Determinants of forest degradation under private and common property regimes:
The case of Ethiopia
Ryo Takahashi
May 2014
Abstract
To examine the relationship between management efforts and different tenure systems, many
empirical studies are carried out. However, the previous studies consider land ownership exogenously,
and thus there is a lack of rigorous empirical studies. Additionally, most of them mainly focused on
forest quantity (e.g., forest area), and less attention was devoted to investigate how different land
tenure systems affected forest quality (e.g., biomass). This study considers the endogenous
determination of land ownership and aims to compare the forest quality between private property
regimes and common property regimes. In addition, we further investigate the determinants of forest
degradation in both property regimes. The study was conducted in Ethiopia, and remote sensing data
of 2005 and 2010 were used to gauge the change in forest quality. Using the Propensity Score
Matching method, we compared the private property areas and common property areas with similar
environmental characteristics and found that the private property areas significantly reduced the forest
degradation compared with the common property areas. In addition, while the forest areas under
private property regimes accelerated the forest degradation in the relatively flat area with better access
to the main road, the common property forest areas isolated from the village and main road are likely
to foster degradation. Moreover, our empirical results indicate that the risk of degradation in private
property areas is partially observed in the low-density forest, whereas the forest degradation was
widely observed in the common property forest areas.
Keywords: property regimes, forest quality, impact evaluation, remote sensing, Ethiopia
1
1. Introduction
Establishing property rights, or land tenure, is well recognized as an important step toward achieving
sustainable forest management (Tucker, 1999; Owubah et al., 2001; Arnot et al., 2011). In fact, many
studies show that undefined property rights induce rapid deforestation (Mendelsohn, 1994; Deacon,
1999; Bohn and Deacon, 2000). However, the debate about whether private or common ownership
leads to more sustainable forest management has not yet been resolved (Glück, 2002). The early
literature on natural resource management suggests that private property regimes are able to manage
natural resources efficiently because individual landholders have incentives to conserve such
resources (Demsetz, 1967; Furubotn and Pejovich, 1972). In contrast, common property regimes have
been indicted as a cause of the degradation of natural resources, which is illustrated as the “tragedy of
the commons” suggested by Hardin (1968).
However, since the late 1980s, common property regimes are recognized as efficient and
sustainable forest management systems. For example, Bromley (1989), McKean and Ostrom (1995),
and Ostrom (1990) suggest that common property regimes can manage natural resources sustainably
under certain conditions, such as closed access, clear resource boundaries, restricted non-member
access, and additional regulation. Other studies argue that common property regimes would function
particularly efficiently in rural areas in less developed countries, where most local communities are
tightly structured based on trust and cooperation among members of a community, leading to the
monitoring and punishment of irresponsible users (Pretty and Ward, 2001; Hayami and Godo, 2005;
Tole, 2010). In fact, many empirical studies were conducted to test the hypothesis of the role of
common property and found evidence of a positive influence on forest conservation (Edmonds, 2002;
Somanathan et al., 2005; Dalle et al., 2006; Matta and Alavalapati, 2006; Ellis and Porter-Bolland,
2008; Lund and Treue, 2008; Takahashi and Todo, 2012). However, it does not necessarily mean that
common property is more efficient than private property because these studies focus only on the effect
of common management practices and the comparison with private property regimes is overlooked.
In contrast, the empirical results concerning forest conservation in private property regimes are
far from conclusive. Although several empirical studies report that private property contributed to
2
forest conservation when property rights are secured (Godoy et al., 1998; Nelson et al., 2001; Araujo
et al., 2009), some studies claim that deforestation accelerates under private property regimes because
forest clearing become a profitable investment to the landholders after the acquisition of property
rights; thus, forest clearing can be a rational choice for the landholders to maximize their profit
(Angelsen, 1999). Such landholder behavior is empirically observed in the studies conducted in South
America (Wood and Walker, 2001; Deininger and Minten, 2002; Arima et al., 2007; Marchand, 2012).
Although these previous empirical studies indicate that private property alone does not assure
sustainable forest management, we cannot conclude the superiority of communal management
without the comparison between two regimes.
To investigate the relationship between management efforts of natural resources and different
tenure systems, several studies compare the effectiveness of forest management between private and
common property regimes within the same forest area (Gibson et al., 2002; Nagendra et al., 2008).
Although land ownership is assumed to be exogenous in these previous studies, there is a high
possibility that land ownership is endogenously determined by other factors, such as the accessibility,
elevation, slope, soil type, and sun condition. Therefore, the results of these previous studies are likely
to be biased due to the endogeneity problem (Imbens and Wooldridge, 2009). Because of a lack of
rigorous empirical studies, the debate between private and common ownership has not yet reached a
consensus (Glück, 2002).
Another shortcoming of the existing literature on the relationship between forest management
and tenure systems is that most previous studies mainly focus on forest quantity (e.g., forest area), and
less attention is devoted to investigate how different land tenure affect forest quality (e.g., biomass
and vegetation structure) and what is the determinants of forest degradation. Hence, there is still a
need for further empirical studies to identify and to compare the determinants of forest degradation
under different land tenure regimes.
Therefore, this study considers the endogenous determination of land ownership and aims to
compare the forest quality between private property regimes and common property regimes. In
addition, we further investigate the determinants of forest degradation in both property regimes. We
selected the Belete-Gera Regional Forest Priority Area (RFPA) located in the southwest of Ethiopia as
3
a case study. In our estimation, we first identify how private property in the forest area is determined,
followed by the comparison of the forest quality between private property regimes and common
property regimes. To control for selection bias, we employed a propensity score matching (PSM)
method. Finally, we use a probit model to investigate the determinants of the forest degradation under
private and common property regimes.
