Draft: Comments are welcome 1 Poor Participants and Even Poorer Free Riders in Nepal’s Community Forestry Programme Baikuntha Aryal 1 * and Arild Angelsen 1Ψ Abstract Common property resource management (CPRM) is often considered a win-win option for conserving resources and biodiversity, and enhancing local development and poverty reduction. This includes community-based forest management schemes, where Nepal has been a pioneer with its community forestry programme (CFP) launched in the late 1970s. While the programme has halted forest degradation, the economic gains are less documented and more debatable. This study investigates the factors determining the participation in community forestry and the resulting impact of participation on forest income. Based on the data of 452 households from 16 villages in central Nepal, our analysis suggests that poverty tends to increase participation in the programme. However, we find no evidence of participation increasing forest income. On the contrary, the free riders are the ones that appear to be gaining from CFP. The free riders are the poorest with an average income more than 40% below that of participants, suggesting membership may be too costly for the very poorest both in terms of membership costs and the restrictions membership imposes on forest use. Key words: Community forestry, Nepal, poverty, participation, free riding. 1 Introduction The Community Forestry Programme (CFP) of Nepal was introduced in 1978 and transferred the management and use rights over national forests to the local communities. Locally formed Community Forest User Groups (CFUG) are responsible for executing operational plans of management of CFP. A steadily growing number of CFUGs – more than 13,000 by 2004 – is a strong indication of the popularity of the programme. 1 Department of Economics and Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N 1432, Ås, Norway Email: * [email protected], Ψ [email protected]
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1
Poor Participants and Even Poorer Free Riders in Nepal’s
Community Forestry Programme
Baikuntha Aryal1* and Arild Angelsen1Ψ
Abstract
Common property resource management (CPRM) is often considered a win-win option for
conserving resources and biodiversity, and enhancing local development and poverty reduction.
This includes community-based forest management schemes, where Nepal has been a pioneer
with its community forestry programme (CFP) launched in the late 1970s. While the programme
has halted forest degradation, the economic gains are less documented and more debatable. This
study investigates the factors determining the participation in community forestry and the
resulting impact of participation on forest income. Based on the data of 452 households from 16
villages in central Nepal, our analysis suggests that poverty tends to increase participation in the
programme. However, we find no evidence of participation increasing forest income. On the
contrary, the free riders are the ones that appear to be gaining from CFP. The free riders are the
poorest with an average income more than 40% below that of participants, suggesting
membership may be too costly for the very poorest both in terms of membership costs and the
restrictions membership imposes on forest use.
Key words: Community forestry, Nepal, poverty, participation, free riding.
1 Introduction The Community Forestry Programme (CFP) of Nepal was introduced in 1978 and transferred the
management and use rights over national forests to the local communities. Locally formed
Community Forest User Groups (CFUG) are responsible for executing operational plans of
management of CFP. A steadily growing number of CFUGs – more than 13,000 by 2004 – is a
strong indication of the popularity of the programme.
1 Department of Economics and Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, N 1432, Ås, Norway Email: * [email protected], Ψ [email protected]
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Although all households in the local communities are supposed to become the members, this is
far from being the case. In our sample less than half (49%) of the villagers were the participants.
In principle, only members are allowed to collect forest products from community forests (CF),
and the prime reason to join the programmes is therefore to get access to basic forest products. In
practice however, many non-members use CF and thereby become free riders (19% in our
sample). Moreover, non-forest benefits can also play an important role for joining a CFUG, for
example, building social network, maintaining social status, or a concern for forest degradation.
Thus, we found that 15% of the members did not collect anything from CF.
This paper addresses two questions: First, what makes people participate in CFP? Second, do
participants gain in terms of higher forest income from participation? As part of answering these
we deal with the issue of free riding explicitly, as free riding is and alternative strategy to
membership. This issue is quite surprisingly missing from most of the literature on CFM in
Nepal.
