Poor Participants and Even Poorer Free Riders in Nepal’s ...

Draft: Comments are welcome

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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, îR from equation (3) as the first stage. Then in second stage, we run regression of forest

income on iP and îR and other poverty, household and spatial and community variables

(equation 4).

ˆ î 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 îR

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 îR ), 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.

Dependent variable

± Increased forest access Dependent variable

Poverty variables totinc totincsq wealth13 wealth2 wealth3

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

CFUG CF Area

(ha) Area per

CFUG (ha) No. of hhs involved

Average size (hhs)

Average income

Average expenditure

Chitawan6 Dolakha7 Kathmandu8 Kavrepalanchowk9 Sindhupalchowk10

14 257 139 390 409

7,453 28,259 4,421 18,178 22,311

532.35 109.96 31.81 46.61 54.55

8,972 38,898 16,022 33,861 45,539

641 151 115 87 111

2,923,403 29,754 12,509 38,005

NA

2,283,634 14,566 9,908 23,539

NA

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**

21-30 38 24 63.2 6,201 2,445 16.0 4.7 31-40 94 54 57.4 7,639 12,950** 13.0 15.0** 41-50 120 49 40.8 12,106 11,177** 12.8 12.3** 51-60 104 54 51.9 7,140 14,754** 8.7 11.5**

Age of the household head

61+ 96 40 41.7 14,609 21,556** 13.2 16.5** Bottom 20% 91 52 57.1 4,488 4,732** 21.7 22.4** 20-40% 90 42 46.7 7,243 4,558** 16.0 9.8* 40-60% 91 38 41.8 7,358 8,266** 9.2 10.7** 60-80% 90 42 46.7 9,016 11,543** 6.9 9.1**

Income Quintiles

Top 20% 90 47 52.2 19,757 44,074** 5.8 16.0** Wealth 1 156 88 56.4 7,579 16,366** 19.2 17.1** Wealth 2 189 90 47.6 8,627 17,839** 8.0 14.4**

Wealth category

Wealth 3 107 43 40.2 15,839 6,434 6.9 7.0** None 139 66 47.5 11,547 21,154** 15.5 17.9** Primary 167 84 50.3 10,034 6,399** 11.3 12.4** High School 78 37 47.4 7,625 26,784** 14.7 13.5** SLC 39 23 59.0 7,608 4,448* 6.8 7.4**

Education level of the household head

Higher 29 11 37.9 5,674 2,560 3.5 1.4 <100 31 14 45.2 3,573 18,604** 7.6 19.0** 100-500 97 62 63.9 12,794 12,942** 12.5 14.7** 500-1000 77 35 45.5 7,484 24,637** 9.4 17.4** 1000-5000 166 95 57.2 9,984 18,228** 14.8 15.8**

Distance to the forest (m)

>5000 81 15 18.5 4,714 2,916** 6.5 5.2** Total 452 221 48.9 9,613 14,245** 12.3 13.1**

Note: Difference between members and non members is ** significant at 1% and * significant at 5% level.

12 Total forest income from all sources.


17

As can be seen from the table 5.2, female-headed households have a larger share of forest

income compared with male-headed households, though the number of female-headed

households is small. An increasing trend of females becoming members of CFUG and resource

awareness was observed during the field visit also.

Forest income share for different age groups has a U shaped form: decreasing at first and

increasing after attaining middle age. This could reflect the alternative income opportunities for

different age groups, with the middle age group having the best opportunities. The share of forest

income decreases with the size of landholdings. This share takes the same pattern for both

members and non-members. As landholding is a wealth indicator, this shows that forest

dependency is higher for the poor households.

When grouping the households in income quintiles, the share of forest income decreases as the

income level increases for the member households, whereas the trend is not clear for the non-

members, especially the highest income group from non-members have larger share of forest in

their total income. As it was found in another analysis (Aryal and Angelesen, 2006 manuscript),

the rich households collect high value forest products, which makes the forest income share

larger for them. Comparing members and non-members in different income quintiles, the pattern

is mixed. But, there is some evidence that the poorest households can benefited from being

members, while among the richest households the forest benefits are higher for non-members.

The same is true when the households are categorized in terms of wealth. In terms of

participation, the pattern is U shaped - poor and rich are participating more than the middle

income group, but wealth categories indicate the poor participate more than the rich.

