DP2013-28

Hometown-Specific Bargaining Power in
an Experim ental Market in C hina
Xiangdong QIN
Junyi SHEN
Ken-Ichi SHIMOMURA
Takehiko YAMATO
Revised January 8, 2016

Hometown-specific Bargaining Power in an Experimental Market in China*
Xiangdong Qin‡,※, Junyi Shen†,
Ken-Ichi Shimomura†and Takehiko Yamato#January, 2016
Abstract
We conducted a market experiment in China to examine potential effects ofsubjects’ hometowns on their behaviors as well as potential differences between teamand individual trades. We observed that group size affected bargaining power andsubsequently payoffs in different directions according to where the subjects were from.
This finding suggests that it is important to take one’s hometown into account whencomparing economic behavior or decision-making between teams and individuals,
especially in the countries or regions where hometown diversity plays a central role inexplaining differences in business management and local market performance.

Keywords: Hometown, Market experiment, Team trade, Individual trade, China
JEL classifications: C91, C92, D51* We thank Tokinao Ohtaka, and Kiyotaka Takahashi for research assistance. Financial support from
the Murata Science Foundation, and the Japanese Ministry of Education, Culture, Sports, Scienceand Technology through Grant-in-aid for Scientific Research 26285045 and Grant-in-aid for
Scientific Research on Priority Areas 19046002 and 19046004 is gratefully acknowledged.
Shimomura and Yamato thank Leeds University Business School for their hospitality during theperiod when this draft was written. All of the views expressed in this paper and any errors are thesole responsibility of the authors.
‡School of Economics, Shanghai Jiao Tong University, 535 Fa Hua Zhen Rd., Shanghai 200052,

China. xdqin@sjtu.edu.cn†

Research Institute for Economics and Business Administration, Kobe University, 2-1 Rokkodai,
Nada, Kobe 657-8501, Japan. shen@rieb.kobe-u.ac.jp ken-ichi@rieb.kobe-u.ac.jp# Department of Social Engineering, Graduate School of Decision Science and Technology, Tokyo

Institute of Technology,
yamato@soc.titech.ac.jp※

2-12-1

Ookayama,

Corresponding author. Email: xdqin@sjtu.edu.cn

1

Meguro-ku,

Tokyo

152-8552,

Japan.

1. Introduction
Hometown diversity has played a central role in explaining differences in business
management and local market performance within a country such as China. In China it
is much easier for people to have good connections with those from the same hometown
than with those from different hometowns. For example, Tsang (1998) mentioned in his
study that similar to kinship, school, and place of work, hometown acts as a key aspectof personal identification to form a guanxi (i.e., relationship or connection in literalmeaning), which is regarded as “the informal connections so essential to gaining
approval for or access to just about everything in China”. In addition, Zhang (2001)
proposed that “hometown connections” have played a critical role in explaining location
choices of the foreign direct investments (FDI) by overseas Chinese in China, because
the overseas Chinese not only share the same language and culture with people in theirhometowns but also have relatives, friends, and former business ties in theirhometowns.
In spite of the importance of hometown diversity, results from economic
experiments are not always characterized in terms of hometowns of subjects because
there is no hypothesis of behaviors or phenomena peculiar to hometowns in economic
theory. Thus, the first purpose of the current study is to examine potential effects ofsubjects’ hometowns (i.e., hometown diversity) on their experimental behaviors,
specifically their bargaining power in the commodity exchange. To make the study
simple, we divide subjects’ hometowns into two categories – coastal and inland areas,
because on the one hand there is an apparent time lag in economic development
between coastal and inland areas in China, and on the other hand communication
abilities are commonly considered to be different between people from coastal areas andpeople from inland areas. Regarding this issue, the evidence in the literature is
extremely scarce.
The second purpose of this study is to examine potential differences between teamand individual trades. A growing body of experimental research has recently
investigated team or group decision-making and its possible differences from individual
decision-making. Experimental evidences have shown that individual and teamdecisions differ in a broad range of economic situations (e.g., Bornstein and Yaniv,
1998; Cason and Mui, 1997; Copper and Kagel, 2005; Kocher and Sutter, 2005; kugler
et al., 2007; Kocher et al., 2006; Sutter, 2005). In a recent survey paper, Charness and2

Sutter (2012) provided an excellent review on the issue of group decision-making. 1
However, to the best of our knowledge, there are no economic evidences regarding theeffect of group size on subjects’ bargaining power in experimental markets, especiallywhen hometown diversity is taken into account.
In order to examine the issues mentioned above, we apply a theoretical model thatis an exchange economy with two types of consumers and two kinds of commodities inwhich three competitive equilibria exist. One type of consumers initially own the moreof the first good, and the less of the second good, than the other type of consumers. The
same type of consumers are all rationed identical commodity bundles of endowment.
We choose the second good as the numeraire, the price of which is always fixed to beone, and focus on the behavior of the relative price of the first good. There are threeequilibrium prices in our model. The lowest relative price is beneficial for the type of
consumers having more of the second good, and the highest relative price isadvantageous to the type of consumers having more of the first good. The intermediate
price gives a “fairly equitable” allocation. The tatonnement dynamics predict that
relative prices go up when the first good is excessively demanded, and go down when it
is excessively supplied, in the market. Therefore, the relative prices observed in our
experiment are served as an index of subjects’ bargaining power.
According to the traditional economic theory, neither hometown diversity norgroup size has an influence on subjects’ bargaining power. However, in our market
experiment, we found that group size affected bargaining power and subsequently
payoffs in different directions according to where the subjects were from. In particular,
increasing group size from one-person to two-person strengthened the bargaining powerof subjects from coastal areas but weakened that of subjects from inland areas whencommodity exchanges were conducted between subjects from different areas. Thisfinding implies that it is important to take one’s hometown into account whencomparing economic behavior or decision-making between teams and individuals,
especially in the countries or regions where hometown diversity plays a central role inexplaining differences in business management and local market performance.
The problem of multiplicity of competitive equilibria in exchange economies with
1

Their main conclusions are that (i) groups are more cognitively sophisticated than individuals; (ii)groups can help with self-control and productivity problems; and (iii) groups may decrease welfare
because of stronger self-interested preferences.
3

two consumers and two goods has recently been investigated by using laboratory
markets. Crockett et al. (2011) designed and conducted an experiment of the Gale’s(1963) example of an exchange economy in the double auction institution using theMarketScape software. In the Gale model, there are three equilibria: two of them are“extreme” (the allocations are respectively supported by the price vectors (1, 0), and (0,1)), and the other is an interior equilibrium. The Walrasian stability depends on theinitial holdings of the consumers: the interior equilibrium is the only stable point insome cases, and it is unstable and the two extreme equilibria are stable in other cases.
Crockett et al. (2011) observed surprisingly strong support for these predictions by the
Walrarian stability theory. The methodology of our experiment is different from theirs:
our institution is a manual experiment of trading in a pit market, while theirs is an
on-line experiment of double auction.
Moreover, Huber et al. (2009) implemented an experiment of the Shapley-Shubik’s(1997) example of an exchange economy. In the Shapley-Shubik model, there are threeequilibria which are all interior, the intermediate equilibrium is unstable, and the twoother equilibria are unstable. Their data shows that there is a tendency that allocations
converge to the intermediate equilibrium in the Edgeworth box. The experimental
methodology in Huber et al. (2009) is also different from ours. They used computers forthe subjects to submit their proposals of trades and to decide whether to accept others’
offers or not only once in a period. The subjects carried over their holdings from the endof one period to the beginning of the next period until the end of one session of tradeperiods. On the other hand, we told subjects to trade face–to-face and reset holdings to
the initial state at the beginning of every trading period in our experiment of trading in apit market. We also compared the cases of individual and team decisions and consider
subjects’ hometowns in the context of a market experiment, while the above mentioned
literature focuses on individual decisions only without investigating either teamdecisions or subjects’ hometown effects.
The remainder of the paper is organized as follows. Section 2 presents thetheoretical model that we applied to conduct our experiment. In Section 3, we explain
the design and procedures of our experiment. Sections 4 and 5 provide the results of theexperiment and the discussions of the results, respectively. Finally, concluding remarks
are made in Section 6.

