Climate Change and use of Common-Pool Resources:
An experimental approach in the Bolivian altiplano
Javier Aliaga L.
IISEC-UCB ∗
Alejandro Herrera J.
IISEC-UCB †
May, 2015
Preliminary: please do not cite.
Abstract
In this paper, we analyze the effects of Climate Change over the use of water for agricultural irrigation in the Bolivian altiplano, considering the hydric resource as a Common-Pool Resource (CPR).
Through a CPR experiment in the field, based on a theoretical model of individual and social profit
equilibrium and developed in three communities of our study area, we introduce different scenarios
on water availability obtaining information about probable changes in patterns of individual and
collective use associated with the effects of Climate Change over CPR availability for irrigation
use. Our results indicated that, small communities highly dependent on agriculture are certainly
susceptible to the Tragedy of the Commons. By introducing the role of a Judge of Water, we also
find that these small communities are, at least, capable of establish collective measures to adapt
to Climate Change and consequently admeasure the collective level of use of the CPR. Instead,
evidence for a greater community exhibit erratic results, mainly influenced by the diversification of
economic activities of its members and its level of social capital. We estimate Data Panel models
to validate our experimental results.
Keywords: Use of water resources, Common-Pool Resources, economic games experimental field
and climate change.
Resumen
En este trabajo, se analizan los efectos del Cambio Climático sobre el uso del agua para riego
agrı́cola en el altiplano boliviano, considerando al agua como un recurso comunal. Mediante el
desarrollo de un juego de campo experimental en tres comunidades de la zona de estudio, basado
en un modelo teórico de equilibrio de beneficios individuales y sociales, introducimos durante el
juego diferentes escenarios sobre la disponibilidad de agua para obtener información sobre los
cambios en los patrones de uso individual y colectivo asociados con el Cambio Climático. Nuestros
resultados establecen que: las pequeñas comunidades altamente dependientes de la agricultura son
ciertamente susceptibles a la Tragedia de los Comunes. Mediante la introducción de la figura de un
Juez de Agua, también encontramos que estas pequeñas comunidades son, por lo menos, capaces
de establecer medidas colectivas para adaptarse al Cambio Climático y, en consecuencia, mesurar el
nivel de uso colectivo del recurso comunal. En cambio, las comunidades grandes muestran resultados
erráticos, influenciado principalmente por la diversificación de las actividades económicas de sus
miembros y su nivel de capital social. Estimamos modelos de datos de panel para validar nuestros
resultados experimentales.
Palabras Clave: Uso de recurso hı́drico, recursos comunales, juegos económicos experimentales
de campo y cambio climático.
JEL Code: C10, C23, C25, C72, C93, D03, D13, D71, O13, Q12, Q15, Q25.
∗
Executive Director - Institute of Socioeconomic Research (IISEC) - Universidad Católica Boliviana “San Pablo”.
Contact: [email protected].
†
Associated Researcher - Institute of Socioeconomic Research (IISEC) - Universidad Católica Boliviana “San Pablo”.
Contact: [email protected].
We thank Jorge Higinio Maldonado, Juan Robalino and Roger Madrigal (members of our review board assigned by
LACEEP project); Dirk Hoffmann (co-author of the working-paper preceding this version) and Gover Barja for their
very helpful comments on this work. We thank the indigenous authorities of the study area for their crucial cooperation.
We also thank students from the Faculty of Economics at the Bolivian Catholic University and IISEC-UCB research
assistants, who collaborated in our experimental missions.
1
Contents
1 Introduction
4
2 Climatic Effects and glacier melting
4
2.1
Climate Change in the Andean region . . . . . . . . . . . . . . . . . . . . . . . . . . .
4
2.2
Glacier melting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
5
2.3
Strategies for Adaptation to Climate Change . . . . . . . . . . . . . . . . . . . . . . .
6
2.4
Local management of water resources and Judge of Water . . . . . . . . . . . . . . . .
6
3 Tragedy of Commons
3.1
7
Experimental Games and Common Pool Resources . . . . . . . . . . . . . . . . . . . .
4 Study Area
8
9
4.1
General Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
4.2
Water Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
10
4.3
Demographic and socioeconomic characteristics . . . . . . . . . . . . . . . . . . . . . .
10
5 Methodology
11
5.1
Theoretical Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
11
5.2
Model parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
13
6 CPR Field Experiment
6.1
6.2
14
Experimental Phases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
6.1.1
Phase I . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
6.1.2
Phase II . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
6.1.3
Phase III . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
15
Experiment Treatments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
16
7 Field Experiment Results
17
7.1
Experimental Missions Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
7.2
Decisions on Resource Use . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
18
7.3
Treatments effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
19
7.4
Econometric approximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
20
8 Conclusions
21
2
References
23
List of Figures
1
Study Area Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
2
Study Area Hydrological Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
26
3
Experimental Field Game Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . .
27
4
Percentages of occurrence of adverse scenarios and adaptation . . . . . . . . . . . . . .
28
5
Average use during 21 rounds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
29
6
Evolution of average use and score during the game
. . . . . . . . . . . . . . . . . . .
30
7
Kernel densities for resource use by localities . . . . . . . . . . . . . . . . . . . . . . .
31
List of Tables
1
Epistemology of the Commons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
8
2
Socio-demographic description of communities in the area of study . . . . . . . . . . .
28
3
Total players by location and treatment . . . . . . . . . . . . . . . . . . . . . . . . . .
28
4
Percentages of occurrence of adverse scenarios and adaptation . . . . . . . . . . . . . .
29
5
Average resource usage (in hours) by phase and state . . . . . . . . . . . . . . . . . . .
29
6
Average scores usage (in hours) by phase and state . . . . . . . . . . . . . . . . . . . .
31
7
Statistical analysis of differences in average water use . . . . . . . . . . . . . . . . . . .
32
8
Comparison of average levels of use between treatments . . . . . . . . . . . . . . . . .
32
9
Comparison of average scores between treatments . . . . . . . . . . . . . . . . . . . . .
33
10
Panel Data Model (GLS) with random effects . . . . . . . . . . . . . . . . . . . . . . .
33
11
Panel Data Model (GLS) with random effects by treatments, localities and rounds . .
34
12
Dynamic Panel Data Model - Arellano & Bond estimations . . . . . . . . . . . . . . .
34
13
Descriptive statistics for resource level of use . . . . . . . . . . . . . . . . . . . . . . .
35
14
Descriptive statistics for resource level of use . . . . . . . . . . . . . . . . . . . . . . .
35
15
Within and Between Frequencies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
36
3
1
Introduction
In recent decades, the altiplano region of Bolivia recorded significant changes in temperature,
rainfall and humidity. These changes were mainly related to the effects of Climate Change (CC)
phenomenon, highlighting in particular the significant effect over the melting ice process of glaciers in
the region (Hoffmann, 2010; Hoffmann et al., 2011). This main effect, generates considerable variations
in water supply from those glaciers that get affected in their melting processes. During the melting
process, it is expected to face a first phase of excess water resource, i.e. a sizable contribution to water
balance (Boero et al., 2015); followed by a second phase of abrupt reduction in water intake, reaching
a certain size of glacial, known as water peak, finally followed by glacial final melt.
Besides ecosystem consequences, these abrupt changes in the availability of water resources for
communities in the region, could also generate social dilemmas on resource management and on
agricultural production, mainly. In this sense, it is probably that individual water use by farmers in
the region, would be considerable altered and their communities would probably face constant conflicts
related with the management of water use available to the community. Considering that access to
water in the region is not excludable and its use is rival (especially in the presence of Climate Change),
economic theory identifies this resource as a Common-Pool Resource (CPR), susceptible to this type
of conflict called The Tragedy of the Commons, a problem in which individual and collective interests
are confronted.
Considering the basins of Tuni-Condoriri, Milluni and Hampaturi located in department of La
Paz-Bolivia as study areas, this research seeks to answer: How do changes in water availability due
to Climate Change would affect decisions on resource use for agricultural production?, Are there
social arrangements that avoid exacerbating collective use of the CPR, when the community faced the
adversities of climate change? Using the methodology of experimental economics focused on human
behavior, we develop an experimental field game with farmers in the study area, game that introduces
stochastic variability in the availability of the CPR, the figure of a Judge of Water, alternatives to
Climate Change adaptation, to analyze changes in individual and group behavior under the possibility
of depletion of a CPR and how local management rules could counteract these changes.
The document is structured as follows: in the second section we present the background to the
problem, in addition to the characteristics of the study area. In the third section, we present relevant
literature ffor this research. The fourth section presents the theoretical model and experimental game
design. In the fifth section the main results from the experimental game are described. Finally, the
last section presents our conclusions and recommendations.
2
Climatic Effects and glacier melting
2.1
Climate Change in the Andean region
Despite the evident changes in temperature, humidity and rainfall in the Andean region of Bolivia,
there is still no certainty about the future impact of Global Warming and Climate Change. In this
sense, Hoffmann and Weggenmann (2013) highlights that the impact of Climate Change will probably
be stronger in tropical high mountain regions, or in regions of high altitude above the sea level
compared to other regions affected by this phenomenon. Additionally, there is no certainty about
future rainfall patterns in the region1 . However, some authors foresee a decrease of 10% in the level
1
Precipitation systems in the Andean highlands are mainly determined by the evapotranspiration from the Amazon
basin, the Pacific Ocean and the presence of Lake Titicaca (Martı́nez et al., 2011). Other factors influencing the climate
system of the region are: the presence of the Eastern Cordillera, that serves as a barrier to condensation, and the winds
from the east and north.
4
of rainfall for the next decade in the Andean highlands2 (Andrade & Blacutt, 2010; Marengo et al.,
2011).