It is surprising to see that the membership appears to be more beneficial to the households far

from the forest, while the dependency is much higher for non-members living close to the forest.

This indicates that there are restrictions imposed by CFP and that non-members living close to

the forest can enjoy better forest access and relatively more forest income.

The overall picture of this table is that income poor and small landholders benefit more from

being members of CFP. However, the total share is higher for non-members given the


18

restrictions to commercial use of forest products from CFP, free riding problem, and richer

households collecting high value forest products.

5.2 Why people join CFP?

During the survey, households were asked to rank the three main reasons for joining the CFM.

Table 5.3 presents their responses.

Table 5.3: Households’ preferences for joining the CFP Rank 1 Rank 2 Rank 3 Reasons

No. of hhs % No. of hhs % No. of hhs %

Score

Economic

Easy access

Benefit sharing

Avoid exclusion from extracting

forest products

166

141

25

0

75.1

63.8

11.3

0

146

21

106

19

66.1

9.5

48.0

8.6

49

13

6

30

22.2

5.9

2.7

13.6

63.2

36.0

22.1

5.1

Environmental (Conserving forests) 42 19.0 19 8.7 63 29.4 17.1

Social and Institutional

Rights over forest products

Others in the society are members

Rights of decision making

Social prestige

Information sharing

Network building

12

8

4

0

0

0

0

5.4

3.6

1.8

0

0

0

0

42

14

9

12

5

1

1

19.1

6.3

4.1

5.4

2.3

0.5

0.5

98

26

27

22

6

16

1

44.4

11.8

12.2

10.0

2.7

7.2

0.5

16.5

5.9

4.3

3.5

1.2

1.4

0.2

Force

Fear of what others say

Because the forest officer asked

1

0

1

0.5

0

0.5

1

0

1

0.5

0

0.5

4

1

3

1.9

0.5

1.4

0.7

0.1

0.6

Note: The score is calculated on the basis of weights given for each ranks (3 for rank 1, 2 for rank 2 and 1 for rank 3).

Percentages are calculated on the basis of total response for each rank.

The table indicates that the economic benefits from community forestry are the predominant

reasons to join CFP. Participation is perceived to provide access to forest resources, and this is

the single most important reasons to join. The environmental and conservation issue is the

second most important reason. This response is mainly given by younger households, which


19

indicates an increasing awareness towards the environmental impact of degrading forest

resources and the need to preserve it for future uses.

A significant number of households also mentioned social and institutional reasons for joining

CFP, although few state this as their primary reason for being member. One notes that what is

often perceived as an important benefit of CFP – rights of decision-making – gets relatively low

scores. This indicates that for the majority of participants, the decision-making process does not

matter a lot, but rather the more tangible benefits they can get. It may also reflect that most

people have limited say in the decision making process, mostly because of social, cultural and

economic domination of some elite members (see also Agrawal and Gupta 2005; Adhikari et al.

2004; Maskey et al. 2006)

One also notes that 33 member households (14.9%) do not collect anything from community

forestry. This clearly shows that membership of CFP is not only a function of economic benefits

but has social and environmental values too. Almost one third of the members told that

participation increased their social status. Very few households join CFP due to force, indicating

that the decision is self-motivated.

5.3 Village/Community level effect in participation

Village or community also has an effect on participation in CFP. Figure A1 shows that the

membership to CFP varies dramatically among the villages from very low to 100% participation

rates. The scatter plot of participation rate against the free riding (left panel of fig 5.1) indicates

that there are three types of villages: (1) low participation - low free riding, (2) high participation

- low free riding and (3) low participation - high free riding. The cut-off lines are 70% for

participation and 20% for free riding.


20

Fig. 5.1: Scatter plots of (a) participation rate against free riding and (b) free riding against CF

area 0

2040

6080

100

Memper

0 20 40 60 80Comfreerider

020

4060

80Freerider

0 .5 1 1.5Cfareaperhh

Table 5.4 gives the overview of villages. The type 1 villages are from Terai (plain land) and mid

hills. The average area of community forest per household is 0.23 ha which is lowest among

these three types. The average number of community forestry user groups (CFUGs) is five, while

two of them have only one CFUG in the village. In general, villages from Terai are close to the

urban area and off-farm income opportunities, and therefore less dependent on the forest than

other villages. The average household income in these villages is highest among these three

types. Additionally, they have relatively small areas of community forests and most households

have their own woodlots.

