The Effect of the Nepal Community Forestry Program on Equity in Benefit Sharing

Abstract

We assessed the effectiveness of Nepalese Community Forestry Program (CFP) in increasing local perceptions of equity in benefit sharing. Our aim is to inform emerging forest policy that aims to mitigate climate change, promote biodiversity conservation, and address poverty and livelihood needs. We collected data from 1,300 households from nationally representative samples of 65 CFP communities and 65 non-CFP communities. By using a robust method of covariates matching, we demonstrate the unique and positive effect of the CFP on perception of equity in benefit sharing at national level and among poor, Dalits, indigenous and women-headed households and in the hills (except Terai). Our results suggest the need to continue the current benefit-sharing practices in CFP except in the Terai, where such practices need to be reviewed. However, caution should be taken in implementing emerging carbon-focused forestry so that it does not alter the CFP management sufficiently to conflict with equity goals and upend the generally positive effects on equity.

Full text

Article
The Effect of the Nepal
Community Forestry
Program on Equity in
Benefit Sharing
Harisharan Luintel
1,2
, Randall A. Bluffstone
3
,
Robert M. Scheller
4
, and Bhim Adhikari
5
Abstract
We assessed the effectiveness of Nepalese Community Forestry Program (CFP)
in increasing local perceptions of equity in benefit sharing. Our aim is to inform
emerging forest policy that aims to mitigate climate change, promote biodiversity
conservation, and address poverty and livelihood needs. We collected data from
1,300 households from nationally representative samples of 65 CFP communities
and 65 non-CFP communities. By using a robust method of covariates matching,
we demonstrate the unique and positive effect of the CFP on perception of equity
in benefit sharing at national level and among poor, Dalits, indigenous and women-
headed households and in the hills (except Terai). Our results suggest the need to
continue the current benefit-sharing practices in CFP except in the Terai, where such
practices need to be reviewed. However, caution should be taken in implementing
emerging carbon-focused forestry so that it does not alter the CFP management suf-
ficiently to conflict with equity goals and upend the generally positive effects on equity.
Keywords
benefit sharing, community forestry, equity, Nepal, social groups
Journal of Environment &
Development
0(0) 1–25
!The Author(s) 2017
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DOI: 10.1177/1070496517707305
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1
School of the Environment, Portland State University, Portland, OR, USA
2
ForestAction Nepal, Patan, Nepal
3
Department of Economics, Portland State University, Portland, OR, USA
4
Department of Environmental Science and Management, Portland State University, Portland, OR, USA
5
International Development Research Center, Ottawa, Canada
Corresponding Author:
Harisharan Luintel, School of the Environment, Portland State University, Portland OR, USA.
Email: hluintel@gmail.com

Many governments in tropical countries have been promoting decentralized
forestry to involve forest-dependent communities and households in the conser-
vation and management of local forests. A key aspect of decentralized forestry is
that it devolves all or part of forest management rights from governments to
local or even household levels (Charnley & Poe, 2007; Larson & Soto, 2008), and
there is a growing consensus that decentralized forestry has the potential to
reconcile sometimes-conflicting goals of social justice, equity, and environmental
sustainability (Gauld, 2000). Concerns over equity have influenced many social
movements related to human rights, global trade, and climate change (Gross,
2007), and in recent years, equity has been one of the central concerns regarding
forest management (Adhikari & Di Falco, 2009). Indeed, it is increasingly con-
sidered to be a legitimate basis for judging the effectiveness of forest commons
management (e.g., Li, 1996), affecting not only the motivation of community
members but also credibility, acceptability, and socioenvironmental outcomes
(McDermott, 2009).
The concept of equity is based on human needs related to survival, self-
concept, and dignity through which people evaluate their social standing in a
society (Mapel, 1989). Equity reflects the existence of social justice that includes
contextual, procedural, and distributional dimensions, where context refers to
the capacity of actors to participate and capture benefits, procedures focus on
decision-making processes, and distribution considers how costs, benefits, and
risks are shared (McDermott, Mahanty, & Schreckenberg, 2013). In the context
of natural resource management, equity reflects a situation of fairness in which
everyone has just and equal opportunities to participate in decision-making
processes and access the resources required to achieve full potential (Mahanty,
Fox, Nurse, Stephen, & McLees, 2006). A core determinant of equity, fairness is
expected in day-to-day interactions (Gross, 2007) and in general requires value
judgments in a particular context (Schreckenberg & Luttrell, 2009). Perspectives
on fairness therefore vary across cultures, time, and space.
Equity and fairness are likely to be especially important for
decentralized and incentive-based forest management. An important example
of incentive-based approach to forest management is Reducing Emissions
from Deforestation and Forest Degradation, Conservation and Enhancement
of Carbon and Sustainable Management of Forest (REDD+), which is an
international climate change initiative that provides financial resources to low-
income countries in exchange for verifiable reductions in forest-based emissions
(Wunder, 2008). While both decentralized forestry and REDD+ have the
potential for poverty reduction and support for forest-dependent communities
(Eliasch, 2008; Luintel, Ojha, Rana, Subedi, & Dhungana, 2009; World Bank,
2004), their incentives may differentially benefit individuals and groups and
thereby create different perceptions of equity in benefit sharing.
Equity has so far been assessed either by using tangible benefits or by using
perception of communities receiving benefits. Equity as measured by tangible
2Journal of Environment & Development 0(0)

