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This is definitely a collective effort. The first gratitude is to Lilliana Mosquera who was the main
research assistant in the field for these experiments. Ana Maria Roldán and Pablo Ramos also
provided major assistance in the field, and Natalia Candelo in processing of data. My colleagues
Diana Maya and Maria Claudia Lopez had a major role in the field and in the design and
adaptation of the experimental design to the context. In the field Ximena Zorrilla and Carmen
Candelo have always been very helpful with their enthusiasm and devotion in the field and with
the communities in Sanquianga. The people of Bazán and Amarales in Sanquianga deserve as well
my gratitude, as well as Eduardo Restrepo, a true scholar on the Pacific coast social systems. Jean
Ensminger, Abigail Barr and Joe Henrich have made this entire project possible and have also
provided valuable inputs for my particular case. An international fellowship from The Santa Fe
Institute also provided ideal conditions for research.
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While the rest of country shows unemployment rates of 11%, such indicator is at the 14% for the
afrocolombians living in various regions and mainly in the pacific coast. The primary schooling
coverage is similar to the rest of the country, but secondary schooling reaches 62% while the rest
of the nation has reached 75% and college level education coverage is of only 14% for this group,
while of 26% in the rest of nation. Uninsured population involves 51% of the afrocolombians
while the same rate is at 35% elsewhere. Only 46% of the population in the afrocolombian
population has access to sewage systems while the rest has reached an 81% level. Further, 7,285
afrocolombians per 10,000 population are at high risk of malaria, while for the rest of the country
such indicator is at less than a third of that (DNP, 2004).
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The core games (DG, SMUG, 3PP) are reported here. Results for the replications with students
and for the Double-Blind experiments are not included in the analysis that follows.
4
Recall that for the DG and SMUG the same group of people played both games, although
matching was anonymous, random and choices for the first game (DG) were not known to player
2s until the second game (SMUG) was finished.
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Cardenas and Carpenter (2005) survey about 15 papers in different countries in the world using
the dictator game. For the students subject pools, the average of offers is around 25% and ranging
from 20-30%. For non-students the average offer found is 40%.
80
Percent of Sample
70
60
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50
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40
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30
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Pairwise comparisons are not feasible since the sample of participants in DG and 3PP were different
For the hypothesis that the SMUG offers were higher than for the 3PP, we obtain a t-test t =
1.3306, P > t = 0.0942, with an average difference in offers of $0.387, about 4% of the initial
endowment.
8
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Expected Income for Player 1
Percentage of Rejections or
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60
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Accept Accept Accept Accept Accept Accept Accept Accept Accept Accept
Received
Accept 0
10
20
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100
ugofr
accept0 accept10 accept20 accept30 accept40 accept50 accept60 accept70 accept80 accept90accept100 SumAccept
5000
1
1
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1
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1
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1
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5000
1
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5000
1
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1
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1
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82%
5000
0
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0
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0
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5000
0
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0
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0
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5000
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% Acceptance
% Rejection
60%
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will be argued later that ex-post incentives outside of the field lab but within the subjects’ group
may also play a key role, as discussed in Cardenas (2004).
11
There was one player 3 who had chosen to punish a 50% offer and not punishing the rest of
superior offers. These decisions were double checked with participants during the decision stage.
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In Cardenas (2004) a series of trust game experiments are reported where college students are
recruited across 4 campuses and the only available information they have about the other player is
the university affiliated with, which maps accurately with the predicted socio-economic status of
the others. Offers by player 1s were smaller when they came from higher socio-economic status
private universities, and especially when they were offers made to to students in public
universities with middle and low income populations.
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have immediate access to fishing resources: “Los peces que se destinan para el autoconsumo, se
utilizan tanto para el consumo doméstico como también pueden distribuir a los parientes o
amigos cercanos que no tienen es ese momento acceso a ellos, o sea, que circulan por las redes de
reciprocidad anotadas para el caso de la cacería o de la recolección”.
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Notice that while wealth has a negative effect on offers in DG and SMUG, education has in fact a
positive effect. Since education and wealth are not correlated for this group, one could reject the argument
that wealthier people behave closer to the homo-economicus prediction because they understand better the
incentives and the rules of the game.