The type 2 villages (high participation and low free riding) are from the area where CFP was

launched initially. These villages have longest history of CFP and the average CF area per

household is 0.33 ha. Almost all the forest area has been handed to the communities. The

average number of CFUGs per village is 11, which is highest among these three village

categories. These villages mainly depend on the community forests for fuel. One can see when

visiting the villages that their forests are well managed. The history and group pressure are the

main reasons for the higher participation in these villages. Moreover, they have few forest

income alternatives.


21

Table 5.4: Villages by CF area, participants and free riders

Type Villages No. of CFUGs

CF area/hhs

Membership (%)

Free riding (%)

Average income (Rs.)

Forest income

share (%) Fulbari 1 0.63 0.0 3.9 168,400 0.6 Chalal 4 0.10 8.0 8.0 122,662 16.1 Shivanagar 4 0.30 23.1 11.5 152,927 3.4 Kushadevi 12 0.08 24.0 0.0 118,503 27.3 Patihani 2 0.28 36.0 4.0 165,685 1.5 Balthali 11 0.13 44.0 0.0 137,495 24.2 Barhabise 7 0.06 48.0 4.0 91,043 7.6

1

Average 5 0.23 26.2 4.5 136,674 11.5 Tukucha 15 0.61 92.0 4.0 130,812 2.9 Paanchkhaal 10 0.31 94.0 4.0 182,366 3.9 Mahadevsthan 13 0.20 100.0 0.0 55,313 47.1 Kuvinde 6 0.22 100.0 0.0 43,302 9.7

2

Average 11 0.33 96.5 2.0 102,948 15.9 Dakshinkali 2 0.11 17.9 53.9 95,743 16.7 Boch 6 1.26 24.0 72.0 54,993 13.2 Chhaimale 13 0.37 27.8 47.2 86,070 12.2 Lakuridanda 7 1.24 64.0 36.0 57,291 12.1 Bansbari 13 0.42 75.0 25.0 51,277 11.5

3

Average 8 0.68 41.7 46.8 69,075 13.2 Total 8 0.39 18.8 48.9 114,727 12.7

Type 3 villages (low participation and high free riding) are from upper hills and Kathmandu, the

capital district. The average CF area per household in this type is 0.68 ha whereas the average

number of CFUG per village is eight. Villages from Kathmandu are less dependent on forest for

fuel, but depend more on tree leaves and fodder. Because of being close to the capital city, the

members may have less interest in patrolling and catching free riders than finding a more

lucrative off-farm work in the capital. The village with highest rate of free riding is far from the

capital, and has the largest area of community forestry (1.26 hectare per household). One

possible explanation is that forest abundance reduces the incentives for good management. These

villages have lowest average household income among three types of villages.

Table 5.4 and the correlation matrix (table 5.5) suggest that free riding is less of a problem when

the number of CFUGs is high and CF area is small. This pattern is further illustrated in the

scatter plot of free riding against the area (right panel of fig. 5.1).


22

This result is consistent with the hypothesis of institutional economics that the incentives for

enforcement of property rights is strongly correlated with the scarcity (and value) of the

resources (e.g. North (1987). Moreover, it may be difficult for the CFUGs to control the larger

area from encroachment and unauthorized use as well as difficult to carry out appropriate

conservation measures.

Table 5.5: Correlation matrix of community level variables and income

CF area No. of CFUG Free riding Participation

rate Forest income

Total income

No. of CFUG -0.01

Free riding 0.48*** -0.14***

Participation rate 0.03 0.61*** -0.35***

Forest income -0.43*** 0.27*** -0.16*** -0.09*

Total income -0.28*** -0.27*** -0.51*** -0.12** 0.00

Market Access -0.24*** 0.09* -0.18** 0.09* 0.17** -0.26***

* Significant at 10%, ** significant at 5% and *** significant at 1% level of significance

Negative and significant correlation between area and forest income also suggest that when the

resource is abundant, benefits may be below optimum as the resources are under utilized.