The emerging consensus suggests that the CFM programme has succeeded in halting the forest
degradation and deforestation (Arnold 1995; Pokharel 2002; Maharjan 2003). Implementation of
community forestry as the primary forest policy in Nepal is leading to rejuvenation of once
degraded forest areas in the mountains. Nepal’s community forestry policy is considered to be
one of the most progressive forest policies in the world (Bhatia 1999). The empirical evidence on
equity and the economic benefits from CFM is, however, rather mixed (Kumar 2002; Adhikari,
Falco et al. 2004). Related to this, both policy makers and researchers have tended to overlook
the programme’s economic incentives to the local users, with most of the analyses being done on
the broader institutional issues rather than individual incentives (Das 2000 cited in Adhikari et al.
2004).
Kumar (2002: 764) argues that participation in common pool resources helps the rich more than
the poor: “Common pool resource management is well suited for the regeneration at the cost of
poor” (see also Graner, 1997). After the forests were handed to the local communities for
management most of them have been protected for regeneration and hence restricted the access
causing significant problems to the households. Despite of a large literature on community
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forestry in Nepal, few studies explicitly tell whether it is benefiting the local users or not.
Exceptionally, Bhattarai and Ojha (2000) found that the poor households because of lack of
interest and rich households do not benefit from participating and middle income households are
benefiting most. Dev et al. (2003) notes that CFP may have had positive impact on community
infrastructure, development activities, income generating opportunities and social capital, but
due to restrictions on use of common property possibly generate destitution to the poorer
households. Similarly, Adhikari (2004) even found that the proportion of benefits of rich is three
times to poor.
This paper thus contributes to filling important gaps in the discussion on the impact of CFP in
Nepal, by both considering the individual incentives for both participation and free-riding, and
by comparing the forest income implications of the decision. The paper is organized as follows:
A brief background to the programme is given in section 2, followed by an outline of the
theoretical framework along with the estimation model in section 3. Section 4 presents the
overview of data and introduction to study area. The results and discussion of key findings in
relation to the two questions asked are given in section 5, before we conclude in the final section.
2 The Community Forestry Programme in Nepal The forest history of Nepal fits well into the larger international picture, where deforestation and
forest degradation has been an alarming issue for several decades. The deforestation rate
increased rapidly soon after the forests were nationalized in 1957. With the realization of the
government’s inability to manage forest under its control, the devolution of forest management
started in 1978 with the transfer of use rights and management responsibilities from centralized
government to Community Forest User Groups (CFUGs). This concept was incorporated in
Nepal’s First National Forestry Plan (1976), and its related legislation of 1977. This legislation,
the rules and the regulations framed under it and ensuing government and donor programmes
have made the development of community forestry possible in Nepal. The Tenth Five-year Plan
(2002-2007) mentioned that 'the user-group approach' is particularly useful in mainstreaming
poor and deprived communities in forestry sector activities (NPC 2003).
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Under the CFP, the CFUGs are responsible for managing the forest patches transferred to them.
Before the forest is transferred, the CFUGs with the close consultation and supervision of Forest
Officers make the operational plan, which details all the necessary rules and regulations for
resource conservation and benefits sharing. The member households are generally barred from
using forest products commercially. However, the CFUGs can sell the forest products to
individuals or groups or anyone for the benefit of the community. Through this sale, membership
fees, donor assistance and other income sources many CFUGs are financially very strong. For
this reason, in some cases, the positions in the executive committee of CFUGs are more
attractive than being the elected leader of the Village Development Committees (VDC). CFUGs
can launch a number of community development projects such as building schools, distributing
piped drinking water, running health posts and constructing village roads. However, these
extended benefits are enjoyed by both the members and non-members, as the exclusion of non-
members is costly and probably would also violate local norms of equal access to such services.
The rural households get forest products such as fuelwood from different sources: community
forest, state forest, or private forest/own woodlots. Not all forests are declared community
forests, thus households can go to the forests that have less restrictions (state or private forest).
Participating in CFP is therefore a real option for most households when alternatives are
available.
Members are allowed to collect forest products from CF for their home consumption. But this is
limited to a fixed quota, which varies from one group to another depending on the resource
abundance. Typically the newer CFUGs open forests for collection less frequently than the older
ones. Although each member household is entitled with the fixed quota, CFUGs have provision
to provide extra amount of forest products to households affected by natural and other calamities.