2. Description of the Study Area
2.1. Description of the Belete-Gera RFPA
The Belete-Gera RFPA is 150,000 ha in size and is located in the Gera and Shabe Sombo Districts in
the Oromiya Region (Figure 1). This region is part of the highland rainforest, and the natural
vegetation in this area is subject to an annual precipitation of 1,500 mm and an annual average air
temperature of approximately 20 degrees Celsius. The topography of the Belete-Gera RFPA is
complex and consists of undulating hills that range from 1,200 to 2,900 m in height, with steep
mountainous terrain in certain locations.
Within the forest area, total 125 sub-villages are located. The boundaries between each
neighboring villages are traditionally determined and the community members use and manage the
forest resources within the registered forest area. However, such human activities induced significant
decline of forest cover in the RFPA. Since most residents engage with farming and rely increasingly
on forest resources, the expansion of their farmland and grazing land into the forest area and wood
extraction from the forest induce significant reduction of forest resources (Takahashi and Todo, 2012).
As a result, despite the government’s prohibition of wood extraction in the forest area, 40 percent of
the forest area was cleared during the 1985-2010 period (Todo and Takahashi, 2011).
2.2. Wild coffee production
Coffee (Coffea Arabica) is a native species and grows wild in the Belete-Gera RFPA. Such wild
coffee in the Belete-Gera RFPA (hereafter, “forest coffee”) is typically found in the forest at an
4
altitude of approximately 2,000 m.
The forest coffee is traditionally grown in the understory of shade trees, called the shaded coffee
system, and the forest coffee areas form dense forest. Many studies documented that the
agroecosystems of shaded coffee system can preserve the forest and provide an important refuge for
biodiversity (Perfecto and Snelling, 1995; Perfecto et al., 1996; Wunderle Jr and Latta, 1996;
Greenberg et al., 1997; Moguel and Toledo, 1999; Mas and Dietsch, 2004). Additionally, another
characteristic of the shaded coffee system is the low productivity. Although the shaded coffee system
is environmental friendly system, its yield is low compared with modern industrial coffee systems
which include very few or no shade trees. According to the statistics by Schmitt et al. (2010), the
average coffee yield of the shaded coffee system was only 2 kg per ha/year, while the modern coffee
system yielded 31 kg per ha/year. Although the coffee yield has been improved by the modern coffee
system, the modern system is accompanied by higher environmental cost, such as forest reduction,
increased erosion, chemical runoff, and consolidation (Perfecto et al., 1996; Staver et al., 2001).
Among 125 sub-villages in the forest area, 62 sub- villages are located in the area suitable for
coffee production (shown in the light and dark gray areas in Figure 1). The right to harvest forest
coffee is given to the producer according to traditional agreement among villagers, and each right
holding producer managed their forest coffee areas. The villagers can identify right holder of each
coffee area and they hesitate to enter the area without permission. Therefore, in this study, we define
these coffee areas as private property and the natural forest areas registered to the community as
common property areas.
3. Data
3.1. Remote sensing data
For our analysis, we used the January 2005 and January 2010 satellite images of Landsat 7 ETM+
(path/row 170/55), with a resolution of 30 m. We used a two-step process to classify the forest areas
based on forest density.
5
First, we distinguished the forest areas from the non-forest areas (such as agricultural lands,
young fallow lands, rangelands, cleared areas, bare soil areas, and urban areas) by utilizing the
normalized difference vegetation index (NDVI). The NDVI is a measure of vegetation that is
commonly used in remote sensing studies (Tucker et al., 1985; Davenport and Nicholson, 1993;
Tucker et al., 2001). Following the studies by Southworth et al. (2004) and Takahashi and Todo
(2012), we determined a threshold value of the NDVI for the forest areas on the basis of the
information from the satellite images and fieldwork. We conducted ground-truthing to collect
locational data for 17 points on the boundaries that delineated the forest regions from the non-forest
areas that existed during the period of our study (according to interviews with several local residents).
We chose the area with the highest NDVI value for each year as the threshold value for the forest
areas.
Second, after eliminating the non-forest areas from the satellite images, we classified the images
using an unsupervised classification technique in which one of the clustering algorithms split the
images into classes based on the NDVI values. One advantage of using unsupervised classification is
that it does not require the user to have foreknowledge of the classes. We first set the number of
clusters and established the clustering criteria, such as the minimum number of pixels per cluster and
the closeness criterion. In this study, we used the following specifications: the minimum number of
pixels per cluster was 20, and the sample interval was 10 cells.
After establishing the criteria, cluster centers are randomly placed and each pixel is assigned to
the closest cluster by Euclidean distance. Then, the centroids of each cluster are recalculated.
Additionally, the established clusters are split into different clusters based on the standard deviation of
the cluster or merged if the distance between the clusters is closer. These processes are repeated until
the clustering criteria are satisfied. The unsupervised classification is commonly used in remote
sensing to classify forests (Mertens et al., 2000; Bray et al., 2004).
We classified the forest areas into five categories that represent the forest density (class 5
indicates a dense deep forest and class 1 is a less dense forest). To confirm the forest condition of each
classification, we conducted a ground truth survey by using sample plots of 20 m by 20 m and
collecting the following information: the number of trees, the tree species, the tree height for each
6
species, the number of strata of trees, and the canopy cover. We tried to investigate the class 5 forest
areas; however, we could not enter these areas due to their rugged terrain. According to local residents,
neither humans nor wild animals can access the deep dense forest.