2. Theoretical Model4We consider an exchange economy model with two kinds of private goods called

X and Y and two types of consumers named 1 and 2. 2 The utility functions ofconsumers 1 and 2 are of the “Leontief-nested” type in the following forms:
U 1 ( x1 , y1 ) = a1 min[ g1 ( x1 ), y1 ] + b1 andU 2 ( x 2 , y 2 ) = a2 min[ g 2 ( x 2 ), y 2 ] + b2

(2.1)

In the experiment, we set a1 = 52.58 , b1 = 669.96 , a2 = 50 , b2 = 695.07 ,g1 ( x1 ) = x1 / 9.8

if x1 ∈ [0,6.2]=
10 x1 − 6.2(10.5 − 1/ 9.8)

if x1 ∈ [6.2,7.5]=x1 / 9.8 + 1.3(10.5 − 1/ 9.8)

if x1 ∈ [7.5,14.9]=
10.5 x1 − 13.6(10.5 − 1/ 9.8)

otherwise;g 2 ( x2 ) = x2 / 9.1

if x2 ∈ [0,7.35]=
11.3x2 − 7.35(11.3 − 1/ 9.1)

if x2 ∈ [7.35,8]x2 / 9.1 + 0.65(11.3 − 1/ 9.1)=

if x2 ∈ [8,17.45]=
11.3x2 − 16.8(11.3 − 1/ 9.1)

if x2 ∈ [17.45,18.45]=
x2 / 9.1 + 1.65(11.3 − 1/ 9.1)

otherwise.

The individual endowment of consumer 1 is given by ( x1 , y1 ) = (25,1) and theindividual endowment of consumer 2 is ( x 2 , y 2 ) = (5,29) .

Figure 1 displays this economy in an Edgeworth box. Notice that the utility
functions satisfy local nonsatiation 3, and are maximized by a unique point on the budget
line for any positive prices. Then, the solid (resp. dashed) piecewise linear line denotes
consumer 1’s (resp. consumer 2’s) “offer curve,” derived by varying prices and asking
the consumer how many units of commodities she would like to trade to maximize her
utility at each price. Notice that the offer curves are given by y1 = g1 ( x1 ) and y 2 = g 2( x 2 ), because the utility maximization points are the loci of the verteces of the L-shaped
indifference curves. There are three competitive equilibrium allocations, A, B, and C,
indicated by the points of intersection of the two offer curves.

2
3

We give formal definitions of concepts in an exchange economy model in Appendix 1.
The utility function U 0 defined over R 2+ satisfies local nonsatiation if, for every ( x 0 , y 0 ) ∈R 2+ and for every ε > 0 , there exists ( x 0′ , y 0′ ) ∈ R 2+ such that || ( x0′ , y0′ ) − ( x0 , y0 ) || < ε andU 0 ( x0′ , y 0′ ) > U 0 ( x0 , y 0 ).
5

<Insert Figure 1 here>
We next consider Walrasian dynamics of price adjustment process, which works
off equilibrium in the market. Let p = Px/ Py be the price of good X relative to good Y.
Figure 2 represents a diagram of the excess demand function for good X which isderived from our two-good economy model. 4 The characteristic of this excess demand
function is to have three equilibrium prices at which excess demand is equal to zero: A(0.4449), B (1.0582), and C (2.1147). According to Walrasian dynamics of priceadjustment process, if the relative price of X, p, is smaller than A (larger than C), thevalue of excess demand for X is positive (negative), so that p increases (decreases). If plies between A and B (between B and C), the value of excess demand is negative(positive), so that p decreases (increases). Therefore, the equilibrium price B is locallyunstable, whereas the other two prices A and C are both locally stable. 5
Figure 1 shows the three equilibrium allocations, A, B, and C, which
respectively associate with the equilibrium prices. The intermediate equilibrium
allocation associated with B = (12.0132, 14.7432), indicated in terms of agent 1’s
consumption bundle, is supported by a locally unstable equilibrium price, and the othertwo equilibrium allocations corresponding to A = (6.99771, 9.00859) and C = (15.4803,
21.1309) are attained with locally stable equilibrium prices. Figure 1 can be also
regarded as demonstrating symmetric equilibrium outcomes in a market with n traderson each side when all traders of the same type take the same action.
<Insert Figure 2 here>
In our experiment, subjects chose integers as trading units, not real numbers as in
the theory. Therefore, it is important to consider a discrete version of the exchange
economy corresponding to the experimental setting to make a rigorous theoreticalprediction. Figure 3 shows this discrete exchange economy in an Edgeworth box. The
locus ofmarks (resp. ´ marks) denotes consumer 1’s (resp. consumer 2’s) offer

4

We only need to focus on trades of good X because, based on the Walras’ law, the market of good
X is clear when that of good Y is clear. Notice that Walras’ law holds in our model since the utility
functions of all consumers satisfy local nonsatiation.
5 See Appendix 1 for the formal definitions of local stability and instability of equilibrium according
to Walrasian price adjustment process.
6

curve, which is thick, in the discrete economy. The two offer curves intersect at sevenpoints indicating competitive equilibria: A1 = (7, 8), A2 = (7, 9), A3 = (8, 9), C1 = (16,20), and C2 = (16, 21) give locally stable equilibria together with the corresponding
equilibrium price ratios p = 0.39, 0.44, 047, 2.11, 2.22, respectively, while B1 = (12,15) and B2 = (12, 16) are locally unstable points with p = 1.08, 1.15, respectively. 6<Insert Figure 3 here>

Moreover, we prohibited subjects from trading commodities at which the priceratio p , the trading ratio of Y to X (= (Amount of Y)/(Amount of X)), was less than 1/4= 0.25. For p < 1/4, there are several competitive equilibria other than the above
seven equilibria. In Figure 3, we omit these equilibria and focus on the seven equilibria
close to the three equilibria in Figure 1 of the usual Edgeworth box.
Table 1 summarizes the equilibrium predictions. There are trade-offs between
stability and “equity” of the competitive equilibria. The equilibria A1, A2, and A3 withlow relative prices p are beneficial for the consumer of type 2 who initially had moreof commodity Y, while the equilibria C1 and C2 with high relative prices of commodity
X is advantageous to the consumer of type 1 who initially had more of commodity X.
These four equilibria are locally stable, but not equitable. On the other hand, theequilibria B1 and B2 with intermediate prices give allocations that generate the minimal
difference between the payoffs to the two types of consumers. We say that theequilibrium and allocation are fair. In particular, each type of consumer receives thesame equilibrium payoff at B1. However, they are locally unstable.
Considering the relation between the above predicted equilibria and the consumers’
bargaining power, we say that any relative price p < 1.08, which includes the equilibria
A1, A2, and A3 but not B1, B2, C1, or C2, indicates that the consumer of type 2 has arelatively higher bargaining power than the consumer of type 1. In contrast, any relative
6

The number of competitive equilibria when only integer units are allowed in trading inexperimental settings could be quite larger than that when trading units are real numbers in theory.
For Shapley-Shubik (1977) exchange economy with two goods, there are only three competitive
equilibria in theory. However, there are 198 equilibria with only integer trading units and no trading
price constraint (see Masui et al. (2010) for detailed results).
7

price p > 1.15, which includes the equilibria C1 and C2 but not A1, A2, A3, B1, or B2,
indicates that the consumer of type 1 has a relatively higher bargaining power than the
consumer of type 2. Finally, bargaining powers of the consumers of both types arealmost the same when the relative price p lies between the middle equilibria B1 (1.08)
and B2 (1.15).
<Insert Table 1 here>