Moreover, households having broad social, ritual or similar functions also get extra quota.
Since the start-up in the mid-hills of Nepal, the programme has expanded to all over the country.
As of May 2004, 1.06 million hectare of forestland, mainly in the mid-hills had been handed
over to 13,125 CFUGs with 1.5 million households (35% of total population of the country)
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involved (CFD, 2003)2. The government of Nepal is planning to hand over most of the potential
community forests to local communities by 2010.
3 Theoretical Framework and Estimation
3.1 A conceptual framework
The individual expected costs and benefits are the starting point for analyzing the decision about
participating in the community forest management. As Ostrom (1999) points out, this approach
starts with “the basic costs benefit calculations of a set of users utilizing a resource. Each user
has to compare the expected net benefits of harvesting from a resource continuing to use the old
rules to the benefits he or she expects to achieve with a new sets of rules.” As also noted by
Ostrom, it is important to find out how the user attributes affect the individual cost benefit
analysis. The decision to participation is also affected by the users’ perception towards the
resource and the status of their resource base.
At the broader level, the decision is influenced by both internal attributes of community forestry
such as community size (Varughese and Ostrom 2001), socioeconomic heterogeneity (Baland
and Platteau 1996), institutional setting, and property rights structure (Baland and Platteau 1999;
Ostrom 1999) and external influences such as national forestry policy (Ostrom 1999). Because
the practice varies across systems and time and the complexity and dynamics involved none of
the previous studies prescribes the single set of factors for the successful management of
community forestry (Agrawal and Gibson 1999; Baland and Platteau 1999; Ostrom 1999; Buchy
and Hoverman 2000; Agrawal 2001; Varughese and Ostrom 2001). It largely depends on the
type of community and socio economic situation in the area. Yet some attempts have been made
to identify common success factors (Agrawal 2001; Pagdee, Kim et al. 2006).
As a synthesis of the above literature, general agricultural household models (e.g., Sadoulet and
de Janvry, 1995), as well as our knowledge of the study area, we specify four different types of
costs and benefits involved in the households’ decision, and then hypothesize how the magnitude
2 Record of Community Forestry Division, Department of Forest, HMG/N, Kathmandu, Nepal
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of these will be determined by various household, spatial and community level variables.
Particular attention is paid to how poverty affects the decision.
The primary benefit of participation is the presumed better access to the community forest. The
importance of these benefits should be higher for poor households with few alternative income
opportunities. Note that the better access is not necessarily guaranteed. Consider the non-
hypothetical situation with no or only weak sanctions against free riders, thus free riding is
common. The enforcement of the rules with respect to members’ use is, however, more effective.
In this situation being a member can actually limit the access: members have to obey to the rules,
while non-members do not.
A second benefit is the “social and political gains” from participation, in the form of social
prestige and networks, but also gaining political power from participation (cf. section 2). The
latter is probably more important for better-off income groups. Thus, a pattern of high
participation among generally better-off and politically influential groups, who do not need the
forest benefits per se, would indicate that this motivation is important.
The growing concern over environmental degradation, particularly among young households, is
another motivating factor for participating in CFP. Therefore, relatively young households, who
may not need forest products for their livelihood, often participate in order to contribute in
resource conservation.
The costs of participation are in terms of time spent in CFUG activities and cash contributions
(membership fee). Generally, we hypothesize that poor households have lower opportunity costs
of labor but value cash more, making the net effect ambiguous. These costs and benefits are
illustrated in figure 3.1.
At the next level, these costs and benefits are determined by a set of poverty variables, (other)
household characteristics, and spatial and community level variables. Poverty variables are
household income and assets/wealth, thus allowing for both welfare (income) and asset based
definitions of poverty (e.g., Vosti and Reardon, 1997). Household characteristics include age and
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education of household head, sex of household head, and household size. Spatial and community
level include area of CF, participation rates, and distance to forest. The full list of variables at
this level is included in Table 3.1.
Figure 3.1: A conceptual framework for participation decisions and impact on forest income.