The description of each classification is presented in Table 1. We observed six different tree
species in the class 1 forest area with a canopy cover that ranged from 60 to 70 %. Although the
number of trees in the lower classes (classes 1 and 2) was greater than in the upper ones (classes 3 and
4), the upper classes had more canopy cover than the lower ones because the upper classes were
formed by a great forest canopy with bigger trees. Approximately 85 and 90 % of the class 3 and 4
forest areas was covered by forest canopy, respectively. For purposes of this study, if the forest areas
moved down the classification scale between 2005 and 2010, we defined such decrement as forest
degradation.
3.2. Forest coffee area and observation grids
We randomly selected two sub-villages (the areas marked with a black color in Figure 1) as the areas
for our study: Dabiye sub-village and Naso sub-village. The producers usually had the rights to
manage and harvest one or two forest coffee areas. To obtain the locational data of the forest coffee
areas, we used a global positioning system (GPS) device to map all of the forest coffee areas managed
by the producers. We investigated all coffee producers in the villages and studied a total of 92 forest
coffee areas.
The general characteristics of the forest coffee areas in the sample are given in Table 2. Columns
1 and 2 provide information on the areas in the Belete and Gera forests, respectively. The average size
of the forest coffee areas in the Gera forest (94.1 are) is approximately four times as large as the size
of the Belete forest areas (24.4 are).
The target forest areas were divided into square-shaped cells (30 m by 30 m). In this study, we
used each grid as an observation for the analysis. The total number of observation grids was 21,591,
which were divided into 2 categories: the private property area (forest coffee area) and the common
property area (forest area without forest coffee). The observation numbers for the private property
7
area and common property area are 629 and 20,962, respectively (Table 3). We observed that all of the
grid characteristics of the private and common property areas were significantly different (p < 0.01).
4. Empirical Framework
In this study, we first used a probit model to identify how land tenure in the forest area is determined.
We used the land tenure dummy as dependent variable which took a value of 1 if a grid was managed
under private property regimes.
In the estimation, the following environmental variables were used as covariates: the distance to
the village, the distance to the main road, the average elevation, the average slope, a dummy variable
for acrisol, a dummy variable for facing south, and a dummy variable for facing north. Acrisol is a soil
with sub-surface accumulation of low-activity clay and low base saturation, or, in other words, acrisol
is infertile. The dummy variables for facing south take a value of 1 if the slope face of a grid faces the
south; this variable controls for the high likelihood of catching the sun. Additionally, we include the
dummy variable for facing north to control for the likelihood of sunless conditions.
Second, we evaluated how different property regimes would affect the forest degradation using
the PSM method to reduce selection bias. We used private property area as the treated group, while
the common property area was employed as the control group. This paper specifically examines the
average effect of treatment on the treated (ATT), which is specified as follows:
ATT  E(Yi (1)  Yi (0) Di  1),
(1)
where Di is a dummy variable indicating that grid i is a private property area or a common property
area. Yi is the change in forest classification between 2005 and 2010. The ATT is the average
difference between the change in forest quality in private property areas and the counter-factual
transition that would exist if these areas were managed as common property forest.
To identify the ATT, we must satisfy the following two assumptions (i.e., conditional
independence and overlap) (Rosenbaum and Rubin, 1983):
8
Y (1), Y (0) D X ,
(2)
0  Pr( D  1 X )  P( X )  1,
(3)
and
The first assumption given by equation (2) implies that a given set of observable characteristics X are
not affected by treatment; the potential outcomes are independent of the treatment assignment. The
second assumption given by (3) ensures that the grids with the same X values have a positive
probability of being under both private and common property. Rosenbaum and Rubin (1983)
designated these two assumptions as ‘strong ignorability’.
To estimate the ATT, this study made use of the PSM method developed by Rosenbaum and
Rubin (1983). The PSM estimator is simply the mean difference in outcomes over the common
support, which is appropriately weighted by the propensity score. Hence, the ATT in equation (1)
becomes
ATT  E(Yi (1) Di  1, P( X i ))  E(Yi (0) Di  0, P( X i )).
(4)
One advantage of using remote sensing data is that we can build panel data for the analysis. If
panel data are available, a difference-in-differences (DID) PSM estimation of the ATT can be
employed (Heckman et al., 1997; Heckman et al., 1998). Thus, we can eliminate time-invariant effects
on the outcome variable.
After comparing the forest quality between both property regimes, we further investigated the
determinants of the forest degradation under different land tenure by using a probit model. As the
dependent variable, we used a dummy variable that took a value of 1 if the observation grid moved
down the classification scale from 2 or greater to 1 or 0.
As mentioned earlier, the traditional shaded coffee system requires shade trees for the coffee
production, while the modern coffee system increases its coffee yield under less shaded
9
agroecosystems. In fact, we observed the modern coffee areas in the non-forest areas and some in the
class 1 forest areas. Therefore, our probit estimation for private property regimes may capture the
conversion effect to the modern coffee system.
In the right-hand side of the probit estimation, we employed the same environmental variables as
those used in the previous probit estimation. In addition, we included the initial condition dummies
for the class 3, 4, and 5 forest areas, which took a value of 1 if forest classification of grid in 2005 is 3,
4, and 5, respectively.
Moreover, the effectiveness of forest management in the private property areas most likely differs
depending on the socioeconomic characteristics of the landholders. To further investigate the
relationship between the forest degradation and landholders’ characteristics, we estimated two
non-linear probability models: one that employs the only environmental variables and one that
additionally includes the socioeconomic characteristics of the landholders (socioeconomic variables).