3. Experimental Design and Procedure
There were two treatments in our experiment, one was the individual treatment andthe other was the team treatment. Both treatments shared basically the same design andprocedures other than that in the individual treatment subjects were individuals, whereas
in the team treatment subjects were teams of two subjects each. In the latter case the two
subjects were seated together to discuss their trading strategies. In order to keep per
capita incentives constant across treatments, we paid each member of a team the sameamount that the team obtained from the experiment.
Each treatment consisted of four sessions, which the four sessions were based on
consumers’ type (type 1 or type 2) and subjects’ hometown (coastal or inland areas).
That is to say that in each treatment we used a 2 × 2 design, hence, eight sessions in
total were conducted. Table 2 provides the information on the time schedule and numberof subjects in each session. We conducted all the sessions at Shanghai Jiao Tong
University on September 21 and 22 of 2011 using paper and pencils. A total of 144
undergraduate students participated in the experimental sessions, 48 in the individual
treatment and 96 in the team treatment. Among them, the hometowns of half of thesubjects locate in the coastal areas and the others locate in the inland areas. In eachsession of the individual (resp. team) treatment, 6 (resp. 12) subjects played the role oftype 1 and 6 (resp. 12) subjects did the role of type 2. Their roles were fixed throughout
the experiment.
<Insert Table 2 here>
In each session of the team treatment, each team of two subjects received two
8experimental instructions, one record sheet, one payoff table and two name tags. 7 The
name tag of each subject indicated her team name (A, B, C, …, or L) and her
identification number (1 or 2). Six teams (A-F) played the role of type 1, and six teams(G-L) played the role of type 2. Each team was given pink cards and/or white cards inan envelope. One pink card was one unit of commodity X, and one white card was one
unit of commodity Y. We explicitly noticed to every subject that she was not allowed toreveal any information regarding her team payoff table or endowed color cards to any
other team.
The subjects of each team walked around a relatively large laboratory room andfound a team to trade. We prohibited any team from trading any amount of commodity
X or Y more than what they held. In addition, as explained in the previous section, thetrading ratio of Y to X should be greater than or equal to 1/4 = 0.25 to exclude
undesirable equilibrium allocations.
We told the subjects to trade commodity X for Y or Y for X when two teams
reached an agreement. After writing the trading results in their record sheets, the teams
reported them to the experimenter. The following information on the results was enteredinto the computer and displayed publicly through a projector: the team name giving
commodity X, the amount of the traded X, the team name giving Y, the amount of the
traded Y, and the trading ratio of the commodities (= Y/X). This was the end of one
trade. The teams had 10 minutes for each period and they were allowed to trade as manytimes as they wanted within the time limit. For the next trading partner, the teams could
choose any team as they wanted. That is to say that the next partner might be the sameas or different from one of the teams they had already traded. After each period, thesubjects went back to their seats and the experimenter collected all commodity cards.
This was the end of one period.
At the beginning of the next period, the teams received the same materials as thoseof the previous period. In particular, holdings of commodities were reset at the end of
the previous period and each team held the same endowment as that at the beginning of
the previous period. After a 2-minute break, the next period started. One session had 5
periods, which means that the above steps were repeated 5 times.
The design and procedures in each session of the individual treatment were
7

The experimental instruction and payoff tables are provided in Appendixes 2 and 3.
9basically the same as those in the team treatment except that: (i) each subject received
one experimental instruction, one record sheet, one payoff table and one name tag; and(ii) the name tag of each subject only indicated her ID name (A, B, C, …, or L).
Earnings of each subject or team depended on the final payoff that she or her teamearned in one randomly selected period from the experiment. This period was chosen by
a random device after the experiment. Each member of the same team received the sameearnings. Sessions lasted about 1.5 hours. The average payoff per subject was 92.5 CNY(about 14.2 USD if 1 USD = 6.5 CNY).

4. Experimental Results
4.1. Trading Ratio of Y to X
Figure 4 plots the trading ratio of Y to X in each session. The three equilibria (i.e.,
the low, middle, and high equilibria) are also presented by the orange, red, and green
lines, respectively. As shown in the figure, a certain level of fluctuation in the trading
ratios can be observed in the early periods of the experiment, while with the passing oftrading periods the fluctuation diminishes and most of the trading ratios lie within theinterval of the low and middle equilibria and never reach the high equilibrium. Resultsof both the Wilcoxon sign-rank test and t-test suggest that in all the sessions except
Inland2-Coast2 (i.e., team Inland-Coast session) the trading ratio of Y to X is
statistically higher than the low equilibrium and lower than the middle equilibrium inthe final period (i.e., period 5), which implies that the trading ratio does not converge toany equilibrium. In the session of Inland2-Coast2, the trading ratios of Y to X fromperiod 2 to 5 are not significantly different from that at the low equilibrium, implying
convergence to the low equilibrium (p-value = 0.3173 in Wilcoxon sign-rank test andp-value = 0.1387 in t-test for period 5).
<Insert Figure 4 here>
A detailed description of mean trading ratios of Y to X in each round of eight
sessions is presented in Table 3. Over all, it seems that the mean trading ratio differs
according to the subjects’ hometowns (represented by sessions of Coast-Coast,
Inland-Inland, Coast-Inland, and Inland-Coast) and/or group size (Individual or Team)10

but varies not too much across periods in each session. This is confirmed by the resultsof a two-way analysis of variance (ANOVA) that both the main and interaction effects
of group size and subjects’ hometown are significant in each period. 8<Insert Table 3 here>
In order to investigate how group size and subjects’ hometown affect trading ratios,
we ran the Wilcoxon rank sum test by comparing trading ratios of individuals with thoseof teams in the corresponding sessions. The results are displayed in Table 4. As shownin the table, except for Coast-Coast session the trading ratios of Y to X in the other three
sessions are significantly different between the individual and team treatments in all
periods. More specifically, the trading ratios of individuals are lower than those ofteams in Coast-Inland session and the trading ratios of individuals are higher than thoseof teams in Inland-Coast and Inland-Inland sessions. 9 Summing up the above evidences,
we obtain the following results.
RESULT 1a. When commodity exchanges were conducted among subjects fromdifferent areas, group size matters significantly in terms of trading ratios, however, thiseffect went into different directions according to where the subjects are from. When thesubjects of type 1 were from coastal areas, increasing group size from one to two raised
the trading ratios. In contrast, when the subjects of type 1 were from inland areas,
increasing group size from one to two lowered the trading ratios.

8

F statistics and p values in each period are as follow. For the main effect of group size, F(1, 93) =4.75, p = 0.0318 in period 1, F(1, 101) = 5.39, p = 0.0222 in period 2, F(1, 96) = 8.67, p = 0.0041 inperiod 3, F(1, 74) = 10.12, p = 0.0021 in period 4, and F(1, 66) = 5.36, p = 0.0237 in period 5. For
the main effect of subjects’ hometown, F(3, 93) = 2.85, p = 0.0441 in period 1, F(3, 101) = 3.98, p =0.0101 in period 2, F(3, 96) = 8.26, p = 0.0001 in period 3, F(3, 74) = 11.30, p = 0.0000 in period 4,
and F(3, 66) = 16.10, p = 0.0000 in period 5. For the interaction effect of group size and subjects’
hometown, F(3, 93) = 3.03, p = 0.0332 in period 1, F(3, 101) = 11.47, p = 0.0000 in period 2, F(3,96) = 8.26, p = 0.0001 in period 3, F(3, 74) = 15.77, p = 0.0000 in period 4, and F(3, 66) = 17.03, p= 0.0000 in period 5.
9 We also ran twelve pairwise comparisons of the trading ratios among sessions separated byindividual and team (i.e., Coast1-Coast1 versus Inland1-Inland1, Coast1-Coast1versus
Coast1-Inland1, Coast1-Coast1 versus Inland1-Coast1, Inland1-Inland1 versus Coast1-Inland1,
Inland1-Inland1 versus Inland1-Coast1, Coast1-Inland1 versus Inland1-Coast1, Coast2-Coast2
versus Inland2-Inland2, Coast2-Coast2 versus Coast2-Inland2, Coast2-Coast2 versus Inland2-Coast2,
Inland2-Inland2 versus Coast2-Inland2, Inland2-Inland2 versus Inland2-Coast2, Coast2-Inland2
versus Inland2-Coast2). The Wilcoxon rank sum test results provide a similar image to that we
obtained from Table 4. Therefore, for the sake of conserving space we omit these results and makethem available upon request.
11

RESULT 1b. When commodity exchanges were conducted among subjects from thesame areas, whether or not group size matters significantly in trading ratios depended
on where the subjects are from. When the subjects of both types were from coastal areas,
increasing group size from one to two did not matter significantly in the trading ratios.
When the subjects of both types were from inland areas, increasing group size from one
to two significantly lowered the trading ratios.
<Insert Table 4 here>
4.2.Payoff
Figures 5a and 5b provide the basic information about subjects’ payoffs. As shownin Figure 5a, it seems that the mean payoffs of subjects including both types differ very
little among different sessions and treatments. However, if we separate subjects’
payoffs by their types, we observe a different pattern. Figure 5b plots subjects’ average
payoffs per period in each session. The horizontal and vertical axes are for the payoffs
of types 1 and 2, respectively. The 45-degree line is drawn here to help understanding
which type of subjects earned more on average in each session. It is clear from thefigure that except for one session (i.e., individual Inland-Inland) the subjects of type 2
earned more than those of type 1 did in the other seven sessions. This observation tells
us that the payoffs earned in our market experiments may depend strongly on subjects’
type. A three-way ANOVA of three factors (i.e., group size, subjects’ hometown, andsubjects’ type) on subjects’ payoffs suggests that although the main and interaction
effects of group size and subjects’ hometown are not significant, the main effect ofsubjects’ type, the interaction effect of group size and subjects’ type, the interaction
effect of subjects’ hometown and subjects’ type, and the interaction effect of all thethree factors are all strongly significant. 10<Insert Figure 5a here><Insert Figure 5b here>10