Other authors also identify other possible consequences of Climate Change on the Andean altiplano. Hoffmann and Weggenmann (2013) argue that deforestation and the large-scale change in land
use along the Amazon basin, will significantly influence precipitation and temperature in the Andean
region. Meanwhile, Nordgren (2011) states that, with high temperatures in the highland region, it is
expected an increase of evapotranspiration, thus influencing significantly soil moisture and favorable
conditions for agricultural production would arise. Also, Bradley et al. (2006) show that there has
been a shift of lower altitudinal limit of frosts for the mountains in the Andean tropics by about
45 meters over the last thirty years. Meanwhile Buytaert et al. (2010), warn about the dangerous
combination of fragile ecosystems with amplified impacts of Climate Change, such as mountain areas,
and the dramatic effects it can bring for their societies.
2.2
Glacier melting
Some estimations indicate that Bolivia has 20% of the tropical glaciers of the Andean highlands
region (approximately 300 km2 ). Jordan (1991) states that Bolivia glacial inventory, based on data
from the eighties, covers 566 km2 of glacier surface. The largest concentration is located in the
Cordillera Real (55%), in the Cordillera Apolobamba (37%) and the Cordillera Quimsa Cruz (10%).
In Bolivia, as experienced in countries of the Andean region, it is possible to prove the disappearance of
a large number of small glaciers and low altitude in the last three decades. Changes in the surface and
volume of twenty one glaciers in the Cordillera Real between 1963 and 2006 based on photogrammetric
measurements show a loss of 43% of its volume and 48% of its surface (Soruco et al., 2009).
As in other regions, the regular processes of glaciers melting in the mountainous region of Bolivia allow maintain minimum flow of rivers in the dry season. This regular feature also ensures the
availability of drinking water to great urban centers and communities in and near the region. In addition, regular melting of glaciers contributes to groundwater recharge, although there is no conclusive
studies on this relationship (Boero 2012). However, in presence of the effects of Climate Change, it
is expected that the process of glaciers melting will suffer alterations involving: i) an oversupply of
water resource from glacial in the short and medium term, and ii) a rapidly decreasing contribution of
water from the glacier to the point of exhaustion in the long term. Obviously, the flow of rivers that
supply water to small farming communities and also supply drinking water to nearby urban centers,
would be seriously affected.
Due to variations in temperature, precipitation and humidity that are evidenced in the Andean
highlands of Bolivia, it is clear that the process of glacial melting has been affecting ecological and
social balances in recent decades (Vuille et al., 2003). These processes could have been affecting the
glaciers snow balances , even attempting to produce an ablation that leads to their disappearance
in the medium to long term when the equilibrium line is above the height of the glacier (Francou et
al., 2004). According to Hoffmann (2010), the glacial retreat will have severe impacts on the water
cycle of the Cordillera Real. For Soruco et al. (2009), the glacial retreat will also affect the flow of
the surrounding basins to Tuni-Condoriri. In the case of the gradual disappearance of Tuni glaciers
(assuming no changes in rainfall), farming communities inhabiting this region would receive 12% less
than the current average annual flow of water. This represents a 9% during the wet season and 25%
less during the dry season.
2
These regions in Bolivia account for approximately a quarter of its territory: the highlands north, central and south;
and the Western and Eastern Cordilleras.
5
2.3
Strategies for Adaptation to Climate Change
Currently, there are many research focused on predicting how people will respond to the impacts of
climate change, and on mechanisms to promote more effective adaptation strategies. In the agricultural
sector these strategies ranging from changes in cultivated varieties ((Challinor et al., 2007), Krishnan
et al., 2007), to changes in planting dates and improvements in irrigation systems (Byjesh et al., 2010,
Srivastava et al., 2010). For Bradshaw et al. (2004), the adaptation in the agricultural sector is a
response to climate variability and change, but the way each farmer should implement is unknown,
the question instead is whether it is consistent with a collective adaptation. For Schimmelpfennig et al.
(1996), is the social behavior that determines which options are taken in a climate adaptation. Thomas
et al. (2007) makes an analysis of the processes of collective action in agricultural communities, finding
that farmers decide to adapt when the cost (risk) to adapt can be distributed among several members
of the community.
In Bolivia the impact of climate change on agriculture, affects the cycle of crop growth and increased vulnerability of crops during drought periods (eg potato production in the Andean region).
According to Aliaga and Paredes (2010), extreme weather events of the last decade have adversely
affected crop yields, producing more food vulnerability and significant impact on the migratory patterns of the rural population and on food prices. This evidence suggests the enormous need to find
effective mechanisms of adaptation to climate change in the region.
2.4
Local management of water resources and Judge of Water
The management of water resources in rural areas of Bolivia, has followed an autonomous and
independent of external interventions historical development, and recently the State intends to take a
more active role through the formulation of laws and programs that regulate the rights of water use.
This historic characteristic, allowed the development of local forms of management and the establishment of rights based mainly on cultural factors. Bustamante (2002) recognizes that, for ecology, social,
cultural and ethnic diversity of Bolivia, it would be naive to try to make an overall characterization
of local ways of managing water resources (even autonomous-independent development).
However, if we could characterize roughly the local water management in Bolivia, we must start
to point out that the “social and territorial logic” is the standard way to identify the sources of
water that are subject to local management. This logic suggests that water sources belonging to
the community territory are for the exclusive use of its members. That is, the fact of belonging to a
community materializes their access to water sources in the territorial extension of the community and
unrestricted water use for different purposes. This logic does not close to a communal demarcation,
moreover there are other cases (like irrigation organizations) that include several communities. While
this may create conflicts of interest between the territory where the resource is used and the territory
in which the water source is located; rights and pre-existing agreements are generally respected.
Once the social and territorial logic defines water sources ownership to the community, different
types of water sources are identified; which in turn raise different types of management. Generally,
the oldest sources e.g. rivers or springs, maintain management standards even from precolonial times.
Then other water sources that are exploited from small reservoirs and ponds, are characterized by
management standards largely dependent on the historical period in which they were set up for use.
Finally, in recent years the extraction of underground water by drilling deep and semi-deep wells,
made the resource management standards functional to technical requirements.
We can not deny that the particular form of water management will be greatly influenced by local
cultural principles, i.e. those defined by the social organization that determine resource use based
on their own worldview. However, as well as culture, geographical features are equally important in
determining how to manage the resource. That is, the local water management has clearly a material
support, expressed in the technical supply needs regarding their use.
6
Bustamante (2002) identifies three rights that express a socially legitimate claim to exercise certain
attributions over the resource, which are: i) Right to enjoyment of the water: i.e. acquirement of a
portion of the water flow; ii) Right to manage: i.e. possibility to regulate the use patterns; and iii)
the right to use infrastructure: i.e. use of channels to lead the water flow to the agricultural property.
The acquisition and access to water rights takes different forms, ranging from simple access to water
under the fact of belonging to the community, to complex correlations with inputs (labor or money)
made by users. These rights in turn entail obligations related to: the maintenance, improvement
and utilization system repair, payment of dues, attending meetings, etc. This definition of rights and
obligations is transparent, because even without being written, most users in rural areas in Bolivia
are familiar with them and understand the penalties caused by its breach.
The organizations involved are usually specialized in water management and recognize the supremacy
of community and supra-community organizations (central, subcentral, federation, etc.). The organizational structure, usually considered an authority inwards usually called Judge of Water, which is
responsible for the distribution of water, the solution of internal conflicts and maintenance supervision
on the one hand; and secondly the authority out, usually the head and other hierarchical charges in
the community organization, that are responsible for managing the affairs and interests of the system
in relation to the State and external institutions. Depending on the organizational structure, it is possible to rotate roles between users more or less continuously (in ayllus for example) or imply a degree
of specialization in which they are held by a group of people for relatively long times. Furthermore,
the complexity of the organizational structure generally varies in relation to the size of the irrigation
system.
3
Tragedy of Commons
In different contexts, there are natural resources or resources that come from the human being,
in contrast to other types of resources or assets, that are free to use for individuals, and a right of
exclusivity would involve significant costs. Such resources are called Common-Pool Resources (CPR),
for which any individual has rights to use and there is no right to exclude any of these (Fuentes-Castro,
2009). The implications of how to use and manage these types of communal property by agents, has
generated extensive discussion in economics.
Within the first studies on communal goods, Gordon (1954) initially proposed that the property
of many is ultimately property of anybody emphasizing the problems created by a collective use of
communal good. Later, Clark (1973) states that if a significant number of users have free access to
a given resource commonly owned, total units drawn probably be higher than the economic optimum
extraction. In his seminal research, Hardin (1968) states that the individual use of communal goods
leads to its depletion, or in the case of CPR (e.g. water), the use would lead to its degradation. In
this sense, Hardin argued that the pursuit of individual profit in the use of a CPR, generates potential
negative externalities for other users 3 . The logic proposed by this author, captured the thinking of
researchers especially in the field of natural, who claimed that indiscriminate use of the human being
seriously affect natural resource management.
However, in later years Elinor Ostrom provides a broader view of the characteristics and possible
way of managing the CPR concept. While Ostrom (1990) agrees with the logic of Hardin only in
extreme examples (oceans, forests, etc.), this author argues that communal goods less extensive or
with less dimension, have shown the opportunity to succeed in establishing procedures to prevent their
overuse and degradation of the communal resource.
To guide the discussion on the subject, Ostrom defines community property as a system of natural resources and man-made, with considerable costs to exclude potential beneficiaries use. This
definition distinguishes the commons of other types of goods according to their usage characteristics
3
In this sense Hardin says: “ . . . the freedom of the commons leads to the ruin of all. ”(sic. Hardin, 1968).
7
and exclusion, as shown in Table 1.
Table 1: Epistemology of the Commons
Use
Rival
Non Rival
Excludable
Private goods
Club goods
Access
Non Excludable
Common goods
Collective (public) goods
Source: Ostrom (1990).
In Ostrom (1990), various schemes for managing common resources are presented. The first
relates to State control, where the latter is responsible for regulating the use of the common resource.
However, Ostrom shows that State control is effective only when the State itself has accurate and has
the ability to monitor and sanction, with low management costs. A second alternative proposes the
privatization of common resource to avoid the tragedy of the commons, however Ostrom shows that
the scheme has not been fully effective.
