subject pairs, it pays for subjects in role 2 to move left at x2 .

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• Across both treatments for Game 1, it does not pay for subjects in role 1 to defect on the SJM outcome 50

• Hence subjects are not behaving in an irrational manner, in the sense that, given the reciprocity types in the sample, and the subjects’ ability to read each other, on average they are collecting more money than if they behaved in a myopic self-interested way, ignoring the reciprocity characteristics of those with whom they are matched.

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• Reciprocity and its origins in trust andror punishment is in need of being modeled to account for a variety of behavioral reputational types:– those who offer cooperation on the basis of

pure trust– those who require the prospect of punishment– those who defect when cooperation is offered– those who, faced with defection, tend to

respond with punishment

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Chaudhuri et al. (2003)

• Use a simple 2 person sequential game to understand trust

• Measure people’s predisposition towards trust using Yamagishi’s Trust Scale - a commonly used psychological measure

• We want to understand if we can correlate the two

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Player 1

Player 2 Player 2

2.501.25

0.750.75

1.255.00

3.753.75

Top Bottom

Left Right Left Right

SPE SJPM

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Yamagishi Trust Scale

• Most people tell a lie when they can benefit by doing so.

• Those devoted to unselfish causes are often exploited by others.

• Some people do not cooperate because they pursue only their own short-term self-interest. Thus, things that can be done well if people cooperate often fail because of these people.

• Most people are basically honest. • One should not trust others until one knows them

well.

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Experimental Procedure

• 76 subjects from Wellesley

• Paired anonymously (different rooms)

• Each player plays both roles of Player 1 and Player 2 except paired with a different person in each role

86

Research Questions

• Do trust and reciprocity based motivations affect the outcome in this game?

• Can trust be explained by what McCabe et al. (1998) refer to as “pure trust”? In other words are trust scores correlated to trusting moves?

• Is the trusting move correlated to reciprocity? Do those who trust, also reciprocate the trust of others?

87

Summary Findings

• What we find is that by and large the “self-interested” outcome is rejected by a majority of individuals.

• People with high trust scores are both trusting and trustworthy in that they trust others and also reciprocate others’ trust.

• People with low trust scores may show trust, but are often not trustworthy in that they often do not reciprocate the trust of others.

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Yamagishi Trust Scale

• Highest score possible is 25

• Minimum is 5 while maximum is 21

• Mean = 12.88

• Median = 13

• Mode = 13

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Player 1

21/76=27.6% 55/76=72.3%

Player 2

21/21 0/21

Player 2

30/55 25/55

2.501.25

0.750.75

1.255.00

3.753.75

90

Results

• Probit analysis of choice as Player 1 regressed against trust score

• Does not yield significant coefficient

• Neither does a probit analysis of choice as Player 2 regressed against trust score

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Results

• Curious fact: 72% choose “trusting” response

• But only 45% choose the reciprocal response as Player 2 when faced with a trusting move by Player 1

• 55% of the players choose to “defect” when faced with a trusting move by player 1

92

Results

• Consistent players - trusted and reciprocated trust

• Inconsistent players - trusted but did not reciprocate trust

• Average trust score for consistent players is 14 while that for inconsistent players is 11.61

• Highly significant difference using t-test (0.03) or Mann-Whitney U-test (0.01)

93

Results

• Probit analysis

• Look at sub-sample of players who trusted as Player 1 and then faced a trusting move as Player 2

• Regress choice as Player 2 against trust score

• Significant coefficient (p-value = 0.03)

• Marginal effect = 5.4%

94

Results

• Compare “high trusters” (those above average of 12.88) and “low trusters” (those below average)

• 16 out of 46 “high trusters” consistent

• 5 out of 30 “low trusters” consistent

• Using sample proportions test z = 1.81

• Significant at 0.08 level using 2-tailed test and 0.05 level using 1-tailed test

95

Discussion of the Results

• Roughly same proportion of high (33 out of 46 = 72%) and low trusters (22 out of 30 = 73%) choose the trusting response of Bottom