F statistics and p values are as follow. For the main effect of group size, F(1, 80) = 0.52, p =0.4719, for the main effect of subjects’ hometown, F(3, 80) = 0.63, p = 0.5971, for the main effect ofsubjects’ type, F(1, 80) = 170.31, p = 0.0000, for the interaction effect of group size and subjects’
hometown, F(3, 80) = 0.52, p = 0.6697, for the interaction effect of group size and subjects’ type,F(1, 80) = 10.42, p = 0.0018, for the interaction effect of subjects’ hometown and subjects’ type, F(3,80) = 11.14, p = 0.0000, and for the interaction effect of all the three factors, F(3, 80) = 26.13, p =0.0000.
12

We ran the Wilcoxon rank sum test to investigate how group size, subjects’
hometown, and subjects’ type affect payoffs. The results are reported in Table 5. As
shown in the table, the payoffs of type 1 are significantly different between theindividual and team treatments in the Coast-Inland and Inland-Coast sessions but not in
the Coast-Coast and Inland-Inland sessions. As for the subjects of type 2, we observe asignificant effect of group size on the payoffs in all the sessions except for the
Coast-Coast session:
RESULT 2a. When commodity exchanges were conducted among subjects fromdifferent areas, group size and subjects’ type matter significantly in payoffs, however,this effect went into different directions according to where the subjects were from.
When the subjects of type 1 were from coastal areas and the subjects of type 2 werefrom inland areas, increasing group size from one to two raised up the payoffs of type 1
and lowered the payoffs of type 2. In contrast, when the subjects of type 1 were frominland areas and the subjects of type 2 were from coastal areas, increasing group size
from one to two lowered the payoffs of type 1 and raised the payoffs of type 2.
RESULT 2b. When commodity exchanges were conducted among subjects from thesame areas, whether group size and subjects’ type matter significantly in payoffs
depended on where the subjects were from. When subjects of both types were fromcoastal areas, increasing group size from one to two did not matter significantly in the
payoffs of both types. When subjects of both types were from inland areas, increasinggroup size from one to two significantly raised up the payoffs of type 2 but left thepayoffs of type 1 unchanged.
<Insert Table 5 here>

4.3.Number of trades
The average number of trades per period is presented in Figure 6. From the figure,
we observe that (i) compared to the team treatment the subjects in the individual
treatment seem to trade more frequently in all sessions; and (ii) the number of trades
also seems to be different according to where subjects are from. These are confirmed bya two-way ANOVA. Although the interaction effect of group size and subjects’
hometown is not significant (F(3, 32) = 1.15, p-value = 0.3429), the main effects of13

each factor are significant (for group size, F(1, 32) = 10.23, p-value = 0.0031, and for
subjects’ hometown, F(3, 32) = 2.73, p-value = 0.0599).
<Insert Figure 6 here>
In order to investigate how group size and subjects’ hometowns affect the numberof trades, we ran the Wilcoxon rank sum test through comparing the number of trades in
the individual treatment with those in the team treatment. The test result indicates thatthe number of trades between the individual and team treatments is significantly
different in the sessions where subjects are from different areas (i.e., the sessions of
Coast-Inland and Inland-Coast) but not in the sessions where subjects are from the same
areas (i.e., the sessions of Coast-Coast and Inland-Inland). 11 More detailed results arepresented as follows.
RESULT 3a. When commodity exchanges were conducted between subjects fromdifferent areas, increasing group size from one to two decreased the number of trades.
RESULT 3b. When commodity exchanges were conducted between subjects from thesame areas, increasing group size from one to two did not have any significant effect on
the number of trades.
5. Discussion
An important issue related to the trading ratio is worth noting that for the subjectsof type 1 (resp. type 2) who have more endowment of X (resp. Y), a low trading ratio of
Y to X stands for a weak (resp. strong) bargaining power while a high trading ratio of Y
to X stands for a strong (resp. weak) bargaining power. Consequently, RESULTS 1a and1b implied that teamwork strengthened the bargaining power of subjects from coastal
areas but weakened that of subjects from inland areas when commodity exchanges wereconducted between subjects from different areas. Why did the effects of teamwork on
the bargaining power of subjects from coastal and inland areas go into a different
direction? Although not yet confirmed by field evidences, several possible explanations
are conceivable.

11

For Coast-Coast, z = 1.167, p = 0.2433, for Inland-Inland, z = 0.318, p = 0.7503, for Coast-Inland,z = 1.956, p = 0.0504, and for Inland-Coast, z = 2.128, p = 0.0333.
14

First, China’s economic development started 15 years earlier in coastal areas thanin inland areas. This conduces to a fact that compared to subjects from inland areas
those from coastal areas are more economic-savvy and their thoughts are morewesternized. Consequently, they are more familiar with teamwork processes than theircounterparts from inland areas, and as a result, their bargaining power was enhanced.
Second, traditional Chinese culture and society feature strong vertical relationships offilial piety, paternalism, and hierarchy. These vertical relationships would promote
teamwork if teams are formed by managerial authority and have strong appointed
leaders (i.e., a top-down control). However, the vertical relationships would demote theoutcome of teamwork if there is no apparent team leader in the team. Our experiment is,indeed, a case of the latter since no one could easily become the leader in a team of two
subjects that were just randomly matched. Therefore, in such a case communicationand cooperation abilities of both team members are crucial to the success of any teameffort. In China, compared to people from inland areas those from coastal areas are, in
general, good at communicating and cooperating 12, our results are consistent with theseobservations.
Regarding the results of subjects’ payoffs, RESULTS 2a and 2b are completely
consistent with RESULTS 1a and 1b. As reflected from the payoff tables in Appendix 3,
for the subjects of type 1 (resp. type 2), a low trading ratio of Y to X means that it costs
them more X (resp. less Y) in exchange of Y (resp. X), which consequently would leadto low (resp. high) payoffs of the subjects of type 1 (resp. type 2). Therefore, on the one
hand, when the subjects of type 1 (resp. type 2) were from coastal areas and the subjectsof type 2 (resp. type 1) were from inland areas, increasing group size from one to tworaised up (resp. lowered) the trading ratios, and as a result the payoffs of type 1 (resp.
type 2) increased and the payoffs of type 2 (resp. type 1) decreased. On the other hand,when subjects of both types were from coastal areas, increasing group size from one totwo did not matter significantly in trading ratios, therefore, did not affect the payoffs ofsubjects of both types significantly. Again, when subjects of both types were frominland areas, increasing group size from one to two significantly lowered the trading
ratios, as a result, the payoffs of type 2 increased and the payoffs of type 1 decreased.
The number of trades significantly differed between the individual and team

12

Although not served as a strongly supportive evidence, we observed that in the team treatment
subjects from coastal areas communicated more actively with their partners than those from inland
areas.
15

treatments, however, only in the cases that commodity exchanges were conductedamong subjects from different areas but not in the other cases that commodity
exchanges were conducted among subjects from the same areas. Table 6 presents analternative but related analysis. In the table, the average amounts of both commodities X
and Y exchanged per trade in the individual and team treatments were separately
calculated by whether subjects were from the same areas or from different areas. The
table shows that there was almost no difference in the average amounts of commodities
exchanged per trade between the individual and team treatments (13.3833 versus
14.1636) if subjects of both types were from the same areas. The average amounts ofcommodities per trade in the team treatment were about 28% more than those in theindividual treatment (15.2647 versus 11.913) if subjects of both types were fromdifferent areas. 13 Based on this related evidence, here, we provide an explanation that
may help to interpret RESULTS 3a and 3b. Compared to when commodity exchanges
were conducted among subjects from the same areas, subjects would be more cautious
in trading with their counterparts who were from different areas, which consequently
caused reduced number of commodities per trade. Meanwhile, the amount exchanged
per trade was generally larger in the team treatment than in the individual treatment,
because in the team treatment team members had chances to discuss together while in
the individual treatment communication was possible only between trading partners.
Based upon the above two considerations, the amount of commodities exchanged per
trade was determined by a combined effect of these two factors. In our case, it seemedthat when commodity exchanges were conducted among subjects from different areas,
the effect of inter-subject discussion in teams overshadowed that of trading with thesubjects from different areas. As a result, the amount of commodities exchanged per
trade was significantly larger and consequently the number of trades was significantly
smaller in the team treatment than in the individual treatment.
<Insert Table 6 here>
Finally, we explain differences between our experimental results and several
previous studies. For example, Kocher and Sutter (2005), Kocher et al. (2006) and
Sutter (2005) offered supportive evidence for groups being more cognitively
sophisticated than individuals. According to this context, in our experiment, the trading
13

