Discussion Paper No. 496

A FOUR COUNTRY COMPARISON
OF SPITE, COOPERATION AND ERRORSIN VOLUNTARY CONTRIBUTIONMECHANISMS

Jordi Brandts
Tatsuyoshi Saijo
and
Arthur Schram

January 2000

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanA Four Country Comparison of Spite, Cooperation andErrors in Voluntary Contribution Mechanisms

byJordi Brandts*
Tatsuyoshi Saijo**
Arthur Schram***June 29, 1999

* Instituto de Analisis Economico (CSIC), Campus UAB, 08193 Bellaterra, Barcelona, Spain and
Department of Economics, University of California, Berkeley CA 94720, USA. Email:brandts@cc.uab.es.
** Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki,
Osaka 567, Japan. Email: saijo@iser.osaka-u.ac.jp*** CREED, Department of Economics, University of Amsterdam, Roetersstraat 11, 1018 WB
Amsterdam, the Netherlands. Email: ArthurS@fee.uva.nl

Abstract
This paper presents data from experiments with a linear voluntary contributions
mechanism for public goods conducted in Japan, the Netherlands, Spain and the USA.
The same experimental design was used in the four countries. Our 'contributionfunction' design allows us to obtain a more complete picture of subjects' behavior thanprevious studies; it yields information about situations where it is a dominant strategy
to contribute all the endowment and about situations where it is a dominant strategy tocontribute nothing. Our results show, first, that differences in behavior across countries
are not large. Second, the evidence for spiteful behavior by Japanese subjects, that has
been observed in other studies, is not confirmed by our results. Third, as a whole ourdata are inconsistent with the explanation that subjects contribute only out of confusion.

This paper is part of the EU-TMR Research Network ENDEAR (FMRX-CT98-0238).

1. Introduction
Traditionally economists have not paid too much attention to the investigationof cross-country differences in behavior. This is probably due to the fact that economic
analysis is based precisely on the idea that human behavior everywhere is guided bycommon principles. This view does not exclude these factors from having some effecton economic behavior; they are, however, considered to be just secondary influences.
The question is, however, an empirical one and needs to be analyzed on the basis ofdata. It is difficult to conduct this kind of analysis using only field data. When
comparing behavior across countries on the basis of information from the field one is
generally looking at data which have been generated in different environments. This
makes a direct evaluation of cultural differences alone problematic.
Experimental methods allow for a very controlled approach to the issue athand. Examples of experimental studies that illustrate the virtues of the experimental
approach in this area are Kachelmeier and Shehata (1990), Roth, Prasnikar, OkuwoFujiwara and Zamir (1991) and Burlando & Hey (1997). The degree of control that canbe achieved in experiments appears to be especially desirable when the objective is theanalysis of cross-country differences in motivational factors of human behavior.
Our objective in this paper is to present a broad comparison of behavior inJapan, the Netherlands, Spain and the United States using a common experimental
design. We use a voluntary contributions mechanism (hereafter, vcm) for linear public
goods to obtain information about motivation in a context of interdependence. Our
design makes it possible to study, at the same time, two different types of behavior, notexclusively motivated by subjects’ own monetary payoffs. In this way we are able toobtain a unifying view of behavior.
We analyze contributions that are made in the case in which they lead to adecrease in the contributor’s monetary payoff and to an increase of others’ payoffs.
There is now abundant evidence of contributions in this kind of situations. Ledyard(1995) and Schram (1999) present overviews of these results. This behavior has often
been attributed to some kind of ‘altruistic’ or ‘cooperative’ motivation: subjects’ utility
may depend positively on others’ payoffs.
We also study behavior for the case where the individual’s direct interest is infull contribution. Saijo and Nakamura (1995) motivate our interest in this case. They
report evidence from a set of experiments conducted at the University of Tsukuba,
1using a standard linear public goods environment. Their central result is that subjects
deviated to a substantial degree from a dominant strategy of full contribution. This kindof behavior has up to this point not been observed in other countries.
This surprising result can be interpreted in terms of a ‘spiteful’ or ‘competitive’value orientation. Subjects may be motivated not only by the absolute amount of thepayoff they obtain, but also by the relation of their own payoff to the payoffs of others.1
In the context considered, a subject’s decision not to contribute his whole endowment
imposes a certain cost on himself, but an even larger cost on the other participants in thevcm; if spite is sufficiently strong subjects may be willing to give up some of their
payoff to hurt others.
Taken together the two types of deviations from the maximization of own
payoff mentioned above point to an alternative interpretation of behavior in terms ofdecision errors. If one adopts this interpretation, one can view the two types ofdepartures from the dominant choice in a consistent way: subjects’ make the mistake ofcontributing when it is a dominant choice not to do so and they also mistakenly fail tocontribute when a dominant strategy prescribes to do it.
We believe that the two general issues, whether behavior varies acrosscountries and what the roles of spite, cooperation and errors are in explaining behavior,call for a more systematic investigation in a common framework. To study whether
subjects’ motivations differ across countries the same design should be used in different
locations; to ascertain whether subjects behave similarly for the two types of situations
described above a common design is also necessary. As will be explained in section 2,
some of the features of our design allow for a very efficient data collection and make itpossible to investigate, at the same time, the two issues mentioned above.
The paper is organized as follows. In section 2 we present the experimental
design (with special attention to how we control for the effects of between-country
variables), the treatments and the procedures. Section 3 describes the results and Section
4 presents a statistical analysis of spite, cooperation and errors. Section 5 summarizes
and concludes.
2. Experimental design.
1 Saijo, Yamato, Yokotani and Cason (1997) contains evidence of a different type of spiteful behavior by

subjects at the University of Tsukuba.

