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Does Common Property Forest Management Change On-Farm Behavior?
Evidence on Tree Planting in Highland Ethiopia
Abstract: Policy reform to increase tree cover is a key priority in a number of low-income developing
countries and Ethiopia is no exception. Reforms in Ethiopia include a forest proclamation issued in
2007 and the first-ever federal forest policy approved the same year. Both these documents allow a
variety of institutional arrangements for investment in forests, including common property forest
management (CPFM), private woodlots and on-farm trees. This paper analyzes behavioral change
spurred by better CPFM, with a focus on on-farm tree planting in Ethiopia.
Results from our theoretical model suggest that on-farm tree stocks, which provide products that can
substitute for those from common forests, should increased by better CPFM systems. We test this
finding using data from household and village surveys conducted in the Ethiopian highlands in 2007.
Using CPFM indices based on survey respondent perceptions, we find that CPFM in the study area is
very loose, informal and could be considered weak. We also find that management varies across sites
and households and is driven by population density, management level, forest type and other variables.
Different aspects of CPFM show strong correlations, which suggests these components tend to .
We find that despite generally loose management, using a variety of estimation techniques, including
treatment effects, count data and IV methods, CPFM has a positive association with the number of
trees grown and in some cases with decisions to grow trees. These findings suggest that what appears
to be informal, loose or ineffective management may have important behavioral effects. One of these
behavioral effects appears to be that households grow more trees on their farms when CPFM is more
stringent. These and likely other behavioral responses should be considered when planning or
evaluating CPFM programs (JEL Q23, Q12).
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I. Introduction
In most low-income developing countries households depend on trees to provide a variety of
products that are essential to daily life, including fuelwood, fodder for animals and building materials.
Furthermore, forests provide important “off-site” benefits, including erosion and flood control. Often
forests are “common,” which creates interdependencies between community members that may result
in open access, which provides few incentives for stewardship, planting, and management (Gordon
1954). Without clear property rights, as long as resources have value, they will be used in less than
ideal ways and almost certainly will be degraded, often to the point where they end up close to
worthless. Sometimes this phenomenon is called the “Tragedy of the Commons” (Hardin 1968) and
reflects the idea that potentially very valuable resources can be degraded when it is not clear who gets
products generated from natural resource investments and/or who has the right to control resources.i
Establishing clear property rights through appropriate institutional arrangements is therefore
perhaps the critical prerequisite to enhanced tree planting, stewardship, management and tree cover in
many low-income countries. Clarifying property rights may not solve all problems, but the economic
literature is doubtful that tree cover can be sustainably increased without clear property rights (Gordon
1954; Hartwick and Olewiler 1998; Field 2001). In Ethiopia, which is the focus of this paper, property
rights in rural areas are often very weak and this is particularly true for common grazing and forest
areas (Mekonnen and Bluffstone 2008). People therefore typically use the land as they want with very
limited management; as rural populations and demands on common lands have increased, it is perhaps
not surprising that forest cover has declined.
What institutional arrangements can be used to establish property rights? Private property, of
course, is a possibility and it certainly creates the right incentives for forest management if there are
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few conflicts between uses. For example, if trees only produce fuelwood, private property may be the
correct institutional arrangement, because there are few ways owners of fuelwood plantations can
infringe on each others’ property rights. Privatization would therefore likely be the correct solution.
But, in Ethiopia as in most low-income countries, common forests typically produce multiple
products, creating technical barriers to privatization. Furthermore, allocation of forest resources is
highly political and villagers may be deprived of historical rights. Poor villagers, who often rely much
more heavily on common lands than the rich (Jodha 1986), may as a result find themselves worse off
than when land access is open.
With an estimated 4.6 percent forest cover, compared with a baseline of perhaps 40 percent
forest cover in the 16th century (EFAP 1994; Tumcha 2004), 0.8 percent deforestation per year and
83.3 percent of the population living in rural areas (World Bank 2005) the forestry situation in Ethiopia
is highly problematic. Ethiopia also has a rapidly growing human population of about 80 million,
largely dependent on low-productivity and rain-fed agriculture. There are also over 70 million
livestock that compete for land and forest resources. As noted by the Food and Agricultural
Organization of the United Nations, “The current demographic and socio-economic conditions have
led to an unprecedented pressure on [the remaining] mountain forest ecosystems.” It found that over 90
percent of energy for household cooking comes from fuelwood.ii Since 2006 floods potentially linked
to lack of tree cover killed hundreds and resulted in significant property damage.
Policy reform to increase tree cover is a government priority, with a forest proclamation issued
in 2007 and the first-ever federal forest policy approved the same year. Both these documents allow a
variety of institutional arrangements for investment in forests, including CPFM, private woodlots, and
on-farm trees. Creating government ownership is also a possibility, but as seems to be the case in
Ethiopia (where all land is state-owned) and many other low-income countries, state property often
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functions as de facto open access. A more promising institutional arrangement is common property in
which users of forests jointly own resources. Devolution of forest management to local-level
institutions with the goal of creating incentives for stewardship, planting, and management of common
lands has therefore become an increasingly important tool when forests mainly generate direct use
values that are not fully compatible (Agrawal 2007).
If common property forest management (CPFM) really offers incentives for better management
than open access, we should observe behavioral differences when better management exists. In this
paper we examine one potential behavioral effect by trying to isolate and understand the effects of
generally-considered better CPFM on households’ incentives to invest in trees on their own farmland.
As a small but growing literature is establishing, private tree planting is a potentially important
behavioral response to better social coordination and higher levels of social capital like CPFM (Nepal
et al. 2007; Bluffstone et al. 2008; Mekonnen [forthcoming]). Furthermore, in East Africa as common
forests have deteriorated, very small private plantations are increasingly producing critical forest
products for rural households. In Kenya a significant proportion of fuelwood and charcoal comes from
private lands (Bluffstone et al. 2007)
In this paper we seek to contribute to the growing—but still inadequate—literature on the
determinants of CPFM and household responses to different aspects of CPFM. To our knowledge, this
is also the first study in Africa that looks into the links between CPFM and private tree planting. Using
an analytical household model, we find that better CPFM creates incentives for tree planting on
household farms. We test this hypothesis using household data from highland Ethiopia and find strong
support for this result using a variety of methods. We therefore conclude, as Bluffstone et al (2008) did
for the Bolivian Andes, that better CPFM causes households to change their behavior and one effect is
more on-farm tree planting.
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The next section discusses the literature related to CPFM. Section 3 presents a household
analytical model that extends that used by Bluffstone et al. (2008). Section 4 discusses data and
descriptive statistics. Section 5 presents the econometric approach, followed by results in section 6.
Section 6 concludes and discusses the implications of the paper.
II.Literature on Common Property Forest Management and Household Behavior
In recent decades there have been important advances in our understanding of the management
of commonly held natural resources. In the process, an enormous literature has emerged that discusses
the incentives for community members to cooperate and emphasizes the need for individual interests to
be deferred to enhance the long-term community interest. This literature implies that with effective
CPFM households would be forced to restrict their collections compared with desired levels (e.g.,
Olson 1965; Dayton-Johnson 2000; Wade 1988; Ostrom 1990; Bromley 1990; Baland and Platteau
1996; Sethi and Somanathan 1996; Baland and Platteau 1999; Bluffstone et al. 2008).
A related literature discusses desirable aspects of CPFM and attempts to disaggregate its
components. This work suggests that effective CPFM systems empower communities, have clear
access and extraction rules, fair and graduated sanctions, public participation, clear quotas and
successful monitoring (Ostrom 1990; Agrawal 2000, 2001). Following Bluffstone et al (2008) we
divide CPFM characteristics into institutional characteristics and management tools. Institutional
characteristics are the basic values and arrangement of institutions. For example, clarity of access to
forest resources, fairness, democracy and public participation are institutional characteristics.