One problem is that one cannot observe all the costs and benefits associated with the
participation. Hence the variables entering the empirical models are those referred to in Table
3.1. However, the framework sketched enables a more consistent and theory based interpretation
of our findings. In the table we have made some hypotheses of how the various variables impact
the participation decision. One hypothesis we will test is that there is a U-shaped relationship for
participation, and poverty (both income and asset): the poorest participate because they need
access and have few alternatives, while the richest participate because of the social and political
benefits. Then, for a middle group the participation rates are lower. However, an alternative
hypothesis is also conceivable: The poor cannot afford being member of CFUG, and in situations
with poor enforcement of rules they choose a strategy of free riding instead.
Participation decision
Forest income
Access to CF
Social & political capital
Resource conservation
Poverty variables
Household characteristics
Spatial & community variables
Cost of participation
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The second research question is how participation (and free riding) affects forest income. The
causal linkages are depicted in Figure 3.1. While CF participation has a direct impact on forest
income, it is also necessary to control for the impact of the three sets of variables at the bottom of
the figure. Their hypothesized impact is also given in Table 3.1. In the empirical analysis, we
distinguish forest income in absolute and relative terms, i.e. as share of total income. We
hypothesize that the share of forest income declines as the total income goes up, i.e. lower forest
dependency among the richer households.
3.2 Estimation
The empirical analysis of section 5 consists of simple descriptive methods (cross-tabulations,
scatter plots, and correlation matrices) and regression (Probit) analysis of the participation
decision, while the forest income analysis uses OLS regression with predicted participation to
control for the endogeneity of the participation decision.
From our conceptual framework, the decision to participate in the CFP depends on the costs and
benefits of joining the programme. Since there are no entry restrictions, participation decision is
an individual choice, and can be modeled as a function of the set of variables described
previously.
The two commonly used models for such discrete choice decisions are the Logit and Probit
models. There is no clear cut demarcation on whether to use Logit or Probit, the main difference
lies in the underlying assumptions: Where logistic regression is based on the assumption that the
categorical dependent reflects an underlying qualitative variable and uses the binomial
distribution, Probit regression assumes the categorical dependent reflects an underlying
quantitative variable and it uses the cumulative normal distribution. In most applications,
however, these models are quite similar, but with one difference seen on the logistic distribution
curve (Logit) being slightly flatter than the standard normal curve (Probit) (Gujarati 2003).
One strength of Probit model is that it analyses rational choice behavior as suggested by
McFadden (1981). Further, participation decision being a latent variable in which utility is
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different for participants and non participants, the binary outcome is a result of individual choice.
Since Probit model is motivated by such latent variable (Cameron and Trivedi 2005) we use a
Probit model to find the factors responsible for participation decisions.
i i i i i iP Y Z Sα β η θ ε= + + + + (1)
Where, Pi is the participation in CFP which takes the value ‘1’ if the household participates and
‘0’ if does not; Yi is the vector of poverty variables; Zi is the vector of household characteristics,
Si is the vector of spatial and community variables including market for forest products; i denotes
households; iα , β , η and θ are unknown parameters and εi is the stochastic error term, whose
distribution is assumed as 2(0, )i Nε σ∼ . The associated log likelihood function is
1 0
log ( , , ) log log 1i i
i i i i i i
P P
Y Z S Y Z SL β η θ β η θβ η θσ σ= =
+ + + + = Φ + −Φ
∑ ∑ (2)
Where, Φ (.) is the cumulative function of the standard normal distribution. By normality
assumption, we optimize this log likelihood function directly by iteration algorithm of a general
non-linear optimization program (Greene 2000) to estimate parameters of the model.
We used a similar approach to analyze the free-riding decision:
i i i i i iR Y Z Sτ ν= + ℘+ + + (3)
Where, Ri is the free riding such that Ri = 1, if the household is free rider and 0 if not. The right
hand side variables have the same meaning as in equation (1); , ,i andτ℘ are the unknown
parameters and iν is error term. The similar log likelihood function holds for the equation (3)
also. The participation and free riding variables are mutually exclusive, as the same household
cannot be participant or free rider at the same time, but they are influenced by the same
variables. Therefore, we use the same set of variables to estimate the fitted value of these two.