For Ostrom, there are five characteristics in common resource systems whose administrations
were successful. First, the common resource and its users should be clearly defined. Second, users
must participate in the acceptance of rules that suit local conditions, and these can not be bypassed.
Third, supervisors should be accountable to resource users, and possibly be users. Fourth, there
must be moderate sanctions for offenders and mechanisms for resolving conflicts. Fifth, less common
resource can be nested in a larger system. However, Ostrom recognized that while it is possible
to find cooperation strategies for sustainable resource use; when the resource is nearing exhaustion,
cooperation strategies lose their meaning and rather dominates selfishness, prompting everyone to
extract more of the resource.
In a recent approach, Baylis, Gong, and Wang (2013) establish that social capital (measured by
ethnic homogeneity) can facilitate community governance, but not all social capital is alike. These
authors distinguish bonding social capital (within a village) from bridging social capital (between
villages), and compare their effects on the management of a CPR showing that bonding social capital
can improve CPR management, while the effect of bridging social capital is mixed. They also find
that bonding social capital decreases the consumption of the CPR, and bridging social capital erodes
the effect of bonding. Bridging social capital also decreases the use of the CPR by villagers who are
near subsistence levels of consumption.
3.1
Experimental Games and Common Pool Resources
Analysis of common resources and the way individuals respond to shocks in the availability of
such resources has been carried out in recent years by applying techniques of experimental economics.
Experiments have examined human behavior under social dilemmas related to resource extraction
commonly used (e.g. Ostrom et al., 1992; Ostrom et al., 1994; Casari & Plott, 2003; Cárdenas &
Ostrom, 2004; Cardenas et al., 2004; Velez et al., 2009; Alpizar et al., 2011 and Moreno-Sánchez
& Maldonado, 2010). Blanco, Lopez, and Villamayor-Tomás (2011) analyze the effect of exogenous
ecological changes on the behavior of users who exploit a common natural resource, in an environment
of relative scarcity. Experimental subjects face different levels of availability at various stages of
the experiment. This technique can assess whether the average individual extractions vary between
different levels of availability of the common resource. The results show that individual behavior
is sensitive to the level of resource availability and that when the level of availability endangers its
conservation, subjects did not change their average individual extractions.
Individuals take some time to react, but do so by restricting their levels of extraction. When
the decrease in the level of resources is large enough that there is an imminent risk of resource
depletion, individuals respond more selfishly increased their withdrawals. These results contrast with
those obtained by Osés-Eraso and Viladrich-Grau (2007), but are consistent with the observations of
8
Ostrom (2010). Resource users need to be exposed to a situation of scarcity earlier than cooperative
strategies emerge. However, when the resource is near exhaustion, users do not see any benefit in
cooperating and then follow selfish strategies. These results suggest that changes in the availability
of a common resource can affect not only the direction of the individual responses of users, but also
their speed of adaptation.
These results should be interpreted as short-term information, related to changes in resource availability. Within the experimental game designed by Blanco et al. (2011) there is no scope for collective
strategies, institutional adaptation or technological improvements that take time to be implemented.
These elements have proven to be relevant in the user responses to the common resource scarcity.
Reactions, both short term and long term, are potentially relevant to the success of sustainability.
The relevance of short-term reactions is explained by the fact that these determine the pressures
on resources during the transition to long-term strategies. If the pressure exceeds the regenerative
capacity of renewable resources, this can become depleted.
Cárdenas and Ostrom (2004) proposed the existence of three types of information (i.e. material
incentives, group-context and individual characteristics) that people use when faced with an experiment of this type. A possible combination of information on subjective material payoffs in a game,
group members, and repeated game incentives may induce cooperative behavior as a rational strategy
within a framework of collective action. This experiment allowed participants to use the information
in its proper context and researchers to examine the impact of this information on decisions. In turn,
there are positive endorsements by arguments derived from the theoretical framework. It seems that
individuals use various levels of information depending on the structure of the game and the context
in which they are playing.
4
4.1
Study Area
General Characteristics
Our study area is located between the Condoriri and Tuni massifs, area where the Zongo glacier
and the imposing mountains of Huayna Potosi and Chacaltaya among others are located. We consider
this area of study, since Ramirez (2006) identifies that glaciers in this area are crucial in the water
supply of the region, and are highly vulnerable to glacial melting, determining an estimated horizon
of life until 2030. Geographically, this area is located in the southern part of the Cordillera Real de
los Andes, northwest of the cities of La Paz and El Alto. In terms of political division, this area has
geographical presence between: Murillo province (municipalities of La Paz and El Alto), Los Andes
province (municipalities of Pucarani and Batallas) and Larecaja province (municipality of Guanay).
Communities of Alto Peñas, Suriquiña, Tuni and Condoriri are in the municipalities of Batallas and
Pucarani. The Condoriri and Tuni communities specifically belong to the macro communities: Palcoco
and Chuñavi respectively (see Figure 1).
The characteristic ecosystems of the region are diverse, among them the following ecoregions
have been identified: i) Nival (4200-5589 m.a.s.l.), ii) High-Andes (3850-4200 m.a.s.l.) and iii) Puna
(3800-3850 m.a.s.l.). This diversity of ecoregions from north to south, allows us to describe three
important zones in the region: a) High Zone: the Nival and High-Andean ecoregions, influenced by
the proximity to the mountains, with low vegetation cover that generates high evapotranspiration,
which consequently it produces a cold climate in the region; b) Central Zone: with the ecoregions
High-Andean and Puna, low temperature but more moisture under the influence of Lake Titicaca; c)
Lower Zone: Puna ecoregion, higher temperature and humidity.
On the one hand these municipalities have a maximum temperature of 16.6o C (61.88o F) and a
minimum -4.3o C (24.26o F), with an average temperature of 8.7o C (44.7o F). Minimum temperatures
occur from May to August, in this period the critical temperature occurs in the month of July which
9
is exploited for the production of dehydrated products. Moreover rainfall occur from December to
March, with greater intensity in January where the average reached 111.3 mm. The lower intensity
are in the months of May to August with zero precipitation in July (SENAMHI, 2012).
4.2
Water Resources
The study area consists of more than 15 watersheds, which have great influence on the central
basin of Lake Titicaca and elsewhere on the Zongo River basin. These micro basins are habitats that
have great influence on the distribution of vegetation in wetlands and wildlife (see Figure 2). Lake
Titicaca basin is important because it supplies water to the eastern slope of the Andes, provides water
to wet grassland and keeps the soil for agriculture. The cities of La Paz and El Alto are dependent
on water supply from this region. Meanwhile watersheds that converge to Zongo River, are used for
the generation of hydroelectric power.
The hydric organization in the study area consists of several levels. First, the glaciers defined
as points of water accumulation and retention of clouds necessary for the maintenance of the water
system. Second, small rivers with steep slopes that shape the landscape and fed lakes that supply
water to smaller communities and urban populations. Third, new small rivers of surface features that,
in many cases, are infiltrated to moisten the soil that feed the grasslands and wetlands.
The influence of snowy mountains in the upper area, allows the concentration of masses of abundant water that give rise to different lakes as Khara Kota, Taypi Chaca Sora Khota, and Labrahuani,
among the most important. So, they give rise primarily to two irrigation systems - the Labrahuani
and Khara Kota system, which together have a storage capacity of 14.5 million cubic meters used
to irrigate about 4,770 hectares - the lagoon system Taypi Chaca and Sora Khota, with a storage
capacity of 13 million cubic meters, which allow water 4,482 hectares.
4.3
Demographic and socioeconomic characteristics
The population of the area is mostly of Aymara origin. The economic resources in these communities have mainly agricultural orientation, the population is engaged in the raising of cattle, sheep,
pigs, camelids, mainly in the highlands and agricultural production as tubers and cereals in the lower
parts of the community, still these their major sources of income. The villagers believe that their main
crops are potatoes, quinoa, wheat and bean. Taking as a secondary agricultural production: oats,
barley, alfalfa. There is an emerging number of people in the study area, which is engaged in trade
and transportation, leaving in background their agricultural activity.
During the time of sowing and harvesting, the demand for labor increases, while the rest of the
year people migrate to different places or regions, including the neighboring countries, seeking to
increase family income. Intermediate villages, as Palcoco, Peñas and Patamanta are used as second
homes. Also these people are the basis for communication with the cities of La Paz and El Alto.
The communities are organized into agrarian unions and ayllus, in recent years several communities have returned to their original form of organization of the ayllu, which had been abandoned
after the National Revolution of 1952 and the Land Reform the following year. However, in some
cases they have not changed structures, but only the name (see Table 2). Social and administrative
organization in these communities is at the forefront of an Agrarian Central which is formed by a set
of Sub stations and these in turn by a set of communities, as in our communities of study. These
communities are organized on the basis of the farmers’ union, emerged after land reform. The board
of the union is at the head of the Secretary General who is assisted by a group of secretaries.
The Tuni community (Chuñavi) presents the traditional organization of native indigenous ayllu.
At the head of a community Mallku, although some people still considered a peasant farmers union.
10
In other communities Tuni management peasant farmers union, and indigenous ayllu originated lasts
one year, after which it is replaced by another community member elected according to their practices
and customs, which the outgoing authority delivers all existing documents and gives a report of the
actions handled during his tenure.
5
Methodology
For this study, we developed a game of experimental economics field based on a theoretical model.
It should be noted, that this study constitutes one of the first experimental approaches in the economic
research in Bolivia. First we describe the theoretical framework and then describe the game.
5.1
Theoretical Model
Recent approaches to analyze individual decisions regarding the degree of availability of a common resource, have emphasized the use and development of methodologies of experimental economics
mainly for its ability to generate information about patterns of behavior of participants, in order to
validated by repeating this behavior.
In this paper, we follow the theoretical development proposed by Bernal et al. (2013), which in
turn constitutes an important extension of the contributions proposed by Ostrom et al. (1992), Ostrom
et al. (1994), Casari and Plott (2003), Cárdenas and Ostrom (2004), Cardenas et al. (2004), Velez et
al. (2009), Alpizar et al. (2011) and Moreno-Sánchez and Maldonado (2010). We start with a function
of single payments of type:
!