Positive correlation between forest income and number of CFUGs also support the above

arguments. One interesting finding is that average household income has a strong negative

correlation with free riding indicating poor being free riders.

The correlation matrix illustrates some causal links between community level variables - higher

number of CFUGs implies low free riding and high participation. This may suggest the

fragmentation of resources reduces free riding, as the enforcement is easier and less costly.

Moreover, negative correlation suggests low free riding in the presence of market possibly

because buying from market is easier, less time consuming and has lower opportunity cost.

The above discussion hence suggests high participation and low free riding when the number of

CFUGs is higher. Free riding is higher when the resource is abundance (larger area of CF) as

discussed above. It is rather surprising that free riding is negatively correlated with market

access, when market creates higher incentives to free riders. But this may indicate that free riders


23

are poor and the CF use is mainly the subsistence one. and there is indication that free riders are

from the villages with low household income. Forest income is negatively correlated with

membership; weakly suggesting members not benefiting in terms of income from forests

possibly because the non participants negatively affecting the benefits of participants. The

possible explanations are – (1) less forest products available for participants and (2) participants

incur costs in terms of fee, time and other liabilities, which the non participants do not need and

with free riding there are fewer members to share the costs.

5.4 Factors determining the participation in CFP

This section moves to the household level and investigates determinants of participation in CFP

by regression analysis. Table 5.6 presents five Probit models with different sets of explanatory

variables. The first model has only income variables. Wealth categories are added in the second

model, and the third model is further extended by adding household characteristics. The fourth

model is the full model with income, household and spatial variables. The fourth model also

includes the community level variables: area of community forestry per household, participation

rate in the village and availability of other sources of forest products. We follow these four

models simultaneously in our discussion. Since we are answering the second research question

‘impact of participation in forest income’, income variables are removed from fifth model, which

will be discussed in section 5.6.

The validity of all models but model 1 is confirmed by the statistically significant Wald Chi-

square statistics, indicating that the control variables in each model are jointly significant. In

addition to these variables, we used village dummies in the model. But since we are not doing

comparative analysis of the villages, their parameters are not presented here.


24

Table 5.6: Determinants of participation in CFP

Dependent variable = Participation in CFP

Model (1) (2) (3) (4) (5)

Total income -0.099 (0.116) 0.025 (0.113) -0.382 (0.195)** -0.133 (0.219)

Total income squared 0.025 (0.020) 0.012 (0.016) 0.081 (0.038)** 0.055 (0.039)

Wealth cat (middle) -0.262 (0.141)* 0.162 (0.188) 0.147 (0.223) 0.163 (0.217)

Wealth cat (rich) -0.492 (0.174)*** 0.251 (0.222) 0.400 (0.267) 0.485 (0.278)*

Age of hh head -0.062 (0.038) -0.049 (0.045) -0.060 (0.045)

Age squared 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)

Gender of hh head (male) -0.150(0.288) -0.294 (0.302) -0.243 (0.293)

Adult equivalence 0.244 (0.054)*** 0.189 (0.063)*** 0.185 (0.065)***

Educated member in hh 0.045 (0.005)*** 0.039 (0.006)*** 0.038 (0.006)***

Caste of hh (lower caste) -0.259 (0.172) -0.466 (0.218)** -0.410 (0.225)*

In migrated hh -0.756 (0.176)*** -0.239 (0.346) -0.341 (0.345)

Distance to CF -0.044 (0.070) -0.053(0.069)

Market for forest products 0.111 (0.233) 0.017(0.225)

CF area 2.521 (1.036)** 1.750(0.669)***

Participation rate 0.063 (0.013)*** 0.054(0.007)***

Other forest source -0.414 (0.285) -0.328 (0.277)

No. of observations 452 452 452 452 452

Pseudo R2 0.00 0.02 0.39 0.55 0.54

Wald χ2 1.99 10.75 101.81 156.56 368.13

Prob. > χ2 0.37 0.03 0.00 0.00 0.00

* Significant at 10%; ** significant at 5%; *** significant at 1%. Robust standard errors in parentheses

Poverty determinants

The probability of being member of CFP decreases as total household income increases.