benefits has been discussed in some previous research, which showed the strong
relationship between private endowment and appropriation of benefits from the
structured forms of collective action (e.g., Adhikari, 2005; Beck & Ghosh, 2000;
Naidu, 2011; Sunam & McCarthy, 2010). Such findings indicate that the tangible
benefit approach in understanding equity might not be adequate, particularly
where the inequality in asset is an inherent problem. Another stream of literature
argues that communities’ perceptions of fairness substantially impact their
participation in conservation programs (Lind & Tyler, 1988; Vatn, 2010).
The success of many conservation programs, including decentralized forestry
and REDD+, is contingent on developing positive attitudes of local commu-
nities (Adams & Hulme, 2001; Struhsaker, Struhsaker, & Siex, 2005). Using
local perceptions in assessing equity in benefit sharing is methodically easier
and widely acceptable in complementing existing knowledge. Perceived lack of
fairness in benefit sharing undermines the effectiveness of conservation programs
even if they deliver tangible benefits (Kanfer, Sawyer, Earley, & Lind, 1987) and
can lead to environmental degradation (Boyce, 1994). Therefore, it is important
to understand the perception of equity in benefit sharing so as to address the
issue of inequity and foster positive local attitudes.
Advocates pitch decentralized forestry as an effective approach to increase
access to forest ecosystem services like fuelwood, fodder, and water quality that
improves rural well-being (Beck & Nesmith, 2001; Rights and Resources
Initiative, 2014). However, studies have shown both positive and negative results
of decentralized forestry on equity, which has led to important debates. If devo-
lution fosters fair and inclusive processes, equity may be enhanced by decentral-
ization (McDermott & Schreckenberg, 2009). Persha and Anderson (2014) and
Luintel (2006), for example, argued that equity has been enhanced by decentralized
forestry primarily due to external support and the involvement of civil society organ-
izations, and such supports are common in legally recognized community forest user
groups (CFUGs) in Nepal (World Bank, 2001). Agrawal and Ostrom (2001), how-
ever, argued that decentralized forestry may not promote equity, and Adhikari
(2005), Agarwal (2001), Iversen et al. (2006), and Thoms (2008) demonstrated that
decentralized forestry in South Asia can be associated with unequal access. As a
result, the gap between rich and poor forest users may be widening and involvement
of poor and marginalized communities may be decreasing (Lamichhane & Parajuli,
2014). Mahanty, Guernier, and Yasmi (2009) and others (e.g., Bista, 1991; Hobley,
2007) argued that inequity in benefit sharing is common due to differential power,
assets, and capacities of forest-managing community members.
Despite this active debate, robust empirical evaluation of the effect of
decentralized forestry on equity in benefit sharing is limited. This knowledge
gap reduces the credibility and legitimacy of decentralized forestry and may
constrain the effective implementation of emerging forest management
programs, such as REDD+ (Arsel & Buscher, 2012; Fairhead, Leach, &
Scoones, 2012; McAfee, 2012). Researchers, policy makers, and practitioners
Luintel et al. 3

have therefore highlighted the need for empirical assessment and greater under-
standing of equity to reduce the potential for social conflicts and environmental
degradation (e.g., Boyce, Narain, & Stanton, 2007; Patel et al., 2013; Poudel,
Thwaites, Race, & Dahal, 2015; Smith & McDonough, 2001).
In this article, we empirically examine whether and to what degree the
Nepalese community forestry program (CFP), which is a formal forest decen-
tralization program, increases perception of equity in benefit sharing, including
among marginalized social groups, such as Dalit
1
groups, indigenous peoples,
2
and women-headed households, and different geographic regions (i.e., hill
3
and
Terai
4
). We use cross-sectional data collected in 2013 from nationally represen-
tative random samples of community forest (CF) and matched non-CF (NCF)
communities. The Nepalese CFP offers a unique opportunity to examine the
effects of decentralized forestry on equity; it has more than 40 years of history of
managing approximately 1.8 million hectares and includes approximately 42%
of the population with a wide range of socioeconomic backgrounds. The pro-
gram legally recognizes approximately 19,000 CFUGs as autonomous public
bodies that can acquire, possess, transfer, and manage property (Ministry of
Law and Justice [MoLJ], 1993).
The most important challenge in such research is to identify an appropriate
counterfactual, which allows the identification of the effect on equity in the
absence of the program. We follow a quasi-experimental, matching method—a
method that mimics randomized experiments. By using only matched samples, we
estimate the average treatment effect on the treated (i.e., the average perception of
household on equity in benefit sharing [ATT
e
]). As national-level estimates may
mask a great deal of variation in the effectiveness of the CFP in different social
groups and geographic regions, we also estimate the ATT
e
by social group and
geographic region. Finally, we use sensitivity analysis to estimate whether and to
what extent our ATT
e
can be affected by hidden bias caused by unobserved
confounders.
We find strong equity-enhancing effects of the CFP virtually across the board.
Whether we compare equity in CFs and NCFs in the overall sample, among the
poor, Dalit, indigenous people, women-headed households, and in the hills, we can
reject that the CFP has no positive effect on equity. Our results and methods are
potentially applicable to a variety of countries that are practicing decentralized
forestry and potentially provide critical insights for policy makers and planners.
Research Methods: Site, Design, and Analytical Model
The data presented are part of an ongoing multidisciplinary research project
funded by the World Bank and jointly implemented by Portland State
University and ForestAction Nepal. The primary aim of the project is to
assess potential synergies and tradeoffs between the Nepal CFP and
REDD+.
4Journal of Environment & Development 0(0)

Sampling Methods, Sample Sites, and Data Collection
We randomly select 65 CFUGs from a nationally representative sample of 137
CFUGs used to conduct a CFP impact study during 2010 to 2012 (Ministry of
Forest and Soil Conservation [MoFSC], 2013). We then selected 65 NCFs in
such a way that they are proximate and similar to CFs based on a variety of
characteristics (e.g., ethnic group, agro-ecological zones, use of forests by local
communities, and forest characteristics). Such NCFs are close, but not next, to
CFs to avoid being used simultaneously by the same people. The selected CFs
and NCFs are distributed in the tropical, subtropical, and temperate climatic
zones in 42 of Nepal’s 75 districts (Figure 1). The CFUGs in our sample have 5
to 40 years of experience in formal forest conservation and by law in principle
are expected to show fairness in their benefit-sharing rules. We randomly sample
10 households from each of our 130 forest user groups to be surveyed and
conduct or use impact evaluation survey results from leaders of all user groups.
We developed and pretested a questionnaire in two CFUGs before conducting
the survey and then recruited a team of 25 Nepalese field researchers having
undergraduate degrees in forestry (12) and graduate degrees in social science
(13). This team was trained to conduct forest user group surveys, forest inven-
tories, and household surveys. Data were collected between March and May 2013.
Variable Selection and Measurement
Treatment and control variable. The treatment variable is participation in a formal
CFP. Local communities and the Nepalese government opt into CF status, and
therefore, the observational data are nonrandom. NCFs are the control
variable and are formally owned by the Nepalese government, which also has
management responsibilities. NCF communities may protect and use forest
Figure 1. Distribution of sample plots.
Luintel et al. 5