Management tools are instruments used to encourage members to mitigate their use of forests or
improve forest quality. Fixed allotments of fuelwood and fodder, monitoring by officials and villagers,
formal and informal sanctions and work to improve forest quality are all management tools.
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Despite what is an emerging conventional wisdom that CPFM is better than other alternatives,
evidence on the effects and efficacy of CPFM components is still limited and the subject of current
empirical research; indeed, empirical work focusing on CPFM elements that spur behavioral change
has only relatively recently emerged (Hegan et al. 2003; Adhikari 2002; Amacher et al. 1996, 1999;
Cooke 2000, 2004; Edmonds 2002; Heltberg 2001; Heltberg et al. 2000; Linde-Rahr 2003).
The work of Nepal et al. (2007), Bluffstone et al. (2008) and Mekonnen (forthcoming) are
directly related to our paper, because of the focus on incentives for on-farm tree planting. Nepal et al.
(2007) looks at a variety of social networks and finds that forest-related institutions particularly spur
on-farm tree planting. Other less forest-related groups have limited effects. Bluffstone et al. (2008)
uses a methodology similar to that used in this paper to examine whether CPFM spurs on-farm tree
planting in Bolivia. They find that better CPFM at its highest level of aggregation—and especially the
institutional characteristics component—is positively correlated with more and higher quality on-farm
trees. In terms of less aggregated indices, relatively few variables are significant, although two specific
property rights aspects of CPFM may reduce on-farm planting. Mekonnen (forthcoming) looks at tree
planting in Ethiopia and finds that a variety of labor, asset and credit market imperfections affect onfarm tree planting.
III. A Household Analytical Model and Comparative Static Resultsiii
Consider a representative farming household living in a large village in a low-income country. The
village being “large” means that many households access a nearby forest and strategic interactions
regarding the use of the forest are not possible.iv The status quo is open access, though rents may not
be completely dissipated (Gordon, 1954). The village is examining the implications of introducing
CPFM regulations to curb open access.
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U  U (F , X )
(1)
The household has a unitary decision process and household utility is an increasing concave
function of cooked food (F) and other goods (X) that must be purchased (equation 1). Food is only
produced by households and is a function of environmental and non-environmental household labor
inputs ( E E and E NE ), common forest quality (Q) and trees planted on-farm (T) that substitute for
common forests. Environmental labor includes activities such as fuelwood collection, grazing and
cutting of fodder for animals, which provides fuel for cooking and feeds animals that produce meat.
Animals also produce dung, which is the main fertilizer in much of the developing world, including
Ethiopia. Non-environmental labor consists of household activities like cooking, cleaning, caring for
children and agriculture. This function is given in (2).v
( 2) F  F ( E E ,
E
NE
, Q, T )
(2)
First derivatives are positive and second derivatives negative due to the existence of quasi-fixed
factors such as tools, animals, etc; common forests do not generate these diminishing returns, because
households are “small” collectors of forest products. Labor cross-partial derivatives are assumed to be
zero, which means that marginal products of any Ei are not affected by any E h . This approach is taken
to reduce dimensionality of the problem in the interest of deriving testable hypotheses. Common forest
quality (Q), however, makes environmental labor more productive (i.e. (
F
/ Q )  0) . On-farm tree
EE
inputs into food production (e.g. by stall feeding animals or collecting branches on-farm) do not
require environmental labor.
Equation 3 is the production function for trees (T), which is only a function of tree planting
labor (ET). This simplification focuses attention on labor, which is the main resource allocation issue.
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Trees may also, however, be purchased. ET includes planting, watering, chasing away grazing animals
and guarding against encroachment. The first derivative of g(ET) is positive and the second derivative
negative due to the existence of fixed land.
T  g ( Et}
(3)
Production occurs subject to the time constraint in (4). All activities discussed above are
included, as well as off-farm wage labor (Ew), which earns incomes used to purchase X and T. Leisure
is omitted, because Ethiopian farmers typically work long hours and the labor-leisure tradeoff is not
germane to our research questions.
E  EE  ENE  ET  Ew
(4)
There is no saving or borrowing. Cash is earned from wages at rate w and spent on X and trees .
Households are assumed to be price takers. Due to highly imperfect financial markets there is no
borrowing or saving (5).
wEw  PTT  PxX
(5)
Effective CPFM by its nature utilizes collective action to restrict household deforesting
behavior in the name of forest regeneration and boosting rents. Because households’ most important
variable factor is labor, similar to the method used by Linde-Rahr (2003) we model these restrictions
as inequality constraints on forest-related labor supply.vi Following Heltberg (2001), it is supposed that
while forest policies may be endogenous, they are given when villagers make their day-to-day
decisions.
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L  U [{F ( EG, ENE , Q, T )  g ( ET )} , (
wE w  PT g ( ET )
)]   1( E  EE  ENE  ET  Ew)
PX
(6)
  2( E w  Ew)   3( E  EE )
*
*
E
We substitute (3) into (2) and (5). After solving (5) for X we substitute the result into (1) and
do the same for (2). The resulting Lagrangian that the household maximizes is given in (6). In
addition to the time constraint represented by constraint λ1, λ2 and λ3 represent restrictions on labor
supply. The Kuhn-Tucker conditions are that if the constraints bind, the Lagrange multipliers are
positive rather than zero. λ2 > 0 therefore says that households are unable to work in the wage labor
market as much as they would like. λ3 > 0 represents restrictions on environmental labor supply
imposed by CPFM.
Though it is easiest to think of these constraints as extraction time limits, as will be shown in
the following section Ethiopian CPFM constraints may be quantitative and specific (e.g. allowable
cutting of fuelwood, maximum days grazing, fees for extraction) or qualitative (e.g. households may
take what they need but not more, face social sanctions for over-use or that allocations are fair).
a.
b
L
U F

(Q )   1   3  0
EE F E E
L
U F

 1  0
ENE F E NE
(7)

U F g
PT g

,
 1  0
ET
F g ET
PX ET
L
w
d.

 2  0
Ew PX
c.
To derive comparative static results, an explicit form of 7c must be assumed. In rural Ethiopia,
because markets are very thin food is virtually always produced on-farm and not purchased or sold. It
is therefore likely there is close to complete separability between X, the purchased good, and food,
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which is produced as part of substistence agriculture. An additive function captures this separability
and so in 7c we assume such a function. Setting 7a=7b=7c gives (8)
U F
g U F PT
U F
(Q)   3 
(

)
F E E
ET F g PX
F ENE
Assuming (
(8)
U F PT

)  0 , which says that on the margin households find it in their interest
F g PX
to plant trees rather than buy them, we examine the effects of λ3 being positive rather than equal to
zero, which indicates that CPFM constraints bind. In the comparative statics we allow λ3 to increase
starting from zero (i.e. open access).
Figure 1
To maintain equality as λ3 increases ∂g/∂ET and ∂g/∂ENE must decline. Given diminishing
returns to labor, this outcome would only occur if ET and ENE increase; labor therefore shifts from
environmental labor based on the use of common forests into non-environmental and tree labor. The
latter result is of interest here, because it indicates that CPFM labor constraints increase tree planting.
These labor shifts are larger the tighter the constraint. ET and ENE are therefore increasing in λ3.
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Figure 1 presents the situation focusing on EE and Figure 2 does something similar with regard
to ET. Figure 1 presents the situation. The horizontal axis is environmental labor and the vertical axis
is marginal utility. Because (
U F PT

) > 0, households trade off environmental labor against onF g PX
farm tree labor and choose environmental labor to maximize utility as in (8).