Although this variable is significant only in third model, a negative sign in all but second model

indicates that poor are more likely to be the members of CFP. A positive sign of square of total

income indicates that the probability of being member increases after the households attains a

certain level of income (Rs. 121,000 in full model), which is more than the average household

income (Rs. 114,700). This makes a weak U shaped pattern, although statistically significant in

third model only, where the turning point (Rs. 236,000) is more than double the average

household income.


25

Wealth categories are statistically significant and have negative signs only in model two, while

adding other variables in model three and four both categories become insignificant and the signs

change to positive. Looking at model two alone, with income and wealth variables, wealth is

associated with decreasing probability of participation. But, wealth is positively correlated with

the new variables included in model 3: education (0.22), household size (0.36) and migration

(0.08), while it is negatively correlated with lower caste (-0.11). Thus, the wealth effect in this

model is captured through these variables.

The probability of the household being a member of CFP increases when the percentage of

educated members in the household increases. The coefficient shows that 1% increase in

educated members in the household increases probability of that household being CFP member

by 0.05. One could argue that the education reduces forest dependency and hence the incentives

for being member of CFP. But the positive sign indicates that other effects are dominating: first,

education can lead to increase awareness towards the resource conservation and therefore

increased participation. Second, educated households may have a better chance to exercise

political influence by joining the CFUG.

Caste system and classification under this system is often taken as the indicator of poverty in

Nepal. The lower the caste, the poorer the households are. A negative sign of the lower caste

variable indicates that the upper class households are more likely to be the member of CFP. This

reflects and confirms the prevalence of upper caste dominancy over lower caste in participating

in major activities all over the country. The data shows that only 38% households from this caste

participate in CFP compared to about 53% upper caste households joining CFP. For the lower

caste households, forest contributes 11.7% in their total income whereas for upper caste

households the contribution is 13%. Some researchers have argued that the elite dominate every

phases of CFUG’s development (e.g. Adhikari et al., 2004; Maskey et al., 2006).

In short, looking at the poverty determinants, we find a weak U shaped pattern between income

and participation decision, though motivation may be different – getting access to forest products

for poor and gaining social status and political influence for rich. It is seen that wealthier

households do not participate in CFP but the variables closely related to wealth such as education


26

and caste are important in participation decision, indicating educated and upper caste households

are more likely to join CFP. An interesting question arises whether relative poor, uneducated and

lower caste households are free riders. We answer to this question will in next section.

Household characteristics

Presence of more adult members in the household increases the probability of participation in

CFP. The variable is statistically significant at 1% level. Having more members in a household

increases the demand of forest products and this can be met through membership of CFP. Unit

increase in adult members in the household leads to 0.24 increase in the probability of that

household being CFP participant. The availability of surplus labor in the household is another

possible reason for this relationship. This result is consistent with the result found by Jumbe and

Angelsen (2006) in Malawi.

Migrants are less likely to join the CFP, as shown by the significant impact of this variable in

model 3 (although not significant in model 4). There are a couple of possible explanations of

this: the migrants may have less concern over the forest resources in their new place of residence

compared with the locals that have a stronger attachment to the forest; and immigrants are less

likely to participate in social life and village activities. Generally, migrants are thought to be

poor and depend more on natural resources, but our analysis shows that migrants are relatively

better off (average total income for migrants is more 15% more than non-migrants) and for this

reason they may be having alternative livelihood strategies.

Spatial and Community/village effects

The probability of a household to join CFP increases when the area of community forest is large.

The area may ensure the availability of forest products and hence attracts households to

participate in its management. From the coefficients of this variable, it is found that one hectare

increase in area of CF leads to 2.5 increase in probability of participation.

The higher participation rate in the village increases the probability of a household participating

in CFP. As discussed in section 5.3, the possible reasons for this are: (1) group pressure –

participation of majority households in the village compels other households to participate, and


27

(2) the participation rate is correlated with the history of CFP indicating the observed benefits

attract households to join the management.

Though statistically insignificant, there is a hint that the distance and availability of other sources

for forest products reduces the probability of participation in CFP.

Overall impression of this section is that the participation decision is motivated by two reasons –

access and socio political influence, former being for poor and latter being for rich. In terms of

wealth, the wealthier households do not participate, however their decision is influenced by other

household level variables associated with wealth indices. Resource abundance and group

pressure are other important factors in making participation decisions.