resources, particularly nontimber forest products, but often NCFs are effectively
open access.
Outcome variable: the equity index. Household members may use a variety of
criteria, indicators, and standards to view equity. Perceptions of fairness in
benefit-sharing processes and outcomes therefore help create a picture of
equity, and it is for this reason that our outcome variables come directly from
survey responses by households who are and are not part of the CFP. Also of
keen importance is that household categories, such as the poor, women-headed,
Dalit, and indigenous, are defined at the household level. In Nepal, it is typical
that household heads speak on behalf of families, and it is for this reason that
surveys are conducted with self-identified household heads or their designees.
On the basis of the survey responses, we construct an equity index, which is a
composite measure of equity in benefit sharing and summarizes different aspects
of equity. Such an index is intended to capture most of the underlying ethics and
assumptions of forest governance and management in relation to benefit shar-
ing. Despite the risk of misinterpretation and misapplication, the single number
index helps us summarize different dimensions of equity in a simple way, order
equity, understand the average of household responses, assess how symmetric
this measure is around the mean by applying various statistical tests, and com-
pare and communicate the equity situation (Organisation for the Economic
Co-operation and Development [OECD], 2008). Such construction of index is
not uncommon in the field of forest commons study (e.g., Agrawal & Chhatre,
2005; Andersson & Agrawal, 2011; Beyene, Bluffstone, & Mekonnen, 2013).
We use four variables that reflect fairness at different stages of benefit-
sharing systems to construct index. First, we select the fairness in locally
developed benefit-sharing rules, which form the foundation for governing
equitable benefit sharing. Communities prepare such rules considering several
factors, including household need and ability to accessing (alternative)
resource, and condition and availability of resource (Gautam & Shivakoti,
2005; Ostrom, 1990). Second, we incorporate fairness in the benefit-sharing
processes that translate benefit-sharing rules into practice, which is guided
by governance principles, decision-making systems, and power relations
among households. As past studies recognized the asymmetric distributions
of wealth and power, different preferences and opportunity costs, and unequal
claims of households while translating benefit-sharing rules into the practice
(Adhikari, 2005; Adhikari & Lovett, 2006), it is important to include in the
index. Third, we consider fairness in actual benefit sharing, which reflects
whether the flow of benefits reached to the household is fair. Finally, we
consider the degree of conflict related to benefit sharing, which is an important
indicator of satisfaction with the post benefit-sharing situation. These variables
are presented in Table 1 and are coded from 0 to 4, with higher levels implying
a higher value of the equity index.
6Journal of Environment & Development 0(0)

We identify the weights of these variables using principal component analysis
(PCA)
5
and standardize the variables to have zero means and unit variance.
Diagnostic checks of the data showed that all variables were correlated or
internally consistent with the principal components (Cronbach’s alpha ¼.71;
95% CI[0.66, 0.75]); sampling adequacy scored as middling to meritorious
(Kaiser–Meyer–Olkin Measure ¼0.64–0.85); and the variables have different
variances (Bartlett test of sphericity ¼211.14, df ¼3, pvalue 2.2e-16). The
assumptions for using PCA were therefore met.
As our interest is to determine the weights for each variable to construct an
equity index (as opposed to minimizing the number of variables), we select
the principal components that have at least one of the following attributes: (a)
factors that have eigenvalues larger than one, which is commonly known as the
Kaiser criteria (Lise, 2007; Manly, 2005); (b) factors that together contribute
>60% to explaining total variance; and (c) factors that individually contrib-
ute at least 10% to explaining overall variance. On the basis of these criteria,
we select all four principal components, which together explain 100% of total
variance (Table 2). We then perform a varimax rotation of the original variables
associated with each of the selected principal components and ensure that
each variable is maximally correlated with one principal component (Jolliffe,
2002). The rotation provides component loadings for each variable. Components
that have a greater than 0.5 loading were identified as important for further analysis.
Table 1. Indicators Used to Create the Equity Index and Their Measurement Units.
Variables Definition of variables Measurement unit
Fair rules Existence of fair system of
benefit sharing (e.g., selecting
forest beneficiaries).
1to4(Strongly disagree to
Strongly agree)
Fair process Existence of fair and acceptable
governance and decision-
making systems of accessing
and distributing forest
benefits.
1to4(Strongly disagree to
Strongly agree)
Fair practice Existence of fair benefits distri-
bution, i.e., fair benefits
flowed to the household.
1to4(Strongly disagree to
Strongly agree)
Presence of
conflicts
Lack of conflicts and problems in
the distribution of forest
benefit
Ye s ¼0, Neutral ¼2, No ¼4
Note. As these four variables, at times, looks overlapping or it is difficult to delineate their boundaries, the
authors were careful, while translating questions in Nepali language. As two of four authors’ native lan-
guage is Nepali and they have worked in Nepalese community forestry for more 10 years, we overcame
this challenge by carefully utilizing our knowledge, getting suggestions from ForestAction Nepal and pre-
testing questionnaires.
Luintel et al. 7