πi = f (xi ) + g S,
X
i
xi
βx2i
= αxi −
−γ
2
S−
n
X
!
xi
(1)
i=1
In Equation 1, the first term f (xi ) represents the function of direct payments the common resource
use or water for irrigation (resource, hereinafter); considering: a first individual profit associated with
the use of the resource with price α for each unit of resource used. A second element associated
with the cost of using an additional unit of the resource, considering β as a positive but decreasing
parameter as more units of the resource are used. Equation 1 also includes a second term g(·) reflecting
the externality on individual benefit generated by the use of water as a group.
Due to the characteristics that constitute the water for irrigation as a CPR, when approaching
potential emerging equilibriums, we must consider that there are two ways to establish a criterion of
optimum extraction: individually and from social or group.
From the individual standpoint, the agent achieves a Nash Equilibrium maximizing her individual
benefit (πi f orall i) under the following objective function:
!
max πi = f (xi , S) + g
xi
X
xi
= αxi −
i
βx2i
2

+ γ S −
n
X

xj − xi  ∀ j 6= i
j=1
Accordingly, the solution to individual profit maximization of Equation 2 is defined as:
∂πi
= α − βxi − γ = 0
∂xi
11
(2)
That after a rearrangement of terms, we determine the optimal level of resource use as the
resulting Nash Equilibrium:
ash
xN
=
i
α−γ
β
(3)
On the other hand, considering the level of resource use from a social perspective (i.e. group),
we maximize the aggregation function of individual benefits, determined by:
max
xi
n
X
πi =
i=1
n
X
i=1
"
βx2i
+γ
αxi −
2
S−
n
X
!#
xi
(4)
i=1
In the case of the Equation 4, the solution derived from maximizing profit aggregation is determined by:
∂πi
= α − βxi − nγ = 0
∂xi
From this solution, we establish the optimal level determined by the Social Equilibrium, of the
form:
xSoc
=
i
α − nγ
β
(5)
Therefore, Equation 3 and Equation 5 determine the individual and social equilibria are not
depending on the status of the resource (i.e. S). Following Bernal et al. (2013), the experimental
design incorporates a stochastic component related to climate fluctuations affecting the level of CPR
availability. Under this stochastic approach, three probable scenarios about the status of the CPR
are introduced: i.) Normal status (SN ), ii.) Low status (SL ) and iii.) Drought status (SD ) 4 .
Considering these three resource status, relationship to the level of availability is set SN > SL > SD .
Based on the individual and social equilibrium (Equation 3 and Equation 5), we can establish the
level of individual benefit, according to the adopted strategy, i.e.:
2
N ash
πit
=
ash
αxN
it
xN ash
ash
− β it
+ γ St − nxN
it
2
(6)
Similarly, we establish the benefit associated with social solution:
2
Soc
πit
=
αxSoc
it
xSoc
− β it + γ St − nxSoc
it
2
(7)
Under this scheme, an experimental game (described in the next section) arises to approach the
individual behavior regarding the use of a CPR, considering the stochastic factor in the availability of
the resource. The experimental set includes three experimental phases:
• Phase I: Under the random establishment of the status of the resource at this stage agents
(players, hereinafter) play under two possible states of the resource: Normal status with a
probability of occurrence of p and Low status with probability (1 − p). If a player adopts its
4
Consider the maximum availability of the resource, it is associated with the status Normal, while the minimum
availability resource is associated with the status of Drought.
12
Nash strategy, her expected profit will be:
h
i
h
i
h
i
N ash
N ash
N ash
E πi,t
= p πi,B
+ (1 − p) πi,N
(8)
• Phase II: In this phase, players’ decisions are taken under two possible availability status of the
resource, Normal status with probability q and the Drought status with probability (1 − q).
If the player adopts its Nash strategy, its expected profit will be:
h
i
h
i
h
i
N ash
N ash
N ash
E πi,t
= q πi,S
+ (1 − q) πi,N
(9)
• Phase III: In this last phase, players have the opportunity to adapt to the effects of climate
change5 through the decision to build a dam. While taking the group decision to build the
dam involves mitigating the adverse effects of climate change, this decision involves a cost to
the group, reflected by the magnitude C. In addition, construction costs account only for K
experimental rounds emph ie has a lifespan of K rounds.
To determine whether adapt to climate change is good strategy, Bernal et al. (2013) suggest that
we solve the game by backward induction. Thus, the individual assumes that regardless of the level of
the resource, the result is that all players (including her) take their individual strategy. The expected
payoff of the player during the K rounds of the third phase is where adapt to climate change:
A=
K
X
i
h
io
n h
N ash
N ash
N ash
+ (1 − q) πi,n
−C
= K q πi,b
E πi,t,k
(10)
k=1
in the case of not adapting to climate change, it would be:
B=
K
X
n h
i
h
io
N ash
N ash
N ash
= K q πi,s
+ (1 − q) πi,n
E πi,t,k
(11)
k=1
Assuming that individuals are risk neutral symmetrical and prefer to adapt and pay a cost of
adapting C, if and only if A > B. In the case where the individual is indifferent between adapt and
do so, we have:
i
h
io
n h
i
h
io
n h
N ash
N ash
N ash
N ash
K q πi,b
+ (1 − q) πi,n
− C = K q πi,s
+ (1 − q) πi,n
n h
io
n h
io
N ash
N ash
K q πi,b
− C = K q πi,s
nh
i h
io
N ash
N ash
C = qK πi,b
− πi,s
(12)
Therefore, if the cost of adapting i) exceed C of Equation 12, the individual prefers to take the risk
of confronting the status Drought in the next K rounds, ii) equals C of Equation 12, the individual
is indifferent to the adaptation and iii) is less than C of Equation 12, the individual will always pay
to adapt.
5.2
Model parameters
Based on the report by Zuazo (2013) for this research, we define specific parameters for the construction of pay tables, which determined the score in every round depending on resource availability
5
Effects on the availability of common resource, ceteris paribus.
13
and balances of the theoretical model. The stock of water resource is denoted by the letter S for different possible climate states (i.e. Normal, Low and Drought). We define the following values according
to the characteristics of the area: SN = 80, SL = 60, and SD = 40 units of water respectively.
Regarding the role of payments, it is estimated that the price value of the water corresponds to
α = 50, while the parameter of the function of cost and externality are respectively the following:
β = 6 and γ = 8. Replacing these values in Equation 3, we get that the optimal level of resource use
ash ' 7. Also, with these parameters and considering
equivalent to Nash equilibrium is equal to xN
i
that the experimental design includes groups of five people (∴ n = 5) , by Equation 4 it states that
the optimal level of resource use equivalent to social balance is xSoc
' 2. For the construction of our
i
pay tables, we remove the experiments extractions zero units (hours), since this level of extraction
is associated with prohibitive policies on the use of resources. Therefore, and based on the above
parameters, in our experiment minimum possible value of extraction in each round of the game is one
hour and the maximum is seven hours of water use.
6
CPR Field Experiment
6.1
Experimental Phases
To perform this game through a CPR experiment in the field, we previously developed an Experimental Protocol6 , in which we detail: i) general characteristics of the experimental game; ii)
description of the designed experimental treatments; iii) selecting, structuring and organization of
experimental groups, establishment of games roles among players and the specific roles for experimental monitors during the game; and iv) a procedure for contingent cases for possible methodological
alteration due to uncontrollable circumstances. Our Experimental Protocol was then used to train our
experimental group monitors (responsible for overseeing the development of the game) and to prepare
the material where the information was collected.
Our experiment standardizes two elements as part of modeling the reality. First, the game is
played under the assumption of potato production cycles only, in order to standardize agricultural
yields considering the representative product of agricultural production in the region. Second, in the
game it is assumed the same extension of agricultural land among all players, also considering the
same level of slope, in order to control the effects of agricultural land by differences between players,
the game results. The parameters of the model reflects the characteristics of potato production in the
highland region (considering wages of labor and potato market price). Another feature of our game is
that, due to traditional metric conventions widely used in the study area, farmers used to measure the
level of water use in number of hours of flow to their land. Therefore, our game measures of water use
were then measured in terms of hours (and not in liters, as it would be expected). This feature favors
the development of the experiment as the game is adapted to the metric conventions of the area and
even facilitate the reading of results.
Each of the experimental phases contemplated in our game, follows a particular procedure. Therefore, briefly we detail these procedures for each experimental phase.
6.1.1
Phase I
In this first phase of the game, two scenarios about resource availability are introduced, i.e.
Normal status (represented by green balls during the game) and Low status (represented by yellow
balls). Through random selection, each group determines the status of the resource to be considered
in each round and then each member shall record on a card her decision about the hours of water use
6
Available by request to the authors.
14
for irrigation, given the current resource status. In this first phase, the probability to play with the
Normal status is 3/4 and the probability of playing with the low status of 1/4.
In each of the six rounds of this phase, group monitor is responsible for reporting the current
status of the resource, under which decisions must be taken and then to register and add individual
decisions to determine the level of group use per round. The expected payoff per round, for the case
where all players choose to adopt a dominant strategy or Nash equilibrium (equivalent to seven hours
ash = 7), equals 54 points.
of usage of the resource between 1 to 7 hours of possible extraction i.e. xN
i
1
3
E[πi,t ] = (35) + (60) = 54
4
4
6.1.2
Phase II
After the first phase, players players start another six new rounds. At this phase, we introduce the
adverse resource status as representing the presence of adversity of Climate Change on the availability
of the resource (i.e. Drought status, represented by red balls). In this case, individuals play under
only two of the possible resource status: Normal and Drought status. The occurrence probability for
the Normal status is 3/5 and 2/5 for the Drought status. The expected payoff per round, considering
the case where all players adopt their dominant strategy or Nash equilibrium, equals 42 points.