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food which they have on hand over and above the immediate requirements of the
family, especially to those who are known to be need. Any individual can relate he
names of many person to whom at one time an another he has given food; some of
these have eventually reciprocated, while the others may be called on when the
need arises. As an instance of the way in which it works, the case may be cited of
man who, though his neighbors declared that he had once been in a comfortable
situation, had been without work for sometime. Though he has no money his
prospect of job were rather dim, both he and his wife seemed well fed. He was
very much given to talking long walks, at the end of which he would inevitably
return home with several large plantains, yuca, and the like, announcing that by
chance he had encountered a friend who, after inquiring about his family an
determining in the typically indirect way that the informant was in need, has given
him these gifts. In conversation, it become clear that the one who had given him
the food had in the past been in the same situation, and had benefited from similar
gifts of food.” (Price, 1955: 20).
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Appendix I: Regression results (explaining offers in DG, SMUG, 3PP and MAO in the SMUG)
Variables
(divided by Std.Deviations)
Age
Female dummy(a)
Education
Individual income (USD)
Household wealth (USD)
Household size
Constant
(1)
perc_offerdg
(2)
perc_offerdg
(3)
perc_offerdg
(4)
perc_offerdg
1.47
(3.43)
4.69
(6.73)
9.36
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(2.96)
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(18.48)
30
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8.27
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0.97
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11.43
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10.56
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16.46
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30
0.002
0.40
8.23
(2.69)***
Observations
Model significance (p-value)
Adjusted R-squared
Standard errors in parentheses
* Significant at 10%; ** significant at 5%; *** significant at 1%
(a) (not divided by std.deviation)
Table A.1. Dictator Game offers explained by demographic variables.
-5.34
(2.39)**
10.56
(2.64)***
16.24
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30
0.001
0.43
Variables
(divided by Std.Deviations)
Age
Female dummy(a)
Education
Individual income (USD)
Household wealth (USD)
Household size
Constant
(1)
per_ugofg
(2)
per_ugofg
5.23
(1.99)**
3.08
(3.90)
8.71
(2.21)***
-1.52
(1.72)
-4.93
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4.48
(1.64)**
15.24
(10.70)
30
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8.46
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4.21
(1.60)**
19.92
(8.85)**
30
0.000
0.50
Observations
Model significance (p-value)
Adjusted R-squared
Standard errors in parentheses
* Significant at 10%; ** significant at 5%; *** significant at 1%
(a) (not divided by std.deviation)
Table A.2. Strategy Method Ultimatum Game offers explained by demographic variables.
Variables
(divided by Std.Deviations)
Age
Female dummy(a)
Education
Individual income (USD)
Household wealth (USD)
Household size
Constant
(1)
mao
(2)
mao
(3)
mao
(4)
mao
(5)
mao
8.12
(3.61)**
2.57
-7.69
4.69
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5.79
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30
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30
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30
0.124
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5.98
(3.23)*
-2.02
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-5.58
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30
0.1
0.12
5.81
(3.18)*
Observations
Model significance (p-value)
Adjusted R-squared
Standard errors in parentheses
* Significant at 10%; ** significant at 5%; *** significant at 1%
(a) (not divided by std.deviation)
-9.06
-9.67
30
0.049
0.14
Table A.3. Minimum Acceptable Offers (MAO) by Player 2s in the Strategy Method Ultimatum Game.
Variables
(divided by Std.Deviations)
Age
Female dummy(a)
Education
Individual income (USD)
Household wealth (USD)
Household size
Constant
(1)
tppofg
(2)
tppofg
1.65
(2.82)
8.37
(6.27)
-3.86
(2.75)
-3.95
(3.03)
5.91
(3.01)*
1.68
(2.87)
33.25
(12.48)**
31
0.24
0.043
7.55
(5.96)
-5.05
(2.54)*
-4.58
(2.95)
5.24
(2.85)*
1.71
(2.82)
41.11
(9.38)***
32
0.26
0.023
(3)
tppofg
-4.61
(2.55)*
-6.41
(2.60)**
4.53
-2.83
2.41
-2.79
44.21
(9.16)***
(4)
tppofg
(5)
tppofg
-4.69
(2.62)*
-5.39
(2.59)**
-5.39
(2.66)*
-5.64
(2.66)**
4.32
-2.59
41.8
(9.28)***
54.83
(5.15)***
Observations
32
32
Adjusted R-squared
0.24
0.2
Prob > F
0.02
0.025
Standard errors in parentheses
* Significant at 10%; ** significant at 5%; *** significant at 1%
(a) (not divided by std.deviation)
Table A.4. Third Party Punishment Offers explained by demographic variables
32
0.15
0.034
(6)
tppofg
-4.61
(2.55)*
-6.41
(2.60)**
4.53
-2.83
2.41
-2.79
44.21
(9.16)***
32
0.24
0.02
36