22.1. General features of the design
Our experimental design is based on the linear public good environment firstused in Brandts and Schram (1998). The key feature of the design is that subjects do notjust make a single decision in each experimental period. Instead, each subject makes 10
decisions, corresponding to 10 different marginal rates of substitution (hereafter, mrs)
between the private and the public good. A set of 10 decisions of this type, which wewill refer to as a ‘contribution function’, will yield information both with respect tocooperative and to spiteful behavior by subjects. This is so because the different mrs forwhich our subjects had to make decisions were basically of two types. For some of the
mrs a spiteful, but not a cooperative, motivation may lead to deviations from dominant
strategy play. For the rest of the mrs, the reverse will be true: a cooperative motivation,and not a spiteful one, may lead to deviations from the dominant strategy.
In our experiments every subject is asked to divide 9 tokens by investing eachin one of two accounts, A and B. An individual’s investment of one token in account A
yields an identical amount of money to every member of the group to which theindividual belongs. A token placed in account B yields a certain amount of money only
to the subject who makes the investment.2 This division has to be made for 10 different‘mrs’, for which the payoff to the public account was kept constant, while the payoff tothe private account varies; in the experiments these different mrs are called ‘situations’.3
We conducted some baseline sessions and some additional sessions; the difference
between them is in the payoff schedule we employed. Table 1 shows the payoffschedules used in the different countries in the baseline sessions and table 2 gives theones used in the additional sessions. For each country column A (B) gives the payoff per
token in the private (public account). The last column on the right of the table gives thedifferent mrs, which are common for all countries.
Note that for the parameter values shown in table 1, it is a dominant strategy toinvest all tokens in account A for situations 1 and 2 and to invest everything in B for
situations 3 to 10. This is due to the differences between the payoffs in the public andprivate accounts. We allocate subjects to groups of size 4. It is easy to see that for thiscase the efficient solution is for all participants to invest all tokens in A (B) for situations
2 Accounts A and B correspond, respectively,

to what is often called a ‘private account’ and a ‘public
acount’. We did, however, not use these terms in the experiments.

3

1 to 8 (9 to 10). For situations 1 and 2 a subject hurts himself by deviating fromdominant strategy play, but he hurts the others in the group even more. At the sametime, by contributing less than others a subject’s payoff will be larger than that of theothers.4 For situations 3 to 10, we have the more standard environment that bycontributing and hence deviating from dominant strategy play a subject reduces his
own payoff but increases that of the other subjects. For situations 3 to 8 a subject’s
contribution lowers his own payoff less than what it increases the total payoff.
Table 1: Payoff schedules, baseline sessions*
Japan

The
Netherlands

U.S.A.

Spain

A

B

A

B

A

B

A

B

MRS

situation 1

8

2

12

3

8

2

8

2

0.25

situation 2

8

6

12

9

8

6

8

6

0.75

situation 3

8

10

12

15

8

10

8

10

1.25

situation 4

8

14

12

21

8

14

8

14

1.75

situation 5

8

18

12

27

8

18

8

18

2.25

situation 6

8

22

12

33

8

22

8

22

2.75

situation 7

8

26

12

39

8

26

8

26

3.25

situation 8

8

30

12

45

8

30

8

30

3.75

situation 9

8

34

12

51

8

34

8

34

4.25

situation 10
8
38
12
57
8
38
8
38
4.75
* The numbers are the payoff per token, in Japanese yen, Dutch cents, American cents, andSpanish pesetas. The numbers in the columns ‘A’ (‘B’) are the payoff per token in the public
account A (private account B). The mrs is defined as the value for an individual of a token
invested in the private account divided by the value for an individual of a token invested in the
public account.

Situations 9 and 10 differ in an important way from all other situations: contributing isinefficient. To see this observe that the mrs of both these situations are larger than 4, the3 The mrs is just the reciprocal of the marginal per capita return (mpcr); we use the term mrs following

Palfrey and Prisbrey (1996).
4 Consider, for instance, situation 1 for a group of four subjects in Japan. If all of them contribute 9 tokens toaccount A, each of them will obtain a payoff of 8x36=288 yen. In comparison if three subjects contribute 9tokens and the fourth subject contributes 0 to the A account, then each of the three subjects who fully
contribute will earn 8x27=216 yen and the remaining subject will earn 216+18=234 yen.

4

number of subjects. The payoff from the private account is more than 4 times the payofffrom the public account and, hence, the decrease in the payoff of who contributes islarger than the increase of the payoffs of the other group members together.
In contrast, in the design used in the additional sessions the efficient solution isfor all to invest all tokens in A in every situation. This can be seen in table 2. For
situations 1 to 5 (6 to 10) the dominant strategy is to invest all tokens in A (B). We chosethe situations inTable 2: Payoff schedules, additional sessions*Japan

the Netherlands

A

B

A

B

MRS

Situation 1

20

2

30

3

0.1

Situation 2

20

6

30

9

0.3

Situation 3

20

10

30

15

0.5

Situation 4

20

14

30

21

0.7

Situation 5

20

18

30

27

0.9

Situation 6

20

22

30

33

1.1

Situation 7

20

26

30

39

1.3

Situation 8

20

30

30

45

1.5

Situation 9

20

34

30

51

1.7

Situation 10
20
38
30
57
1.9
* The numbers are the payoff per token, in Japanese yen andDutch cents. The numbers in the columns ‘A’ (‘B’) are thepayoff per token in the public account A (private account B).
The mrs is defined as in table 1.

the additional sessions to be ‘symmetric’ around mrs=1. Therefore, from here onward
we will denote the baseline sessions as the ‘Asymmetric’ (AS) sessions and the
additional sessions as the ‘Symmetric’ (S) ones. The two types of sessions are meant toprovide different perspectives on the issues we are interested in.
Besides the cross-country comparison and the differense between the AS andthe S sessions, we added one more treatment to our design. Since Andreoni (1988) there
has been a discussion about the differences in behavior in a ‘partners’ and ‘strangers’
5

