Presented by Sandy Nofyanza (CIFOR-ICRAF) at "2023 FLARE Annual Meeting - Parallel Session 16: REDD+ and local livelihoods", Nairobi, Kenya, on 14 Oct 2023

1. REDD+ project and its impact households’ incomes in Indonesian
Borneo: Preliminary findings
Sandy Nofyanza*, Zahra Avia, Colas Chervier, Agus M Maulana, Bimo Dwisatrio
*PhD student (Sustainable Forest Transition Project), Global Development Institute, the University of Manchester
Research Consultant (Global Comparative Study on REDD+), CIFOR-ICRAF
2023 FLARE Annual Meeting
CIFOR-ICRAF Campus, Nairobi
12-16 October 2023

2. Background, objective, data
Background
• REDD+  D&D reductions + co-benefits
• Multiple proponents of projects (govt-led, private-led)
• No cash-transfers from REDD+ projects, or result-based
payments (RBPs) from REDD+ programs between 2010-
2018 – can projects still provide co-benefits?
Objective
• to assess the impact of participation in REDD+ toward
agriculture and overall household income
Data
• GCS REDD+ household dataset, three phases (2010,
2014, and 2018)
• 450 Indonesian HHs across three surveys
Participating in
REDD+
Agri inc - ***
Overall HH inc + ***
Agricultural income
1. Farming (including tree crops)
2. Livestock
3. Livestock products (e.g., eggs, milk, wool)
Overall household income
1. Agriculture
2. Forest product collection (timber and non-timber)
3. Off-farm (e.g., businesses, social assistance,
pension account)

3. Study sites
Private-led: Katingan-Mentaya Project (KMP) Govt-led: Berau Forest Carbon Program (BFCP)
Before-After Control-Intervention (BACI); often locations of REDD+ initiatives are not random – matching was conducted to
obtain a balanced sample of intervention and control households (Sills et al,. 2017).

4. Quasi-experimental method
Task 1.1: REDD+ impact on incomes of participating HHs
Nearest neighbour matching (k=1 and k=3)
Treated group (n KMP: 18, n BFCP: 49)
• HHs living in REDD+ village and participated in REDD plus-related activities
both in 2014 and 2018
Control group (n KMP: 18, n BFCP: 49)
• HHs living in REDD+ villages but participated only either in 2014 or 2018
• HHs living in REDD+ villages but have never participated in REDD+
activities at all
• HHs living in control villages
• Matching  SMD < 0.25 threshold (Stuart, 2010)
• Longitudinal impact assessment  Panel difference-in-differences (DID)
Task 1.2: Intracommunity spillover effect
“Optimal pair” matching and “full” matching
Treated group (n KMP: 66, n BFCP: 8)
• HHs living in REDD+ villages but have never
participated in REDD+ activities at all
Control group (n KMP: 66, n BFCP: 22)
• HHs living in control villages
1
2 (Cohen’s d) Estimation of the magnitude of project’s impact/the size of the difference:
• Task 2.1: Between participating HHs and non-participating HHs
• Task 2.2: Between non-participating HHs in REDD+ village and HHs in control villages

5. Covariates used
Multiple trial using
combinations of
covariates..
.. to obtain a
balanced sample
Variable Description
Gender Gender of household head, 0=male, 1=female
Age Age of household head
Ethnicity 1=household head belongs to the largest ethnic group in the village, 0=otherwise
TREAT 1=household participated in REDD plus-related activity in 2014 and 2018, 0=otherwise
Household size Number of household member(s), including the head
Productivity index Number of assets (0-4) owned to support work (car, truck, motorcycle, and boat)
Precipitation Average annual rainfall in the village for three years prior to each survey (mm)*
Ha of agricultural land Ha of land used for agriculture purposes
Agricultural income Annual income (net) from agriculture (incl. value of livestock and livestock products)
Total household income Annual income (net) from all sources