The results of Wilcoxon rank sum test supported these results. z = -0.744, p = 0.4568 in the casethat subjects of both types were from the same areas and z = -2.272, p = 0.0231 in the case that
subjects of both types were from different areas, comparing the amounts exchanged per trade in theindividual treatment to those in the team treatment.
16

ratio of Y to X should reach one of the three equilibria more easily in the team treatment
than in the individual treatment. However, this was not observed in our results except in
the team Inland-Coast session where the trading ratio of Y to X converged to the low
equilibrium. In addition, Kugler et al. (2007) provided evidence for supporting thatgroups may decrease welfare because of stronger self-interested preferences. This waspartly observed in our results, however, opposite evidence was also observed. We foundthat group size affected payoffs in different directions according to where the subjects
were from. This finding implies that it is important to take one’s hometown into accountwhen comparing economic behavior or decision-making between teams and individuals,
especially in the countries or regions where hometown diversity plays a central role inexplaining differences in business management and local market performance.
6. Conclusion
We have examined whether there are potential differences between team andindividual trades and whether there are potential effects of subjects’ hometowns on theirtrading behaviors in an experimental market. To the best of our knowledge, ourexperiment is the first to document these group-size and hometown effects in China. We
found that teamwork strengthened the bargaining power of subjects from coastal areas
but weakened that of subjects from inland areas when commodity exchanges wereconducted between subjects from different areas.
We observed that when the subjects of both types were from inland areas,
increasing group size from one to two significantly lowered the trading ratios (seeRESULT 1b). A possible explanation is that since the hometowns of our subjects were
simply categorized into two areas (i.e., coastal and inland areas) and the number ofhometowns in inland areas categorized by provinces (i.e., 19) was about twice as large
as that in coastal areas (i.e., 9), hence, there may exist heterogeneous characters even in
the subjects from inland areas. Our experimental data may not have offered a precise
test on the above explanation. We leave this issue as a future task by providing moresophisticated categorization of subjects’ hometowns.

References
Bornstein G., and Yaniv I., 1998. Individual and group behaviour in the ultimatum
game: are groups more “rational” players?. Experimental Economics, 1, 101-108.
17

Cason T. N., and Mui V., 1997. A laboratory study of group polarization in the team
dictator game. Economic Journal, 107, 1465-1483.
Charness G., and Sutter M., 2012. Groups make better self-interested decisions. Journalof Economic Perspectives, 26(3), 157-176.
Cooper D. J., and Kagel J. H., 2005. Are two heads better than one? Team versus
individual play in signaling games. American Economic Review, 95(3), 477-509.
Crockett S., Oprea R., and Plott C., 2011. Extreme Walrasian dynamics: the Gale
example in the lab. American Economic Review, 101(7), 3196-3220.
Gale D., 1963. A note on global instability of competitive equilibrium. Naval Research
Logistics Quarterly, 10, 81-87.
Huber J., Shubik M., and Sunder S., 2009. Default penalty as a disciplinary andselection mechanism in presence of multiple equilibria. Cowles Foundation Discussion
Paper No. 1730, Yale University.
Kocher M. G., and Sutter M., 2005. The decision maker matters: Individual versus
group behavior in experimental beauty-contest games. Economic Journal, 115, 200-223.
Kocher M. G., Strauß S., and Sutter M., 2006. Individual or team decision making –Cause and consequences of self-selection. Games and Economic Behavior, 56(2),259-270.
Kugler T., Bornstein G., Kocher M. G., and Sutter M., 2007. Trust between individuals
and groups: Groups are less trusting than individuals but just as trustworthy. Journal of
Economic Psychology, 28(6), 646-657.
Shapley L. S., and Shubik M., 1977. An example of a trading economy with threecompetitive equilibria. Journal of Political Economy, 85, 873-875.
Sutter M., 2005. Are four heads better than two? An experimental beauty-contest gamewith teams of different size. Economic Letters, 88(1), 41-46.
18

Tsang E. W. K., 1998. Can guanxi be a source of sustained competitive advantage fordoing business in China?. The Academy of Management Executive, 12(2), 64-73.
Zhang K. H., 2001. What attracts foreign multinational corporations to China?.
Contemporary Economic Policy, 19(3), 336-346.

19

Appendix 1.

Formal Definitions of Concepts in an Exchange Economy ModelFirst of all, we give formal definitions of “two-good exchange economy,” “budget
correspondence” and “individual demand function”:
DEFINITION 1. A two-good exchange economy is a list ( I ,U , ω ) such that I is anonempty finite set, U = (U i )i∈I is a profile of real-valued functions defined over R 2+ ,and ω = (ωi )i∈I , where ωi = ( xi , y i ) , is a profile of points of R 2+ . Each element of I
is called a consumer. For each i ∈ I , U i and ωi are called the utility function, andthe individual endowment, of consumer i , respectively.

DEFINITION 2. Let ( I ,U , ω ) be a two-good exchange economy. The budget
correspondence Bi and the individual demand function d i of consumer i aredefined by
Bi ( p X , pY ) = {( xi , yi ) ∈ R 2+ | p X xi + pY yi ≤ p X xi + pY yi }; andd i ( p X , pY ) = {( xi , yi ) ∈ Bi ( p X , pY ) | U i ( xi , yi ) ≥ U i ( xi′, yi′ )for each( xi′, yi′ ) ∈Bi ( p X , pY ) }for each ( p X , pY ) ∈ R 2++ . For each consumer i , define the functions d Xi andd Yi

by d i ( p X , pY ) = ( d Xi ( p X , pY ), d Yi ( p X , pY ) ) for each ( p X , pY ) ∈ R 2++ , then d Xi andd Yi are respectively called the individual demand functions for X

and Y ofconsumer i .

Notice that the budget correspondence and the individual demand function of aconsumer are zero-homogeneous. It means that their intrinsic valuable is the ratio ofp X to pY , namely Bi ( p X , pY ) = Bi ( p X / pY ,1) and d i ( p X , pY ) = d i ( p X / pY ,1) . To
make our analysis simpler, we consider the relative price p = p X / pY as anindependent valuable. We are now ready to define the classical market equilibrium
concept for prediction.

20

DEFINITION 3. Let ( I ,U , ω ) be a two-good exchange economy. The profile ofnon-negative vectors

is called an allocation of ( I ,U , ω ) if

( xi , yi ) i∈I∑ x =∑ x and ∑ y =∑ y . The vector ( ( x *, y *) , p*) ∈ (R ) I × R is a
i∈I

i∈I

i

i

i∈I

i

i∈I

i

i

i

2
+

i∈I

competitive equilibrium, or simply equilibrium, for ( I ,U , ω ) if ( xi *, y i *) = d i ( p*,1)
for each i ∈ I ; and ( xi *, y i *) i∈I is an allocation of ( I ,U , ω ).

The allocation( xi *, yi *)i∈I , and the real number p * are called an equilibrium allocation, and an
equilibrium price, for ( I ,U , ω ), respectively.