5.5 Free riders in CFP

The existence of free riders may affect the participation decisions and the benefits from

participation and from CF. In this section we analyze who are the free riders and in the following

section we try to find free riding effects in forest income.

We found that 85 households (31% of total users) use community forest without being member

in CFP. The table 5.7 presents the comparison of members, free riders and others. The table

shows that free riders have lowest income, smallest land size and highest forest dependency.

Some of the poor may be too poor to join the CFP, despite the need of forest products. It is

notable that out of 85 free riders, 74 are having total income less than average household income

(Rs. 114,700).

Compared to non free riding households, the free riding households have older heads. It is

interesting to note that among free riders, 35% households are from lower caste and only 10%

are migrants. Out of total migrants only 7.7% are free riders.


28

Table 5.7: Comparison of characteristics among members, free riders and others Members Free riders Others

Number of hhs 221 85 146

Total income (Rs.) 119,424 70,267 133,502

Forest income share 12.3 16.3 11.3

Land size (ha) 0.64 0.63 0.74

Age of hh head (years) 48.6 52 50.7

Educated members in the hh (%) 55 12.7 11.8

Lower caste hh 42 30 39

Migrated hh (%) 41.4 7.7 50.9

Distance to forest (km.) 2.3 3.1 4.1

To identify the factors responsible for a household to be the free riders we run 5 Probit models

(table 5.8) of free riders with the same variables that we used to find the factors of participation

in CFP. The significant Wald chi-square confirms the validity of all models suggesting the joint

effects of control variables are statistically significant.

The table 5.7 and Probit result (table 5.8) suggest that the uneducated and lower caste and non-

migrants households are more likely to be free riders. Moreover, probability of free riding

increases when there is no market for forest products and when the participation rate in the

village is low. This is consistent with the results found in Probit of participation in CFP.

The Probit results indicate that probability of a household being free rider significantly decreases

when the household gets richer. Income variables are statistically significant in models (1), (2)

and (3). When we added the spatial and community level variables in model (4), the income

became insignificant. This suggests that the free riding is concentrated in some villages and these

variables capture the income effect in this model.


29

Table 5.8: Factors determining free riding in CFP

Dependent variable = Free riding in CFP

Model (1) (2) (3) (4) (5)

Total income -0.524 (0.137)*** -0.575 (0.153)*** -0.505 (0.154)*** -0.208 (0.245)

Total income sq. 0.036 (0.013)*** 0.040 (0.014)*** 0.031 (0.015)** -0.008 (0.034)

Wealth cat (middle) 0.381 (0.167)** 0.192 (0.186) 0.232 (0.228) 0.147 (0.213)

Wealth cat (rich) 0.130 (0.214) -0.182 (0.242) 0.048 (0.299) -0.170 (0.287)

Age of hh head -0.028 (0.037) -0.027 (0.045) -0.015 (0.045)

Age squared 0.000 (0.000) 0.000 (0.000) 0.000 (0.000)

Gender of hh head (male) -0.199 (0.272) -0.381 (0.307) -0.418 (0.306)

Adult equivalence 0.031 (0.051) -0.049 (0.065) -0.058 (0.064)

Educated member in hh -0.018 (0.003)*** -0.028 (0.005)*** -0.027(0.004)***

Caste of hh (lower caste) 0.261 (0.167) 0.763 (0.215)*** 0.764 (0.211)***

In migrated hh -0.471 (0.207)** -0.256 (0.416) -0.186 (0.403)

Distance to CF 0.173 (0.072)** 0.180 (0.072)**

Market for forest products -0.606 (0.257)** -0.525 (0.245)**

CF area 3.387 (0.683)*** 3.494 (0.680)***

Participation rate 0.018 (0.009)** 0.018 (0.009)**

Other forest source -0.079 (0.337) -0.181 (0.329)

No. of observations 452 452 452 452 452

Pseudo R2 0.05 0.06 0.19 0.47 0.46

Wald χ2 15.25 17.04 65.80 170.29 174.88

Prob. > χ2 0.00 0.00 0.00 0.00 0.00


As it was found in section 5.4, the educated households are more likely to join the CFP. One

reason can be the concern towards resource conservation. Same argument may explain the

reasons for likelihood of uneducated households to be free riders. The residents of the area are

more likely to free ride than the migrants. The reason could be that the immigrants may have

alternative fuel sources and they may have alternative livelihood strategies. So they neither

participate in CFP nor free ride over the participants. In general, it is thought that migrants are

poor and depend more on forest, either being free riders or members, but our analysis shows that

they are relatively better off (cf. section 5.4) and depend less on the forest.