design.5 His surprising result that partners cooperate less (free-ride more) than strangers
has not always been replicated (see Keser and van Winden, 1997, for a recent
discussion). Weimann (1994) finds the opposite result and Burlando and Hey (1997)
attribute the differences to national differences. We believe that this issue is of interest
because it yields information about the importance of strategic thinking in subjects’
behavior. Our data set provides a good opportunity to test this hypothesis across four
countries. Therefore we distinguished partners and strangers sessions in our AS
sessions. In the S sessions we only used the partners treatment.
2.2. Controlling for between-country variables.
Following Roth et al. (1991) we consider three aspects of the design which
require special attention when conducting a multi-national experiment: experimenter
effects, language effects, and currency effects.
The term experimenter effect refers to the possibility that different sessions of thesame experimental treatment may yield different results, due to possible effects ofuncontrolled procedural differences across locations, including the fact that the sessions
were conducted by different experimenters. We dealt with these difficulties by firstestablishing a protocol for the procedures6 in the initial sessions in Amsterdam, andthen using this standard in the sessions at the other locations. The first Amsterdam
sessions were conducted by Schram in Amsterdam with Brandts observing. Brandts
then ran the sessions in Barcelona, with Schram observing in some of these.7
Subsequently, Schram ran the sessions in Arizona. The sessions in Osaka were run by
Saijo and his collaborators with Brandts observing.8 In this way we ensured that thestandard established in Amsterdam ‘trickled down’ to the other locations. We did not
explicitly deal with possible uncontrolled personal differences among the experimenters.
We believe that the fact that all our sessions were computerized and that allcommunication with subjects was explicitly included in the English description of the
5 During a typical experimental session, subjects are allocated into different groups; a group is defined by

the set of subjects that are connected through the public goods payoff function. In partners, groups areconstant across the different periods of an experimental session, whereas in strangers subjects arereallocated to new groups in every period.
6 A detailed description of the procedures was used. This is available from the authors.
7 Joep Sonnemans of CREED also observed some of the first Amsterdam sessions. He later went toBarcelona and helped run some sessions there. We thank Joep for his help and note that his presence creates
an additional link between the two locations in terms of procedural control.
8 We would like to thank Yasuyo Hamaguchi for her help in running the Japanese sessions and Steve6

protocol (which included written texts for all interaction with subjects) reduces thepotential effects of this kind of influences.
Second, to control for unwanted language effects the instructions for theexperiment were initially written in English, and then translated into Dutch, Spanish
and Japanese. The experimenter responsible for each translation is a national of thecountry in question who has lived extended periods in the United States. We, therefore,
believe that all the translators had a clear understanding of all the possible connotations
and nuances present in the English instructions.
The translation of our instructions into Japanese raised some special design
problems. In the sessions run in the Netherlands, in Spain and in the United States wetranslated the computer interface of the experiment into the language of the country; theinstructions that appeared on the computer screen were also in the language of thecountry in question. Doing this for the Japanese case would have presented us withsome very important difficulties having to do with the use of Japanese characters. The
solution we chose for the Japanese sessions was to have the English version of the
instructions on the computer and, at the same time, to give a Japanese translation to thesubjects. The translation consisted of a separate sheet for each screen of the computerized instructions. The layoff of each page reproduced exactly the layout of thecorresponding screen. We believe that this is a reasonable solution, even though it does
introduce a certain asymmetry between the Japanese sessions and the rest.9
With respect to currency effects, first note that one of the most importantvariables in our design is the mrs, for which the same values are used in all countries. At
the time of the experiments, the exchange rates were $1 ≈ f 1,60 ≈ ¥ 90 ≈ Ptas 114. Notethat the numbers in tables 2 and 3 imply that we have used the exchange rate $1 ≈ f 1,50
≈ ¥ 100 ≈ Ptas 100. Hence, using the exchange rates to compare payments in othercountries with those in the United States, the stakes were slightly higher in Japan (11%)
and slightly lower in the Netherlands (6%) and Spain (12%). These differences are small
and in the direction of our observations of purchasing power in these countries.
Therefore, we do not expect that differences in payoff saliency will affect our results.
Backerman and Matt Cox for their help in organizing the U.S. sessions.
9 If this asymmetry is responsible for an observed difference in behavior between countries, a reasonable
conjecture may be that it led to more confusion among Japanese subjects than among subjects in the otherthree countries. In this case, one should probably observe more deviations from the dominant strategy,
independently of the mrs. As will be discussed below, this does not appear to be the case in our data.

72.3. Experimental procedures and subject pools
The experiments were all conducted in computerized laboratories. The
Japanese sessions were run at the Institute of Social and Economic Research at Osaka
University, the Dutch sessions at the CREED laboratory at the University of Amsterdam,
the Spanish sessions at the LEEX laboratory of the Pompeu Fabra University inBarcelona and the United States sessions at the ESL laboratory at the University of
Arizona.10 13 subjects per session were recruited according to the usual practice at thevarious sites. One of these subjects (randomly selected) was appointed monitor. The
monitor was allowed to observe the experiment to make sure everything was done as
explained in the instructions. In addition, he made the random draws (see below).
In each session the 13 subjects were brought into the laboratory and randomly
appointed seats. They were separated by partitions and no communication was allowed.
Instructions were computerized and subjects could read them at their own pace; see theappendix for the English version of the instructions. After subjects had finished reading
the instructions, two practice periods were run, in which it was made clear that there
was no interaction: the computer randomly determined the ‘decisions of the otherparticipants’. Then the 10 periods of the experiment began.
The S sessions were only run in Japan and the Netherlands and all used thepartners design (two sessions in each country). In each country, we ran two AS-partners
and two AS-strangers sessions. Hence we have data from 6 sessions in Japan and theNetherlands, 4 sessions in the United States and in Spain.
Each experimental session consisted of ten periods; we used a multi-period
procedure to follow standard practice. In each period, subjects were anonymously
allocated to three groups of four, in accordance with either the strangers or the partners
treatment. In each period each subject selected a complete contribution function. In thesessions the payoff schedule was presented in tabular form on a handout, on an
overhead projector, and on the computer screen. Each subject could determine in whatorder he wanted to decide on the 10 situations. The decision for any situation could be
revised as long as all decisions had not been finalized.
After all the participants had finalized the division of tokens for all situations
10 We would like to thank Antoni Bosch of LEEX and Vernon Smith of ESL for allowing us to use theirfacilities. More details about the sessions are available from the authors.