DEFINITION 4. Let ( I ,U , ω ) be a two-good exchange economy. For each p > 0, we
define the set of the demanders ID( p ) for X and the set of the suppliers IS ( p ) of

X byID( p) = { i ∈ I | d Xi ( p,1) − x i ≥ 0 }; andIS ( p) = { i ∈ I | d Xi ( p,1) − x i < 0 }.
The market demand function, simply demand function for X , is defined by[]

D X ( p ) = ∑i∈ID ( p ) d Xi ( p,1) − x i ,the market supply function, simply supply function of X , is defined by[]

S X ( p ) = ∑i∈IS ( p ) x i − d Xi ( p,1) ,and the market excess demand function, simply excess demand function for X , isdefined by[

E X ( p ) = ∑i∈I d Xi ( p,1) − x i]for each p > 0.
Notice that E X ( p ) = D X ( p ) − S X ( p ) , and p * is an equilibrium price if andonly if E X ( p*) = 0. Then, by using the excess demand function, we define thefollowing classical concepts to describe whether an equilibrium is likely to be attained
or not:DEFINITION 5. Let E X be the excess demand function for X in the two-good21

economy ( I ,U , ω ). Then Walrasian adjustment process with the excess demand
function E X is the ordinary differential equation: p = W ( E X ( p ))14

, where W is areal-valued function defined over R such that W (0) = 0 and W ′( z ) > 0 for each z ∈R. Suppose that E X ( p*) = 0. We say that p * is locally stable (resp. locally unstable)
if E ′X ( p*) < 0 (resp. E ′X ( p*) > 0). The competitive equilibrium ( ( xi *, y i *) i∈I , ( p*,1) )is called a locally stable equilibrium (resp. locally unstable equilibrium) if p * islocally stable (resp. locally unstable) .

14

The notation p indicates the derivative of p with respect to time.
22Appendix 2. Experimental Instruction
This is an experiment about decision making and economics. The instructions are
simple. If you follow them carefully and make good decisions, you might earn aconsiderable amount of money that will be paid to you. In this experiment, you will
make decisions to trade or hold two kinds of commodities, called X and Y in a sequenceof trading periods.

I. Introduction
1. There are ( 12
) trading teams in total, and each team has one/twoparticipants. 15 Please make sure that you have the following items:a)“Payoff Table” (one)b)“Record Sheet” (three)c)
Name tag (one)2. Please check whether your team name (A, B, C…) in your “Record Sheet” is thesame as the first letter in your name tag.
3. The “Payoff Table” of your team is your own SECRET information. You are NOT
allowed to reveal the information regarding your “Payoff Table” to any other team. We
will show you how to read the “Payoff Table” later.
4. At the beginning of each period of the experiment, your team will be given someamounts of Commodity X and/or Y. These amounts are shown in the first row of your
“Record Sheet”. This endowment is also your own SECRET information, so you are
NOT allowed to reveal the information regarding your endowment to any other team.

II. Trading Rules
The trading rules are as follows:
1. First of all, please put on your team name tag so that the other teams can see yourteam name.

15

Here, “each team has one participant” refers to the individual treatment, and “each team has twoparticipants” refers to the team treatment.
23

2. Each team will be given pink cards and/or white cards in an envelope. One pinkcard is one unit of commodity X, and one white card is one unit of commodity Y. The
number of pink cards (resp., white cards) equals to the amount of Commodity X (resp.,
Commodity Y) in the first row of your “Record Sheet.” You can take pink and whitecards (Commodities X and Y) out of the envelope only when you check the number ofcards or trade them.
3. Walk around this room and find a team to trade. Be careful NOT to reveal to any
other team the “Payoff Table” and the “Record Sheet” of your team. The same notice
applies to the following steps 4, 5, and 6.
4. Start a negotiation when you find a team that wants to trade with you. You
CANNOT give Commodity X or Y more than you hold to the team. Moreover, thetrading ratio of Y to X (= (Amount of Y)/(Amount of X)) should be greater than orequal to ( 1/4 = 0.25). Remember that the trading ratio of Y to X cannot beless than (1/4 = 0.25).
5. If you reach an agreement, then report the agreement to an experimenter. In front of
the experimenter, trade Commodity X with Commodity Y according to the agreement.
After that, write the trading result in your “Record Sheet.” This is the end of one trade.
6. Repeat the above steps 3-5 after one trade is completed. You have (10)
minutes for each period. You can trade as many times as you want within the time limit.
For the next team to trade with, you can choose any team: it may be the same as ordifferent from one of the teams you have already traded. We accept only agreementsthat have reached within the time limit.
7. Please go back to your seat after each period. Please make sure that all commodity
cards you traded are in your envelope. This is the end of the first period.
8. At the beginning of the second period, you will receive the same materials as thoseof the first period. That is, the amounts of Commodities X and Y you initially have at
Period 2 are the same as those at the beginning of Period 1. We will distribute pinkcards and /or white cards in an envelope. Those amounts are shown in your “Record
Sheet”. We will also collect the commodity cards and the envelope used in Period 1.
After a 2-minute break, Period 2 starts. This experiment has 5 periods. The above steps
24

are repeated 5 times.

III. An Example
We will give you an example to explain how to read the “Payoff Table” and how to fillout the “Record Sheet” by way of an example. In the following explanations, we will
use the “Pilot Payoff Table” which has nothing to do with the “Payoff Table,” but the“Pilot Payoff Table” should help you read the “Payoff Table” in the experiment.
1.

Suppose that your team name is “A”.

2. Take a look at Table 1-(a). The endowment of your team is shown in the second
row of the “Record Sheet.” In this example, your team is given endowment of 9 units ofCommodity X and 8 units of Commodity Y.

Record Sheet
Date (day/month/year):Team Name:Your Name Tag ID:

A

Time:
xxxxx

Your Name

xxxxx

~
xxxxx

Period 1
Trade
Amount of Amount of Team You Amount of Amount ofXY
Number Change in X Change in Y Trade with

9

0

8

Payoff4905

1

Table 1-(a)Next please see Table 2. This table represents a part of the “Pilot Payoff Table.” In thistable, the horizontally aligned numbers denote the amounts of Commodity X and thevertically aligned numbers denotes the amounts of Commodity Y. Your team is endowedwith 9 units of Commodity X and 8 units of Commodity Y, so the initial payoff of yourteam is the number in the cell of column 9 - row 8, that is, 4905. This number is thevalue shown in the “Payoff” of “Trade Number 0” in the “Record Sheet”.
25Amount of Y

Pilot Payoff Table
25 ・・・・
24 ・・・・
23 ・・・・
22 ・・・・
21 ・・・・
20 ・・・・
19 ・・・・
18 ・・・・
17 ・・・・
16 ・・・・
15 ・・・・
14 ・・・・
13 ・・・・
12 ・・・・
11 ・・・・
10 ・・・・
9 ・・・・
8 ・・・・
7 ・・・・
6 ・・・・
5 ・・・・
4 ・・・・
3 ・・・・
2 ・・・・
1 ・・・・
0 ・・・・
・・・・

7209
7145
7073
6994
6907
6810
6703
6585
6455
6311
6152
5976
5781
5566
5328
5066
4776
4455
4101
3709
3276
2798
2269
1685
1039
325
3

7284
7220
7148
7069
6982
6885
6778
6660
6530
6386
6227
6051
5856
5641
5403
5141
4851
4530
4176
3784
3351
2873
2344
1760
1114
400
4

7359
7295
7223
7144
7057
6960
6853
6735
6605
6461
6302
6126
5931
5716
5478
5216
4926
4605
4251
3859
3426
2948
2419
1835
1189
475
5

7434
7370
7298
7219
7132
7035
6928
6810
6680
6536
6377
6201
6006
5791
5553
5291
5001
4680
4326
3934
3501
3023
2494
1910
1264
550
6

7509
7445
7373
7294
7207
7110
7003
6885
6755
6611
6452
6276
6081
5866
5628
5366
5076
4755
4401
4009
3576
3098
2569
1985
1339
625
7

7584
7520
7448
7369
7282
7185
7078
6960
6830
6686
6527
6351
6156
5941
5703
5441
5151
4830
4476
4084
3651
3173
2644
2060
1414
700
8

7659
7595
7523
7444
7357
7260
7153
7035
6905
6761
6602
6426
6231
6016
5778
5516
5226
4905
4551
4159
3726
3248
2719
2135
1489
775
9

7734 ・・・・
7670 ・・・・
7598 ・・・・
7519 ・・・・
7432 ・・・・
7335 ・・・・
7228 ・・・・
7110 ・・・・
6980 ・・・・
6836 ・・・・
6677 ・・・・
6501 ・・・・
6306 ・・・・
6091 ・・・・
5853 ・・・・
5591 ・・・・
5301 ・・・・
4980 ・・・・
4626 ・・・・
4234 ・・・・
3801 ・・・・
3323 ・・・・
2794 ・・・・
2210 ・・・・
1564 ・・・・
850 ・・・・
10 ・・・・