Component 1 accounts for 40% of the total variance and received mod-
erately negative loadings from process (0.50) and conflict (0.66).
Component 2 explains 27% of the variance and received moderately negative
loadings from conflict (0.75). Component 3 accounts for 19% of total
variance and largely depended on the actual practice of benefit distribution
(0.92). Component 4 accounts for 14% of the variance and received positive
leadings from rules (0.71) and negative leadings from existence of conflicts
(0.71).
Using the factor loadings and the proportion of variance explained by princi-
pal factors, we calculate the weight for each component (Table 2). The weights
for rules, process, practice, and conflict-related indicators in the equity index
are 0.1080, 0.1518, 0.2450, and 0.4970, respectively. We use these weights to
construct the equity index for each household. The equity index ranges from
0 to 1, where 0 means perception of no equity at all and 1 means full
equity. The descriptive statistics of the equity index and its components are
given in Table 3. At the mean, the index is 0.588 and the median is 0.621,
which suggests moderate equity across the sample.
Table 2. Eigenvalues of the Reduced Correlation Matrix, Factor Pattern, and Weight
Factor.
Principal
components
Eigen
values
Proportion
of variance Cumulative proportion of variance
1 1.60 0.40 0.40
2 1.06 0.27 0.67
3 0.77 0.19 0.86
4 0.57 0.14 1
Cronbach’s alpha ¼.71
Variables/
components
Component
1
Component
2
Component
3
Component
4
Component
scores
a
Variable
weights
b
Fair rule 0.47 0.43 0.32 0.71 0.071 0.1080
Fair process 0.50 0.44 0.25 0.71 0.100 0.1518
Practice 0.33 0.23 0.92 0.05 0.161 0.2450
Absence of conflicts 0.66 0.75 0.04 0.01 0.327 0.4970
Explained variance 1.60 1.06 0.77 0.57 0.659 1
Proportion of
explained variance
0.40 0.27 0.19 0.14
Note. Numbers in bold face denote a dominating indicator (factor loading .5 or .5).
a
Factor scores: square the significant loading factor (>.5) and multiply by the proportion of explained variance.
b
Variable weights: Factor scores scaled to 1.
8Journal of Environment & Development 0(0)

Confounding variables. Because communities choose to participate in the CFP,
there are a number of potential confounders that may affect treatment status
and cause bias in ATT
e
estimates (Heinrich, Maffioli, & Va
´zquez, 2010). We
therefore use data from NCF plots to develop a counterfactual control group
combined with matching, an ex post identification technique, to identify what
would have happened to equity in benefit sharing in the absence of the CFP
(Pattanayak, 2009). Of critical importance is that we control for confounders in
the matching process, which helps minimize bias by identifying the best matches
between CF and NCF households.
On the basis of the literature, focus group discussions with 10 different
forest-managing communities and one consultation meeting with experts in
Kathmandu in 2012, we identify 14 observable forest or topographical charac-
teristics and communities confounders that could affect whether forests (and the
households who use them) are part of the CFP. As far as possible, we tried to
include all observable variables that determined whether a forest was part of the
CFP or not, including relevant biophysical and socioeconomic confounders; by
adjusting for these observables, we attempt to reduce any bias that potential
confounders might cause.
As households very rarely opt out of community-level decisions and the opt-
in decision is at the community or forest level, it is forest- and community-level
characteristics that drive CFP assignment. Therefore, despite our analysis being
at the household level, most of the confounder variables are at the community or
forest level. We analyze relationships between potential confounders and CFP
assignment at the national level and across social groups and geographic regions
using a Generalized Linear Mixed Model Probit (GLMM). We use GLMM
because analysis is at the household level, but assignment is determined at the
community or forest level. The model tells us the change in the log odds ratio
associated with assignment to the treatment, given the observables. Our results
are presented in Table 4.
If treatments were randomly assigned, coefficients should be zero or statistic-
ally insignificant. As shown in Table 4, very few variables have coefficients that
Table 3. Descriptive Statistics of Equity Index and the Variables
Used to Construct Index.
Variables MSDMinimum Maximum
Equity index 0.588 0.177 0 0.802
Fair rule 2.57 1.00 0 4
Fair process 2.49 1.03 0 4
Fair practice 2.62 0.91 0 4
Absence of conflicts 3.27 1.32 0 4
Luintel et al. 9

Table 4. Potential Confounders and Their Relationships With CFP Assignment (GLMM Probit Model) by Region and Household Type.
Intercept Overall Poor Dalit
Indigenous
people
Women-headed
household Hill Terai
Intercept 4.7807 (0.821) 6.3900 (0.5966) 5.3460 (0.563) 3.2570 (0.794) 4.6678 (0.641) 9.3334 (0.843) 8.6736 (0.782)
Forest area 0.0229 (0.201) 0.0136 (0.004) 0.0125 (0.177) 0.0150 (0.0427) 0.0260 (0.0454) 0.0185 (0.225)
Number of forest user
households
0.0003 (0.921) 0.0005 (0.802) 0.0023 (0.687) 0.0003 (0.879) 0.0013 (0.626) 0.0874 (0.129) 0.0008 (0.799)
Travel time to nearest road 0.1290 (0.965) 0.0036 (0.998) 0.9081 (0.529) 0.9320 (0.618) 0.1298 (0.915) 0.5000 (0.906) 1.4275 (0.761)
Altitude 0.0043 (0.552) 0.0019 (0.521) 0.0028 (0.435) 0.0010 (0.774) 0.0018 (0.520) 0.0017 (0.889) 0.0024 (0.834)
Slope 0.4287 (0.191) 0.2889 (0.041) 0.2136 (0.175) 0.3871 (0.000) 0.1936 (0.173) 0.077 (0.902) 0.2785 (0.581)
Years of communities
conserving forest
0.0185 (0.850) 0.0095 (0.875) 0.0076 (0.904) 0.0698 (0.533) 0.0411 (0.765) 0.0533 (0.693)
Moisture gradient 0.5303 (0.849) 0.1289 (0.930) 0.0171 (0.991) 0.0897 (0.939) 1.1895 (0.845) 0.2624 (0.946)
Broadleaf-conifer gradient 0.0152 (0.997) 0.1528 (0.951) 1.2342 (0.638) 0.0857 (0.979) 0.5451 (0.799) 0.3970 (0.961)
Presence of Shorea robusta
a
2.4642 (0.736) 2.1250 (0.537) 1.4444 (0.672) 2.4325 (0.570) 2.2193 (0.482) 0.0166 (0.999) 6.5473 (0.541)
Presence of soil erosion
a
2.3708 (0.703) 1.6544 (0.6157) 1.1956 (0.714) 2.3963 (0.544) 1.0150 (0.703) 0.5564 (0.962) 0.3563 (0.969)
NDVI 1989 5.0557 (0.863) 3.2999 (0.817) 4.6764 (0.756) 3.7593 (0.833) 5.8121 (0.648) 0.8161 (0.989) 7.0555 (0.875)
Proportion of households
living in the village for 2
generations
2.8127 (0.775) 3.8418 (0.464) 3.3199 (0.578) 2.1773 (0.738) 3.8504 (0.464) 3.8518 (0.894) 2.4360 (0.855)
Proportion of ethnic
population
0.6005 (0.937) 0.6275 (0.838) 0.0622 (0.988) 1.7862 (0.267) 0.2088 (0.941) 1.1531 (0.915) 2.5174 (0.817)
Proportion of poor
population
2.8359 (0.749) 2.6501 (0.603) 3.5630 (0.528) 1.9762 (0.627) 5.5278 (0.791) 8.1204 (0.509)
Note. N ¼1,300. CPF ¼Community Forestry Program. CFP status is dependent variable. pvalues are in parentheses and statistically significant estimates (p.05)
are in bold. A blank space indicates that a confounder is not used in the model to achieve matching in the particular forest category.
a
The presence of erosion and Shorea robusta could be functions of CFP status. As they are primarily determined by ecological characteristics and are likely
important determinants of whether forests are assigned to CFP, we use them in the GLMM probit model used to estimate propensity scores.
10