We see that without CPFM restrictions households would choose EE_OA1, which is the open
access equilibrium. With binding CPFM restrictions households are constrained to environmental
labor of E E , which is consistent with a higher MU of environmental labor and lower MU of tree
planting labor (consistent with higher labor supply). Households forgo forest rents and would like to
move labor into forestry activities, but is not permitted to do so. With diminishing marginal returns to
ET, Figure 1 indicates that tree planting increases due to CPFM restrictions.
Over time it can be expected that effective CPFM will cause common forest quality (Q) to
increase. Such an outcome increases the marginal productivity of EE and MU EE therefore shifts to the
right as represented in MU EE (Q2 ) . We see that the desired level of environmental labor increases to
EE_OA2. Allowing households to respond to increased Q degrades the resource, however, reducing Q
and shifting MU EE back to MU EE (Q1 ) . EE_OA2 is therefore not a long-run equilibrium and labor
supply would revert back to EE_OA1. Allowing households to take advantage of increased forest rents
through increased labor supply is therefore counterproductive. Rents must therefore be taken in terms
of households gathering more forest products per hour collected. As shown in Figure 1, even at EE_OA1
rents increase.
Households are better off from improvements in forest quality not because they can
reallocate labor to forest dependent activities, but because each unit of (constrained) forest labor is
more productive. Because of the restrictions on EE the increase in Q has no effect on ET or on-farm
tree planting.
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Figure 2
Figure 2 presents a diagram similar to Figure 1, except in terms of ET. We see that under open
access to common forests tree planting labor would be ETE-OA. Under CPFM EE is restricted and from
the open access equilibrium labor shifts into on-farm trees and ET = ET_ E E . As common forest quality
increases the marginal cost of ET, which is MU_EE, increases and households would like to reduce
their on-farm tree effort even below ET_OA1. This level of effort is not an equilibrium, however,
because shifts out of ET and into EE as ET falls below ET_ E E reduces Q.
As long as CPFM is in place
ET = ET_ E E . On-farm tree effort is therefore unambiguously increasing in CPFM stringency.
IV.Data and CPFM Descriptive Statistics
The key prediction of the model that households experiencing more stringent CPFM will plant
more trees on their farms is now tested using household and community level data collected in 2007.
The sample is made up largely by subsistence rural households that rely on mixed crop and livestock
farming in East Gojam and South Wollo zones of the Amhara Regional State in Ethiopia. Ten village
areas called kebeles (often translated as peasant associations) are covered by the survey. Kebeles are
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chosen to ensure variation in terms of characteristics, such as agro-ecology and tree cover, with
households randomly selected. Figure 3 shows the location of the study sites.
Figure 3
The goals of the empirical analysis are to identify household and community-level determinants
of CPFM and test whether better CPFM spurs on-farm tree planting. Because there is very limited
literature that analyzes the relationship between tree planting and CPFM in the disaggregated fashion
we attempt, our hypothesis is that all indices positively affect agro-forestry. Descriptive statistics are
presented in tables 1 to 5.
Table 1 here
Table 1 shows that 82 percent of households grow trees and on average households have about
241 trees, with a large variation across households. Such a finding suggests that on-farm trees are key
household assets. Indeed, as Bluffstone et al (2007) note, on-farm trees on average make up over 50%
of household assets. This percentage is so high, because households in Ethiopia do not own the
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agricultural land they use. The government owns all land, though recent initiatives to certify use rights
appear to be gaining momentum (Mekonnen and Bluffstone, 2008). As also shown in table 1, about
70% grow eucalyptus and average eucalyptus trees per household is about 197, making eucalyptus the
most important planted tree species.vii
k
Indexij   ( Aij  Min i ) /( Maxi  Min i )
(9)
i 1
Table 2 here
To estimate the effect of CPFM on tree investments, we use household survey responses to
create CPFM indices. We will discuss these critical data in some detail. The survey questions that
represent the raw data are based on well-established criteria for well-functioning common property
suggested by Ostrom (1990) and Agrawal (2001). Nine indices are created for each household using
the formula in (9), which is used by UNDP to compute the human development index and is  [0,1].
Aij is the value of index component i for household j and Mini and Maxi are the sample minimum and
maximum for component i. CPFM variable definitions, means and standard deviations are presented in
Table 2.
There are a number of reasons household head perceptions rather than village “leaders” provide
the main CPFM data.
First, on-the-ground policy implementation in developing countries often
corresponds poorly with stated policies. This could be for a number of reasons, including leader misassessments, deliberate attempts to portray local institutions in a positive light or simple difficulties
associated with characterizing CPFM details (i.e. imprecision). For example, we know from Ostrom
(1990) and Agrawal (2001) that fairness, public participation, clarity of access rules and appropriate
social sanctions are key aspects of successful CBFM. These are subjective and subtle aspects that
households are likely to perceive differently than leaders.
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For these reasons it is important to focus on household assessments of CPFM. Because CPFM
indices in the final analysis are village or kebele variables and all members should in principle (though
in actuality may not) be subject to the same rules, perception data are averaged across all kebele
members, with one value for each type of CPFM index applied to all member households. These
indices are then results are presented in Table 2.
At the top of Table 2 is what we call the overall CPFM index, which is an average of
institutional characteristics and management tools sub-indices, which are themselves averages of
specialized sub-indices. Specialized sub-indices are calculated as averages of the Likert scale survey
questions, which are also included in Table 2. For example, different types of community labor are
often required to maintain common forests and our labor index includes inputs for planting, thinning,
watering and fertilizing. Similarly, social sanctions, penalties, monitoring, payments and clarity of
quotas are elements of management tools and those indices are created based on two to four elements,
as are the institutional characteristics sub-indices, participation and democracy and fairness. The
clarity of access index is based on one survey question. All components are weighted equally, because
we have no priors to suggest otherwise.
What do we find? Table 2 indicates rather loose management. The average overall CPFM
index is 0.39 with standard deviation of 0.20, suggesting relatively weak management with
considerable variation across households. Indices for institutional characteristics and management
tools are similar at 0.41 and 0.37. Mean sub-indices of the management tools index range from 0.02
(labor requirements) to 0.58 (social sanctions), suggesting that respondents perceive informal social
penalties from taking too much forest products. Components of institutional characteristics range from
0.33 (clarity of access) to 0.49 (participation and democracy). These findings, particularly at the
aggregated level, are similar to those of Bluffstone et al (2008) focusing on the Bolivian Andes (mean
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overall CPFM index of 0.31 and standard deviation of 0.15). Though more research is needed, this
suggests that barring major policy interventions commonalities in CPFM may exist across low-income
countries.
Table 3 here
Table 3 presents correlation coefficients between site dummies and the three high-level CPFM
indices, with P-values in parentheses. We find both negative and positive relationships, with many
statistically significant at least at the 10% level, suggesting the presence of systematic variation in
CPFM across study sites. Substantial cross-site variation was also found for Bolivia by Bluffstone et al
(2008).
Table 4 here
A difference from the analysis in Bolivia is that as shown in Table 4 we find high and
statistically significant correlations between CPFM sub-indices. For example, correlations between the
overall CPFM index and its two sub-indices are over 0.93, suggesting for analytical purposes we could
meaningfully proxy overall CPFM with either of its sub-indices. Indeed, the correlation coefficient of
management tools and institutional characteristics is 0.77, which is higher than virtually any found by
Bluffstone et al (2008); they find that management tools and institutional characteristics are very
weakly correlated (ρ = -0.13).
Many specific indices are also highly correlated. For example, while theory may lead us to
predict that mutually reinforcing aspects of formal penalties and social sanctions would lead to high
correlation (ρ = 0.96), such a tight linkage between access clarity and labor requirements indices is not
immediately obvious.
We nevertheless find a correlation of 0.74.