For the second research question in the impact of participation and free riding on forest income,
we followed the steps of Two-Stage Least Squares suggested in Wooldridge (2002) and
discussed in Cameron and Trivedi (2005). For the two endogenous variables – participation in
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CFP and free riding in CFP, we used the fitted participation, iP from equation (1) and fitted free
riding, ˆiR from equation (3) as the first stage. Then in second stage, we run regression of forest
income on iP and ˆiR and other poverty, household and spatial and community variables
(equation 4).
ˆ ˆi i i i i i i iF W Z S P Rξ γ ψ δ ς ϑ υ= + + + + + + (4)
Where, Fi is the income from forest; Wi is the vector of wealth variables; Zi is the vector of
household characteristics and Si is the vector of spatial and community level variables; iP and ˆiR
are the predicted participation and free riding estimated from equation (1) and (3) respectively;
, , , ,i andξ γ ψ δ ς ϑ are unknown parameters and iυ is error term. To avoid the inconsistency of
instruments ( iP and ˆiR ), the same Wi, Zi and Si used in equation (4) are used to estimate the
instruments (Wooldridge 2002) but income variables used in the first stage regression are
skipped.
In our analysis, equation (4) represents two different regression equations – one for absolute
forest income and another for relative forest income. We define absolute forest income as the
total forest product collected valued at market prices without deducting the costs associated with
it and relative forest income is defined as the share of absolute forest income in total household
income.
3.3 Description of variables
The variables are categorized into three broad categories: poverty-related variables, (other)
household characteristics, and spatial and community level variables. The definition of the
variables and their expected signs in the two regressions are given in the following table. The
signs of the variables in first stage Probit regression of free riding is hence discussed in chapter 5
and not presented here.
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Table 3.1: Variables used in Probit and regression models and their expected signs Probit model of CF participation Regression of forest income Variables Indicators/Description Expected
sign Reasons Expected
sign Reasons
Dependent and predicted variable cfmember forinc
Participation in community forestry programme Income from forest in 100,000 Rs.
Total Income in 100,000 Rs. Square of total income Wealth category (poorest) Wealth category (middle) Wealth category (rich)
- + - -
Poor depend more on access Political motives to participate Default As for income As for income
+ - ? ?
Poor have fewer alternatives Dependency decreases for rich Demand of more inputs from forest, but also more alternatives
Household characteristics agehead agesq edumem sexhead migrated castel adultequi4 freerider
Age of the household head Square of age of the household head Share of educated members in the households Gender of the household head (1=male, 0=female) 1 = The HH in-migrated: 0 otherwise 1 = Lower caste: 0 otherwise Household size (adult equivalence) 1 = Household is free rider, 0 otherwise
+ - + - - + +
Resource management Opportunity declines Awareness of resource mgmt. Females resource friendly Exploit more They are relatively poor Demand of more forest product
- + - + + + + +
Alternative sources Fewer alternatives Awareness/more opportunities Physical strength Exploit more Poor have fewer alternatives More hands in forest use Get benefits w/o cost sharing
Spatial variables distance market vdc
Distance to the community forest (kms) 1 = Market for forest products in villages: 0 otherwise Village dummies
- -
Distance reduces incentives More other opportunities
- +
Distance reduces the benefits Opportunity to buy FPs
Community level variables cfarea memper othsource cfnumber
Area of CF per household in the village Participation rate 1 = Availability and use of other source for forest products Number of CFUGs in the village
+ + -
FPs more available Group pressure Alternatives reduce prob.