are significantly different from zero, which suggests that CFP assignment eval-
uated at the household level was largely random. The two exceptions are forest
area and average forest slope, which in three and two models had a statistically
significant effect on the log odd ratio of a forest being selected into the CFP.
6
For example, the log odds ratio that a poor household was part of a CF
increased by 0.0136 for each additional hectare of forest. The log odds ratio
also increased for indigenous households by 0.015 with each additional hectare
and for women-headed households by 0.026. This is reasonable, as local
communities prefer larger forests, which provide more forest products; house-
holds in communities with larger forests are therefore more likely to opt into the
CFP, and the Nepalese government has a policy of handing over forests accord-
ing to communities’ willingness to manage (MoLJ, 1995). Finally, these three
generally disadvantaged groups are likely to have access to smaller forests if they
are members of CFs made up of similar households. The marginal value of
organizing to gain access to larger forests is therefore likely higher than for
other groups.
Second, in two models, average forest slope had a significant positive effect on
the log odds ratio that households are part of CFs. Among poor households, the
log odds of selection increase by 0.29 for each additional degree of slope, and
the equivalent effect is 0.39 for indigenous households. As is well known, the
Nepalese government especially promoted the CFP in the hills and has largely
retained forests in the Terai.
According to our data, most of the potential confounders are not significantly
related to CFP assignment. However, these confounders were important deci-
sion criteria during the initial years of the CFP, and we therefore keep them in
our analytical models.
Specification of Analytical Models
Addressing confounding through matching. Because our study is observational, the
principal problem in the estimation of ATT
e
is identifying counterfactuals and
dealing with confounders induced by selection bias. Selection bias arises when a
forest (and the households that use it) is nonrandomly assigned to the CFP.
Adjusting for these confounders is important to avoid biased ATT
e
estimates
and make matched households as close as possible to randomly assigned.
Matched CF and NCF households in principle allow unbiased estimates of
ATT
e
(Ho, Imai, King, & Stuart, 2007; Imbens, 2004; Rosenbaum & Rubin,
1983; Sekhon, 2011).
We use a two-step nonparametric matching method to identify counterfac-
tuals and estimate ATT
e
. There is a lack of consensus on exactly how matching
ought to be done, how to measure the success of the matching procedure, and
whether matching estimators are sufficiently robust to misspecification
(Heckman, Ichimura, Smith, & Todd, 1998), but it is generally agreed that
Luintel et al. 11

appropriate matching asymptotically balances observed confounders and
reduces or eliminates bias (Rosenbaum & Rubin, 1983).
As presented in Table 4, we control for 10 to 14 observed confounders
7
that
are believed to affect the assignment into CFP and equity in benefit sharing. We
then feed the estimated propensity scores from the GLMM model into the
matching model. We find almost all variances (>99%) of random effects are
attributable to community- or forest-level effects.
We use 0.25 standardized mean difference (SMD) as a cutoff point for
matching adjustment, which is a common numerical balance diagnostic criter-
ion, to check whether matching is satisfactory and acceptable (Rubin, 2001).
The SMD expresses the standardized bias is similar to an effect size relative to
the variability observed and is estimated by dividing the difference in mean
outcomes between CF and NCF households by standard deviations of outcomes
across CF households. Reducing SMD minimizes overt bias in ATT
e
estimates
due to measured covariates (Imai, King, & Stuart, 2008; Rubin & Thomas,
1996).
We match CF and NCF households based on observed confounders using the
MatchIt package of R 3.2.2 (Ho et al., 2007). We use matching with replace-
ment, which allows each NCF household to be matched with 1 CF household,
as it produces the highest degree of balance and lowest conditional bias (Abadie
& Imbens, 2006; Dehejia & Wahba, 1999). In addition, to better optimize
matches, we use genetic matching, a multivariate matching method that opti-
mizes the balance between CF and NCF households by automating the process
of finding good matches using an evolutionary search algorithm (Diamond &
Sekhon, 2013). This is a generalization of propensity score and Mahalanobis
distance matching (Rosenbaum & Rubin, 1985), which maximizes balance using
pvalues. The Mahalanobis metric is considered a useful tool for determining
similarities between treatment and control observations even when there are
several, correlated confounders (Mahalanobis, 1936; Rubin, 1980).
Genetic matching most effectively balanced the maximum number of con-
founders while keeping the SMD below the acceptable limit. The postmatching
SMD for confounders is less than 0.25 standard deviations for virtually all
variables and the average SMD across all covariates are 0.11, 0.16, 0.14, 0.14,
0.12, 0.09, and 0.15 for the whole sample and poor, Dalit, indigenous and
women-headed households, and households in the hills and Terai, respectively.
It was not possible to bring the SMD down to 0.25 for travel time to the
nearest road-head in poor and women-headed households and presence of
Shorea robusta (a high value tree found in the Terai region) in the Terai,
while keeping as many covariates as possible in the matching models.
However, we keep those confounders in the matching models, as they contribute
positively to overall balance. A total of 20% to 63% of NCF households are
matched with CF households in the full sample and across different social and
12 Journal of Environment & Development 0(0)