8

for a period, one situation was randomly selected to determine payments. The monitor
selected one situation per group by throwing a ten-sided die. Thus, the investments in
the group account and the payoffs were determined for the same mrs for each of thefour subjects in a group. The monitor entered the result for each group in his computer,and the subjects were told the result for their group privately, through their computers.
In summary, each period consisted in each subject making his decisions for each of thesituations and in one random draw of a situation to determine payoffs.11 The relevant
payoff table as well as the other information about procedures was known to all subjects
in a session.12
Table 3: Subject Background; AS sessions*

GenderJapan

Netherlands

U.S.A.

Spain

male

94.3

74.5

60.5

66.7

female

5.7

24.5

39.5

33.3

1.9

65.9

57.6

89.6

minimum

18

19

18

18

mean

21.35

22.8

21.9

19.6

maximum

25

31

33

24

major in economics

Age* The numbers in the cells are percentages from the post-experimental questionnaire, for thebaseline treatments. Missing cases have been dropped.

Table 3 gives some background data for the subjects in the baseline treatments. We
obtained these data in a post-experimental questionnaire we used at all locations.
Observe that males are overrepresented in all countries and that the fraction of
economics students varies strongly across countries. In our analysis below we take into
account the possible impact of these demographic variables.

11 Cubbitt, Starmer and Sugden (1998) investigate the impact of a random lottery incentive system, similar
to the one we use, on subjects’ behavior in an individual choice experiment. They find no significant effect.
12 All calculations and registration were computerized. Subjects received an overview of all previous
periods when the results for a period were given and could simply recall this information at any time. Per
period, the information included the situation selected for that subject, her/his division of tokens for thatsituation, the group investment in A, and the earnings. It also gave the total earnings at that point. In
addition, subjects could easily recall their decisions for all situations in the previous period.

9

3. Presentation of the results using contribution functions
In this section, we present a general overview of our results. We do so by using
average contribution functions as a convenient device for describing behavior acrosscountries and treatments. We do not present statistical tests in this section. These tests
are reported in section 4. A contribution function gives the number of tokens invested in
the public account as a function of the mrs. Note that standard theory predicts an
individual’s contribution to be 9 tokens if mrs < 1 and 0 tokens for mrs > 1. The
standard prediction, that the contribution function is a stepfunction with only one stepfrom 9 to 0 at mrs = 1, serves as a benchmark to which actual behavior can be compared.
Figure 1 (a-d) presents contribution functions per country for the AS design,where for each mrs the contributions have been averaged over all participants in a given
country. At this point we wish to single out one fundamental trait of our data: all eightaggregate contribution functions for all four countries and both treatments aremonotonically decreasing.13At first sight, the eight contribution functions also appear to
be rather similar in a more quantitative sense, i.e. for given mrs contribution levels arerather similar.
(Figure 1 about here)
Figure 2 shows the results from the S sessions conducted in the Netherlands
and Japan. Just as for the AS sessions, cross-country differences in behavior do notappear to be large. Observe also that the deviations from the dominant choice insituations 1-5 seem to be smaller than for situations 6-10.
(Figure 2 about here)
All in all, behavior follows a common pattern across countries and treatments
and the quantitative differences do not appear to be large. Nevertheless, thesedifferences might be significant. This is tested in the following section.
4. Statistical analysis of behavior across countries and treatments
13 Sufficiently strong non-monotonic behavior would directly suggest that errors are an importantcomponent of behavior.

10In this section, we present a more detailed analysis of our results. We willdefine two indices of behavior that will be used in a statistical analysis of several
hypotheses. For this purpose it is useful to distinguish between behavior for mrs < 1 andbehavior for mrs > 1. In the former case, where the dominant strategy is to contribute all
the tokens to the public account, contributions to the private account may be attributed
to either some kind of error, as in Palfrey and Prisbrey (1996), or to spite, as suggested
by Saijo and Nakamura (1995).
To measure deviations from the dominant strategy we define two broad-based
measures of behavior. We start with the number of tokens contributed to the private
account in any period and define the Gross Spite Index (GSI) as the ratio between thisnumber and the number of tokens available per period for situations with mrs < 1 (18tokens in A and 45 tokens in S). The term ‘gross’ is used to indicate that, at this point,
we do not wish to distinguish between error and spite, or even other motivational forces,
as explanations for the lack of contributions when mrs < 1; we do not attempt toattribute specific fractions of the deviations to different factors.
For mrs > 1 (where the dominant strategy is to invest all tokens in the private
account) we consider the situations where it is efficient to contribute everything to the
public account (mrs < 4).14 Parallel to the GSI, we define the Gross Cooperation Index(GCI) as the fraction of the total number of tokens contributed to the public good for 1 <mrs < 4. We use cooperation here in a general sense without referring to any specificformulation of cooperative preferences. We qualify our cooperation index with the termgross with the same intention as for the case of the GSI.
In section 4.1. we discuss the hypotheses to be tested. Section 4.2. presents
comparisons of the GSI and the GCI for both the AS and the S sessions. Section 4.3.
presents tests for country treatment and subject background effects and 4.4. presents asummary view of our data.
4.1 Hypotheses with respect to the GSI and the GCI
According to standard theory, subjects will follow the dominant strategy for all
mrs. This implies the prediction that GSI=GCI=0. In experiments, however, one has to
14 We do not take into account the third kind of situations for which it is dominant and efficient tocontribute nothing to the public account (situations 9 and 10 in A). Contributions are extremely low in thesesituations. Moreover, it is not easy to think of a motivation for contributing in those situations, and we