2
3
E[πi,t ] = (15) + (60) = 42
5
5
6.1.3
Phase III
In the third phase of the game, we introduced different group treatments (next section provides
a complete description of these). In this third phase, nine final rounds are played. According to the
experimental treatment of each group, we introduce a possibility of adaptation to the effects of climate
change represented by the build of a reservoir or dam, simulating the possibility of reserving water
that counteracts Drought status affecting the availability of the resource.
Those groups who decide to adapt, change two red balls associated with Drought status for two
yellow balls associated with the low status of the resource. Within the game, we establish a reservoir
useful life of three experimental rounds, which means that the decision to adapt is performed every
three rounds (K = 3). Because in this third phase nine rounds are played, the groups that have the
ability to adapt, make their decisions on three occasions.
Adaptation to climate change by building the reservoir carries a construction cost to be covered
by the five members of the group. Players spend a fraction of their profits accumulated during the
previous rounds of the game. The cost of building the reservoir is calculated using the difference
between the expected profit under the Low status, and the expected profit under the Drought status,
considering the assumption of risk neutrality.
2
C = (3)[35 − 15] = 24
5
Assuming that individuals are risk neutral, this is the value that should be the cost of adaptation
for each player and also will be the minimum contribution required to perform the construction of the
reservoir. To facilitate the calculations during the game, we round the individual cost to 30 points.
Therefore, the group cost to Climate Change adaptation would be:
15
C ∗ = (5)(30) = 150
6.2
Experiment Treatments
Based on the model developed in the previous section, we developed an experimental field game
with the aim of studying: i) possible changes in the level of resource use (hours of water use) among
farmers in the study region through introducing effects on resource availability associated with Climate
Change, and ii) the effects over the pattern of resource use of introducing Climate Change adaptation
alternatives and social arrangements of the study region. With this aim, we developed a game through
a CPR experiment in the field based on Bernal et al. (2013) with own adjustments and own innovations
in experimental treatments that are detailed below
In general, our experimental game consists of: i) five treatments and ii) three phases, considering
two initial phases of six rounds each one and nine final rounds where treatments are played (twenty one
rounds in total, representing potato production cycle, from planting to harvest). The game structure
is presented in Figure 3. Within these five different experimental treatments, we consider two baseline
or control treatments, in order to estimate the effect of the other three treatments. In these three
groups (not baselines), we propose the introduction of various factors associated with Climate Change
and collective decisions about resource use.
T(1): Baseline without Climate Change Treatment:
In this treatment, players in the experimental groups take their decision about the amount of hours
of resource to be used individually in each of the twenty rounds of the game, just under two random
status of the resource: Normal and Low. In these control groups, the monitor records the amounts
of individual use per round and announces the group level of use, according to this result each player
calculate their level of profit per round using tables of payments, according to the resource status,
built based on the theoretical model of the game.
T(2) Baseline with Climate Change Treatment:
In this treatment, players make their individual decisions about resource use under two random status:
for the first six rounds, decision making is conducted under the Normal and Low status. Subsequently,
with the aim of introducing the effect of Climate Change and analyze its permanent effects, we replace
the Low status by the Drought status, in all the next fifteen rounds of the game. Thus, players make
decisions about resource use under the Normal and Drought status during the next fifteen rounds (up
to end the game).
T(3) Voting without Communication:
In this treatment, during rounds one to twelve players follow the structure of previous treatment
(i.e. T(2)). From thirteen round onwards, we propose the possibility of Climate Change adaptation
by building a water dam or reservoir. These groups are treated with a strategy of simple majority
vote, in which individuals privately and confidentially (without communicating or seeking consensus),
decide to vote for or against the adoption of the adaptation strategy. The group monitor collects the
votes and announce the final result; if there is a simple majority in favor of the adoption of adaptation
measure, the five players contribute 1/5 of their accumulated profits and the next three rounds are
played with the Normal and Low status. However, if the decision by simple majority is to reject the
measure, the following three rounds are played with the Normal and Drought status.
T(4) Voting with Communication:
This experimental treatment have similar structure of previous treatment (T(3)). The main difference
is that prior to making the group decision to adopt or reject the measure of adaptation, players will
have five minutes to exchange views about the possible benefits and harms of taking such action. It
is not necessary to reach a group consensus. After these five minutes communication, voting is done
and the scheme of the previous treatment continues.
16
T(5) Voluntary contributions - Judge of Water:
Finally, in addition to the possibility of voting for the adoption or rejection of the adaptation measure,
the individual contribution for the construction of the dam in this treatment is voluntary (after taking
the decision to adapt), unlike previous treatments where this contribution is fixed (1/5 of accumulated
profits). If the decision by simple majority is to reject the measure, the following three rounds are
played with the Normal and Drought status. However, if by a simple majority the group opts for the
adoption of the adaptation measure, then there are two new scenarios: i) if voluntary contributions
are greater than or equal to the cost of building the dam, the cost of the dam is covered and players
make their decisions under Normal and Low status; or ii) if voluntary contributions are insufficient
to cover the cost of building the dam, in these circumstances we introduce the figure of the Judge of
Water (traditionally accepted authority on the social structure of the study area).
Since the Judge of Water arises in terms of adaptation decision and insufficient contributions,
the mission of this judge is to redistribute or impose higher contributions in order to cover the cost
of the dam (after of being provided of information on registered voluntary contributions and profit
levels of group members). With this experimental role, we analyze the way the free-rider attitudes are
punishing in the study communities.
7
Field Experiment Results
In this section, we present the results of the game through a CPR experiment in the field, first
detailing the levels of water use hours, both individually and as a group. Based on this results, we
analyze statistically variations in terms of average and standard deviations of use. We also describe the
distributions and densities resulting by phase, treatment and status of the CPR. After this descriptive
analysis, we present the estimation of panel data models, in order to validate our intuitions about
patterns of use and scoring averages, and in order to quantify the effect of treatments on individual
and group decisions.
7.1
Experimental Missions Characteristics
Between August and October 2014, we performed three experimental missions in three localities
in our study area: Chuñavi Alto, Chuñavi Bajo and Batallas. The first two localities are characterized by low population density, dedicated to farming, organized under the rules of indigenous-native
communities, as described in third section of this paper. The third population (Batallas) is identified
as an intermediate city with greater population density compared to Chuñavi Alto and Chuñavi Bajo,
with diversified economic activity (mainly trade, transport and agriculture) and a significant flow of
trade with the nearby urban centers (city of La Paz and El Alto). In these missions, we developed our
experimental game with 200 people7 (players, hereinafter), with a distribution between communities
detailed in Table 3.
Our experimental missions were characterized by the voluntary participation of communities
members, considering farmers mainly, but also indigenous authorities, teachers and some secondary
to high education students. Gender and age compositions in all the missions, achieved to reflect the
communities demographic composition. Also in each experimental mission, before to start the development of the game, we signed an ethical disclosure agreement with each one of the players, in which
we committed to an anonymous and responsible use of information generated by the experimental
game.
Before we present the observed patterns on hours of resource use and patterns on profits for
players, in Figure 4 we plot the percentages of occurrence of the stochastic scenarios associated with
7
In this results section, we take into account only those players whose experimental groups correctly developed the
twenty one rounds without methodological alterations. Other groups were discarded.
17
Climate Change and percentage about the willingness to Climate Change adaptation considering its
acceptance rate. In Figure 4a, we plot the percentages recorded for the occurrence of stochastic
scenarios associated with the effects of Climate Change during the rounds of the game. We can see
that the percentage of occurrence of these stochastic scenarios are highly symmetrical in our three
study sites.
In Phase I, we obtained a 41 percent of occurrence for Low and Drought status (average between
missions), 48 percent of occurrence for Low and Drought statusin Phase II and 43 percent of occurrence
for Low and Drought status during Phase III. In Figure 4b, we present percentages of acceptance to
the adoption of adaptation to Climate Change during Phase III. These percentages show us a first
important result: there is a high willingness to adopt measures to adapt to climate change among
players, with a 89 per cent acceptance to such measures among all the players.
According to the previous analysis, Table 3 presents disaggregated results for percentages of
occurrence for scenarios associated with Climate Change that reduce the availability of the resource,
by locality and the experimental treatment. These percentages also reflect a symmetry in the five
treatments for the three localities. The average occurrence for the resource states ”Low” and ”drought”
is equal to 43.8 per cent between treatments. We also note, as mentioned above, a high disposition
by players to take the measure of adaptation to climate change submitted in the game.
7.2
Decisions on Resource Use
Based on the information resulting from the experimental game, we initially present a description
of the patterns of average resource use and average scores achieved in each experimental phase of the
game. Table 5 shows averages for the resource use chosen by the players for each phase and stochastic
status of the resource. Considering this information, in Table 6 we try to identify if the resource use
among players is exacerbated while we introduce reductions in terms of resource availability due to
Climate Change. As we can see in Table 6, considering Phase I, the average use was equal to 3.66
hours for the Normal status while the average use for the Low status equals 4.41 hours. These values
represent an increase of 0.75 hours between Normal availability scenario and the moderate reduction
over resource availability. This difference in average use between the Normal and Low status, is
statistically significant (|t| = 8.12); this suggests that players facing a moderate reduction in water
availability increase their water use in almost 20 percent. This result is further corroborated by the
higher standard deviation reported for the Low status.
Before describing the observed average use during Phase II, we must remember that according
to our experimental design, we should consider two instances of play during this phase. In the first
instance, those groups playing under Baseline without Climate Change treatmentcontinue alternating
both resource status of Phase I. Thus, for these groups, the average use in the Normal status is equal
to 3.64 hours; while for the Low status, the average is 4.52 hours reflecting a significant statistically
difference of 0.88 hours (|t| = 5.52), repeating the increase in the average use observed in the first
phase. In parallel the other treatments are facing at this stage the adversities of Climate Change.
For these groups, we compute an average use equal to 4.62 hours under Drought status. Considering
the average for the Normal status mentioned above, we compute a statistically significant use increase
equivalent to 0.98 hours (|t| = 7.88) between these extreme resource status, i.e. an extreme reduction
in water availability leads to an increase of 30 percent in its use. These results are also verified with
our calculations of the standard deviations.