These results suggest that
respondents perceive CPFM in Ethiopia as a set of attributes, which may indicate important synergies.
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V. Econometric Approach
The basic empirical approach is to estimate IV models of tree planting (2SLS and GMM),
where models allow for a variety of econometric issues, including the possibility that CPFM may be
endogenous. Trees are defined in terms of whether households plant any trees on their farms and the
number of trees planted.
The IV models are all over-identified. We therefore test over-identification restrictions using
Sargan, Basmann and Hansen’s J Χ2 methods and confirm all pass these tests. Weak instruments are
tested using F and minimum eigenvalue tests and it is found that the set of instruments performs well.
The treatment effects models estimate the average treatment effect on the treated (ATT) using
nearest neighbor propensity score matching based on observables.viii “Treatment” in these models
indicates that households experience kebele average CPFM greater than the median value of the
relevant CPFM measure (overall CPFM, institutional characteristics, management tools). Propensity
scores are estimated using all exogenous variables in Table 5 subject to satisfying the balancing
property. ATTs are estimated with all exogenous covariates from Table 5 as right hand side variables.
We find no effect of CPFM indices on tree planting. This finding is robust to tree planting definition
(e.g. number of trees, whether trees were planted, number of eucalyptus trees planted, number of trees
planted in last five) and CPFM index (overall, institutional characteristics, management tools) tested.
We also estimate treatment effects models based on propensity score nearest neighbor matching
as an alternative to explicitly modeling endogeneity.
Binomial and continuous regression and treatment effects tree planting models are each
estimated separately for the overall CPFM index and its two main sub-indices (institutional
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characteristics and management tools). Because of the high correlation between sub-indices, multicollinearity prevents including both variables simultaneously. As was already discussed, though 82%
of households plant trees, tree planting is not universal. If this decision process involves sample
selection, IV regression would lead to bias (Heckman, 1979; Linde-Rahr, 2003;). We test for sample
selection and find evidence of sample selection at the 10% significance levels. We therefore also report
Heckman results, which turn out to be very similar to those from models that do not adjust for sample
selection. Probit selection equations are estimated using all exogenous covariates in Table 5.
Without sample selection the standard method when data are left-censored is to use Tobit, but
this is correct only if the process for deciding whether to plant trees is the same as for choosing the
number of trees planted. We test this restriction by comparing the Tobit with the model of Cragg
(1971), which utilizes a Probit for the first stage followed by a truncated regression model. Using
likelihood ratio tests, we cannot reject the Tobit as too restrictive at better than the 10% level in any
model. Finally, trees are a count data variable. We therefore estimate the models using Poission
regression and find based on a goodness-of-fit Χ2 tests (Prob > Χ2 = 0.00 for all models) that the
negative binomial is more appropriate.
When results are deliberately selective in the presentation of results.
Our independent variables of interest are the overall common property (top of Table 2),
institutional characteristics and management tools sub-indices (second row of Table 2). Results on
lower-level indices are available from the authors, but to focus the discussion are not reported. CPFM
variables are potentially endogenous and indeed recent literature has specifically emphasized
endogeneity of forest management institutions (Heltberg, 2001; Baland et al forthcoming). We do not
assume endogeneity, however, but test using Wu-Hausman F, Durban Χ2 and GMM C Χ2 tests.ix
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Table 5 here
Table 5 presents descriptive statistics for exogenous covariates and excluded exogenous
variables along with expected signs and reasons for including.
Investments in trees are private
decisions and therefore only household variables are included. Variables representing wealth, labor
endowments, human capital, market access, land tenure, information and improved stove use are
included, because the literature suggests they may affect a variety of investments, including in trees.
The excluded instruments presented in Table 5 identify the first stage models of CPFM indices, which
endogeneity tests suggest must be treated as endogenous variables. Instruments are chosen, because
they are correlated with CPFM indices, uncorrelated with tree plantingxand believed to affect village
norms that are related to CPFM formation (e.g. Ostrom, 1990; Agrawal, 2001). Data are from
household and village leader surveys.
About 20% of households use improved stoves, with the rest relying on three-stone fires.
Households in the sample have an average of 1.15 hectares of land, 3.84 tropical livestock units of
animals and family size of 5.5. On average households grow a large number of trees on relatively
limited land—land that is also used for producing crops. About 17% of household heads in the sample
are female and on average household heads are much less educated (0.81 years) than the most educated
household members (5.14 years). This difference reflects major education initiatives since 1995.
Land is owned by the government and the possibility of land redistribution exists. Indeed, a
land redistribution occurred in the study area in 1997. Some farmers have been issued certificates
confirming rights to use land and we find that 44% of households believe land belongs to them. On the
other hand, about 19% expect to lose land due to land redistributions within 5 years. We also find
about 17% plant trees to increase land tenure security.
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Social capital has also been found to be an important feature of investments (e.g. see Nyangena,
forthcoming; Kabubo-Mariara and Linderhof, forthcoming). As Nepal (2007) notes, social capital has
a number of implications for tree planting and in our study area we believe the primary effect is
through information sharing. Whether households report that they trust people in their villages is used
to capture social capital and in our sample about 67% of respondents say they trust other villagers.
Also related to information is agricultural extension and farmer-to-farmer extension. While on average
households are visited by an extension agent once in the previous year, about 36% also benefit from
farmer-to-farmer extension.
VI. Results
We begin our discussion of results with the decision of whether households grew any trees. As
shown in Table 1, 82% of households grow trees and variation is rather limited. We test for whether
CPFM indices are endogenous to the tree growing decision and cannot reject the null of exogeneity.
Table 6 therefore presents Probit results.
Table 6 here
We see in the table that if anything CPFM is negatively correlated with tree growing. Indeed,
the overall CPFM index is negatively and statistically significantly correlated with the existence of onfarm trees, which does not support the predictions of the analytical model. Significant positive
covariates include livestock holdings, household head age, distance to town, trust in other villagers and
corrugated roof. In addition to the CPFM index, negatively correlated is distance to the nearest road.
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Table 7 here
Table 7 presents OLS models of on-farm tree planting. In no models are CPFM indices
significant. Significant variables across models include endowments like fraction of boys and girls and
livestock holdings. In particular we see that an additional TLU of livestock (e.g. one cow) increases
on-farm trees by 41 trees.
We test for exogeneity of CPFM variables and find, as presented in table 9, we can reject
exogeneity of all CPFM indices at much better than the 1% significance level. There is therefore
reason to believe that OLS estimates of the effect of CPFM on tree planting would be biased. Based
on these test results we estimate a variety of IV models.
Table 8 here
Table 8 presents first-stage OLS models of CPFM indices.
A number of variables are
significant, including household land and animal endowments. Three others—whether a household
gave a loan in the last year, whether respondents believe they own their land and whether they expect
to lose land in the next five years—have positive and statistically significant associations with all three
indices. These results suggest that those rich enough to make loans and those who feel more confident
about their security of land tenure perceive more strict CPFM. The results also suggest that households
who perceive insecurity in land tenure have perceptions of stricter CPFM. Excluded exogenous
variables, including population density, tenure in village and kebele level forest management are also
significant and positively associated with CPFM indices.
Robust standard errors are adjusted for
kebele clustering.
Table 9 here
Table 9 presents IV results estimated using 2SLS and GMM and we see that results from the
two estimation methods are very similar. Relationships between covariates and tree planting are
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limited, which is similar to the OLS results, though in two IV models farmer-to-farmer extension is
positively correlated with tree planting and significant at the 1% level. The main difference with the
OLS results is that after instrumenting, all CPFM indices are positively correlated with tree planting.