We employed the following landholders’ characteristics as independent variables: the age of the
household head, a female household head dummy, the educational years of the household head, the
number of household members, the total area of agricultural land, and the number of large livestock.
The number of large livestock includes cattle, sheep, and goat. We are only able to use these
socioeconomic variables in the probit estimation for private property regimes because there is
no-socioeconomic characteristic in the common property areas.
5. Estimation Results
5.1. Matching procedure
The results of the probit estimation for the benchmark estimation are presented in Table 4. We found
that most variables, except the acrisol dummy, had a significant effect. The goodness-of-fit can be
measured by the pseudo R-squared, and it showed a large pseudo R-squared, such as 0.30.
The results of the probit estimation indicate the environmental characteristics of the private
property areas. The negative significant effects of the distance to the village and main road suggest
10
that the forest coffee areas are located close to the village and main road. Furthermore, the results of
the facing south and north dummies imply that the forest coffee trees are managed under a relative
better light condition compared with the natural forest in the common property area.
Based on the propensity score from the probit estimation, we created a new control observation
group such that the treatment group and the new control group would have similar characteristics. In
this study, we employed one-to-one matching with a caliper of 0.01. A common support condition
must be implemented to satisfy the overlap assumption (i.e., equation (3)). In other words, in the
treatment group, we dropped the observations whose propensity scores were higher than the
maximum score or lower than the minimum score of the observations in the control group. Each
treatment observation was compared with the weighted average of all of the control observations in
the common support region.
To check the characteristics of the treatment group and the control group after the matching, we
conducted two types of balancing tests. First, a t-test was used to compare the mean of each covariate
between the treatment group and the control group after the matching. The results of the t-test for the
three PSM estimations are presented in Table 5. The first column shows the mean difference between
the treatment group and the control group for each covariate before the matching, and the second
column represents the difference after the matching with the t-values in parentheses. The results
showed that the difference in all of the covariates before the matching turned to be insignificant after
the matching, indicating that the characteristics of the control group are sufficiently similar after
matching.
Next, we ran the probit estimation using the sample after the matching and compared the pseudo
R-squared with that obtained from the probit estimation using the sample before the matching. If the
matching was successful, the pseudo R-squared after the matching would have a lower value than that
before the matching, which would indicate that the after-matching probit has no explanatory power.
The results shown in the lower rows of Table 5 indicated that the pseudo R-squared values of the
benchmark estimation declined; in particular, the value of the benchmark estimation decreased
drastically to 0.01. Hence, second balancing test also confirms that there is no systematic difference
between the treatment and after-matching control groups.
11
5.2. Results of the PSM estimation
After the matching procedure, we computed the PSM estimation based on the treatment and control
groups. In most studies, the standard error is obtained by bootstrapping (Caliendo and Kopeinig,
2008). In this study, we also used the bootstrapping standard error based on 100 replications,
following Smith and Todd (2005).
The results of the benchmark PSM estimation, which are given in Table 6, show that the private
property forest coffee areas slightly decrease forest quality by 0.92. One of the reasons for the
degradation in the private property areas may be transformation to the modern coffee system. Because
of the low yield of the forest coffee, the landholders have an incentive to convert their forest coffee
areas into areas operating under the modern coffee system with fewer shade trees. In fact, we
observed the modern coffee trees, particularly around the areas with better accessibility. Although the
modern coffee system improves productivity, this improvement comes with increased environmental
costs, such as forest reduction (Perfecto et al., 1996; Staver et al., 2001; Rappole et al., 2003). The
negative effect observed in our estimation results may be the result of the transformation impact.
Meanwhile, the common property forest areas indicate forest quality depreciation measuring 1.45.
This negative impact may be due to the agricultural expansion and high dependency on firewood;
however, we cannot be sure from our current analysis.
Overall, our empirical results indicate that although forest quality in the target area deteriorates in
a wide sphere, the magnitude of degradation in common property areas is significantly larger than that
of the private property areas, i.e., the difference between the treatment and control groups is 0.53.
These results indicate that the private property areas alleviate the forest degradation more than the
common property areas, even though both areas have similar environmental characteristics.
5.3. Determinants of forest degradation in private and common property areas
The estimation results for the probit model of the determinants of the forest degradation are presented
in Table 7. Columns 1 and 2 present the estimation results of private property regimes without and
12
with the socioeconomic variables, respectively.
We found that the distance to the main road had a negative effect on forest degradation,
indicating that the forest degradation occurred in the areas with better accessibility to the main road.
This negative coefficient may be the result of the conversion of the forest coffee areas into the modern
coffee areas. Because better access to the main road most likely implies better market accessibility,
the landholders of those areas with better access have more incentive for conversion to maximize their
profit. We also found that three environmental variables, such as the average elevation, average slope
rate, and facing north dummy, negatively affected the forest degradation. These results suggest that
the elevated sloping areas under sunless conditions increase the likelihood of conservation of forest
quality.
In the case of the landholders’ characteristics, the total area of agricultural land had a
significantly negative effect on the degradation, while the number of large livestock affected
positively. These results are reasonable. The result of the total area of agricultural land suggests that
the landholders with large agricultural land reduce the dependence on the forest coffee production and
decrease the incentive to invest their forest coffee areas, resulting the conservation of forest quality.