Free riding reduces if the market for forest products exists in the village. In the absence of

market, the households are compelled to depend on the forests to fulfill the demand of forest


30

products and free riding increases, if they are unable to participate for one and another reasons.

When market is available, they can buy the forest products from market which is easier, needs

less physical work, reduces the chances of being caught and is less time consuming. Free riding

is more likely when the participation rate is low in the village. The crowd behavior well suits

here such that if someone is not participating then why should I?

We found some intriguing results from section 5.4 and 5.5 – participation is high among poor

households and poor are the one who are more likely to be free riding. Comparisons of basic

characteristics of free riders and the participants (table 5.7), however suggest that free riders are

poorer than the participants (average income for free riders is 41% lower than that of

participants) and have higher share of forest income.

5.6 Community forestry and the poor

In this section we try to answer the second research question, i.e. what are the benefits in terms

of higher forest income from participation? For this purpose, we estimated the Probit of

community forestry participation on the basis of model 5 from table 5.6. Since we are looking at

the impact of participation in CFP on forest income – a component of total income, income

variables are removed from the model. The signs and significance levels are almost same for

other variables in fourth and fifth models. Predicted participation in CFP is thus estimated on the

basis of fifth model. As free riding is an important factor in benefiting by the forest use, we

estimated the predicted free riding from Probit of free riders. For the same reason as in Probit of

participation, income variables are removed from Probit of free riding when predicting. To

control for endogeneity of the participation and free riding, we used both predicted participation

and predicted free riding as regressors in the regression of forest income (table 5.9).


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Table 5.9: Regression of forest income on different variables

Dependent variable Absolute forest income Relative forest income

Total income 0.165 (0.048)*** -1.216 (1.921)

Total income squared -0.008 (0.006) 0.183 (0.217)

Wealth cat (middle) -0.052 (0.039) -5.434 (2.239)**

Wealth cat (rich) -0.138 (0.052)*** -8.202 (2.928)***

Age of hh head -0.005 (0.010) 0.041 (0.439)

Age squared 0.000 (0.000) -0.000 (0.004)

Gender of hh head (male) 0.008 (0.069) 2.975 (2.963)

Adult equivalence 0.011 (0.011) 0.961 (0.604)

Educated member in hh 0.001 (0.000)** 0.076 (0.035)**

Caste of hh (lower caste) -0.033 (0.027) -3.501 (1.859)*

In migrated hh 0.007 (0.027) 8.127 (2.717)***

Distance to CF 0.001 (0.008) 0.539 (0.750)

Market for forest products 0.165 (0.038)*** 9.343 (2.170)***

CF area -0.097 (0.038)** -10.198 (2.856)***

No. of CFUGs 0.018 (0.005)*** 1.214 (0.248)***

Participation rate -0.002 (0.001)* -0.040 (0.046)

Other forest source 0.049 (0.027)* 4.460 (2.525)*

Predicted participation -0.121 (0.102) -8.005 (5.723)

Predicted free riding 0.108 (0.105) 14.940 (4.816)***

No. of observations 452 452

R2 0.26 0.21

F-statistics 2.23 8.20

Prob. > F 0.00 0.00


Validity of both regression equations is confirmed by the significant F-statistics, indicating the

joint effect of control variables is statistically significant.

It is surprising to see that the coefficients for predicted participation are negative but not

significant for both absolute and relative forest income. Thus the hypothesis that participation

does not affect forest income cannot be rejected, lending support to our earlier findings that

programme participation does not have any major positive effects on forest income, but possibly

the opposite. The possible explanation for this result is that most of the CFUGs limit the forest

use to household consumption and prohibits the sale of forest products by individual.


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On the contrary, coefficients of predicted free riding are positive in both absolute and relative

forest income, and statistically significant for relative forest income. This suggests that free

riding increase forest income. In other words, it is more beneficial to be free rider than a

participant in the CFM, which in one way is just what any economic text book would suggest.