Elasticities of tree planting with respect to CPFM indices are also fairly high at from 1.43
(management tools index) to 1.54 (institutional characteristics) and 1.55 (overall CPFM index),
suggesting responsiveness. Indeed, with the exception of the strong positive effect of livestock on tree
planting (ε ≈ 0.55), the relationship between CPFM and tree planting is the most robust result.
Table 10 here
Table 10 takes on the issue of lower limit censoring by estimating the model using IV Tobit
after testing for whether a double hurdle model using the method of Cragg (1971) is more appropriate.
As already noted, we were unable to reject the more restrictive Tobit specification. We see from the
table that results are virtually identical to the 2SLS and GMM IV estimates and all CPFM indices are
significant at least at the 10% level.
Table 11 here
Table 11 takes account of the count data nature of the dependent variable by estimating the
model using a negative binomial regression. We see little difference with previous CPFM results and
elasticities are similar at 1.30 to 1.44. Covariate results are in many cases similar, but more variables
are significant than was the case for the IV estimates. In particular, those that gave loans had fewer
trees, while more educated households with older household heads, more land and livestock, improved
stoves and corrugated roofs on their houses planted more trees. These findings suggest that in addition
to CPFM, household wealth and endowments may be important.
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Table 12 here
Finally, we adjust for sample selection. Table 12 presents the results of two-step Heckman
selection models. We find results that are highly consistent with previous findings and all CPFM
variables are once again positively related to tree planting and in these models significant at least at the
1.0% level. Elasticities are similar to those previously quoted.
VII. Conclusion
On-farm trees are a critical source of household wealth in highland Ethiopia and in East Africa
a key supplier of tree products like fuelwood. The key research question this paper attempts to analyze
is whether the nature of common forest management affects on-farm tree planting behavior. We
believe this question is of interest not only for understanding whether more private trees can be
expected as common forest management improves in the low-income developing world, but also as
part of the more general issue of whether CPFM really affects household behavior. Relatively little
research has focused on this general question, though low-income countries across the world have
turned to CPFM to bolster declining forest stocks and as it has been recognized that despite the benefit
of egalitarianism, open access degrades key forest resources.
Our theoretical model strongly suggests that if we observe better CPFM we should also observe
more on-farm tree planting. We test this hypothesis using data from the Ethiopian highlands and find
that econometric results strongly support the theoretical model; these findings suggest that decisions
about numbers of trees to grow are very much influenced by the nature of community forest
management. Indeed, overall CPFM, management tools and institutional characteristics indices in all
models are positively correlated with numbers of trees grown. CPFM does not, however, encourage
the existence of on-farm trees and indeed results suggest that households perceiving better CPFM are
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less likely to plant trees. We note, however, that in the sample 82% of households plant trees,
indicating a very widespread practice. Our results suggest that the existence of trees is primarily
affected by wealth and market variables.
Our results suggest that better social coordination between households dependent on common
forests may have profound effects on individual household behaviors. Tree planting on-farm is one
example and our findings broadly support those of others, including Nepal et al. (2007) and Mekonnen
(forthcoming). A potentially important implication of this paper is for climate change initiatives related
to forests, such as the
United Nations Collaborative Programme on Reducing Emissions from
Deforestation and Forest Degradation in Developing Countries (REDD). Our findings indeed suggest
that an important carbon benefit of better CPFM is more planting of on-farm trees. Little is known
about such benefits, however.
Indeed, several research questions remain. As the relationship between CPFM and on-farm tree
planting is clarified, it is useful to evaluate whether relationships also exist with other technologies that
could substitute for common forests. Such measures may include commercial fuels and improved
agricultural inputs. Of perhaps critical importance to the long-term research agenda is to evaluate if,
and under what circumstances, constraints imposed by reducing open access actually increase rural
incomes. Evaluating policy instruments that increase rents and assure gains from better management
reach all parts of households and societies is also critical.
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Table 1
Descriptive Statistics on Tree Growing
Variable label
Mean
Std.
Dev.
Min
Max
Grows trees
0.82
0.39
0
1
Grows eucalyptus trees
0.70
0.46
0
1
Number of trees grown
241.38
492.58
0
4011
Number of eucalyptus trees grown
196.95
459.84
0
3950
Table 2 Definitions and Descriptive Statistics of CPFM Indices. All indicesi  [0,1] as per (9).
Unless noted, survey answers coded such that (5=definitely, 1=definitely not)
Overall Common Property Index (CPFMINDEX)
X =0.39, σ = 0.20
Institutional Characteristics Sub-Index
(INSTICHAR) X = 0.41, σ =.22
Management Tools Sub-Index
(MGTOOLS) X = 0.37, σ = 0.15
SPECIFIC SUB-INDICES MAKING UP EACH SUB-INDEX
AND ASSOCIATED SURVEY QUESTIONS
CLARITY OF FOREST ACCESS INDEX
X = 0.33, σ=0.21
*Is the system that determines who is allowed to gather forest
products clear and understandable?
FAIRNESS IINDEX
X = 0.43, σ = 0.30
* Do you feel you and others can take the amount of forest
products that is needed, but not more?
* Are you get enough forest products to meet your needs, but
not more?
PARTICIPATION & DEMOCRACY INDEX
X = 0.49, σ = 0.29
* Do you have influence on policies for deciding how much
forest products people can take?
*Do you help decide who are the managers of the forest? *
Do you expect that in the future you will have the opportunity
to manage the common forest? * Are the managers
democratically chosen?
CLARITY OF QUOTA INDEX X = 0.21, σ= 0.24
* Are you allocated a fixed allotment of fuelwood per year?
* Are you allocated a fixed allotment of fodder and grazing rights
per year?
MONITORING INDEX X = 0.53, σ = 0.34
* Do village authorities carefully monitor who takes what
products?
* Do villagers generally watch who takes forest products?
* Are you either formally or informally involved in monitoring
common forest lands?
FORMAL PENALTIES INDEX
X = 0.52, σ = 0.33
* If you took more fuelwood from the forest than you were
allowed to take would you be penalized?
* If you took more fodder from the forest than you were allowed
to take would you be penalized?
* Could you lose some or all of your rights to collect forest
products if you were caught taking more than your allotment?
SOCIAL SANCTION INDEX
X = 0.58, σ= 0.35
* Would other villagers be very unhappy with you if they found
that you had taken more than your allotment?
* Would you be embarrassed or feel bad if you took more than
your allotment of forest products?
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LABOR INPUT INDEX X = 0.02, σ = 0.05
All coding below (0, 1, 2, 3,>3 days during past month)
* Number of days planting common forests
* Number of days watering common forests
* Number of days thinning common forests
* Number of days fertilizing common forests
Table 3
Correlation between Site Dummies and CPFM Indices
(p-values in parentheses)
Site
Amanuel
D. Elias
Kebi
Wolkie
Telma
Sekladebir
Godguadit
Adismender
Chorisa
Management
tools index
Institutional
characteristics
index
0.141
0.0728
0.1596
(0.0000)
(0.0267)
(0.0000)
-0.0464
-0.1470
0.0423
(0.1671)
(0.0000)
(0.1776)
-0.0996
-0.1577
-0.0315
(0.003)
(0.0000)
(0.3159)
0.1449
0.1304
0.1481
(0.0000)
(0.0000)
(0.0000)
-0.0167
0.0537
-0.0703
(0.6197)
(0.1024)
(0.0248)
-0.352
-0.3492
-0.3098
(0.0000)
(0.0000)
(0.0000)
-0.1189
-0.038
-0.1565
(0.0004)
(0.2474)
(0.0001)
-0.0046
0.0423
-0.0409
(0.8915)
(0.1989)
(0.1925)
0.2253
0.2588
0.1585
(0.0000)
(0.0000)
(0.0000)
0.0862
0.1066
0.0691
(0.0101)
(0.0012)
(0.0275)
CPFM
index
Adisgulit
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Table 4
Correlation between Aggregated and Disaggregated CPFM Indices
Overall
CPFM
Overall CPFM
Institutional
characteristics
Labor
requirements
Sanction
Penalty
Monitoring
Clarity of quota
Participation and
democracy
Fairness
Clarity of access
Institutional
characteristics
Labor
requirements
Sanction
Penalty
Monitoring
Clarity
of quota
Participation
and
democracy
Fairness
Clarity
of
access
1
0.93
Management tools
Management tools
1
(0.00)
0.94
0.77
(0.00)
(0.00)
0.70
0.72
0.62
(0.00)
(0.00)
(0.00)
0.79
0.93
0.57
0.58
(0.00)
(0.00)
(0.00)
(0.00)
0.84
0.96
0.64
0.67
0.96
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.93
0.86
0.89
0.65
0.73
0.75
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.73
0.73
0.63
0.64
0.56
0.69
0.51
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.88
0.70
0.95
0.49
0.52
0.56
0.88
0.48
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.82
0.62
0.91
0.52
0.40
0.49
0.74
0.60
0.80
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
0.95
0.84
0.94
0.74
0.68
0.76
0.84
0.73
0.84
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
(0.00)
1
1
1
1
1
1
1
1
0.80
1
(0.00)
* p-values in parentheses; predicted values of indices used.