11allow for the presence of decision error. This introduces the possibility that people
deviate from the dominant choices and, hence, that the GCI and the GSI take on
positive values. Decision error can be conceived of in different ways. Ledyard (1995), in
his review of public goods experiments, refers to subjects making mistakes andchoosing their allocations randomly. According to this view, there is no a priori reason to
expect errors to be larger for mrs<1 than for the mrs>1. A somewhat different view is
the one proposed by Anderson, Goeree and Holt (1998). After referring to different
possible sources of error they posit that the cost of a mistake affects its likelihood:
relatively costly mistakes are less likely. Our design makes it possible to evaluate bothmistakes hypotheses.
The combination of the standard view of purposefully individualistic behavior
and purely random errors yields the following null hypothesis:
H0: GCI=GSIThe hypothesis that mistakes are related to costs gives rise to a slight variation of thenull. We will return to this in section 4.2.
With respect to alternative hypotheses, we do not just consider the most generic
one stating that there will be some difference between the GCI and the GSI. Instead, we
formulate a number of specific alternative hypotheses, suggested by previous
experimental evidence, and test the null against each of them. We consider two kinds ofalternative hypotheses. The first kind refers to the differences between GCI and GSI; thecorresponding hypotheses will be denoted as HA1A, HA1B and HA1C below. The
hypotheses of the second kind, HA2 and H3A, refer to differences in either the GCI or the
GSI across countries or treatments.
The first alternative hypothesis we consider is HA1A:
HA1A: GCI > GSI.
It reflects the notion that cooperation is a stronger motivation than spite and that thisdifference is not swamped by the presence of error. HA1A is written with reference toknow of no experimental evidence that shows that subjects contribute for a case like this.

12average behavior. A more specific version of this alternative hypothesis posits that theabove inequality holds for each and every country:
HA1B: GCI(JP)>GSI(JP) and GCI(NL)>GSI(NL) and GCI(US)>GSI(USA) andGCI(SP)>GSI(SP).
A common observation when analyzing data from public goods experiments isthat cooperation may decline as subjects gain experience with the environment. A
similar decay may be present in spite, however. Our conjecture is that, in spite ot these
decays, cooperation remains a stronger motivational force than spite throughout the 10
periods of play. We can formulate this hypothesis as:
HA1C: GCI(period 1)>GSI(period 1) and GCI(period 10)>GSI(period 10).
Our two next hypotheses have to be seen as alternatives to an interpretation of
the H0 as stating that the GCI and the GSI are equal across countries and treatments. The
Saijo and Nakamura (1996) results on observed spite in Japan mentioned in section 1
lead to:
HA2: GSI (JP) > GSI (NL) = GSI (USA) = GSI (SP).
As far as partners and strangers are concerned, the alternative hypothesis suggested by
the Andreoni (1988) results is:
HA3: GCI (strangers) > GCI (partners).
These hypotheses are tested in the following subsections. Subsection 4.2. presents theresults of testing hypotheses HA1A , HA1B and HA1C while subsection 4.3. presents resultsfor HA2 and HA3.
4.2 Hypotheses tests: comparisons of the GSI and the GCI
We want to conduct all our tests on the basis of statistically independent
observations. To ensure this we use data at the group level and deal differently with13

data from partners than from strangers sessions. Recall that in the strangers condition
subjects are reallocated to new groups in every period; from period 2 on the reshufflingof the groups contaminates behavior across these groups. In contrast, in the partners
condition group observations are independent from each other for all periods. For
strangers we, therefore, only use data from period 1, while for partners we consider
data from period 1 as well as averages over all periods
The GSI and the GCI are the basis of our statistical tests. Table 4 presents an
overview of the values of the average GSI and GCI for all the different conditions
studied in the AS sessions.
Table 4: GSI and GCI per country and treatment in the AS sessions*
GSI
JP

GCI
JP

GSI
NL

GCI
NL

GSI
US

GCI
US

GSI
SP

GCI
SP

GSI
ALL

GCI
ALL

Part

7.8

22.7

4.6

29.0

8.2

25.8

1.6

17.2

15.6

23.7

Stra

14.8

20.3

7.1

27.5

18.3

19.4

4.0

15.5

11.1

20.7

Average

11.3

21.5

5.9

28.3

13.3

22.6

2.8

16.4

8.4

22.2

* The numbers in the cells are percentages. ’part’=partners; ‘stra’=strangers, ‘JP’=Japan,‘NL’=Netherlands, ‘SP’=Spain and ‘US’=USA.

HA1A:
We use the matched pairs of GSI and GCI and the Wilcoxon Signed Rank test
to test the null hypothesis against our first alternative hypothesis, HA1A, that GCI>GSI.
We begin with the AS sessions. We can easily reject the null using both partners andstrangers data for period 1 (p=.000, N=48) and partners data averaged over all periods(p=.000, N=24).15
For the S sessions the aggregate GSI is 8.7% in Japan and 9.1% in the Netherlands (8.9% on average) and the GCI is 33.2% in Japan and 38.8% in the Netherlands(36.0% on average). Like for the AS sessions we can use a Wilcoxon Signed Rank test tocompare the GCI and the GSI. Since all the S sessions where based on the partners
design we can compare average values using groups as observations. The difference
between GSI and GCI is statistically significant when tested at the group level, in spiteof only 12 matched pairs of observations (p<0.01).

14

These tests reject the null hypothesis presented above, H0. But how can they beused in relation to a modified null that reflects the notion that mistakes depend on thecosts of making them? For the S sessions, a special feature of our design implies that thetest we have just presented also rejects the modified null. It can be seen in table 2 that,for both countries, the costs of deviating from the dominant choices are symmetricaround an mrs=1. For instance, in Japan the costs of a mistake are 2 yen for bothsituations 5 and 6, 6 yen for both situations 4 and 7 and they keep on taking the samevalues as one moves away from mrs=1 in both directions. As a consequence of this fact,
the GCI and the GSI represent aggregate measures of deviations that correspond to thesame set of costs of deviations. Hence, a rejection of the equality of the two indices canbe seen directly as a rejection of the costly mistakes hypothesis.
For the AS sessions we need to introduce one modification to be able to makethe same argument. In order to use aggregate indices that reflect the same range interms of costs of mistakes we need to restrict the GCI to situations 3 and 4, while we cancontinue using the same GSI as before.16 On the basis of a Wilcoxon Signed-Rank test we
can reject the equality of the GSI and the modified GCI, denoted hereafter by GCI34,
using both data from period 1 (p=.000, N=48) and averages over all periods for thepartners treatment (p=.000, N=24).
HA1B:
Table 5 presents the results of our per country comparisons of GSI and GCI.