Looking at the relative forest income, free riders enjoy nearly 15% more income from forest than

those who are not free riding. In terms of absolute income they get 10,000 more than non-free

riding households.

The similar impression can be drawn from the higher participation rate in CFP at the village

level. The households from the high participating villages get less forest income in both absolute

and relative terms. Though this variable is significant in absolute forest income only, the

negative signs in both regressions indicate that more members in CFP leads to reduced income

from forests. However, this finding is justified, as the benefit is smaller when there are more

beneficiaries.

The above discussion clearly shows that participation in CFP does not have significant effect on

forest income, and it is weakly negative. Free riders are gaining, but from our discussion in

section 5.4, it was found that poor, uneducated, lower caste and non-migrant households were the

free riders and they are from the villages with no market for forest products, low participation

rate and large forest area. Being the poorest and having low opportunity cost of labor, the free

riders do not have much to loose by violating the rules and getting benefited with forest use.

Other determinants

As the household gets richer, absolute forest income increases while relative forest income or

forest dependence decreases. However, this variable is not statistically significant for relative

forest income. In terms of wealth indices, it is found that income from forest decreases when the

household becomes wealthier. The wealthy households may use alternative fuel source than

fuelwood and may use less forest products.

It is interesting to note that migrated households get more forest income than the locals in both

absolute and relative term. From our earlier discussions, it was found that migrants were less


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likely to participate in the program and less likely to be free riders. This may indicate that they

get the forest products either from private or state forest or buy from the market.

On one hand area of community forestry decreases the income from forest for a household and

on other hand number of CFUGs increases the forest income in both absolute and relative term.

This is again in line with the conventional hypothesis that management is less efficient when the

resource is abundant and hence output from it is less. This may be the reason for negative sign of

area of community forest. More user groups imply close monitoring and effective execution of

rules and regulations. The distribution of forest products is efficient when more CFUGs exist and

hence the income from forest also increases.

It is likely that diversified sources provide more income. Positive and significant coefficients of

availability of alternative source and market for forest products indicate this. Market access and

existence of other forest source increase the forest income share by 9% and 4% respectively.

6 Conclusions Most of the rural population in Nepal depends on forest to fulfill the need of basic forest

products. A landmark shift in Nepal’s forestry policy transferred the management responsibilities

to local communities, who had the traditional use rights. Researches have found that this

devolution has been successful in halting the degradation of natural resources, but the

programme’s impact on improving the livelihoods and reducing poverty is not as positive as

often claimed.

Analyzing the household data of 452 household data from central Nepal, we focused on two

major reasons to join the CFP. First, participation provides the better (legal) access to forest

products. Relatively poor households participate in the programme to benefiting from the access.

Second, participants can gain political and social influence from the programme. These

households do not necessarily get any products from CF, but holds the power to influence other

participants and how to spend the income. A third reason, raised by young and educated

households, is the concern over forest degradation.


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We found a weak U curve between the household income and participation. As mentioned

above, the motivations are different for different income groups. Wealth (an index of household

physical assets, landholding and livestock unit) affects participation decision negatively, i.e.

wealthier households do not join the programme. But other variables like household size, caste

and migration, which are closely associated with wealth, are also important in making the

decision. Growing concern over resource conservation is reflected in the educated household’s

decision to participate in the programme.

The participation decision is affected by the community and village level variables also. The

resource abundance may ensure the availability of better access and attracts the participation.

Group pressure is another factor that implies higher participation in CFP.

Existence of free riding in the programme cannot be rejected and we found that poor, uneducated

and lower caste households to be the free riders. Because of weak sanctions against free riders,

the number of free riders is large.

At the next level, we tried to answer what impact participation has on forest income. We found

that participation does not yield any significant positive effect on the forest income, but rather

tends to reduce it. On the contrary, free riders are found to be having higher income from forest

and free riders have higher forest incomes, although generally being much poorer than members.

Although free riding is bad from efficiency point of view, one may argue that it has positive

distributional effects.

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Appendix

Fig A1: Histogram of the participants of CFP by villages

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Villages

Hou

seho

ld P

erce

ntag

e

Member Non member


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