6. Result 1/3: Participating vs nonparticipating HHs
We are not able to conclude that these REDD+ projects had significant impacts on income
• Similar to Peru’s case: REDD+ ‘did no harm’ because agriculture revenue decrease was not statistically
attributable to REDD+ (Solis et al., 2021)
• But our finding does not indicate that REDD+ ‘did better’ in improving HHs incomes
Outcome variable
KMP
Central Kalimantan
BFCP
East Kalimantan
3-year panel
data
Short-term
(2010-14)
Long-term
(2010-18)
3-year panel
data
Short-term
(2010-14)
Long-term
(2010-18)
Agricultural
income
TREAT � 2014
-1,119.08
(890.84)
-1,024.76
(867.94)
-607.49
(773.07)
-604.09
(758.94)
TREAT � 2018
-1,612.97
(992.97)
-1,484.55
(1,000.12)
613.31
(1,225.56)
636.78
(1,254.28)
Total
HH
income
TREAT � 2014
-1,517.85
(2,229.81)
-168.71
(2,036.09)
86.14
(2,459.30)
11.02
(2,461.39)
TREAT � 2018
2,227.44
(3,422.53)
2,284.71
(3,540.98)
133.27
(3,408.36)
64.56
(3,427.94)

7. Result 2/3: Intracommunity spillover effect
• BFCP: in the long-term, non-participating HHs inside REDD+ villages significantly experienced a drop in
agricultural income compared to HHs in control village
• BFCP is a jurisdictional scale program, wider coverage, those who chose not participate at all may miss
out: the long-term impact of not taking part in REDD+?
• Caveat: very small sample size (only 8 nonparticipating HHs in BFCP site)
Outcome variable
KMP
Central Kalimantan
BFCP
East Kalimantan
3-year panel
data
Short-term
(2010-14)
Long-term
(2010-18)
3-year panel
data
Short-term
(2010-14)
Long-term
(2010-18)
Agricultural
income
TREAT � 2014
-712.17
(516.32)
-727.73
(517.88)
-1,277.22
(1,545.73)
-1,171.97
(1,620.34)
TREAT � 2018
-331.83
(621.12)
-354.83
(616.02)
-3,367.58**
(1,502.51)
-3,383.15**
(1,504.89)
Total
HH
income
TREAT � 2014
-1,605.02
(1,472.41)
-1733.76
(1,476.98)
-2,544.02
(4,447.91)
-1,944.53
(4,098.55)
TREAT � 2018
-2,228.75
(1,625.12)
-2,201.62
(1,620.91)
-5,650.56
(3,664.41)
-5,752.72
(3,758.28)

8. Result 3/3: Difference between group/effect size
Effect size Sawilowsky (2009)
0.01 Very small
0.2 Small
0.5 Medium
0.8 Large
1.2 Very large
2.0 Huge
Site Variable
Participating HHs
vs
Nonparticipating HHs
(Task 1)
Nonparticipating HHs in
REDD+ village
vs
HHs in control village
(Task 2)
KMP
Agricultural income -0.176 -0.182**
Total HH income 0.279 -0.148
BFCP
Agricultural income -0.016 -0.123
Total HH income 0.042 -0.253
• Participation in REDD+ activities alone so far has only had a negligible impact on household incomes
• The patterns aligned with what we hypothesized – but again, based on DID, impacts were not statistically
attributable to REDD+ participation

9. Key lessons learned and conclusion
1. BFCP has larger scale and scope, more people participated in
BFCP than KMP – might explain why nonparticipation = missed
opportunities
a) but Δ between voluntarily not participate vs not having the
opportunity to participate is very small
2. Policy implications:
a) Must REDD+/govt provides “safety net” to those choosing
not to participate in REDD+?
b) Should REDD+ just continue focusing on improving the
livelihoods of those participating? If so, how?
3. Further study: analyzing the impact of RBPs on participating HHs’
wellbeing and income improvement
Site Type of activity REDD+
village
Control
village
# of activity # of activity
KMP
Environmental education 8 -
Forest enhancement 10 2
Non-conditional livelihood enhancement 26 13
Restrictions on forest access and/or conversion 12 4
Tenure clarification 4 2
Total KMP – C Kalimantan 60 21
BFCP
Environmental education 17 2
Forest enhancement 18 6
Non-conditional livelihood enhancement 41 5
Restrictions on forest access and/or conversion 26 5
Tenure clarification 4 5
Total BFCP – E Kalimantan 106 23

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The Center for International Forestry Research (CIFOR) and World Agroforestry (ICRAF) envision a more equitable world where forestry and
landscapes enhance the environment and well-being for all. CIFOR–ICRAF are CGIAR Research Centers.
cifor-icraf.org/gcs
Thank you
sandy.nofyanza@postgrad.manchester.ac.uk
s.nofyanza@cifor-icraf.org