N denotes sample size.
Taking, for instance, the payoff schedule for Japan in table 1 one can see that the costs of deviations fromthe dominant choice are 6, 2, 2 and 6 for situations 1 through 4.
15
16

15

Table 5: Comparisons of the GSI and the GCI per country for the AS sessions*.
DataJP
p=.031
N=12

NL
p=.028N=12SP
p=.002
N=12

US
p=.136
N=12

GSI vs. GCIAll periods
Onlypartners
GSI vs.
GCI34
Period 1

p=.116N=6

p=.028N=6

p=.028N=6

p=.046N=6

p=.005N=12

p=.008N=12

p=.002
N=12

p=.015
N=12

GSI vs.
GCI34
All periods
Onlypartners

p=.046N=6

p=.028N=6

p=.028N=6

p=.028
N=6

GSI vs. GCI
Period 1* The numbers in each cell correspond to the p-value for the Wilcoxon test and to the sample size.

Note first that if we center on the results using GCI34, we can in all instances
reject equality of the two indices at the 5% level. If we use the GCI, then there are twocases, US in period 1 and Japan over all periods, for which we can not reject equality atconventional significance levels. For the S sessions we can reject equality for both Japanand the Netherlands (p=.028, N=6 for both countries). Taking together the results for the
GCI34 test in the AS sessions and the results from the S sessions, our judgement is that
we can safely reject our H0 vs. the alternative hypothesis HA1B that the GCI is larger thanthe GSI in all four countries.
HA1C:
We now want to analyze whether the difference between the GCI and the GSI
persists throughout the ten periods of play; in addition we want to explore whether the
GCI is still substantial in the last period of play. The use of the two indices we havedefined makes it possible to present the evolution of behavior over time in a simple way.
The four panels of figure 3 (a-d) present the development of the GSI and the GCI overtime, for both the AS and the S sessions.
(Figure 3 about here)

16

For the AS sessions the GCI clearly declines in all four countries, whereas thechange in the GSI can not be described as easily. If, however, one computes averages
over countries one finds that the GCI declines from 30% to 16% and the GSI from 13%
to 7%. For the S sessions the GSI appears to be constant in time and the GCI exhibits
some decline in later periods. Again, the GCI appears to remain larger than the GSI
throughout the experiment.
Our tests confirm the above impressions. Since, overall, behavior appears to be
similar across countries, we only report results from aggregate tests. Wilcoxon SignedRank tests using group data only from the partners sessions find that the differences
between GSI and GCI are highly significant in all relevant instances: AS sessions inperiod 1 (p=.000, N=12) AS sessions in period 10 (p=.000, N=12), S sessions in period 1(p=.002, N=12) and S sessions in period 10 (p=.004, N=12).17 We can, therefore, reject thenull hypothesis in favor of HA1C.
4.3. Hypotheses tests: country and treatment effects
We now move to the two hypotheses that refer to country and treatment effects,
HA2 and HA3. These can be tested straightforwardly using regression analysis. Table 6
presents evidence about the effects of different variables on the GCI and the GSI, basedon the AS sessions; the results from the S sessions will be evaluated later. We present
results from linear regressions with GCI and GSI as dependent variables; due to thedifferences in subject pool composition shown in table 3 we not only consider the effectsof country and the partners vs. strangers distinction, but also include variables tocapture the possible impact of age, gender and study major. As in the previous section
we use period 1 group observations for both treatments together and groupobservations averaged over all periods for partners only.
The first row in table 6 shows result of regressing the average GCI per group inperiod 1 on average age in the group, the fraction of economics/econometrics students
in theTable 6: Effects of country, treatment and individual variables on GCI and GSI; AS
A more complete analysis of behavior over time is beyond the scope of this paper. However, the declineof the GCI over time and the fact that it still has a sizeable value in period 10, together with the absence of adifference for partners and strangers (which will be discussed in section 4.3.), suggest the following simple
interpretation of the behavior we observe. Subjects wish to cooperate conditional on others also cooperating.
17

17

sessions.*

Dependent
Variable

Const.

Age

Econ

GenderStranger

JP

NL

US

GCI
Period 1
N=48.960(2.44)(p=.02)-.443(1.22)(p=.23).546(.82)(p=.43)-.027(1.52)(p=.14).021(1.28)(p=.21)-.11(.36)(p=.72)-.169(1.62)(p=.11).059(.61)(p=.55)-.166(.93)(p=.37).065(.68)(p=.5)-.048(.54)(p=.59)-.028(.16)(p=.88)

-.022
(.53)
(p=.6).055(1.45)(p=.16)

-.074
(.74)
(p=.46).141(1.52)(p=.14)-.086(.48)(p=.64).121(1.51)(p=.14).017(.23)(p=.81).109(.84)(p=.42)

-.027
(.37)
(p=.72).091(1.34)(p=.19)
.079
(.72)
(.48)

-.514
(1.36)(p=.19).021(1.24)(p=.23).076(.75)(p=.46).169(1.69)(p=.11).137(1.36)(p=.19)

-.024
(.33)
(p=.75).000(.01)(p=.99)GSI
Period 1
N=48
GCI
All
periods
Only
partners
N=24
GSI
All
periods
Only
partners
N=24* The independent variables are defined as follows: Const.=Regression constant, Age=Averageage in the group, Econ=Fraction of economics or econometrics students in the group,
Gender=Fraction of women in the group, Stranger=Dummy equal to 1 in case of strangers,
JP=Dummy equal to 1 in case of Japan, NL=Dummy equal to 1 in case of Netherlands amd
US=Dummy equal to 1 in case of USA.

group and the fraction of women in the group as well as on a dummy for the strangers
treatment and country dummies. The second row shows the same for the GSI per groupin period 1 as dependent variable. These regressions are based on 4 (countries) x 4(sessions) x 3 (groups) = 48 observations. The results of the regressions using only datafrom the partners sessions are shown in the third and fourth row of table 6. In each cell,
the first number is the coefficient; below it one can first see the absolute t-value and thenthe p-value.
The results allow us to test H0 against HA2 and HA3; they also provide
additional information on some other issues of interest. For all four regressions shownin table 6 none of the variables has a significant effect at conventional significant levels.
We can not reject our null hypothesis in favor of either HA2 or HA3, i. e. the GSI does notvary across countries and we find no difference between partners and strangers. In
addition we also no effect of the country variables on the GCI and no significant effects

They start out contributing considerable amounts, but are disappointed by the cooperation levels of others.
They, therefore, revise their cooperation levels downwards and this goes on period over period.