+ ? + +
Increased availability Lower share available/ more effective mgmt. Alternatives increase income Well managed and more access
3 Wealth categories are made up of landholdings, household assets and total livestock unit. These three variables are classified into three groups each. The categories are as follows: (1) land less than .50 ha, between .50 and 1 ha, and more than 1 ha, (2) assets less than 100000, between 100000 and 500000, and more than 500000 and (3) livestock unit less than 1, between 1 and 2, and more than 2. The scores are given in ascending order for each category in each groups, smallest landholders get score 1, for example. The scores are summed up in order to construct the wealth category such that wealth category 1 consists sum 3 and 4; wealth category 2 consists sum 5 and 6; and wealth category 3 consists sum 7, 8 and 9. 4 On the basis of weights estimated by Deaton (1982) (presented in Cavendish 2002).
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4 Study area and data The study site lies in the Central Development Region of Nepal. The region is more developed
compared to the other four development regions in the country, having the capital city in the
region being one of the main reasons. The region extends from the Northern border of the
country to the Southern border and has large geographical diversities from high mountain to the
plain land extending from 270 21’ to 280 13’ north latitude, and from the altitude of 141 m to
7134 m asl. The study area covers the altitude from 141 m to nearly 4000 m. It has a great
variation in geographic, level of development, access to benefits and availability and use of
resources.
Household data collected during fieldwork between January and May 2004 together with data
from secondary sources will be the basis for this analysis. The household survey covered 452
households selected on a random basis from 16 villages in five districts in central Nepal. The
criteria of selecting the villages were distance to market and forests, existence of community
forestry, distance to the main road and location (plain land and mid hills). The secondary
information was collected through direct interviews, key informants interviews, various
publication from different agencies and focus group interviews. Leaders of community forestry
user groups (CFUG), chairmen of Village Development Committees, District Forest Officers and
personnel from donor assisted community forest projects were interviewed directly. Data
entering took place in the field in order to give room for consistency check, gap-filling and
follow-up interviews. The software DAD and Stata were used for the poverty and econometric
analysis.
CFM is being practiced in all five districts and in all 16 villages. The number of CFM is
relatively larger in the mid hills than in the plain land and the capital district of the country. The
following table shows the status of CFUG in the five districts.
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Table 4.1 Status of CFUG in the study area5. District No. of
Chitawan district has by far the highest income levels for the CFUGs, which is only partly
explained by the larger average size of the groups (and the higher forest area per groups). The
district is in more fertile Terai region with good availability of high value forest products.
Furthermore, donors’ support to some CFUGs and a small number of CFUGs may have also
contributed to the high income. Kathmandu has the lowest average income due to small amount
of forest handed over. Three other districts from mid hills have about the same level of income
and they have more CFUGs than other two districts.
5 Results and Discussion This section first presents some basic figures on forest use and participation. The next three sub-
sections analyses participation from three complementary perspectives: household responses to
the question of why they are members, a village level analysis of participation rates, and then a
household level regression (Probit) analysis of participation. The last section uses the result of
the latter to estimate the impact of participation on forest income. 5 Source: Community Forestry Division, Department of Forest, Kathmandu, Nepal (May 2004). 6 This district is in the Terai region of Nepal. This is a plain area and has more fertile land growing varieties of crops. Being the main transit point to western and eastern part of the country, this has a good market access and trade relation to India. This district has a large amount of forests (including shrubs and grassland, forest is nearly 69% of total area). 7 This is one of the undeveloped districts of the country. Bordering with China in the north, it does not have much arable land. 47% of total land area of this district is covered by forest. Lack of arable land, low economic activities and few alternatives to livelihood characterize this district. 8 This is the capital of Nepal. Though most developed district of the country, it still has backward rural areas where the forest is still main sources of fuel. Forest cover is 34% of total land area. All types of economic activities are found in this district. 9 Community forestry program was first launched in this district in late 1970s. Most forest area is under community forestry that accounts 28% of total land area of the districts. Agriculture is the dominant economic activity in this district. 10 This district is adjacent to Kavrepalanchowk and has large forest area under community management. Total forests account for 30%. Agriculture is the basic economic activity in this district. Being one of the transits to China, business of Chinese goods can be found en route to Kathmandu.