geographic categories. The average ratios of matched NCF to CF households
range from 1:2.43 to 1:4.69.
Comparing equity. The ATT
e
is estimated on the basis of the average difference in
the equity index between matched CF and NCF households. As tests of average
differences rely on their distributions, we check whether the distributions are
normal using graphical plots (e.g., histograms and qq plots) and the Shapiro–
Wilk test. We find that differences are not normally distributed and therefore
using ttests is not appropriate. We therefore use a pairwise Wilcoxon signed-
rank sum test to identify the (median) ATT
e
, by deducting NCF values from CF
values. We compare perceptions of equity for the overall sample, in poor, Dalit,
indigenous, and women-headed households, and within hill and Terai regions.
Sensitivity analysis. The legitimacy of matching is based on the assumption that
assignment to CFP is ignorable when all the confounding covariates are included
(Thoemmes & Kim, 2011). Matching methods are therefore not robust to bias
arising from unobserved confounders that simultaneously affect assignment to
CFP and equity outcomes. We properly measured and included all identifiable
and measurable confounders in our model. However, we cannot rule out the
possibility of unidentified confounders. Therefore, we carried out sensitivity
analysis to help understand the robustness of our findings, as unfortunately
testing for the existence of unobservable confounders is impossible.
Following the sensitivity analysis approach proposed by Rosenbaum (2002,
Chapter 4) and using the sensitivitymv package in R 1.3, we explore how robust
are ATT
e
estimates in view of the potential effects of unobserved confounders.
We quantify the degree to which a key model assumption, that CFP assignment
is effectively random conditional on the matches, must be violated in order for
results to be reversed. In other words, we estimate how strong the effects of
unobserved confounders would have to be to change the probability of assign-
ment to CFP and significantly change ATT
e
estimates.
As is standard practice, we use a sensitivity parameter, gamma ()thatshows
critical levels of hidden bias as a measure of difference in the odds of CFP assign-
ment for two forests with the same observed confounders but that diverge on
unobserved confounders. A higher implies that the estimated ATT
e
results are
more robust to potential selection bias, while a low implies that even a mild
selection bias could make the estimate insignificant (¼1 indicates no hidden bias
or fully random assignment). We determine the smallest value of that will
change the pvalue of the true ATT
e
to a nonsignificant level (>0.05). When the
pvalue exceeds 0.05, the value indicates the CF to NCF odds ratio at which
ATT
e
estimates are sensitive to hidden bias. Since the sensitivity analysis for
statistically insignificant ATT
e
estimates is not meaningful, we only compute crit-
ical levels for the significant CFP effects (Hujer, Caliendo, & Thomsen, 2004).
Luintel et al. 13

Effect of the CFP on Equity in Benefit Sharing
As shown in Table 5, we find for all social and geographic categories that the
median value of the equity index is consistently higher for households in CFs
than in NCFs. The highest median value of the equity index in CF households is
in the hill subsample (0.6591) and the lowest is in Dalit households (0.6044). In
NCFs, the highest median equity index is in women-headed households (0.6032),
and the lowest is in indigenous households (0.5228).
The estimated ATT
e
nationally (i.e., the full sample) is 0.0937 (p.00), which
is about 15.1% of the sample median of 0.621. This means that households that
are part of CFs perceive 15% more equity than the average household in the
sample. The ATT
e
is very similar for poor households (0.0921) but is a bit lower
for hill (0.0794, p.00) and Dalit (0.0505, p.02) households but substantially
higher for indigenous households (0.1391, p.00) or 22.4% of the median.
Although point estimates are positive, as the ATT
e
estimates for households
in the Terai (p.27) and marginally for women-headed households (p.062),
are not significantly different from zero at the 5% significance level, we cannot
reject that there is no effect of CF status on equity.
8
We also find that ATT
e
estimates overlap within a 95% confidence interval in the full sample, hill, poor,
Dalit, and indigenous household samples, and therefore, we cannot reject that
the estimated effects are statistically similar.
The higher the CF to NCF odds ratio (i.e., critical level of bias) and the
narrower the confidence interval of ATT
e
, the more precise and less sensitive
to unobserved confounders are our ATT
e
estimates. The sensitivity analysis
suggests that these results can be nullified by the influence of unobserved con-
founders if the odds ratio of CFs to NCFs is changed by 2.01, 1.74, 1.91, 1.14,
and 2.61 in the overall sample, hill, poor, Dalit, and indigenous households,
respectively. As estimates are generally substantially above 1.0 (i.e., at which full
randomization could flip the results), these results are very robust to unobserv-
ables for all household types except perhaps Dalit households.
Discussion
Our analysis contributes to the emerging literature on the impact of formal
forest decentralization on equity (e.g., Adhikari & Lovett, 2006; Luintel, 2006;
Naidu, 2009; Thoms, 2008). By using nationally representative samples from
formal CFUGs and government forest NCFs, and by utilizing robust analytical
methods that reduce bias, we demonstrate the positive effect of CFP on percep-
tion of equity in benefit sharing.
At the national level and among marginalized social groups, such as the poor,
Dalit, indigenous, and (at slightly above the 5% significance level cutoff)
women-headed households, as well as households in the hills, our results clearly
demonstrate that the CFP has a positive effect on household head perceptions of
14 Journal of Environment & Development 0(0)