Amount of X

Table 2
Let us begin the first trading.
3. Suppose that you negotiate with Team B, and the two teams reach the agreementthat “4 units of Commodity X that your team has are traded for 7 units of Commodity Y
that Team B has.”
4. Report the agreement to the experimenter. The experimenter will fill out thefollowing table on the blackboard for you.
26

Deal
Team that Amount Team that Amount
Ratio(=Y/X)
gave Yof Y
gave X
of X
A

4

B

7

1.75

Table 3-(a)In this case, as shown in Table 3-(a), the experimenter writes “A” in the blank of “Teamthat gave X”, “4” in “Amount of X”, “B” in “Team that gave Y” and “7” in “Amount ofY.” Remember that the trading ratio of Y to X (=Y/X) should be greater than or equal to(
1/4 = 0.25).
5. Following the experimenter’s guidance, trade Commodity X for Y according to theagreement. Your team gives 4 pink cards (Commodity X) to Team B and instead
receives 7 white cards (Commodity Y) from Team B.
6. Next please fill out your “Record Sheet.” See Table 1-(b). Write “-4” in the blank of“Amount of Change in X”, “7” in “Amount of Change in Y”, and “B” in “Team You
Trade with” in the second row of “Trade Number 1” of the “Record Sheet”.
As a result of this trade, your team now holds 5 units of Commodity X and 15 units ofCommodity Y. According to the “Pilot Payoff Table” (Table 2), you will find that thepayoff of your team is “6302.”
Write “5” in the blank of “Amount of X,” “15” in “Amount of Y,” and “6302” in“Payoff”.

27

Record Sheet
Date (day/month/year):Team Name:Your Name Tag ID:

A

Time:
xxxxx

Your Name

xxxxx

~
xxxxx

Period 1
Trade
Amount ofAmount of Team You Amount of Amount of
Number Change in X Change in Y Trade with
X
Y
0
1

-4

7

B

9
5

8
15

Payoff

4905
6302

2

Table 1-(b)Then, the first trading is completed. Now, let us move on to the second trading.
7. Suppose that you negotiate with Team K and agree “to trade your 2 units ofcommodity Y for Team K’s 3 units of commodity X.”
8. Report the agreement to the experimenter. The experimenter will fill out thefollowing table on the blackboard for you.
Deal
Team that Amount Team that Amount
Ratio(=Y/X)
gave Xof X
gave Y
of Y
A

4

B

7

1.75

G

5

H

4

0.8

L

12

F

14

1.17

K

3

A

2

0.67

Table 3-(b)As shown in Table 3-(b), the experimenter writes “K” in the blank of “Team that gave
28

X”, “3” in “Amount of X,” “A” in “Team that gave Y” and “2” in “Amount of Y.”
9. Following the experimenter’s guidance, trade commodities according to theagreement. You will give 2 white cards (commodity Y) to Team K, and in turn take 3
pink cards (commodity X).
10. Then, fill out your “Record Sheet” as follows. Look at the row of Trade 2 of Table
1-(c). Write “3” in the blank of “Amount of Change in X,” “-2” in “Amount of Change
in Y” and “K” in “Team You Trade with.”
As a result of this trading, your team owns 8 units of X and 13 units of Y. According to
the “Pilot Payoff Table,” your team’s payoff is found to be 6156. So write “8” in theblank of “Amount of X,” “13” in “Amount of Y” and “6156” in “Payoff.”

Record Sheet
Date (day/month/year):Team Name:Your Name Tag ID:

Time:

A

xxxxx

Your Name

xxxxx

~
xxxxx

Period 1
Trade
Amount of Amount of Team You Amount of Amount of
Number Change in X Change in Y Trade with
X
Y
0
1
2

-4
3

7
-2

B
K

9
5
8

8
15
13

Payoff

4905
6302
6156

Table 1-(c)Then, the second trading is completed.
Arrows in the “Pilot Payoff Table” indicate the changes in your payoff. In our
experiment, you can make as many trades as you want within 10 minutes.

29

Now we will explain about your earnings. Your earnings depend on the final payoff thatyour team earns in one randomly selected period from the experiment. This period ischosen by a random device after the experiment, so nobody can tell during theexperiment. Your earnings are computed in the following way:Your earnings =(the final payoff of your team at one period randomly chosen) × (
RMB

0.07

)

For example, suppose that (i) Period 1 is randomly selected after the experiment, and(ii) your final payoff at the end of Period 1 is ( 4156
) as in Table 1-(c). Then yourearnings are (291) RMB because (
4156
) × (
0.07
) =
( 291
).
We round off the decimal places. Each member of the same team receives the sameearnings.
That’s all. If you have any questions, please raise your hand.
Now, let us start the experiment. First, look at the “Payoff Table.” We will give you 5
minutes so that you can look over the table and understand it very well. During thisperiod, please make sure that you completely understand all the rules.
If you have any questions, please let us know quietly. Our staff will come to help you.
Please remember that you are NOT allowed to communicate with any other team until
the experiment starts.
Meantime, our staff will distribute pink and/or white cards in an envelope. Notice thatthe trading period number is printed in each card. You can only use cards with the same
number as the numbers of the period going on. For example, in Period 3, you can only
use cards with “3”.
Remember that you are NOT allowed to reveal the information regarding your “Payoff
Table” or the information regarding your “Record Sheet” to any other team. If thishappens, the experiment will be stopped at that point.

30

A mo u n t o f Y

Appendix 3. Payoff Tables
A. Payoff table for the type 1 subjects
30 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 2068 2247 2247 2247 2247 2247 2247 2247 2247 2247 2247 2247 2247 2247
29 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 2068 2195 2195 2195 2195 2195 2195 2195 2195 2195 2195 2195 2195 2195
28 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 2068 2142 2142 2142 2142 2142 2142 2142 2142 2142 2142 2142 2142 2142
27 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 2068 2090 2090 2090 2090 2090 2090 2090 2090 2090 2090 2090 2090 2090
26 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 2037 2037 2037 2037 2037 2037 2037 2037 2037 2037 2037 2037 2037 2037
25 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984 1984
24 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932 1932
23 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1879 1879 1879 1879 1879 1879 1879 1879 1879 1879 1879 1879 1879 1879
22 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1827 1827 1827 1827 1827 1827 1827 1827 1827 1827 1827 1827 1827 1827
21 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1774 1774 1774 1774 1774 1774 1774 1774 1774 1774 1774 1774 1774 1774
20 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1722 1722 1722 1722 1722 1722 1722 1722 1722 1722 1722 1722 1722 1722
19 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1669 1669 1669 1669 1669 1669 1669 1669 1669 1669 1669 1669 1669 1669
18 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1616 1616 1616 1616 1616 1616 1616 1616 1616 1616 1616 1616 1616 1616
17 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1516 1564 1564 1564 1564 1564 1564 1564 1564 1564 1564 1564 1564 1564 1564
16 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511 1511
15 670 675 681 686 691 697 702 1145 1424 1429 1434 1440 1445 1450 1456 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459 1459
14 670 675 681 686 691 697 702 1145 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406 1406
13 670 675 681 686 691 697 702 1145 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354 1354
12 670 675 681 686 691 697 702 1145 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301 1301
11 670 675 681 686 691 697 702 1145 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248 1248
10 670 675 681 686 691 697 702 1145 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196 1196
9 670 675 681 686 691 697 702 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143 1143
8 670 675 681 686 691 697 702 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091 1091
7 670 675 681 686 691 697 702 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038 1038
6 670 675 681 686 691 697 702 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985 985
5 670 675 681 686 691 697 702 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933 933
4 670 675 681 686 691 697 702 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880 880
3 670 675 681 686 691 697 702 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828 828
2 670 675 681 686 691 697 702 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775 775
1 670 675 681 686 691 697 702 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723 723
0 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670 670
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29
87
0 1 2 3 4 5 6Amount of X