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5.1 Membership and forest use
Nearly half (48.9%) of the 452 sample households participate in community forestry programme
(CFP), with rates of participation being much higher in the mid hills than in the Terai (plain
land). It is found that 85 households (19%) collect at least one forest product from community
forestry without being member, i.e. are free riders. With the weak enforcement of operational
plans, some CFUGs have not taken any initiatives to make them the members. It is also notable
that 33 member households (14.9%) do not collect anything from community forestry. Adding
the non-member users and deducting non-use members, a total of 273 (67.7%) households use
community forestry for the forest products.
In addition to the community level benefits discussed in section 2, the primary benefit is the
basic forest products for household use. The table 5.1 shows the types of forest product the
households get from different sources, and how these differ between members and non-members.
Fuelwood is the main forest product that the households, whether they are participating in CFP
or not, need and collect from forests followed by fodder, building materials (mainly poles) and
tree leaves. Tree leaves are mainly collected for making leaf plates that are used during the
parties and rituals.
Table 5.1: Percentage of households getting forest products by sources* Source
Types Own farm/
Private forest State forest Community
forest Buying from
Market
Members 70.6 4.1 63.3 6.8 Fuelwood
Non members 61.9 12.1 29.0 16.5
Members 16.7 1.4 36.7 0.5 Building
materials Non members 9.1 5.2 6.1 3.0
Members 0.9 18.6 10.4 0.0 Timber
Non members 0.4 10.0 2.6 0.0
Members 31.2 3.6 14.9 0.9 Tree leaves
Non members 11.3 4.3 6.5 4.8
Members 42.1 4.5 70.1 0.5 Fodder
Non members 27.7 10.8 28.6 0.4
* Most households get forest products from more than one source. So the share does not add to 100.
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The table identifies private forest/own woodlots and community forest as the most important
sources of forest products except timber. All other forest products than timber are collected from
these sources and only a small amounts are collected from state forests. Restriction to timber
collection from community forests could be the reason for this. It is interesting to note that the
members use more forest resources of any kind of forest resources and there is only a weak
indication that non-members use more state forest than the members.
The table points to the problem of free riding in community forestry: up to 30% non members
use community forests for fuelwood and fodder. This might be explained by weak enforcement
of operational plan (e.g. lack of patrolling) and therefore inability to take action against the free
riders. Some poor may be too poor to become member (fees and labor inputs), but require forest
products for fulfilling basic needs. There is a clear negative correlation (-0.19) between total
income and free riding.
Comparing benefits for the users and non-users (members and non-members in this case) is a
possible indicator of success of any programme. The main problem is, however, selection bias
and we will address that issue below. But a simple comparison can be an illustrative first step,
and table 5.2 presents the absolute and relative forest income for different household groups.
The overall picture of table 5.2 is that the share of forest income is higher for the non-members
than the members of community forestry. This can be given at least two interpretations. It can
question the success of community forestry in economic aspects and the role of CFP in poverty
reduction. One possible explanation for this is that the access is limited to domestic use of forest
products and any kind of commercialization of forest products is prohibited. This indicates the
CFP is still more conservation oriented than economic benefit oriented. People from community
forestry user groups and the organizations supporting them also admitted that the programme
was too much focused on the conservation side and had given minimum attention to utilize the
resource as economic goods11.
11 Personal discussion with leaders of CFUGs and donor organisations.
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But, it may equally be used as an argument for the CFP being vital for those with few alternative
forest income sources. In most cases alternative sources are available, but they are not distributed
equally across villages and households. And further caveat is that CFP may contribute to the long
term sustainability of forest use, and our study does not inform us about these aspects. But CFP
has been operating for some years and the medium term benefits should be visible by now. On
the conservation side, more than one-third households (34.7%) think that the forest cover has
increased due to community forestry. But on the economic side, even the average income from
forest is NRs. 9,613 and NRs. 14,245 for members and non-members respectively. This means
the members are poorer and it may support the distributional issue of the resources.
Table 5.2: Forest income12 in total household income for members and non members of CFP Absolute forest income Relative forest incomeHousehold group Total
hhs Members
of CF % of total hhs
Member Non-member Member Non-member
Female 35 15 42.9 19,440 16,406** 14.7 13.5** Sex of the household head Male 417 206 49.4 8,897 14,040** 12.1 13.1**