18

of the subject background variables on either GSI or GCI.
4.4. A final view: step function representations of our aggregated contributionfunctions.
The analysis of the previous sections has shown that differences of behavior
across countries are small. Our data also exhibit a lack of systematic difference between
the partners and the strangers treatments. We, therefore, think that it is safe to look ataggregate data without losing too much information in the process. Figure 4 (a and b)
presents the aggregate contribution functions over all sessions, treatments, and periods
for the AS and S sessions. It also shows the step functions with steplevels at those
integer levels that are closest to the values of the aggregate contribution functions at
each mrs.
(Figure 4 about here)
Figure 4 reflects the behavior of a total of 250 subjects: 192 subjects for the AS sessions
and 48 subjects for the S sessions, where each of these subjects specified 10 complete
contribution functions.
Note that for both the AS and the S sessions the estimated step function iswithin 1 token of the dominant strategy prediction for all cases where mrs < 1 and mrs >4, i.e., in those cases where the dominant strategy coincides with efficiency. The
deviation from the dominant strategy is considerably larger for 1 < mrs < 3, however.
Note also from figure 3, that the four mrs values in the AS sessions that are comparablewith those in the S sessions (mrs = 0.25, 0.75, 1.25, 1.75) show contribution levels which
would fit very nicely in the contribution function for S. Hence, the different situations oftables 2 and 3 do not appear to elicit different behavior for comparable mrs values.
The strong asymmetry of the step function around mrs=1 in the S sessions
makes it specially clear that the costly mistakes hypothesis is not consistent with ourdata.
5. Summary and conclusions
There are two central features of our data that we wish to highlight in this final
section. First, there appear to be only minor differences in behavior between the four19

countries we investigate. The broad behavioral patterns are common across countries
and any quantitative differences are small. Our results give support to the notion that,when people from different countries play “the same game”, they behave similarly. This
does not necessarily contradict the general impression that people in different countries
do behave differently. Observations of this kind may, however, just reflect the fact thatdifferent institutional environments lead to different behavior, i.e. people in different
countries may just be playing according to different rules.18Informal observations about
alleged cultural differences stem from very uncontrolled environments and, moreover,may be influenced by the observer´s prejudices and biases. In contrast, experimental
analysis makes it possible to have different subject pools play literally the same gameand to uncover similarities in behavior that are difficult to see with field observations.
A specific feature of our cross-country comparison is that we find less evidenceof spite than in Saijo and Nakamura (1995). How can this difference be explained? Theirresults come out of a linear vcm environment related to ours, albeit with just one, fixed,
mrs. At this point, one candidate for an explanation of the discrepancies between ourand their data are the differences in the presentation of the payoff information; boththeir rough and their detailed payoff tables differ from our presentation. It is also
possible that subjects at the University of Tsukuba are different from subjects in all four
locations we studied in this paper. Another explanation is that the new design we use inthis paper elicits more reflective responses from subjects than an environment with justone mrs. Having to make decisions for ten different mrs, subjects may mentally take a
step back and think more carefully about their choices. Spiteful behavior may be theresult of a very impulsive reaction and that may be the explanation for why it only
appears as a rather weak force in the data obtained in our design.19 Economists aremainly interested in thoughtful behavior and we feel that, in this respect, the use of ourdesign is appropriate for the task at hand.
The second result that we wish to stress is that deviations from the standard
prediction are larger for mrs>1 than for mrs<1; in all four countries the average GCI islarger than the GSI and this difference does not disappear over time. The difference
between the two indices is especially stark for the S design, as shown in figure 4. Models
18 For an anthropologist’s view of the universal basis of human behavior see Brown (1991).

20

based only on decision error, like the one used in Palfrey and Prisbrey (1996) or the one
proposed by Anderson, Goeree and Holt (1998) which considers that mistakes depend
negatively on their cost, can not accomodate our evidence. Some kind of cooperative
motive is needed to accomodate the data. If we attribute part of the GSI to the presenceof spite then our data allow us to make the following statement: spite is not as strong a
motivation as cooperation and this difference is large enough not to be overwhelmed by
the presence of error.
In this paper we have concentrated on showing that cooperation has asignificant weight in our data across countries and have not attempted to identify more
specifically subjects’ cooperative motives. Some recent research, however, has combined
specific cooperative motives with the presence of error to explain observed behavior.
Palfrey and Prisbrey (1997) present an econometric analysis of experimental data on thebasis of a model that combines ‘warm-glow’ and linear altruism. Anderson, Goeree andHolt (1998) present a theoretical model that combines linear altruism and error to
explain a number of specific features of behavior found in public goods experiments.
The evidence we present in this paper is, in principle, compatible with this kind ofmodeling. However, Brandts and Schram (1998) and Bolton, Brandts and Katok (1999)
present data that are at odds with these linear formulations of other-regarding behavior.
The absence of cross-country differences may surprise some readers, sincenational ‘cultures’ appear to be such strong forces in shaping human behavior. Our
results are, of course, very far from a last word on the matter; a general picture of the
impact of country variables on behavior in experiments will only emerge after a
substantial body of research will have been completed. We do, however, believe thatour design has allowed us to study behavior in a systematic manner and to generate arich data set of thoughtful decisions that needs to be taken into account in the debate.