On the other hand, livestock production requires the sufficient size of grazing land. We assume that
those landholders raising livestock may pasture their livestock on their forest area and such
landholders’ behavior significantly induces the reduction of the biomass. In fact, we observed sheep
and cattle grazing in the forest area and forest experts assigned to the Belete-Gera RFPA mentioned
that such livestock grazing is one major cause of the destruction of forest.
Furthermore, the initial condition dummy for the class 5 forest areas negatively affected the
degradation in both probit estimations. These results indicate that the forest coffee areas with dense
shade trees are likely to sustain its forest condition. In contrast, we found the positive effect of the
initial condition dummies for the class 3 forest in the estimation with the socioeconomic variables,
while the class 4 forest dummy had a negative effect in the first estimation in column 1. Although
these results are not consistently significant in both estimations, these results imply that the forest
condition in the private property areas does not equally decline; instead, forest degradation intensively
occurs in the lower classes.
13
With respect to the determinants of the forest degradation in common property regimes, which
are given in column 3 of Table 7, we found that three environmental characteristics, such as the
distance to the village and main road and average slope rate, had significantly positive effects on
forest degradation. These results indicate that the forest degradation occurred in the distanced high
steep areas. We assume the reason for degradation in those areas is the logging by the villagers. To
avoid detection by other villagers, some villagers may engage in logging at the isolated areas,
resulting in declined forest quality.
Moreover, we found the significant effects on forest degradation from all initial condition
dummies, yet the only class 5 dummy had a negative coefficient. These results imply that the common
property forest widely faces the risk of forest degradation.
6. Discussion and Conclusion
In this study, we employed the PSM method and probit model to explore the conservation effects of
different property regimes and to identify the determinants of the forest degradation. Using the PSM
method to control for the selection bias, we compared the private property areas and common
property areas with similar environmental characteristics and found that the private property areas
significantly reduced the forest degradation compared with the common property areas.
In addition, our empirical results indicate that the determinants of forest degradation differ
between private and common property regimes. While the forest areas under private property regimes
accelerated the forest degradation in the relatively flat area with better access to the main road, the
common property forest areas isolated from the village and main road are likely to foster degradation.
Additionally, the results of the initial condition dummies employed in the probit estimation for private
property regimes suggest that the high risk of degradation is partially observed in the low-density
forest. In contrast, except the areas classified as the class 5 forest areas, the forest degradation was
widely observed in the common property forest areas.
Overall, we found that both property regimes led to the forest degradation. These results suggest
that additional conservation efforts are needed to sustain and to improve forest quality in both
14
property regimes.
To encourage conservation efforts of the producers in the private property, the most important
factor is producers’ incentive. As mentioned before, if the benefit from transformation to modern
system is the most economical and rational option for the producers, the probability of transformation
would be fairly high, as suggested by Angelsen (1999). Hence, it is essential to offer an alternative
option that links environmental and economic goals so that the producers themselves increase the
willingness to conserve their forest land.
In recent years, to reduce producers’ conversion behavior, ecological certification programs have
attracted increasing attention from conservation and development organizations (Fleischer and
Varangis, 2002; Perfecto et al., 2005; Taylor, 2005). The certification programs provide a premium
price to producers who maintain shade trees, and thus the programs contribute to the protection of
environment and producers’ satisfaction. In fact, the different empirical studies conducted in the
Belete-Gera forest documented that acquiring the ecological certification had a positive impact on the
forest conservation (Takahashi and Todo, 2013; Takahashi and Todo, 2014). Therefore, such economic
incentive by ecological certification programs has a good possibility to offer the alternative option
motivating producers to manage their forest coffee areas more sustainably.
With respect to the common property regimes, as the previous studies showed, there were several
essential conditions for successful community management: the closed access, the clear resource
boundaries, the restricted non-member access, and the additional regulation (Bromley, 1989; Ostrom,
1990; McKean and Ostrom, 1995). In the case of the Belete-Gera forest, particularly, two conditions,
such as the clear boundaries and additional regulation, failed to qualify the criteria. Because the
border between farmland and forest was unclear, such unclarity made easier to expand the farmland
into the forest area. Additionally, even if the communities are willing to protect their registered forest
area, there is no official and traditional regulation to prohibit illegal or excessive logging, as well as to
enforce the community members to participate in the management activities and forest monitoring.
However, because the establishment of such agreement restricts the use of natural resources and
induces additional cost of protection, we may not be able to expect community members to establish a
common agreement by their own efforts, unless there is a strong incentive for doing so. Therefore, in
15
order to strengthen the community-based forest management in common property regimes, the
challenge is to create the incentives that would motivate all community members to participate in joint
forest management schemes.
16
Acknowledgement
This work was supported by JSPS KAKENHI Grant-in-Aid for JSPS Fellows (25:3204). This study
was conducted as part of the research project “Impact Evaluation of Aid Projects of the Japan
International Cooperation Agency” in the JICA Research Institute. The authors would like to thank Y.
Sawada (the project leader) and K. Tsunekawa (the former Director of the JICA Research Institute) for
providing us the opportunity to perform this research. The authors would also like to thank N. Ando, S.
Ogawa, Y. Takahashi, and, in particular, T. Nishimura, and F. Saso for their great help in data
collection. We are grateful to Y. Todo and T. Matsumoto for the constructive comments and support.
17
References
Angelsen, A. (1999) Agricultural expansion and deforestation: Modelling the impact of population,
market forces and property rights. Journal of Development Economics 58(1): 185-218.
Araujo, C., C.A. Bonjean, J.L. Combes, P. Combes Motel, and E.J. Reis (2009) Property rights and
deforestation in the brazilian amazon. Ecological Economics 68(8): 2461-2468.