A mo u n t o f Y

B. Payoff table for the type 2 subjects
30 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
29 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
28 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
27 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
26 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
25 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
24 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
23 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
22 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1750 1756 1761 1767 1772 1778 1783
21 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1723 1728 1734 1739 1745 1745 1745 1745 1745 1745 1745 1745
20 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1695 1695 1695 1695 1695 1695 1695 1695 1695 1695 1695 1695
19 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1645 1645 1645 1645 1645 1645 1645 1645 1645 1645 1645 1645
18 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1595 1595 1595 1595 1595 1595 1595 1595 1595 1595 1595 1595
17 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1545 1545 1545 1545 1545 1545 1545 1545 1545 1545 1545 1545
16 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1465 1495 1495 1495 1495 1495 1495 1495 1495 1495 1495 1495 1495
15 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1445 1445 1445 1445 1445 1445 1445 1445 1445 1445 1445 1445 1445
14 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1395 1395 1395 1395 1395 1395 1395 1395 1395 1395 1395 1395 1395
13 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1345 1345 1345 1345 1345 1345 1345 1345 1345 1345 1345 1345 1345
12 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1295 1295 1295 1295 1295 1295 1295 1295 1295 1295 1295 1295 1295
11 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1245 1245 1245 1245 1245 1245 1245 1245 1245 1245 1245 1245 1245
10 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1147 1152 1195 1195 1195 1195 1195 1195 1195 1195 1195 1195 1195 1195 1195
9 695 701 706 712 717 723 728 734 1103 1108 1114 1119 1125 1130 1136 1141 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145 1145
8 695 701 706 712 717 723 728 734 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095 1095
7 695 701 706 712 717 723 728 734 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045 1045
6 695 701 706 712 717 723 728 734 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995 995
5 695 701 706 712 717 723 728 734 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945 945
4 695 701 706 712 717 723 728 734 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895 895
3 695 701 706 712 717 723 728 734 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845 845
2 695 701 706 712 717 723 728 734 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795 795
1 695 701 706 712 717 723 728 734 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745 745
0 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695 695
0 1 2 3 4 5 6 7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
Amount of X

31

x2

02

30
25

•C
•B

15

•A

10
5

y2

y1

20

u1

u2

0
0 0

•( x, y )
5

1

10

15

20

25

30

x1
Figure 1. Exchange Economy with Three Competitive Equilibria

Excess Demand For X= E X

5

C

A

1

B

2

3

5

Figure 2. Excess Demand Function for X.

32

4



Px/Py = pA3

Figure 3. Discrete Version of the Exchange Economy

33

Inland1-Inland1

Coast1-Coast1
3

2.5

2.5

2

Y/X ratio

Y/X ratio

3

1.5

2
1.5

1

1

0.5

0.5
0

0

period 1

period 2

period 3

period 4

period 1

period 5

3

2.5

2.5

2
1.5
1

period 5

period 4

period 5

period 4

period 5

period 4

period 5

2
1.5
1

0

0

period 1

period 2

period 3

period 4

period 5

period 1

Coast2-Coast2

period 2

period 3

Inland2-Inland2

3

3

2.5

2.5

2

Y/X ratio

Y/X ratio

period 4

0.5

0.5

1.5
1

2
1.5
1

0.5

0.5

0

0

period 1

period 2

period 3

period 4

period 5

period 1

Coast2-Inland2

period 2

period 3

Inland2-Coast2

3

3

2.5

2.5

2

Y/X ratio

Y/X ratio

period 3

Inland1-Coast1

3

Y/X ratio

Y/X ratio

Coast1-Inland1

period 2

1.5
1

2
1.5
1

0.5

0.5

0

0

period 1

period 2

period 3

period 4

period 5

period 1

period 2

Figure 4. Trading ratio of Y to X in each session

34

period 3

1600
1400
1200
1000
800
600
400
200
0
Coast-Coast

Coast-Inland

Inland-Inland
Individual

Inland-CoastTeam

Figure 5a. Subjects’ mean payoffs per period

2100

1900

Low Equilibrium

Type 2

1700

1500

Middle Equilibrium

1300

High Equilibrium
1100

900

Endowment
700
700

900

1100

1300

1500
Type 1

1700

1900

Coast1-Coast1

Inland1-Inland1 Coast1-Inland1

Inland1-Coast1

Coast2-Coast2

Inland2-Inland2 Coast2-Inland2

Inland2-Coast2

2100

Figure 5b. Subjects’ mean payoffs per period separated by subjects’ types
35

16

14.6

14
12
10

12.8
11.4

13

12.2

11.6

9.8

8.8

8
6
4
2
0
Coast-CoastInland-Inland

Coast-Inland

Individual

Team

Figure 6. Average number of trades per period

36

Inland-CoastTable 1. Theoretical Predictions about Discrete Equilibria
Allocation
Type 1

Type 2(x1, y1)(x2, y2)

A1(7, 8)(23, 22)A2(7, 9)

A3

PayoffWalrasianType 1

Type 2

Stability

U1

U2

0.39

stable1091

1745(23, 21)0.44

stable1143

1745(8, 9)(22, 21)0.47

stable

1143

1739

B1

(12, 15)(18, 15)

1.08

unstable

1445

1445

B2

(12, 16)(18, 14)

1.15

unstable

1445

1395

C1

(16, 20)(14, 10)

2.11

stable

1722

1136

C2

(16, 21)(14, 9)2.22

stable

1669

1136

Price

Table 2. Time schedule of the experimentAMPM

9/21/2011

9/22/2011

Coast1-Coast1 session: 12 subjects

Coast1-Inland1 session: 12 subjects

Inland1-Inland1 session: 12subjects

Inland1-Coast1 session: 12 subjects

Coast2-Coast2 session: 24 subjects

Coast2-Inland2 session: 24 subjects

Inland2-Inland2 session: 24 subjects

Inland2-Coast2 session: 24 subjects

Notes: The roles of type 1 and type 2 that subjects played are indicated before and after the hyphen,respectively. Coast and Inland refer to the hometowns of subjects locate in coastal and inland areas,
respectively. The numbers of 1 and 2 in the sessions name refer to the individual and team treatments,
respectively. For example, Coast2-Inland2 session means subjects from coastal areas played the roleof type 1 and subjects from inland areas played the role of type 2 in the team treatment.
Table 3. Mean trading ratios of Y to X (standard deviation in parentheses)

Individual

Team

Coast-CoastInland-Inland

Coast-Inland

Inland-Coast

Period 10.850 (0.793)

1.228 (0.422)

0.835 (0.257)

1.136 (0.901)

Period 2

0.760 (0.292)

0.949 (0.277)

0.829 (0.218)

1.003 (0.282)

Period 3

0.766 (0.173)

0.992 (0.316)

0.888 (0.137)

0.968 (0.186)

Period 40.770 (0.240)

1.008 (0.145)

0.835 (0.141)

0.941 (0.186)

Period 50.630 (0.079)

0.961 (0.191)

0.805 (0.147)

0.903 (0.228)

Period 1

0.801 (0.370)

0.835 (0.370)

1.007 (0.293)

0.483 (0.254)

Period 2

0.784 (0.254)

0.752 (0.234)

1.082 (0.320)

0.455 (0.120)

Period 3

0.832 (0.310)

0.906 (0.264)

1.036 (0.111)

0.448 (0.066)

Period 4

0.732 (0.179)

0.834 (0.136)

1.065 (0.098)

0.463 (0.021)

Period 50.664 (0.134)0.800 (0.169)

1.051 (0.073)

0.454 (0.023)

37

Table 4. Wilcoxon rank sum test for trading ratios of Y to X (Individual versus Team)
Coast-CoastInland-Inland

Coast-Inland

Inland-Coastz valuep valuez valuep valuez valuep valuez valuep value

Period 1-0.5300.59632.056

0.0398-1.675

0.09392.800

0.0051

Period 2-0.5350.59282.199

0.0279-2.6330.00854.077

0.0000Period 3-0.2050.83741.868

0.0617-2.8020.00514.181

0.0000Period 4

0.191

0.84822.627

0.0086-3.1270.00183.514

0.0004

Period 5-0.826

0.4091

2.253

0.0243-3.4940.00053.253

0.0011

Table 5. Wilcoxon rank sum test for payoffs (Individual versus Team)
Coast-CoastInland-Inland

Coast-Inland

Inland-Coastz valuep valuez valuep valuez valuep valuez valuep valueType 1-1.121

0.26231.441

0.1495-2.7220.00652.887

0.0039Type 20.480

0.6310-2.4200.01632.082

0.0374-2.8820.0039

Table 6. The average amounts of both commodities X and Y exchanged per trade
IndividualTeam

Subjects from the same areas

13.3833

14.1636

Subjects from different areas

11.9130

15.264738