Table 5. Average Effect of the CFP on Equity at Household Level and the Results of Sensitivity Analysis by Social Group and Geographic
Region.
Social category/
geographic
regions
No. of
CF/NCF
households
(communities)
Mean SMD of
observed
confounders
(before/after
matching)
Median
equity –
CF
Median
equity –
NCF
ATT
e
(Comparison of medians) Hidden bias
Point
estimate
Lower
confidence
limit 95%
Upper
confidence
limit 95% p
Critical
level
of bias ()p
Overall 650/199 (65/34) 0.40/0.11 0.6395 0.5613 0.0937 0.0705 0.1103 .000 2.01 .055
Poor 253/73 (62/34) 0.41/0.16 0.6344 0.5493 0.0921 0.0577 0.1226 .000 1.91 .053
Dalit 94/33 (42/17) 0.70/0.14 0.6044 0.5542 0.0505 0.0108 0.0974 .017 1.14 .054
Indigenous
people
284/114 (53/38) 0.33/0.14 0.6393 0.5228 0.1391 0.1102 0.1699 .000 2.61 .051
Women-headed
house
122/26 (55/20) 0.47/0.12 0.6210 0.6032 0.0324 0.0000 0.0705 .062 – –
Hill 410/101 (41/13) 0.20/0.09 0.6591 0.5776 0.0794 0.0505 0.1066 .000 1.74 .051
Terai 240/99 (24/15) 0.40/0.15 0.6058 0.5938 0.0215 0.0108 0.0597 .268 – –
Note. CF ¼community forest; NCF ¼noncommunity forest; SMD ¼standardized mean difference; ATT
e
¼average treatment effect on the treated;
CPF ¼Community Forestry Program. Column 1 is the social and geographic categories of households. Columns 2 and 3 contain the number of CF/NCF
plots and average SMD of confounders before and after matching in different social and geographic categories. Columns 4 and 5 present the mean equity ofCF
and NCF, respectively. Columns 6 to 9 depict the ATT
e
, lower and upper confidence levels of ATT
e
, and pvalues, respectively. The last two columns provide
information about the sensitivities of estimated ATT
e
to the unobserved confounders. For sensitivity estimation, trimming was carried out at 2.5 times the median
of the absolute matched difference, which is analogous to a trimmed mean that trims 5% outliers from each tail. We computed the critical level of hidden bias
only for the significant CFP effects at a 5% level of significance.
15

equity in benefit sharing. Our results show some variations in ATT
e
across social
and geographical groups, which likely reflect household perceptions about the
implementation of locally generated benefit-sharing mechanisms in CFUGs.
As per the legal requirement, each CFUG makes locally suitable provisions
for benefit sharing, including provisioning for special benefits to the poor and
marginalized groups (MoFSC, 2008; MoLJ, 1993, 1995).
Our ATT
e
estimates are in line with a recent study by Khanal Chhetri,
Asante, and Yoshimoto (2016), who demonstrate, taking a Gini decomposition
approach in five CFUGs, that CFs have an equalizing effect on household
income distribution in the Nepalese hills. Significant positive ATT
e
reflects the
contribution of the CFP in institutionalizing rules and practices of benefit shar-
ing in an equitable way as provided for in the Community Forestry Directives
(MoFSC, 2008).
The CFUGs receive support from a range of state and nonstate actors (World
Bank, 2001) that likely help reduce elite capture and promote more equitable
benefit sharing (Luintel, 2006; Persha & Anderson, 2014). Formal forest decen-
tralization delegates certain rights to CFUGs, resulting in formal opportunities
to participate in forestry activities, thereby potentially increasing ownership in
decision making (Adhikari, Kingi, & Ganesh, 2014; Ribot & Peluso, 2003).
Households participating in forestry are more likely to benefit from forest
resources because of better access to information and ability to voice concerns
(Agrawal & Gupta, 2005). We add to this literature in that we also find that CFP
results in more equitable access to forest resources, as perceived by the local
communities.
Community and household surveys carried out as part of this research indi-
cate that 80% of NCFs have written rules and >60% of households engage in
forest management. Utilizing both traditional and scientific knowledge on forest
ecosystem and sociocultural practices, local communities might have imple-
mented locally appropriate forest management plans that increased forest pro-
ductivity. As documented by Naidu (2011) for the western Himalayas, increased
forest productivity generally increases the ability of communities to access
higher quantities of products from forests. Formally registered CFUGs regulate
extraction and distribution of forest products (Meynen & Dornboos, 2005) and
perhaps better control free riding, reduce unauthorized extractions, and, we find,
establish equitable benefit-sharing systems.
The insignificant ATT
e
for Terai households indicates a lack of dedicated
institutional rules and practices by communities and supporting agencies,
including government forest agencies, civil society organizations, and donor
funded projects, to enhance equity. The Nepalese government is known to
have placed a low priority on community forestry in the Terai (e.g., Bhattarai,
2006; MoFSC, 2000; World Bank, 2001), resulting in poor institutional and
procedural support for equitable benefit-sharing practices. Terai CFUGs often
sell forest products to increase their CFUG funds, which they generally spend on
16 Journal of Environment & Development 0(0)

community development activities rather than fulfilling the forest product needs
of households (Lamichhane & Parajuli, 2014). Elite domination in Terai CF
decision-making processes might have also inhibited equity.
Our differential ATT
e
estimates across social and geographic categories dem-
onstrates the importance of evaluating the effect of forest decentralization at
disaggregated levels. Our results show smaller effects than the national-level aver-
age for poor, Dalit, indigenous, women-headed, and Terai households and higher
effects for hill households. While the positive ATT
e
estimates indicate the need to
continue existing CFP practices, the neutral effect of CFP in the Terai suggests
equity benefits from reviewing community practices. These estimates suggest the
need for flexible and social group and area-specific policies for promoting equit-
able benefit sharing. More targeted policies might be able to address the deficien-
cies for the groups and areas that have perceived lesser benefits thus far.
These results point to the need for further research exploring why CFP is
effective in promoting equity in different social groups and hills, but not in the
Terai, how communities interpret and implement benefit-sharing provisions
under the CFP, and what capacity building would be useful to ensure and
strengthen equitable benefit sharing. Such research would contribute to amend-
ing the current CFP in such a way that improve intended biodiversity and
carbon outcomes associated with the Convention on Biodiversity and
REDD+ and offer lessons for other forest decentralization efforts.
Our research is one of the first to rigorously examine the equity effects of a
formal CFP. Estimating ATT
e
is challenging with observational data, but a ran-
domized control trial is likely to be infeasible in most circumstances and matching
based on a large number of potential confounders increases our confidence in the
results. As there are several matching algorithms, each with pros and cons, there is
always room for questions related to the quality of the matching method. The use
of SMD as a criterion to check the acceptability of the match balance and the
existence of a few cases where we could not achieve balance while including all
potentially confounding variables could also potentially be critiqued.
Despite careful use of sensitivity analysis, the possibility of spurious correl-
ation cannot be completely ruled out. Unobservable confounders affecting the
probability of assignment to the CFP may include the existence of strong leaders
and communities’ intrinsic motivations. Although we include the key ecological
variables and, of special importance, the pre-CFP 1989 forest-level NDVI in our
model of CFP assignment, it is impossible to include all biological (e.g., life
form, species, growth rate, wood density, and stage of life cycle) and ecological
(e.g., successional stage, species composition, and disturbance regime) factors.
Conclusion
With the commencement of formalized and incentive-based forest management,
including decentralized forestry and REDD+, equity has become recognized as
Luintel et al. 17