In Phase III, we also prove that the use of the resource is increased when we introduce more
adverse availability status of the resource. Considering the Normal status, we compute an average of
3.77 hours in use, while for Low and Drought status, the computed averages are 4.02 hours and 4.75
hours respectively. With these results, we found a statistically significant use increase of 0.25 hours
between Normal and Low status (|t| = 2.81) and 0.98 hours between Normal and Drought status
(|t| = 2.81) . While these differences are smaller than those recorded in the previous phases, we must
18
remember that at this stage players face the three possible status over availability resource, besides
the possibility to adapt to Climate Change. Thus, considering these features the results of Phase III
are consistent with the results of previous phases.
Derived from this analysis, we can establish empirically two aspects: first, it is clear that the use
of the resource is individually exacerbated when farmers face more adverse consequences of Climate
Change over resource availability. Second, that our introduction of stochastic scenarios (resource
status) adequately captures this expected relationship. The evolution in the averages recorded by
phase and by resource status, show a clear behavior to increase the hours of use of the resource when
scenarios involving smaller water resource availability are faced, and therefore decision are closer to
the Nash Equilibrium value (7 units) established for the experiment.
Figure 5 shows that relative frequencies computed for resource use, expressed in hours, have
different distributions according to each status of the resource. When the averages for Phases I and II
are analyzed, it is possible to see that the use of the players tends to increasing use values given lower
resource availability. So, as detailed in Figure 5, a 22.02% of decisions under the Normal status were
concentrated around three to four hours of use; Instead decisions made under the Low status were
concentrated on a 22.03% around five hours of use, while 24.42% of decisions were concentrated around
the six hours of resource use for the Drought status. A similar feature is observed when considering
all phases of the game.
Another interesting aspect is that, given the tendency to increase the use individually when
players face the effects of climate change, this affects individual earnings affected by group behavior.
Considering the effect of negative externality (typical of a CPR and considered in the theoretical
model), an exaggerated extraction group reflects lower average individual gain for players. This is
evidenced in Figure 6b and Table 6.
7.3
Treatments effect
Our experimental game considers five experimental treatments as detailed above, in order to
introduce in each one of these factors associated with the effects of Climate Change and the possibility
of collective action in the localities of study. In Table 8, we present the average resource usage for
each of these treatments and our computations of statistical differences. As we can see, the average
use between baselines and voting treatments did not show statistical differences. Instead, there is a
statistically significant differences between the average use in the treatment of voluntary contributions
compared to the other four treatments. In general, these results are compared with the calculation of
the standard deviations. Under this analysis, we can determine that the treatment of baseline with
Climate Change, despite having a lower use average than those computed in other treatments , has
instead the highest standard deviation as we expected. While the treatment with the higher average
use is Voluntary Contributions - Judge of Water, it also has the lowest deviation around the average.
Disaggregating the above information by localities in which the experimental missions were developed, we present the results of Table 8. In this table, we can see that the average resource use,
increase when we introduce the effects of Climate Change (as we expected), while the average computed for Batallas show instead mixed results, i.e. while the determination of the standard deviations
corroborates what we expected, the averages do not necessarily reflect increase in use against adversities associated with climate change for this locality. This leads us to initially think that localities
dependent exclusive on farming have two types of resource use increase in case of facing the adversities
of climate change: i) an increase with high dispersion (i.e. a “uncontrolled” increase) in resource use
due to independent behavior, and ii) an increase with low dispersion (i.e. a “controlled” increase) in
the presence of a regulator such as the figure of Judge of Water. In contrast, the locality of Batallas
with diversified economic activity does not present a clear pattern in average use, reflecting mixed
performances. These characteristics becomes visually clear with Kernel densities presented in Figure 7.
19
In relation to scores recorded during the experimental game, Table 9 shows an interesting feature:
the two treatments with the highest average score are the treatment of Baseline without Climate
Change and the treatment of Voluntary contributions - Judge of Water, i.e. under the fifth treatment,
with the option of climate change adaptation and the participation of the Judge of Water, players
reached an average profit similar to the treatment in absence of Climate Change adversities.
7.4
Econometric approximation
To estimate the effect of each treatment and resource status over the players determination of the
resource use, we build a balanced data panel database8 . As a first econometric model, we consider
the following variables in the estimations to validate our previous intuitions:
• low : dummy variable that takes the value of one when the player’s decision is taken under the
Low status of the resource and zero otherwise.
• drought: dummy variable that takes the value of one when the decision by the player is taken
under the Drought status and zero otherwise.
• treatments: dummy variable determining membership of a particular player to each of the treatments considered in the experiment.
• adoption of adaptation strategies: variable that determines whether the group of the i−th player,
accepts or not to adopt the strategy of adaptation, according to their respective treatment.
• missions: variable identifying each player with their respective experimental mission (location).
• judge of water dummy variable that determines participation or not the Judge of Water as a
decision-maker in voluntary contributions treatment.
Table 10 presents our estimates of a data panel model with random effects, both generally and
for each of the missions developed. After applying the Hausman exogeneity test, the suitability of
this technique based on generalized least squares method is accepted. In the general estimate, we find
that the parameters of water resources for Low and Drought status reflect that a reduction in the
availability of water leads to an increase over the resource use. As expected, this increase is higher for
Drought status. When we consider the missions of Chuñavi Alto and Chuñavi Bajo, We found that
this effect reflects greater magnitude. However, results for the mission of Batallas shows an inverse
behavior, where a reduction is observed in the use of resources when more adverse climate scenarios
are faced. We argue that those localities that are most dependent on agricultural activity exacerbate
resource use, while those localities with more diversified labor activity have mixed results.
The parameters for the overall estimation show that the most significant treatment over the
determination of individual use is the Voluntary Contribution - Judge of Water treatment. In relation
to the adoption of adaptation strategies, Table 10 show that the willingness to adapt to the different
states of the resource, reduce water use, especially in the treatment of Voting without communication.
Also, it is verified that in rounds in which the Judge of Water is involved players tend to reduce their
resource use, especially in Chuñavi Alto and Chuñavi Bajo. These results show that these communities
accept the Judge of Water, as management and supervisory institution of the CPR.
As a second model, in Table 11 the dependent variable reflects the hours of use of the resource
used by each individual. In this second model, we construct a variable of intention that expresses
the decision of the players (in terms of their profits) to contribute to the construction of the reservoir. Estimates for Phase III show that players who made their decisions under the treatment of
Voluntary contributions - Judge of Water, contributed to a greater extent for the construction of the
8
Within and Between frequencies are presented in Table 15.
20
reservoir, compared to players with voting treatments and symmetrical contributions. However, we
also evidenced that this intention was decreasing during rounds of this phase.
Finally, Brañas-Garza, Bucheli, and Garcı́a-Muñoz (2011) recommends the use of Dynamic Data
Panel models in experimental approaches that satisfies: i) the composition of groups is preserved
throughout the experimental game (Partner design), ii) Players take decisions on the same variable
several times and iii) each player receives a feedback at the end of each decision. Table 12 presents the
dynamic estimates following Arellano and Bond (1991), using the Generalized Method of Moments
(GMM). For the determination of the dependent variable use, we consider now a lagged variable
with other explanatory variables exogenous type. In this model, γ represents the estimated parameter
associated with the use, considering its first lag (usei,t−1 ). This parameter shows in general terms, that
the use of an earlier round induced a slight reduction in determining the use in the next round. This
relationship is not the same when dynamic panel regressions are estimated by experimental missions.
Significantly, the lag in the use variable determines a clear reduction for the Batallas mission
regarding the overall model. However, this lag determines an increase on the determination of the use
in the current round for the mission of Chuñavi Alto. As expected, gradual reduction of water resource
availability leads to higher level of use under the status of Drought. Analyzing the effect of moderate
reductions (Low status) and Drought, the dynamic model determines that the increase on resource use
are given in greater amounts for players corresponding to the mission of Chuñavi Alto. Incorporating
the score lag seems not significant in the estimated models for Chuñavi Bajo and Batallas. However,
this parameter determines a negative value on the determination of the hours of use in the current
round, i.e. points earned in round t−1 determines a decrease in the hours of use of the resource for the
round t. Finally, the parameter estimated associated with adaptation to climate change, determines
a negative value on the hours of individual use for the general model and the models estimated for
missions Chuñavi Alto and Bajo. While a positive value on individual use for the mission of Battles
is estimated.
8
Conclusions
In recent years, the region of the Bolivian Altiplano has not been exempt from the consequences
of climate change, a phenomenon that greatly affects the processes of melting glaciers and therefore
attempt to alter the water supply of a wide region. Changes in humidity, temperature and rainfall
recorded in the last decades account for potential future changes on the availability of water resources
in the region. Having identified the irrigation water as a community resource, we wonder how the
effects of climate change on water availability, they would lead to changes in the pattern of resource
use. Our study is one of the first experimental applied research in Bolivia. We have the participation
of 200 experimental players, allowing identify patterns of individual and group resource use. Our
results show significant implications for our research questions.
In line with the approach of Ostrom (1992,1994), in small communities highly dependent on
agriculture for their livelihoods, we found that lower availability of water resources leads to increase
the level of extraction by users. That is, when the resource is running low, cooperation strategies
begin to meaningless and selfishness dominates, pushing a higher level of resource use. Therefore, our
results establish that this type of agriculture-dependent communities in the Bolivian highlands are
highly susceptible to the Tragedy of the Commons. In the results, this is reflected by higher average
resource usage in the drought status, also validated by the econometric estimates. In another sense,
the largest community shows mixed results, which do not establish a clear change in the pattern of
use under the impact of climate change.