• But high trusters exhibit more reciprocal behavior as Player 2

96


• It seems that the high trusters are both trusting and trustworthy

• The low trusters on the other hand seem to do the following: As player 1 they choose Bottom hoping to get a reciprocal response

• But as player 2 they maximize payoff by choosing Left

97


• Trust and trustworthiness thus seem to be two very different constructs

• Trust seems to have two very distinct components - one a social orientation towards others - relational trust - the other is a calculative attitude towards risk -

98


• The first component - relational trust - is a social virtue in the sense of Fukuyama - the second is more ambiguous

• The “low trusters” would probably require an explicit punishment mechanism for them to exhibit reciprocity

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Social Capital, Trust and Reciprocity

• Social capital - current buzzword

• A growing body of research suggests that “social capital” as embodied in the tendencies to “trust” strangers and “reciprocate” such trust influence a wide range of economic phenomena

• Trust reduces transactions costs and acts as the essential “lubricant” of the economy

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Social Capital, Trust and Reciprocity

• Fukuyama (1995) argues that differences in the level of trust among citizens might explain differences in their levels of development

• Knack and Keefer (1997) find strong correlation between rates of growth and fractions of citizens who said they generally trust people.

101

Does Social Capital have an economic payoff?

• Trust reduces transactions costs• Contracts that may not be enforceable might be

undertaken in the presence of mutual trust and reciprocity – leading to increases in efficiency

• Knack and Keefer (1997) find that trust and civic cooperation are associated with stronger economic performance

• Trust and norms of civic cooperation stronger in countries that (1) effectively protect contract and property rights and (2) more homogeneous

102


• World Values Survey from 29 market economies – 21 in 1981– 28 in 1990-91

• Trust assessed by asking “Generally speaking, would you say that most people can be trusted, of that you can’t be too careful in dealing with people?”

• Trust measured by percentage of people who said “most people can be trusted” – Mean 35.8% (Don’t know responses deleted)

103


• Norms of civic cooperation measured by creating composite index on the basis of multiple questions such as– Avoiding fare on public transport– Cheating on taxes given the chance– Keeping money you have found– Claiming govt. benefits one is not entitled to

• Responses range from “can always be justified” to “never justified” or something in between

105


• Test the impact of trust and civic norms on both growth and investment rates using WVS indicators

• To minimize endogeneity problems measure performance subsequent to the measurement of trust and civic cooperation

106


• Other explanatory variables include– Percent of primary and secondary school enrollment

in 1960– Per capita income at the beginning of the period– Price level of investment goods relative to the US

• Dependent variables– Average annual growth rate of per capita income

1980 – 1992– Investment/GDP (averaged over 1980-92)

107


• Social Capital variables exhibit a strong and significant relationship to growth– The coefficient of TRUST in Eq. 1 indicates that a 10

percentage point rise in that variable is associated with an increase in growth of four-fifths of a percentage point

– one standard deviation growth in trust is associated with one-half of a standard deviation increase in growth

– Four point rise in the CIVIC scale is associated with an increase in growth of more than one percentage point

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• Impact of TRUST higher in poorer countries

• Coefficient of interaction term TRUST*GDP80 negative and significant

• For a country with a 1980 per capita GDP of $1000, the coefficient of the TRUST variable is double that for the entire sample

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• Social capital variables measured near the beginning of sample period for 21 out of 29 countries

• For the other 8 measured in 1990 – Could cause reverse causality problems

• Deleting these 8 countries increases the coefficients for the social capital variables suggesting that reverse causality is not a problem

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• Gini coefficient for inequality is strongly correlated with TRUST

• But TRUST is still significant at 5% when Gini Coefficient added to the regressions

• This suggests that TRUST and CIVIC are not solely capturing re-distributional or other effects of inequality

1 Economics 776: Experimental Economics First Semester 2007 Topic 2: Norms and Preferences: Evidence...

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