one of the critical outcomes. It has been crucial in motivating forest-managing
communities and for gaining support for effective CF management. Using
cross-sectional data and robust analytical methods for estimating ATT
e
,we
demonstrate the positive effect of the Nepalese CFP on perception of equity
in benefit sharing in all circumstances except the Terai, which shows no effect.
Our results suggest the need to review benefit-sharing practices in the Terai and
continue or strengthen such practices at the national level and across margin-
alized households and in the hills.
Our findings suggest that the CFP could potentially support equity goals as
Nepal implements REDD+. REDD+ brings unconventional benefits to forest-
managing communities, but it will also impose restrictions on harvesting forest
products (e.g., timber, fuelwood) that are important parts of local livelihoods and
call on communities to invest in better forest management. In such cases, if the
actual or perceived net benefits to households are negative, it is possible that the
currently beneficial equity effects of the CFP could be reversed. Caution in imple-
menting carbon-focused policies is therefore warranted because it is possible that
such incentives could alter CFP management sufficiently to conflict with equity
goals and upend the generally positive effects on equity of the Nepal CFP.
Acknowledgments
The authors acknowledge the critical support of Michael Toman in the design stage of
this research project, ForestAction Nepal in the collection of field data, and Alec
Kretchun for the preparation of research site map. Finally, we gratefully acknowledge
review comments of Jeremy Spoon, Max Nielsen-Pincus, Veronica Dujon, and three
anonymous reviewers at different stages of this article development.
Declaration of Conflicting Interest
The authors declared no potential conflicts of interest with respect to the research,
authorship, and/or publication of this article.
Funding
The authors disclosed receipt of the following financial support for the research, author-
ship, and/or publication of this article: The authors would like to acknowledge the finan-
cial support of The World Bank Group (7161880 and 7176679) to carry out the research.
Notes
1. Dalit refers to caste communities who have historically been disadvantaged in social,
economic, educational, political, and religious spheres and are often deprived of
human dignity and social justice due to caste-based discrimination.
2. Indigenous peoples in Nepal are listed by Nepalese Government Indigenous Act 2002
and defined as settlers prior to the formation of Nepal as a state. Indigenous groups
are excluded from the Hindu cast system and typically have their own languages,
cultures, and religions.
18 Journal of Environment & Development 0(0)

3. Hill areas make up the majority of the country’s land, and the Nepalese government
and donor communities prioritized the CFP in the hills. Hill forests mainly supply
forest products and watershed services for local consumption. Market access is very
limited for these forests.
4. The Terai constitutes the southern flat land bordering India. CFs are fewer in the
Terai, and forests are under severe deforestation and degradation pressures. Forests in
the Terai often include high-value timber species that are not present in the hills.
Communities in the Terai are often big and diverse.
5. For a description, please see OECD (2008). PCA is a nonparametric method to
address problems of multicollinearity and identify weights when constructing indices.
It reduces dimensionality by performing a covariance analysis between factors and
maximizes the correlation between the original variables and new uncorrelated factors
that are mutually orthogonal. Then the Eigen technique transforms the original set of
variables into a new set with an equal number of independent uncorrelated factors and
is used for factor analysis. The principal factors are then classified in decreasing order
according to the percentage of the variance they represent so that most of the variation
in the data can be described by the most important factors.
6. For additional information on the plot sampling and analysis, see Bluffstone
et al. (2015).
7. Depending on the model, with the goal to maximize the number of potential confoun-
ders included in the matching subject to assuring balance.
8. At the 10% significance level cutoff, the estimate for women-headed households would
be considered significantly different from zero.
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Author Biographies
Harisharan Luintel is visiting scholar at Portland State University and researcher
at ForestAction. He received his PhD in Environmental Science and
Management from the Portland State University. His research focuses on the
analysis of forest policy and program in relation to their impact on forest eco-
systems, community governance and management practices and local
livelihoods.
Randall A. Bluffstone is professor of Economics and director of the Institute for
Economics and the Environment at Portland State University. His research and
teaching interests focus on environmental and resource economics, including
climate change, energy, pollution control and deforestation in low-income coun-
tries. Professor Bluffstone is associate editor of the journal Forest Economics and
a research associate of the Environment for Development (EfD) Initiative, where
he co-coordinates the EfD Forest Collaborative.
Robert M. Scheller is professor of Landscape Ecology at North Carolina State
University. He received his PhD in Forest Ecology from the University of
Wisconsin. His research focuses on forest landscape change: how forests have
changed, how they will change, and why it matters. Specifically, his research
examines how forest management and natural disturbances generate or reduce
24 Journal of Environment & Development 0(0)

forest health, specifically in regards to climate change. He forecasts landscape
change to inform forest policy, regionally and globally.
Bhim Adhikari is a senior program specialist at Canada’s International
Development Research Centre. He received his PhD in Environmental
Economics and Management from the University of York, UK. He has pub-
lished extensively in international peer-reviewed journals such as the
Environment and Development Economics,Ecological Economics,Development
Studies,Environment and Development,Climate and Development and
European Journal of Development Research.
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