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Table 5 Descriptive Statistics for Covariates, Expected Sign and Reason for Including in
Models of Tree-Growing
Variable label
Mean
Std.
Dev.
Min
Expected Sign and Reason for
Including
Max
EXOGENOUS COVARIATES
Wealth Endowments – with imperfect credit markets, own wealth finances trees
Land size in hectares (LANDSIZ)
1.15
1.02
0
6.02
Corrugated roof (1 if yes) (CRRGTHS)
0.77
0.42
0
1
Livestock in TLU (tropical livestock units)
(LIVESTOK)
3.84
3.25
0
31.19
Gave loan in last year (1 if yes)
(givenloan)
0.10
0.29
0
1
(+) Wealth indicator; complements trees
(+) Wealth indicator
(+) Wealth effect; animals fed by trees
(+) Indicator of liquidity
Labor Endowments – labor complements trees, but investing in on-farm trees saves labor
Family size (FAMSIZE)
5.48
2.18
1
15
(+/-) Labor complement or substitute
Fraction of boys (6–14 years)
(FRACTIONBOYS)
0.15
0.15
0
0.67
(+) Higher dependency incents labor
saving
Fraction of girls (6–14 years)
(FRACTIONGIRLS)
0.14
0.15
0
0.75
(+) Higher dependency incents labor
saving
Fraction of female adults (>14 years)
(FRACTIONFEMAD)
0.32
0.19
0
1
Fraction of male adults (>14 years)
(FRACTIONMALAD)
0.31
0.19
0
1
(+/-) Labor complement or substitute
(+/-) Labor complement or substitute
Human Capital – education and experience may complement trees
Max. years of education of household
members (MAXEDUCHH)
5.14
3.71
0
16
Gender of head (1 if male) (HEADSEX)
0.83
0.38
0
1
51.05
14.87
20
97
0.81
2.21
0
12
Age of head in years (HEADAGE)
Years of education of head
(HEADEDUC)
(+) Education
(+/-)
(+) Experience
(+) Education
Market Access – trees may be planted for sale and therefore proximity may increase planting
Walking distance to town in minutes
(DISTTOWN)
82.76
58.32
0
280
(-) Market access
Walking distance to road in minutes
(DISTROAD)
38.42
37.25
0
180
(-) Transport costs
Land Tenure – trees are long-term investments and are therefore promoted by tenure security
Believes the land belongs to household
(OWNSLAND)
0.44
0.50
0
1
(+)
Expects to lose land in next 5 years (1 if
yes) (EXPLNSZ5YRS)
0.19
0.39
0
1
(-)
Information – tree planting often requires silvacultural and market information to be successful
Trusts people in village (TRUSTGOT)
0.67
0.47
0
1
(+) Trust encourages information search
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Number of visits by extension agent
(NUMDAVISIT)
0.94
1.32
0
4
Farmer to farmer extension (1 if yes)
(FARMERTOFARMER)
0.36
0.48
0
1
(+) Extension agents offer silvacultural
info.
(+)
Technological Substitute – improved stove reduces wood consumption and substitutes for trees on-farm
Uses improved stove (1 if yes)
(IMPROVEDSTOVE)
(-)
0.20
0.40
0
1
EXCLUDED EXOGENOUS VARIABLES – EXPECTED RELATIONSHIP IS WITH CPFM VARIABLES
Village-Level Survey
Woreda* numerical indicator (WOREDA)
N/A
Village Population density
(POPDENSITY)
2.79
2.21
.55
8.78
Kebele-level forest management dummy
(KEBELEMGT)
.513
.500
0
1
(+) Lower-level administration of forests
reduces transactions costs
Community forestry dummy
(COMMUNITYFOREST)
0.79
0.41
0
1
(+) Forest perceived as community forest
easier to coordinate
20.66
12.26
0
78
? Location fixed effects.
(+) Higher population density encourages
social coordination
Household-Level Survey
Years respondent in village (YEARS)
(+) More years in village improves
knowledge of norms and increases
perception of coordination
* Woreda is an administrative district that is roughly comparable to a county.
34 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Table 7
Dependent Variable Number of Trees. Ordinary Least Squares with Kebele Clustering
Model 1
CPFMINDEX
Model 2
Model 3
235.2
(156.2)
INSTICHAR
459.4***
(129.3)
MGTOOLS
146.0
(176.4)
OWNSLAND
LANDSIZ
CRRGTHS
LIVESTOK
GIVENLOAN
FRACTIONBOYS
FRACTIONGIRLS
FRACTIONFEMAD
FRACTIONMALAD
FAMSIZE
MAXEDUCHH
HEADSEX
HEADAGE
27.41
27.34
27.92
(43.48)
(43.71)
(44.03)
35.92
28.56
37.63
(39.53)
(38.60)
(38.19)
13.54
10.17
14.29
(30.88)
(29.74)
(31.32)
39.88**
39.42**
39.94**
(12.73)
(12.57)
(12.76)
11.17
8.813
13.29
(68.29)
(67.57)
(70.63)
187.3
197.4
185.2
(138.8)
(136.1)
(141.3)
371.4*
384.7*
367.7*
(176.5)
(175.3)
(180.3)
204.7
223.2
199.1
(133.6)
(133.9)
(138.7)
59.45
81.69
54.41
(78.55)
(71.95)
(79.96)
13.21
13.54
13.30
(13.16)
(13.01)
(13.31)
0.548
0.347
0.619
(4.979)
(4.934)
(4.994)
13.46
13.71
13.72
(47.87)
(47.32)
(47.82)
1.649
1.610
1.658
(1.556)
(1.564)
(1.562)
35 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
HEADEDUC
11.31
11.46
11.17
(8.317)
(8.376)
(8.294)
-0.00549
-0.0797
0.0387
(0.385)
(0.362)
(0.424)
-0.0747
-0.0997
-0.0818
(0.345)
(0.347)
(0.338)
21.59
24.66
20.26
(45.61)
(44.57)
(43.98)
20.14
19.76
19.84
(49.06)
(48.70)
(49.12)
17.61
20.67
16.35
(20.78)
(20.46)
(19.66)
33.67
38.38
31.45
(36.14)
(36.53)
(35.64)
55.83
52.98
57.85
(50.22)
(49.80)
(49.55)
-446.3***
-542.2***
-412.0***
(110.5)
(123.1)
(102.0)
Observations
1,080
1,080
1,080
R-squared
0.116
0.120
0.116
DISTTOWN
DISTROAD
EXPLNSZ5YRS
TRUSTGOT
NUMDAVISIT
FARMERTOFARMER
IMPROVEDSTOVE
CONSTANT
Unless otherwise noted, robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Kebele is often translated as “peasant association” and includes several village settlements