With the information generated by our experimental game, we can also establish that the players
showed a high willingness to take measures to adapt to climate change. The introduction of an
alternative adaptation by building a dam, has been able to show that 91% of the players who faced
21
this decision chose to adopt such strategies. An important policy implication derived from this result is
that decisions on adaptation, by our experimental design, were taken in front of an eventual depletion
of the resource. Therefore we could ensure that prior to a drastic reduction in the availability of water
stages are the best time to implement policies for adapting to climate change in the region of study.
About the local management of water resources; through the introduction of Water Judge we
observed that before a drastic reduction of the resource, it is feasible to establish strategies of cooperation to safeguard the availability of the resource for all community members. That is, the functions of
the Judge of water to avoid behaviors of free-rider and realize a fair contribution when adopting adaptation strategies, in addition to the high availability of adaptation measures counteract the impulses
to exacerbate water extraction . These collective strategies may also be technological innovations,
institutional changes and collective cooperation, however, they can take time. Long-term strategies
will depend on the possibility of overcoming the short-term pressures.
Another interesting implication from the results of our experiment, is the importance of social
capital at the time of approaching the management of communal resources. If we adopt the ethnic
homogeneity as a measure of social capital, we reach the same conclusion drawn in Baylis et al. (2013),
who claim that bonding social capital (Chuñavi Alto and Chuñavi Bajo) can improve common pool
resource management, while the effect of bridging social capital is mixed (Batallas). However, it is
necessary to make new experimental approaches to more specifically study such relationships relevant
to the communities of the Altiplano region of Bolivia.
22
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25
Figure 1: Study Area Map
Source: Bolivian Mountain Institute (2014).
Figure 2: Study Area Hydrological Map
Source: Bolivian Mountain Institute (2014).
26
Figure 3: Experimental Field Game Structure
EXPERIMENTAL DESIGN
I
2
3
4
5
6
7
8
II
9 10 11 12
III
PreDeA
13-15
PreDeA
19-21
NORMAL-DROUGHT
NORMAL-LOW
Nadap
NORMAL-DROUGHT
Adap
NORMAL-LOW
Nadap
NORMAL-DROUGHT
Adap
NORMAL-LOW
Nadap
NORMAL-DROUGHT
Adap
NORMAL-LOW
Nadap
NORMAL-DROUGHT
NORMAL-LOW
NORMAL-DROUGHT
Adap
VC<20 JoW
Nadap
Where: - PreDeA: Previous Decision to Adaptation.
- Adap:
Adaptation strategy adoption.
- NAdap: Adaptation strategy rejection.
- VC:
Voluntary Contributions.
- JdW
Judge of Water Intervention.
Source: Authors' Protocol of field experiment (available by request).
NORMALLOW
NORMAL-DROUGHT
Adap
NORMAL-LOW
Nadap
NORMAL-DROUGHT
Adap
NORMAL-LOW
Nadap
NORMAL-DROUGHT
VC≥20 NORMAL-LOW
VOTING
VC≥20 NORMAL-LOW
VOTING
NORMAL-LOW
Adap
VOTING
T(4): Voting with
Communication
NORMAL-DROUGHT
Adap
VC<20 JoW
Nadap
NORMALLOW
NORMAL-DROUGHT
VC≥20 NORMAL-LOW
VOTING
NORMAL-LOW
VOTING
T(3): Voting
without
Communication
NORMAL-DROUGHT
VOTING
NORMAL-LOW
VOTING
T(2): Baseline
with Climate
Change
T(5): Voluntary
Contributions Judge of Water
16-18
NORMAL-LOW
VOTING
27
GROUP & TREATMENT
T(1): Baseline
without Climate
Change
PreDeA
VOTING
PHASE →
ROUND → 1
Adap
VC<20 JoW
Nadap
NORMALLOW
NORMAL-DROUGHT
Figure 4: Percentages of occurrence of adverse scenarios and adaptation
Chuñavi Alto
Phase I
50%
45%
40%
35%
30%
25%
20%
15%
10%
5%
0%
100%
90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
Phase III
Batallas
Phase II
Chuñavi Alto
Chuñavi Bajo
Chuñavi Bajo
Batallas
Adaptation
(a) Percentages of adverse scenarios
(b) Percentages of adaptation
Source: Authors’ computations base on field experiment
Table 2: Socio-demographic description of communities in the area of study
Community
Name
Number
of families
Altitudes in
m.a.s.l.
Tuni (Chuñavi)
50
4.437 –
3.850
Condoriri
(Palcoco)
40
4.588
Alto Peñas
70
Suriquiña
85
4.500 –
3.900
4.600 3.850
Type of
organization
Native
Indigenous
ayllu
Peasant union
Form of land management
Community management in the part of ayllu;
individual management on the part
of the farmers union
No community management
and community management
Peasant union
No community management
Peasant union
No community management
Table 3: Total players by location and treatment
Locality
Chuñavi Alto
Chuñavi Bajo
Batallas
Total
T(1)
15
10
15
40
Treatment
T(2)
T(3)
15
15
10
10
15
15
40
40
T(4)
15
10
15
40
T(5)
15
10
15
40
Total
75
50
75
200
Source: Authors’ computations base on field experiment, where treatments are T(1): Baseline without Climate Change; T(2): Baseline with Climate
Change; T(3): Voting without Communication; T(4): Voting with Communication; T(5): Voluntary contributions - Judge of Water.
28
Figure 5: Average use during 21 rounds
30%
25%
20%
15%
10%
5%
0%
1
2
3
4
5
6
7
Resource use (hours)
NORMAL
LOW
DROUGHT
Source: Authors’ computations base on field experiment.
Table 4: Percentages of occurrence of adverse scenarios and adaptation
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Total
Chuñavi Alto
42.6%
46.9%
43.8%
77.8%
46.3%
88.9%
44.4%
77.8%
44.8%
81.5%
Event
Adaptation
Event
Adaptation
Event
Adaptation
Event
Adaptation
Event
Adaptation
Event
Adaptation
Locality
Chuñavi Alto Batallas
43.5%
42.6%
45.4%
40.7%
48.1%
45.1%
100%
88.9%
50.9%
41.4%
83.3%
88.9%
32.4%
43.2%
100%
100%
44.1%
42.6%
94.4%
92.6%
Total
42.9%
44.3%
45.7%
88.9%
46.2%
87.0%
40.0%
92.2%
43.8%
89.5%
Source: Authors’ computations based on field experiment.
Table 5: Average resource usage (in hours) by phase and state
Game Phases
Phase I - Normal cycles
Phase II - Climate Change
Phase III - Possibility to adapt
Total
Resource Status
Low
Drought
4.41
-
Total
3.97
(1.54)
(1.63)
-
(1.62)
3.64
4.52
4.62
4.08
(1.58)
(1.75)
(2.03)
(1.84)
Normal
3.66
3.77
4.02
4.75
3.98
(1.70)
(1.55)
(1.81)
(1.70)
3.71
4.24
4.60
4.00
Source: Authors’ computations base on field experiment, where (·) present standard deviations.
29
Figure 6: Evolution of average use and score during the game
5.0
Resource use (average hours)
4.5
4.0
3.5
3.0
2.5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
16
17
18
19
20
21
Round
CHUÑAVI ALTO
CHUÑAVI BAJO
BATALLAS
(a) Average resource use evolution
65
60
Average score
55
50
45
40
35
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Round
CHUÑAVI ALTO
CHUÑAVI BAJO
BATALLAS
(b) Average score evolution
Source: Authors’ computations base on field experiment.
30
Figure 7: Kernel densities for resource use by localities
.25
.3
.2
Density
Density
.2
.15
.1
.1
.05
0
1
2
3
4
5
6
7
1
2
3
Resource use (hours)
T(2)
4
5
6
7
Resource use (hours)
T(5)
T(2)
(a) Overall
T(5)
(b) Chuñavi Alto
.25
.3
.2
Density
Density
.2
.15
.1
.1
.05
0
1
2
3
4
5
6
7
1
2
Resource use (hours)
T(2)
3
4
5
6
Resource use (hours)
T(5)
T(2)
(c) Chuñavi Bajo
T(5)
(d) Batallas
Source: Authors’ computations base on field experiment.
Table 6: Average scores usage (in hours) by phase and state
Game Phases
Phase I - Normal cycles
Phase II - Climate Change
Normal
66.18
(4.20)
66.14
(4.19)
Resource status
Low
Drought
39.60
(4.27)
39.02
19.00
(4.64)
(5.17)
Total
55.33
(13.74)
45.22
(22.94)
40.49
(4.29)
30.72
(4.29)
39.98
51.94
(17.10)
44.32
(17.10)
50.99
Phase III - Adaptation possibility
Before paying for adaptation
After paying for adaptation
Total
65.85
(4.45)
57.69
(4.45)
66.03
18.68
(4.70)
18.40
(4.70)
18.89
Source: Authors’ computations base on field experiment, where (·) present standard deviations.