Arima, E.Y., C.S. Simmons, R.T. Walker, and M.A. Cochrane (2007) Fire in the brazilian amazon: A
spatially explicit model for policy impact analysis. Journal of Regional Science 47(3): 541-567.
Arnot, C.D., M.K. Luckert, and P.C. Boxall (2011) What is tenure security? Conceptual implications
for empirical analysis. Land Economics 87(2): 297-311.
Bohn, H., and R.T. Deacon (2000) Ownership risk, investment, and the use of natural resources.
American Economic Review 90(3): 526-549.
Bray, D.B., E.A. Ellis, N. Armijo-Canto, and C.T. Beck (2004) The institutional drivers of sustainable
landscapes: A case study of the ‘mayan zone’in quintana roo, mexico. Land Use Policy 21(4):
333-346.
Bromley, D.W. (1989) Property relations and economic development: The other land reform. World
Development 17(6): 867-877.
Caliendo, M., and S. Kopeinig (2008) Some practical guidance for the implementation of propensity
score matching. Journal of Economic Surveys 22(1): 31-72.
Dalle, Sarah Paule, Sylvie de Blois, Javier Caballero, and Timothy Johns (2006) Integrating analyses
of local land-use regulations, cultural perceptions and land-use/land cover data for assessing the
success of community-based conservation. Forest Ecology and Management 222(1): 370-383.
Davenport, ML, and SE Nicholson (1993) On the relation between rainfall and the normalized
difference vegetation index for diverse vegetation types in east africa. International Journal of
Remote Sensing 14(12): 2369-2389.
Deacon, R.T. (1999) Deforestation and ownership: Evidence from historical accounts and
contemporary data. Land Economics: 341-359.
Deininger, K., and B. Minten (2002) Determinants of deforestation and the economics of protection:
An application to mexico. American Journal of Agricultural Economics 84(4): 943-960.
Demsetz, Harold (1967) Toward a theory of property rights. The American Economic Review 57(2):
347-359.
Edmonds, E.V. (2002) Government-initiated community resource management and local resource
extraction from nepal's forests. Journal of Development Economics 68(1): 89-115.
Ellis, Edward A, and Luciana Porter-Bolland (2008) Is community-based forest management more
effective than protected areas?: A comparison of land use/land cover change in two neighboring
study areas of the central yucatan peninsula, mexico. Forest Ecology and Management 256(11):
18
1971-1983.
Fleischer, G., and P. Varangis (2002) Toward more sustainable coffee: Consumers fuel demand for
more sustainable agriculture. Agriculture Technology Notes 23.
Furubotn, Eirik G, and Svetozar Pejovich (1972) Property rights and economic theory: A survey of
recent literature. Journal of Economic Literature 10(4): 1137-1162.
Gibson, C.C., F.E. Lehoucq, and J.T. Williams (2002) Does privatization protect natural resources?
Property rights and forests in guatemala. Social Science Quarterly 83(1): 206-225.
Glück, P. (2002) Property rights and multipurpose mountain forest management. Forest Policy and
Economics 4(2): 125-134.
Godoy, R., M. Jacobson, J. De Castro, V. Aliaga, J. Romero, and A. Davis (1998) The role of tenure
security and private time preference in neotropical deforestation. Land Economics: 162-170.
Greenberg, R., P. Bichier, A.C. Angon, and R. Reitsma (1997) Bird populations in shade and sun
coffee plantations in central guatemala. Conservation Biology 11(2): 448-459.
Hardin, Garrett (1968) The tragedy of the commons. Science 162(3859): 1243-1248.
Hayami, Y., and Y. Godo (2005) Development economics: From the poverty to the wealth of nations.
Oxford University Press, New York, USA.
Heckman, J.J., H. Ichimura, and P. Todd (1998) Matching as an econometric evaluation estimator.
Review of Economic Studies 65(2): 261-294.
Heckman, J.J., H. Ichimura, and P.E. Todd (1997) Matching as an econometric evaluation estimator:
Evidence from evaluating a job training programme. Review of Economic Studies 64(4): 605-654.
Imbens, G.M., and J.M. Wooldridge (2009) Recent developments in the econometrics of program
evaluation. Journal of Economic Literature 47(1): 5-86.
Lund, Jens Friis, and Thorsten Treue (2008) Are we getting there? Evidence of decentralized forest
management from the tanzanian miombo woodlands. World Development 36(12): 2780-2800.
Marchand, S. (2012) The relationship between technical efficiency in agriculture and deforestation in
the brazilian amazon. Ecological Economics.
Mas, A.H., and T.V. Dietsch (2004) Linking shade coffee certification to biodiversity conservation:
Butterflies and birds in chiapas, mexico. Ecological Applications 14(3): 642-654.
Matta, Jagannadha R, and Janaki RR Alavalapati (2006) Perceptions of collective action and its
success in community based natural resource management: An empirical analysis. Forest Policy
and Economics 9(3): 274-284.
McKean, M., and E. Ostrom (1995) Common property regimes in the forest: Just a relic from the past.
Unasylva 46(180): 3-15.
Mendelsohn, R. (1994) Property rights and tropical deforestation. Oxford Economic Papers: 750-756.
Mertens, Benoı̂ t, William D Sunderlin, Ousseynou Ndoye, and Eric F Lambin (2000) Impact of
19
macroeconomic change on deforestation in south cameroon: Integration of household survey and
remotely-sensed data. World Development 28(6): 983-999.
Moguel, P., and V.M. Toledo (1999) Biodiversity conservation in traditional coffee systems of mexico.