36 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Table 8 First Stage Regression (OLS with Kebele Clustering)
Dependent Variables →
Overall CPFM
Index
Institutional
Characteristics
Management Tools
Index
0.0219**
0.0170*
0.0248***
(0.00718)
(0.00875)
(0.00592)
-0.0157
-0.00846
-0.0209*
(0.00987)
(0.00926)
(0.00966)
-0.0226**
-0.0165
-0.0259***
(0.00768)
(0.00999)
(0.00621)
0.000773
0.00203
-1.21e-06
(0.00126)
(0.00174)
(0.000935)
0.0335***
0.0282***
0.0323***
(0.00778)
(0.00629)
(0.00923)
-0.0181*
-0.0247*
-0.00988
(0.00913)
(0.0119)
(0.00950)
-0.0243**
-0.0297**
-0.0171
(0.00971)
(0.0112)
(0.0113)
-0.0347*
-0.0509**
-0.0139
(0.0171)
(0.0194)
(0.0183)
-0.0105
-0.0332
0.0104
(0.0251)
(0.0298)
(0.0220)
0.00587**
0.00435*
0.00634**
(0.00212)
(0.00209)
(0.00211)
0.000419
0.000880
0.000345
(0.000746)
(0.00111)
(0.000599)
0.000355
-0.00167
0.00356
(0.00598)
(0.00639)
(0.00577)
0.000234**
0.000331**
0.000140
(8.20e-05)
(0.000113)
(8.16e-05)
0.000435
0.000118
0.000864*
(0.000420)
(0.000462)
(0.000389)
1.30e-05
-0.000109
0.000155
(0.000124)
(0.000168)
(9.79e-05)
Exogenous Covariates
OWNSLAND
LANDSIZ
CRRGTHS
LIVESTOK
GIVENLOAN
FRACTIONBOYS
FRACTIONGIRLS
FRACTIONFEMAD
FRACTIONMALAD
FAMSIZE
MAXEDUCHH
HEADSEX
HEADAGE
HEADEDUC
DISTTOWN
37 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
DISTROAD
0.000126
0.000427**
-0.000101
(0.000227)
(0.000174)
(0.000280)
-0.0164**
-0.0125*
-0.0176**
(0.00637)
(0.00666)
(0.00569)
0.00661***
0.00975***
0.00333
(0.00189)
(0.00258)
(0.00190)
-0.00894***
-0.0106***
-0.00674***
(0.00184)
(0.00204)
(0.00201)
-0.00867
-0.0110
-0.00698
(0.00517)
(0.00628)
(0.00444)
0.0121**
0.0121***
0.0106**
(0.00411)
(0.00343)
(0.00429)
-0.00583
-0.00413
0.00518
(0.00893)
(0.00804)
(0.00874)
0.0184
0.0179
0.0166
(0.0106)
(0.0101)
(0.00911)
0.122**
0.136**
0.131**
(0.0429)
(0.0449)
(0.0422)
0.00120***
0.000900**
0.00127***
(0.000312)
(0.000305)
(0.000299)
0.0708
0.0638
0.0712
(0.0606)
(0.0595)
(0.0615)
0.211**
0.231***
0.150*
(0.0759)
(0.0674)
(0.0774)
Observations
1,106
1,106
1,106
R-squared
0.57
0.56
0.65
EXPLNSZ5YRS
TRUSTGOT
NUMDAVISIT
FARMERTOFARMER
IMPROVEDSTOVE
Excluded Exogenous Variables
WOREDA
POPDENSITY
KEBELEMGT
YEARS
COMMUNITYFOREST
CONSTANT
Unless otherwise noted, robust standard errors in parentheses ** p<0.01, ** p<0.05, * p<0.1
Kebele is often translated as “peasant association” and includes several village settlements
38 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Table 9 Dependent Variable is Number of Trees Planted by Households. IV GMM Estimates
Marginal Effects
Model 1
Model 2
Model 3
Endogenous Variables
CPFMINDEX
875.7***
(251.0)
INSTICHAR
813.1***
(231.7)
MGTOOLS
869.5***
(246.4)
Exogenous Covariates
OWNSLAND
LANDSIZ
CRRGTHS
LIVESTOK
GIVENLOAN
FRACTIONBOYS
FRACTIONGIRLS
FRACTIONFEMAD
FRACTIONMALAD
FAMSIZE
MAXEDUCHH
HEADSEX
26.25
28.99
26.49
(33.05)
(32.85)
(33.02)
31.01
27.23
37.52
(28.59)
(28.72)
(27.90)
15.05
8.138
24.73
(31.09)
(30.35)
(31.62)
37.79***
36.17***
36.29***
(9.850)
(9.677)
(9.860)
13.79
19.49
4.047
(66.62)
(66.35)
(67.20)
229.1*
237.0*
230.4*
(137.1)
(136.7)
(137.4)
441.0***
450.1***
447.3***
(170.8)
(170.3)
(171.5)
211.9
238.5*
194.7
(135.5)
(135.2)
(135.5)
88.49
125.6
52.20
(141.1)
(141.4)
(140.5)
1.726
3.947
-2.160
(13.55)
(13.58)
(13.49)
2.663
2.613
2.648
(6.144)
(6.127)
(6.151)
16.05
17.63
26.65
(42.10)
(41.63)
(41.89)
39 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
HEADAGE
HEADEDUC
DISTTOWN
DISTROAD
EXPLNSZ5YRS
TRUSTGOT
NUMDAVISIT
FARMERTOFARMER
IMPROVEDSTOVE
CONSTANT
Observations
0.996
0.853
0.950
(1.098)
(1.084)
(1.102)
9.609
9.295
8.708
(8.423)
(8.380)
(8.426)
-0.444
-0.299
-0.500
(0.337)
(0.313)
(0.344)
0.484
0.213
0.670
(0.503)
(0.484)
(0.522)
50.93
42.87
57.49
(41.61)
(41.27)
(41.97)
23.64
23.09
26.80
(34.48)
(34.33)
(34.61)
23.73
23.25
20.29
(16.17)
(16.06)
(16.14)
34.56
37.14
27.75
(41.09)
(40.74)
(41.51)
45.83
47.77
54.59
(45.74)
(45.50)
(45.91)
-627.6***
-635.1***
-595.2***
(183.8)
(184.2)
(178.0)
1,080
1,080
1,080
Goodness of Fit Tests
R-squared
0.106
Wald Χ (22)
2
93.55 (p = 0.000)
0.115
***
94.25 (p = 0.000)
0.101
***
93.15 (p = 0.000) ***
Exogeneity Tests
GMM C Statistic Χ2 (1)
13.776 (p = 0.000) ***
5.959 (p = 0.015) **
19.071 (p = 0.00) ***
Overidentifying Restriction Tests
Hansen’s J Χ2 (4)
3.929 (p = 0.416)
4.036 (p =0.401)
3.584 (p = 0.4652)
Weak Instrument Tests
F Test
F(5, 1053) = 165.86
F(5, 1053) = 182.32
(p = 0.000) ***
(p = 0.000) ***
F( 5, 1053) = 203.89
(p = 0.000) ***
Unless otherwise noted, robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Kebele
is often translated as “peasant association” and includes several village settlements
40 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Table 11 Dependent Variable Number of Trees. Negative Binomial Regression
Marginal Effects
Endogenous Variables (Predicted Values Used)
CPFMINDEX
1000.053**
(453.7)
925.7307**
INSTICHAR
(388.99)
897.9774**
MGTOOLS
(375.63)
Exogenous Covariates
OWNSLAND
LANDSIZ
CRRGTHS
LIVESTOK
GIVENLOAN
FRACTIONBOYS
FRACTIONGIRLS
FRACTIONFEMAD
FRACTIONMALAD
FAMSIZE
MAXEDUCHH
HEADSEX
-1.911566
6.672677
-3.45183
(39.635)
(35.916)
(35.891)
33.18507**
25.63066
34.93438**
(16.478)
(18.279)
(16.444)
63.01478*
56.53123*
62.59852**
(34.634)
(32.033 )
(30.628)
16.32231***
15.67822***
17.45322***
