Poor Participants and Even Poorer Free Riders in Nepal’s Community Forestry Programme

Poor Participants and Even Poorer Free Riders in Nepal’s Community Forestry Programme

Baikuntha Aryal¹* 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]

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

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).

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)

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

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

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

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

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 i

P= +α Yβ+Zη+Sθ 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;α_i,β, η and θ are unknown parameters and εi is the stochastic error term, whose distribution is assumed as ε_i∼N(0,σ²). The associated log likelihood function is

1 0

log ( , , ) log log 1

i i

i i i i i i

P P

Y Z S Y Z S

L β η θ β η θ β η θ

σ σ

= =

  + +    + + 

= Φ +  − Φ 

   

   

∑ ∑

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 i

R = + ℘+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 ν_iis 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

CFP and free riding in CFP, we used the fitted participation, P^ˆ_i from equation (1) and fitted free riding, R^ˆ_i from equation (3) as the first stage. Then in second stage, we run regression of forest income on P^ˆ_i and R^ˆ_i and other poverty, household and spatial and community variables (equation 4).

ˆ ˆ

i i i i i i i i

F = +ξ Wγ +Zψ +Sδ+Pς+Rϑ 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; P^ˆ_i andR^ˆ_i are the predicted participation and free riding estimated from equation (1) and (3) respectively;

, , , ,

i and

ξ γ ψ δ ς ϑ are unknown parameters and υ_i is error term. To avoid the inconsistency of instruments (P^ˆ_i andR^ˆ_i), 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.

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

3Wealth 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).

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.

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.

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.

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.

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 income

Household 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.

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

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

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.

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

020406080100Memper

0 20 40 60 80

Comfreerider

020406080Freerider

0 .5 1 1.5

Cfareaperhh

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.

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).

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

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.

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.

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

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 –