31
7
Table 7: Statistical analysis of differences in average water use
Resource status
Normal
Low
Drought
Resource status
Normal
Low
Drought
Resource status
Normal
Low
Drought
Chuñavi Alto
Average use
3.32
4.49
5.44
Chuñavi Bajo
Average use
3.24
4.32
5.11
Batallas
Average use
4.39
3.93
3.37
Normal
·
1.18∗∗∗
2.12∗∗∗
Normal
·
1.07∗∗∗
1.87∗∗∗
Normal
·
-0.46∗∗∗
-1.03∗∗∗
Low
·
0.94∗∗∗
Low
·
0.79∗∗∗
Low
·
-0.56∗∗∗
Source: Authors’ computations base on field experiment.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
Table 8: Comparison of average levels of use between treatments
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Average use
3.96
3.93
3.89
3.92
4.31
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Average use
3.76
3.90
3.96
3.61
4.08
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Average use
4.05
3.98
4.13
4.14
4.20
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Average use
4.00
3.92
3.60
3.91
4.59
Overall
Standar Deviation
T(1)
1.63
1.86
-0.03 ns
1.74
-0.07 ns
1.71
-0.04 ns
1.61
0.35***
Chuñavi Alto
Standar Deviation
T(1)
1.55
1.91
0.13*
1.68
0.21*
1.70
-0.14*
1.73
0.32***
Chuñavi Bajo
Standar Deviation
T(1)
1.67
1.89
-0.07 ns
1.78
0.07 ns
1.81
0.08ns
1.63
0.14**
Batallas
Standar Deviation
T(1)
1.64
1.81
-0.08 ns
1.70
-0.40 ns
1.58
-0.09 ns
1.46
0.58***
T(2)
T(3)
T(4)
-0.04 ns
-0.01 ns
0.38***
0.03 ns
0.42***
0.39***
T(2)
T(3)
T(4)
0.09 ns
-0.28**
0.19*
-0.37***
0.10*
0.47***
T(2)
T(3)
T(4)
0.15*
0.16*
0.22**
0.01 ns
0.06 ns
0.05 ns
T(2)
T(3)
T(4)
-0.32***
-0.01 ns
0.67***
0.31***
0.98***
0.68***
Source: Authors’ computations base on field experiment.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
32
Table 9: Comparison of average scores between treatments
T(1)
T(2)
T(3)
T(4)
T(5)
Average use
54.82
47.42
50.68
50.53
51.50
T(1)
T(2)
T(3)
T(4)
-7.40***
-4.14***
-4.29***
-3.32***
3.26***
3.11***
4.08***
-0.15 ns
0.83 ns
0.97*
***significativo al 99% **significativo al 95% *significativo al 90% ns No Significativo
Source: Authors’ computations base on field experiment.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
Table 10: Panel Data Model (GLS) with random effects
Overall
Chuñavi Alto
Chuñavi Bajo
Batallas
0.563***
1.172***
1.246***
-0.463***
(0.059)
(0.083)
(0.109)
(0.099)
0.911***
2.142***
1.945***
-0.977***
(0.073)
(0.102)
(0.134)
(0.119)
-0.155*
-0.428***
-0.266ns
0.089ns
(0.141)
(0.161)
(0.247)
(0.275)
-0.027ns
-0.413***
0.297*
0.095ns
(0.146)
(0.168)
(0.260)
(0.283)
T(4)
-0.069ns
-0.172*
-0.128ns
0.035ns
(0.146)
(0.170)
(0.256)
(0.283)
T(5)
0.236**
0.305**
0.201ns
0.191ns
(0.147)
(0.169)
(0.257)
(0.287)
-0.314***
-0.525***
-0.589**
0.202*
Resource status
Low
Drought
Treatments
T(2)
T(3)
Adaptation strategies adoption
Adaptation on T(3)
(0.117)
(0.169)
(0.207)
(0.192)
Adaptation on T(4)
-0.133*
-0.292**
-0.756***
0.380***
(0.115)
(0.163)
(0.214)
(0.188)
Adaptation on T(5)
0.297***
-0.139ns
0.799***
0.172*
(0.153)
(0.276)
(0.255)
(0.227)
0.146***
(0.113)
·
·
·
·
·
·
·
·
·
·
·
·
-0.199*
-0.445**
-0.668***
-0.193ns
(0.183)
(0.299)
(0.310)
(0.296)
3.572***
3.500***
3.223***
4.253***
(0.126)
(0.116)
(0.177)
(0.196)
4200
200
243.15
0.000
1575
75
631.08
0.000
1050
50
319.53
0.000
1575
75
95.48
0.000
Localities
Chuñavi Alto
(0.113)
Batallas
Judge of Water
Judge of Water intervention
Constant
Number of observations
Númber of individuals
Wald-χ2
Prob> χ2
0.249**
Source: Author’s computations base on field experiment, where treatments are T(1): Baseline without Climate Change; T(2): Baseline with Climate Change;
T(3): Voting without Communication; T(4): Voting with Communication; T(5): Voluntary contributions - Judge of Water.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
33
Table 11: Panel Data Model (GLS) with random effects by treatments, localities and rounds
Parameter
Standard Error
Significance
11.667
11.667
19.155
0.974
0.974
1.058
***
***
***
-0.549
-0.279
0.919
0.919
ns
ns
-0.146
-8.749
1.040
1800
200
440.01
0.000
0.065
0.824
0.895
***
***
ns
Treatments
T(3)
T(4)
T(5)
Missions
Batallas
Chuñavi Alto
Rounds
Phase III rounds
Judge of Water
Constant
Number of observations
Number of individuals
Wald - χ2
Prob > χ2
Source: Authors’ computations base on field experiment, where treatments are T(1): Baseline without Climate Change; T(2): Baseline with Climate Change;
T(3): Voting without Communication; T(4): Voting with Communication; T(5): Voluntary contributions - Judge of Water.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
Table 12: Dynamic Panel Data Model - Arellano & Bond estimations
Variable
γ (Uset−1 )
Overall
-0.034***
(0.018)
(0.025)
(0.033)
(0.027)
Low
3.565***
6.291***
5.151***
3.954***
(0.189)
(0.273)
(0.353)
(0.240)
Drought
6.383***
11.310***
9.221***
7.042***
(0.332)
(0.482)
(0.625)
(0.415)
0.155***
0.181***
0.145***
0.187***
(0.007)
(0.009)
(0.012)
(0.009)
-0.002*
-0.006***
0.001ns
0.001ns
(0.001)
(0.002)
(0.003)
(0.002)
-0.098*
-0.388***
-0.372***
0.526***
Scoret
Scoret−1
Adaptation
Constant
Number of observations
Number of groups
Sargan test
Chuñavi Alto
0.029*
Chuñavi Bajo
-0.018ns
Batallas
-0.074***
(0.082)
(0.105)
(0.130)
(0.117)
-3.648***
-8.628***
-6.449***
-7.513***
(0.475)
(0.670)
(0.892)
(0.622)
3800
200
0.000
1425
75
0.000
950
50
0.000
1425
75
Source: Author’s computations base on field experiment, where treatments are T(1): Baseline without Climate Change; T(2): Baseline with Climate Change;
T(3): Voting without Communication; T(4): Voting with Communication; T(5): Voluntary contributions - Judge of Water.
Note: (***): 99% significance - (**): 95% significance - (*): 90% significance - (ns): Not significant.
34
Table 13: Descriptive statistics for resource level of use
Overall
Chuñavi Bajo
Batallas
Chuñavi Alto
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
Phase
I
II
III
I
II
III
I
II
III
I
II
III
Observations
1200
1200
1800
Observations
300
300
450
Observations
450
450
675
Observations
450
450
675
Average use
3.969
4.083
3.975
Average use
3.870
4.033
3.740
Average use
4.060
3.880
4.273
Average use
3.944
4.320
3.834
St. Dev.
1.619
1.838
1.699
St. Dev.
1.682
1.817
1.678
St. Dev.
1.579
1.944
1.719
St. Dev.
1.615
1.715
1.653
Min
1
1
1
Min
1
1
1
Min
1
1
1
Min
1
1
1
Max
7
7
7
Max
7
7
7
Max
7
7
7
Max
7
7
7
Min
1
1
1
1
1
Min
1
1
1
1
1
Min
1
1
1
1
1
Min
1
1
1
1
1
Max
7
7
7
7
7
Max
7
7
7
7
7
Max
7
7
7
7
7
Max
7
7
7
7
7
Source: Authors’ computations base on field experiment.
Table 14: Descriptive statistics for resource level of use
Overall
Chuñavi Bajo
Chuñavi Alto
Batallas
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Treatment
T(1)
T(2)
T(3)
T(4)
T(5)
Observations
840
840
840
840
840
Observations
210
210
210
210
210
Observations
315
315
315
315
315
Observations
315
315
315
315
315
Average use
3.961
3.935
3.893
3.920
4.313
Average use
3.757
3.886
3.976
3.610
4.076
Average use
4.003
3.921
3.603
3.908
4.587
Average use
4.054
3.981
4.127
4.140
4.197
St. Dev.
1.629
1.863
1.739
1.709
1.608
St. Dev.
1.548
1.906
1.690
1.703
1.726
St. Dev.
1.639
1.814
1.696
1.580
1.463
St. Dev.
1.667
1.887
1.778
1.806
1.631
Source: Authors’ computations base on field experiment, where treatments are T(1): Baseline without Climate Change; T(2): Baseline with Climate Change;
T(3): Voting without Communication; T(4): Voting with Communication; T(5): Voluntary contributions - Judge of Water.
35
Table 15: Within and Between Frequencies
Use
1
2
3
4
5
6
7
Use
1
2
3
4
5
6
7
Use
1
2
3
4
5
6
7
Use
1
2
3
4
5
6
7
Overall
Total
Within
Frequency Percentage Frequency Percentage
296
7.05
117
58.5
669
15.93
179
89.5
773
18.4
191
95.5
745
17.74
187
93.5
752
17.9
191
95.5
630
15
186
93
335
7.98
132
66
Chuñavi Alto
Total
Within
Frequency Percentage Frequency Percentage
77
7.33
30
60
194
18.48
47
94
221
21.05
48
96
159
15.14
45
90
184
17.52
46
92
135
12.86
48
96
80
7.62
31
62
Chuñavi Bajo
Total
Within
Frequency Percentage Frequency Percentage
127
8.06
43
57.33
217
13.78
65
86.67
261
16.57
69
92
280
17.78
70
93.33
285
18.1
71
94.67
267
16.95
68
90.67
138
8.76
47
62.67
Batallas
Total
Within
Frequency Percentage Frequency Percentage
92
5.84
44
58.67
258
16.38
67
89.33
291
18.48
74
98.67
306
19.43
72
96
283
17.97
74
98.67
228
14.48
70
93.33
117
7.43
54
72
Source: Authors’ computations base on field experiment.
36
Between
Percentage
12.05
17.8
19.27
18.97
18.75
16.13
12.09
Between
Percentage
12.22
19.66
21.92
16.83
19.05
13.39
12.29
Between
Percentage
14.06
15.9
18.01
19.05
19.11
18.7
13.98
Between
Percentage
9.96
18.34
18.73
20.24
18.21
15.51
10.32