(5.75394)
(5.29226)
(6.27335)
-51.14181*
-44.0903
-48.6115*
(27.35)
(31.675)
(29.109)
316.5914*
321.9054*
326.2179*
(184.71)
(173.28)
(184.17)
499.898***
500.9367***
512.0168***
(189.2)
(176.82 )
(197.42)
240.7476
251.8902*
222.8084
(160.47)
(154.14)
(138.1)
189.7823
214.7518*
178.0077
(140.03)
(131)
(146.36)
-1.612992
0.374738
-2.36674
(4.56769)
(4.76142)
(5.44339)
9.772137***
9.367929***
10.1945***
(2.7289)
(3.06867)
(3.24048)
46.79797
47.64126
43.62842
41 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
HEADAGE
HEADEDUC
DISTTOWN
DISTROAD
EXPLNSZ5YRS
TRUSTGOT
NUMDAVISIT
FARMERTOFARMER
IMPROVEDSTOVE
CONSTANT
Wald Χ2
Goodness of Fit Χ2
Observations
(37.047)
(39.067)
(33.884)
1.500204
1.53426
1.621104*
(1.00233)
(1.0603)
(0.98168)
5.049276
5.385478
4.270956
(6.29778)
(6.58252)
(5.33613)
-0.3154592
-0.16609
-0.31176
(0.34221)
(0.39111)
(0.51431)
0.2991159
-0.05067
0.566199
(0.53443)
(0.44664)
(0.5121)
34.4642
28.96023
39.47187
(22.101)
(19.704)
(24.666)
18.62002
15.64525
16.08052
(32.841)
(30.5120
(31.201)
22.31148
22.67942
20.51303
(16.576)
(15.036)
(13.202)
42.58449
45.42149*
38.42002
(31.376)
(25.724)
(29.882)
22.16745
25.05938
28.35921
(29.175)
(28.529 )
(32.395)
0.340
0.342
0.533
(0.8520)
(0.7760
(0.704)
Χ2 (22) = 1467
Χ2 (22) = 2030
Χ2 (22) = 1696
Prob > Χ2 = 0.00
Prob > Χ2 = 0.00
Prob > Χ2 = 0.00
Χ2 (1057) = 52055
Χ2 (1057) = 517552
Χ2 (1057) = 52195
Prob > Χ2 = 0.00
Prob > Χ2 = 0.00
Prob > Χ2 = 0.00
1,080
1,080
1,080
Robust Bootstrapped (100 Repetition) Standard Errors Adjusted for Kebele Clustering.;
Kebele is often translated as “peasant association” and includes several village settlements
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
42 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Table 12 Dependent Variable Number of Trees. Heckman Selection Model
Marginal Effects
Endogenous Variables (Predicted Values Used)
CPFMINDEX
1,179***
(318.1)
MGTOOLS
1,103***
(332.1)
INSTICHAR
1,004***
(323.3)
Exogenous Covariates
OWNSLAND
LANDSIZ
CRRGTHS
LIVESTOK
GIVENLOAN
FRACTIONBOYS
FRACTIONGIRLS
FRACTIONFEMAD
FRACTIONMALAD
FAMSIZE
MAXEDUCHH
HEADSEX
HEADAGE
32.91
40.83
29.21
(42.67)
(49.61)
(42.86)
26.38
17.22
31.27
(38.68)
(33.46)
(30.71)
43.10
34.54
49.16
(38.44)
(42.99)
(46.65)
38.18***
36.95***
38.79***
(13.63)
(11.81)
(12.33)
-31.05
-21.37
-27.36
(81.82)
(69.88)
(84.53)
291.0
299.9*
284.4
(187.6)
(171.2)
(177.7)
532.5**
538.1**
526.5**
(229.9)
(216.0)
(238.1)
326.2*
348.0**
302.3*
(180.0)
(166.9)
(170.4)
173.1
206.8
146.9
(193.4)
(186.7)
(187.5)
12.80
15.48
12.24
(19.08)
(17.96)
(18.84)
-1.754
-2.222
-1.117
(8.156)
(6.915)
(7.591)
16.52
19.88
18.52
(54.68)
(55.81)
(64.09)
0.954
0.937
1.041
43 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
(1.618)
(1.280)
(1.567)
15.72
15.92*
14.91
(11.61)
(9.091)
(11.98)
-0.657*
-0.482
-0.591
(0.397)
(0.342)
(0.363)
0.529
0.127
0.738
(0.629)
(0.630)
(0.565)
35.16
29.21
35.33
(56.76)
(53.66)
(56.81)
8.500
5.692
9.902
(36.66)
(38.98)
(41.42)
26.90
27.31
23.43
(19.53)
(21.18)
(21.39)
37.70
39.48
30.81
(52.67)
(44.15)
(52.37)
32.83
34.94
42.57
(51.82)
(51.04)
(50.67)
-4.472e+10*
-4.489e+10**
-5.018e+10**
(2.470e+10)
(2.192e+10)
(2.221e+10)
-812.1***
-819.8***
-745.8***
(252.2)
(217.5)
(245.4)
HEADEDUC
DISTTOWN
DISTROAD
EXPLNSZ5YRS
TRUSTGOT
NUMDAVISIT
FARMERTOFARMER
IMPROVEDSTOVE
INVERSE MILLS RATIO
CONSTANT
Observations
Wald Χ
2
1,080
1,080
1,080
Χ (22) = 94.48
Χ (22) = 90.70
Χ (22) = 108.84
Prob > Χ2 = 0.000
Prob > Χ2 = 0.000
Prob > Χ2 = 0.000
2
2
2
Robust, Bootstrapped (100 Repetition) Standard Errors Adjusted for Kebele Clustering.;
Kebele is often translated as “peasant association” and includes several village settlements
Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
44 | P a g e
Draft – Not for Citation. Comments Welcome. October 25, 2010
Endnotes
i
Because it is now recognized that it is not the common nature of resources but the openness of access that causes
the tragedy, in modern terminology, it would be referred to as the tragedy of open access.
ii
Food and Agricultural Organization, http://www.fao.org/forestry/site/countryinfo/en/.
iii
This model is based on, but extends the theoretical treatment in Bluffstone et al (2008).
Such behavior is sometimes described as “myopic,” but this label does not seem appropriate given the
constraints.
iv
v
This parsimonious arrangement, where we particularly abstract from the details of food production, is done in the
interest of focusing on our variables of interest.
vi
This approach is taken merely for convenience. Formulating constraints on forest extractions would be
equivalent.
vii
Surveys in 2000, 2002, and 2005 of the same households showed that eucalyptus trees were the most important
trees in terms of number of households growing and trees grown (see Mekonnen [forthcoming]).
viii
In all models the propensity score specification satisfies the balancing property.
ix
As tree leaves may be an important source of fodder for animals, we test for exogeneity of animal stocks to the
tree planting decision. In no models, however, can we reject exogeneity. Animals are therefore treated as exogenous.
x
Mean and median Spearman correlations between excluded exogenous variables and the number of trees are 0.06
and 0.08.
45 | P a g e