Conservation Biology 13(1): 11-21.
Nagendra, H., S. Pareeth, B. Sharma, C.M. Schweik, and K.R. Adhikari (2008) Forest fragmentation
and regrowth in an institutional mosaic of community, government and private ownership in
nepal. Landscape Ecology 23(1): 41-54.
Nelson, G.C., V. Harris, and S.W. Stone (2001) Deforestation, land use, and property rights: Empirical
evidence from darien, panama. Land Economics 77(2): 187-205.
Ostrom, E. (1990) Governing the commons: The evolution of institutions for collective action.
Cambridge University Press.
Owubah, C.E., D.C. Le Master, J.M. Bowker, and J.G. Lee (2001) Forest tenure systems and
sustainable forest management: The case of ghana. Forest Ecology and Management 149(1):
253-264.
Perfecto, I., R.A. Rice, R. Greenberg, and M.E. Van der Voort (1996) Shade coffee: A disappearing
refuge for biodiversity. BioScience 46(8): 598-608.
Perfecto, I., and R. Snelling (1995) Biodiversity and the transformation of a tropical agroecosystem:
Ants in coffee plantations. Ecological Applications 5(4): 1084-1097.
Perfecto, I., J. Vandermeer, A. Mas, and L.S. Pinto (2005) Biodiversity, yield, and shade coffee
certification. Ecological Economics 54(4): 435-446.
Pretty, Jules, and Hugh Ward (2001) Social capital and the environment. World Development 29(2):
209-227.
Rappole, J.H., D.I. King, and J.H.V. Rivera (2003) Coffee and conservation. Conservation Biology
17(1): 334-336.
Rosenbaum, P.R., and D.B. Rubin (1983) The central role of the propensity score in observational
studies for causal effects. Biometrika 70(1): 41-55.
Schmitt, Christine B, Feyera Senbeta, Manfred Denich, Helmut Preisinger, and Hans Juergen
Boehmer (2010) Wild coffee management and plant diversity in the montane rainforest of
southwestern ethiopia. African Journal of Ecology 48(1): 78-86.
Smith, J.A., and P.E. Todd (2005) Does matching overcome lalonde's critique of nonexperimental
estimators? Journal of Econometrics 125(1): 305-353.
Somanathan, Eswaran, Rajagopal Prabhakar, and Bhupendra Singh Mehta (2005) Does
decentralization work? Forest conservation in the himalayas. BREAD Working Paper 096.
Southworth, J., D. Munroe, and H. Nagendra (2004) Land cover change and landscape
fragmentation—comparing the utility of continuous and discrete analyses for a western honduras
20
region. Agriculture, ecosystems & environment 101(2): 185-205.
Staver, C., F. Guharay, D. Monterroso, and RG Muschler (2001) Designing pest-suppressive
multistrata perennial crop systems: Shade-grown coffee in central america. Agroforestry Systems
53(2): 151-170.
Takahashi, R., and Y. Todo (2012) Impact of community-based forest management on forest
protection: Evidence from an aid-funded project in ethiopia. Environmental management 50(3):
396-404.
Takahashi, Ryo, and Yasuyuki Todo (2013) The impact of a shade coffee certification program on
forest conservation: A case study from a wild coffee forest in ethiopia. Journal of environmental
management 130: 48-54.
Takahashi, Ryo, and Yasuyuki Todo (2014) The impact of a shade coffee certification program on
forest conservation using remote sensing and household data. Environmental Impact Assessment
Review 44: 76-81.
Taylor, P.L. (2005) A fair trade approach to community forest certification? A framework for
discussion. Journal of Rural Studies 21(4): 433-447.
Todo, Y., and R. Takahashi (2011) Impact of farmer field schools on agricultural income and skills:
Evidence from an aid-funded project in rural ethiopia. Journal of International Development
25(3): 362-381.
Tole, Lise (2010) Reforms from the ground up: A review of community-based forest management in
tropical developing countries. Environmental management 45(6): 1312-1331.
Tucker, C.J., TE Goff, and JRG Townshend (1985) African land-cover classification using satellite
data. Science 227(4685): 369-375.
Tucker, C.J., D.A. Slayback, J.E. Pinzon, S.O. Los, R.B. Myneni, and M.G. Taylor (2001) Higher
northern latitude normalized difference vegetation index and growing season trends from 1982 to
1999. International Journal of Biometeorology 45(4): 184-190.
Tucker, C.M. (1999) Private versus common property forests: Forest conditions and tenure in a
honduran community. Human Ecology 27(2): 201-230.
Wood, C.H., and R. Walker (2001) Land titles, tenure security and resource use among small farmers
in the brazilian amazon. Paper in S 67.
Wunderle Jr, J.M., and S.C. Latta (1996) Avian abundance in sun and shade coffee plantations and
remnant pine forest in the cordillera central, dominican republic. Ornitología Neotropical 7:
19-34.
21
Figure 1: The Belete-Gera regional forest priority area
The areas shown in gray represent the sub-villages that produce forest coffee, and the white areas are
the sub-villages without forest coffee. The areas marked with a dark gray color are the study areas for
this investigation.