19 The contribution levels we observed for mrs > 1 are, however, in line with what is typically found in thistype of vcm experiments. The same holds for the decline in contributions over periods and the fact that thisdecline is not converging to zero.

21References
Anderson, S. P., J. K. Goeree and C. A. Holt (1998): “A theoretical analysis of altruism
and decision error in public good games”, Journal of Public Economics, 70, 297-323.
Andreoni, J. (1988): "Why free ride? Strategies and learning in public goods experiments", Journal of Public Economics, 37, pp.291-304.
Bolton, G.E., J. Brandts and E. Katok (1999): “How strategy sensitive are contributions?
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Brandts, J. and A. Schram (1997): “Cooperation and noise in public goods experiments:
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Brown, D.E. (1991): Human Universals, New York, MacGraw-Hill.
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22

Palfrey, T.R. and J.E. Prisbrey (1996): “Altruism, reputation and noise in linear public
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Roth, A.E., V. Prasnikar, M. Okuwo-Fujiwara, and S. Zamir (1991): “Bargaining andmarket behavior in Jerusalem, Ljubiljana, Pittsburgh and Tokyo: an experimental study”,
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23

Figure 1a: Japan
9

8

8.41
7.88

8.19
7.45

7

number of tokens

6

5

4

3.99

3

3.08

2.86
2.33

2

1.93
1.47

1.85

1.21

1.44

1

1.14

1.03
0.84

0
0.25

0.75

1.25

1.75

2.25

2.75

3.25

3.75

0.54

0.52

0.51

0.49

4.25

4.75

mrs
Partners, JP

Strangers, JP

Figure 1b: Netherlands
9

8

8.62
8.42

8.56
8.3

7

numbe of tokens

6

5

4.72

4

4.51

3

3.52

3.48

2.85
2.16
2.56

2

1.51
1.88

1.11

1

1.3

0.34
0.89

0
0.25

0.75

1.25

1.75

2.25

2.75

3.25

mrs
Partners, NL

24Strangers, NL

3.75

0.32
4.25

0.29
0.23
4.75

Figure 1c: Spain
8.9

9

8.81
8.58

8.7
8

7

number of tokens

6

5

4
2.97

3

2.33
2.94
2

1.76

2.03

1.32

1.42

1

0.68

1.1

0.58

0
0.25

0.75

1.25

1.75

2.25

2.75

3.25

0.26
0.32
3.75

0.19
0.01
4.25

0.01
0.12
4.75

mrs
Partners, ES

Strangers, ES

Figure 1d: United States
9
8.33
8.2

8
7.63

7.08

7

number of tokens

6

5

4

3.69
3.32

3.15

3

2.67
2.2

2.35
2

1.87
1.23

1.38
1.02

1

0.85
0.68

0
0.25

0.75

1.25

1.75

2.25

2.75

3.25

mrs
Partners, US

25

Strangers, US

3.75

0.52

0.53

0.5

0.4

4.25

4.75

Figure 2: Symmetric Sessions
9

8

8.59

8.5

8.56

8.45

8.27
8.21

8
7.99

7.72
7.7

7

number of tokens

6
5.24

5

4.44
4.14

4

3.53
3

3.31
2.85

2.55
2.32

2

2.22
1.8

1

0
0.1

0.3

0.5

0.7

0.9

1.1
mrs
NL

26

JP

1.3

1.5

1.7

1.9

Figure 3a: GCI per country over time; AS sessions
40
35.7
33.8

35

31.1
29.9

30.6

30

28.6

25
GCI

33.4

24.7

26.9

26.5

26.6

25.9

27.9
25.1

25.6

25

24.9

24

21.5

23.3

20

23.6

20

21.2

21

20.6

18.4

15

17.9

16.6

16.7

14.7

16.4

20

17.9

18

19.9

13

14.4
10

10

7.9
7.8

5

0
1

2

3

4

5

6

7

8

9

10

periodGCI-NLGCI-JP

GCI-US

GCI-ES

Figure 3b: GSI per country over time; AS sessions
18
16.3

16

16

15.5

15.2

14

13.2

14.1

13.8

13.7

13.5
13.2

12

12

12.2

12
11.1

10.9

10.5

10

10

GSI

9

9.1

8.3

8.7
8
6.5

6.9
6

6

5.8

5.8

4.5
4

3.9

3.5

4.4
3.7

3.5
3.2

2

2.3

1.9

1.6

3.4

3.4

2.7

0.8

0
1

2

3

4

5

6

7

8

periodGSI-NL

GSI-JP

27

GSI-US

GSI-ES

910

Figure 3c: GCI per country over time; S-sessions
0.5
0.48

0.47
0.46

0.45

0.44

0.4

0.4

0.37

0.39
0.38

0.37

0.36

0.37

0.35

0.35

0.35
0.33

0.31

0.3

0.31

GCI

0.28
0.25

0.24

0.24
0.22

0.2
0.15
0.1
0.05
0
1

2

3

4

5

6

7

8

9

10

periodGCI-NLGCI-JP

Figure 3d: GSI per country over time; S-sessions
0.12
0.116
0.112
0.108
0.1

0.098
0.093

0.093

0.089

0.089
0.08

0.107

0.091
0.089

0.087
0.082

0.085

0.079

0.074
GSI

0.069
0.06
0.056
0.052
0.04

0.02

0
1

2

3

4

5

6

7

periodGSI-NL

28

GSI-JP8910

Figure 4a: Aggregate behavior; AS sessions

9
8.36
8.15

8
7

# tokens

6
5
4
3.65
3

2.76
2.11

2

1.6
1.1

1

0.75
0.37

0.33

0
0.25

0.75

1.25

1.75

2.25

2.75

3.25

3.75

4.25

4.75

mrs
Contribution Functions, All Data

Figure 4b: Aggregate behavior; S sessions
9

8.57

8.47

8.24

8

8

7.71
7

6

5

4.84

4

3.84
3.08

3
2.43
2.01

2

1

0
0.1

0.3

0.5

0.7

0.9

1.1

1.3

mrs
Contribution Function, all data

29

1.5

1.7

1.9