1. REDD+ project and its impact on HH agriculture and forest revenues
in Indonesian Borneo: Preliminary findings
Sandy Nofyanza, Zahra Avia, Agus M Maulana
December 14, 2022
Science and Policy Dialogue IV, Santika Hotel Bogor

2. Objective and method
Aim
• to explore the average effect of REDD+ on forest and
agricultural revenue (ATET) and their changes overtime (DiD)
Method
• k-nearest neighbor matching (k=3 and k=5)
• Kernel matching (weighted avg of control HHs based on their similar
characteristics to a treated HH)
Data
• GCS REDD+ household dataset, three phases (2010, 14, 18)
• 450 HHs across three surveys

3. Study
sites

4. Study
sites

5. Forest products
1. Logs, sawn timber, poles, bamboo, rattan, firewood, charcoal
2. Rubber, resin, forage/fodder, thatch
3. Lianas and vines
4. Various plants and animals for food and medicine (incl wild honey)
5. Mineral, ore, rock
6. Tree barks, leaves, roots, branches, seedlings, seeds
Agricultural products
1. Various agricultural crops
2. Livestock
3. Livestock products (eggs, milk, wool, etc)

6. Propensity score matching
Multiple trial using
combinations of
covariates..
.. to obtain a
balance propensity
score
Variable Description
Information on household head
Gender Gender of household head, 0=male, 1=female
Marital status 1=married, 0=otherwise (incl widow/widower)
Age Age of household head
Years of schooling Years of formal education
Born at village 1=household head was born in this village, 0=otherwise
Local ethnicity 1=household head belongs to the largest ethnic group in the village, 0=otherwise
Years living at village* Number of years of living in the village
Information on household
Household size* Number of household member(s)
Years formed Years since household first formed
Asset value Total value of household assets (USD PPP 2021)
House index Relative value of household house conditions, from 3 (low) to 9 (high)
Utility index Relative value of household access to water, electricity, and sanitation, from 3 (low) to 9 (high)
Forest revenue Annual revenue from forest-related activities
Agricultural revenue Annual revenue from agriculture (including the value of livestock and livestock products)
Ha of agricultural land Ha of land used for agriculture purposes
Ha of land for other use Ha of land used for other purposes
Information on villages
Village type 1=REDD+ villages, 0=control villages
Basic infrastructure* Number of basic infrastructures in the village, scale 1-6. Infrastructure include elementary and secondary
schools, accessible road all year, regular phone access, healthcare facility, and financial institution
(formal/informal)
Forest cover change* Perceived forest cover change in the last two years; 1=increased or stayed the same, 0=decreased

7. Propensity score matching
Median bias %
Forest revenue Agricultural revenue
nn match
(k=3)
nn match
(k=5)
Kernel nn match
(k=3)
nn match
(k=5)
Kernel
2010 Unmatched 13.7 13.7 n/a* 13.7 13.7 13.7
Matched 9.1 8.6 n/a* 5.4 6.6 6.1
2014 Unmatched 14.5 14.5 14.5 14.5 14.5 14.5
Matched 7.7 4.1 7.7 2.4 2.7 2
2018 Unmatched 14.2 14.2 14.2 14.2 14.2 14.2
Matched 14.3 16 14.3 5.4 2.7 0.7
• Good overlap
• Good reduce of bias (i.e., all models are well-
balanced), but the 2018 forest revenue
model is not as balanced as the rest

8. Result: Forest revenue
1890,8
2380,4
3243,7
1115,2 1083,2
1446,7
0,0
500,0
1000,0
1500,0
2000,0
2500,0
3000,0
3500,0
2010 2014 2018
USD
(PPP
2021)
Avg change in annual forest revenue
REDD+ villages Control villages
Unmatched samples
0
500
1000
1500
2000
2500
2010 2014 2018
USD
(PPP
2021)
ATET, annual forest revenue
PS NNmatch (k=3) PS kernel matching PS NNmatch (k=5)
Matching 2010 2014 2018
T-test (unmatched) Diff 935.8 1206.93 2005.95
PS NNmatch (k=3) ATET 774.77 1323.57 1781.59
S.E. 393.12 460.21 898.63
p-value 0.050 0.004 0.048
PS NNmatch (k=5) ATET 809.720 1266.95 1840.06
S.E. 369.120 451.76 884.10
p-value 0.029 0.005 0.038
PS kernel matching ATET 742.55 1300.92 1769.44
S.E.
360.63 442.83 872.59
p-value 0.040 0.003 0.043
n with common support:
• 2010: 189 (out of 194)
• 2014: 179 (out of 184)
• 2018: 198 (out of 200)

9. Result: Agricultural revenue
2302,7
1987,2
2236,7
2036,7
2881,7
2335,0
0,0
500,0
1000,0
1500,0
2000,0
2500,0
3000,0
3500,0
2010 2014 2018
USD
(PPP
2021)
Avg change in annual agricultural revenue
REDD+ villages Control villages
Unmatched samples
-1400
-1200
-1000
-800
-600
-400
-200
0
200
400
600
2010 2014 2018
USD
(PPP
2021)
ATET, annual agricultural revenue
PS NNmatch (k=3) PS kernel matching PS NNmatch (k=5)
Matching 2010 2014 2018
T-test (unmatched) Diff -229.18 -987.08 -1227.32
PS NNmatch (k=3) ATET 445.53 -1170.45 -62.31
S.E. 621.60 440.14 594.96
p-value 0.474 0.008 0.916
PS NNmatch (k=5) ATET 249.49 -805.29 -105.81
S.E. 636.43 429.92 569.90
p-value 0.695 0.061 0.852
PS kernel matching ATET 103.01 -707.77 -126.81
S.E. 611.21 418.01 554.68
p-value 0.866 0.091 0.819
n with common support:
• 2010: 286 (out of 290)
• 2014: 347 (out of 362)
• 2018: 347 (out of 358)

10. Discussion and conclusion
• On average, communities in REDD+ villages earned an extra ….
• US$ 742-809/year (2010)
• US$ 1,266-1,323/year (2014)
• US$ 1,769-1,840/year (2018)
from forestry-related economic activities compared to those in control villages
• In 2010, farmers in REDD+ villages earned between US$ 103-445 annually more than those in control villages
• But in 2014 farmers in control villages earned US$ 707-1,170/year more than those in REDD+ villages
• Revenue gap got smaller in 2018, with farmers in control villages earned about US$ 62-126/year more
than farmers in REDD+ villages
FOR
AGRI

11. Discussion and conclusion
• REDD+ significantly influenced the rise in forest revenues
• But there is a considerable gap in the baseline (2010) as forest revenue in REDD+ villages were already
1.7 times larger than in the control villages
• This indicates that REDD+ sites were in general more suitable for forest-based economic activities in the
first place
• REDD+ had statistically significant effect on the decrease of agricultural revenue in 2014
• This may be explained by the various agricultural restrictions imposed by the project such as prohibition
of new land clearing
• Average agricultural revenue in REDD+ villages bounced back in 2018 (to about the same level in 2010) and
the revenue gap with control villages became narrower
• But we did not find statistical evidence that allows us to say REDD+ influenced such increases
FOR
AGRI

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