22
Table 1: Description of five classifications
Number of
Number of
Range of
Number of
Canopy
trees
tree species
height (m)
strata of trees
cover (%)
Class 1
14
6
20−35
2
60−70
Class 2
21
4
15−35
2
80
Class 3
10
6
20−45
2
85
Class 4
11
6
15−50
3
90
Class 5
N/A
N/A
N/A
N/A
N/A
23
Table 2: Description of private property forest coffee areas in the sample
Belete
forest
Number of plots
Size of forest coffee area (a)
Distance to the village (m)
Average elevation (m)
Average slope (%)
Gera forest
Total
(1)
(2)
(3)
71
21
92
24.4
94.1***
40.3
(30.0)
(138.4)
(75.9)
79.5
110.7
86.6
(129.0)
(161.9)
(136.9)
1,922.6
1,880.7***
1,913.0
(65.6)
(68.5)
(68.2)
12.1
13.0
12.3
(5.9)
(4.7)
(5.6)
Note: standard deviations are in parentheses; *** column 2 indicates that the means in the two groups
(columns 1 and 2) are significantly different at the 1 percent level.
24
Table 3: Characteristics of the observation grids
Private property
area
(Forest coffee
area)
(1)
Number of observations
Distance to the village (m)
Common
property area
(Forest area
without forest
coffee)
(2)
629
Total
(3)
20,962
21,591
202.5
979.0***
956.4
(157.8)
(677.5)
(680.8)
2.0
3.3***
3.3
(1.2)
(1.8)
(1.8)
1,884.6
1,900.9***
1,900.5
(93.9)
(127.3)
(126.5)
Average slope (%)
12.8
15.6***
15.5
Proportion of Acrisol over the observations
(5.5)
1.6
(7.6)
18.2***
(7.6)
17.7
(12.5)
(38.6)
(38.2)
58.5
26.8***
27.7
(49.3)
(44.3)
(44.8)
17.6
54.3***
53.3
(38.2)
(49.8)
(49.9)
Distance to the main road (km)
Average elevation (m)
Proportion of grid facing south (%)
Proportion of grid facing north (%)
Note: standard deviations are in parentheses; *** column 2 indicates that the means in the two groups
(columns 1 and 2) are significantly different at the 1 percent level.
25
Table 4: Determinants of private property area
Benchmark
estimation
(1)
Distance to the village (km)
−2.165***
(−21.320)
Distance to the main road (km)
−0.206***
(−9.815)
Average elevation (m)
−0.001***
(−3.218)
Average slope (%)
−0.015***
(−3.732)
Acrisol dummy
−0.031
(−0.201)
Facing south dummy
0.271***
(4.993)
Facing north dummy
−0.491***
(−7.701)
Constant
1.292**
(2.575)
Observations
2
Pseudo R
21,591
0.30
Note: z−statistics are in parentheses; *** indicates statistical significance at the 1 percent level.
26
Table 5: Balancing tests
Benchmark estimation
Difference
before
matching
(1)
Distance to the village (km)
−0.78***
(−28.72)
Distance to the main road (km)
−1.31***
(−18.63)
Average elevation (m)
−16.30***
(−3.20)
Average slope (%)
−2.87***
(−9.40)
Acrisol dummy
−0.17***
(−10.78)
Facing south dummy
0.32***
(17.62)
Facing north dummy
2
Pseudo R
−0.37***
(−18.30)
0.30
Difference
after matching
(2)
0.01
(0.99)
−0.16
(−2.36)
9.10
(1.69)
0.31
(1.05)
0.01
(1.01)
0.03
(1.14)
0.02
(0.91)
0.01
Note: t−values are in parentheses; *** indicates statistical significance at the 1 percent level.
27
Table 6: Forest quality comparison between private and common property regimes
Benchmark estimation
(1)
Treatment group
Control group
Private property
(Forest coffee area)
Common property
Mean of treatment group
−0.924
Mean of matched control group
−1.450
Difference: average treatment
effect
Standard error
t−value
Number of observations
0.526
0.11
4.81***
1,244
Note: *** indicates statistical significance at the 1 percent level.
28
Table 7: Determinants of forest degradation
Distance to the village (km)
Distance to the main road (km)
Private
property
(1)
Private
property
(2)
Common
property
(3)
0.606
0.170
0.067***
(1.447)
(0.310)
−0.581***
−1.196***
(−9.376)
−0.004***
Average elevation (m)
(−4.793)
Average slope (%)
Acrisol dummy
Facing south dummy
Facing north dummy
Initial condition dummy (Class 3)
Initial condition dummy (Class 4)
Initial condition dummy (Class 5)
(−6.638)
(9.290)
−0.002***
(−22.654)
−0.095***
0.003*
(−4.616)
−0.283
(−0.509)
0.148
(1.040)
−0.533***
(−2.844)
0.192
(1.327)
−0.375**
(−2.066)
−1.005***
(−2.825)
(−6.454)
0.587
(0.837)
−0.091
(−0.553)
−0.504**
(−2.330)
0.336**
(2.124)
−0.108
(−0.533)
−0.973**
(−2.438)
0.007
(0.959)
−0.680
(−1.617)
0.027
(0.791)
0.029
(0.999)
−0.004***
(−3.185)
0.284***
(5.735)
(1.808)
−0.150***
(−4.800)
−0.034
(−1.262)
0.058**
(2.204)
0.586***
(25.376)
0.185***
(8.029)
−0.231***
(−6.847)
7.868***
15.057***
3.025***
Female household head dummy (1=Yes)
Years of formal education of the household head
Number of household members
Total area of agricultural land (a)
Number of large livestock
(5.294)
629
0.18
Observations
Pseudo R2
−0.007***
0.063***
−0.053***
Age of the household head
Constant
(−8.051)
(3.998)
(6.921)
629
0.28
(17.860)
20,962
0.08
Note: z−statistics are in parentheses; *, **, and *** indicate statistical significance at the 10, 5, and 1
percent level, respectively.
29