Presentation by Maurice J. Ogada at the 28th triennial conference of the International Association of Agricultural Economists (IAAE), Foz do Iguaçu, Brazil, 18-24 August 2012.

1. Forest management decentralization in Kenya:
Effects on household farm forestry decisions
in Kakamega
By
Maurice J. Ogada
Presented at the 28th triennial conference of the International Association of
Agricultural Economists, Foz do Iguaçu, Brazil, 20 August 2012

2. Presentation Outline
• Background to the study
• Decentralized forest management &
environmental outcomes
• Methodology
• Results and discussion
•Policy implications

3. Background
• Colonial government established Forest
Department in 1902
• Conservation was the main objective, community
interest was peripheral & management was highly
centralized
• The centralized management continued even
after independence
• In 1980s, conflicts between communities &
Forest Department intensified

4. Background……
•The conflicts necessitated forest reform & in 2005,
a new Forest Act was formulated
•The new Act transformed FD into KFS to facilitate
new management arrangements
•This marked the beginning of PFM with
communities getting involved through CFAs. The
guiding principle is “integrated forest management”.
•CFAs rely on membership fees & periodic
contributions to undertake their activities

5. Decentralized forest management
& environmental outcomes
• Decentralization policies may not affect behaviour
of communities directly
• But such policies change local incentive structures
• A variety of outcomes, both positive & negative, is
thus possible
• For instance, communities initially thought the new
regime would allow them to convert forests into
farmlands
• Basically outcomes are dependent on community
experiences & traditions, and capacity to take
advantage of prevailing market conditions

6. Decentralized forest management
& environmental outcomes….
• At best results of decentralized forest
management are mixed
• This is what motivates the current study→ to
investigate the results of forest management
reform in Kenya on environmental outcomes
• Farm forestry is used as the indicator of
environmental outcome
• So the study investigates how household’s
engagement in PFM impacts on its farm forestry
decisions

7. Methodology
• Twin objectives are simultaneously pursued:
– Identifying determinants of household’s participation in CFA
– Estimating impact of household participation in CFA on
farm forestry investment decisions
• Participation in CFA has potential costs & benefits.
Thus, it can be modeled in a random utility
framework
• We model it as a binary choice based on utility
maximization subject to household resource
constraints

8. Methodology….
• In assessing impact of participation in CFA on farm
forestry, the interest is to estimate the average
treatment effect on the treated (ATT)
• We are unable to observe what the results would
have been without participation. So we have to deal
with missing data on the counterfactual
• This informs choice of PSM in this study. PSM uses
information on non-participants to create counterfeit
counterfactual
• PSM is not able to control for unobservable
heterogeneity among households. But we test
robustness of our results using different specifications
• We also use ESR (reveals absence of selectivity due
to unobserved factors)

9. Data
• Analysis is based on cross-sectional data collected from
Kakamega forest communities
• Kakamega is the only remaining rain forest in Kenya
(remnant of the Guinea-Congolean rain forest to the
east)
• 318 households were randomly selected & interviewed
using a detailed semi-structured questionnaire by EfD-
Kenya in 2010
• The forest has 3 management agencies→ KFS, QC &
KWS. KFS & QC are the same in practice.

10. Results
Determinants of CFA Participation
Variable Marginal Effects
Distance to forest (walking time) -0.006*(0.004)
Access to credit 0.253**(0.097)
Household size 0.034* (0.02)
Landholding size -0.048**(0.021)
No. of social groups 0.107*** (0.037)
Aware of Forest Act 0.487*** (0.062)
Management agency=KFS 0.217** (0.096)

11. Results….
Propensity score distribution & common support
0 .2 .4 .6 .8 1
Propensity Score
Untreated: Off support Untreated: On support
Treated: On support Treated: Off support

12. Results….
Average treatment effects
Matching Outcome ATT Critical Number of Number of
Algorithm level of Treated Control
hidden bias
(Γ)
NNM Acreage 0.428*** 2.65-2.70 140 157
under trees (4.43)
KBM Acreage 0.428*** 2.00-2.05 140 157
under trees (4.13)

13. Results….
• Imposition of common support condition is useful
in avoiding bad matches
• Participation in CFA exerts positive & significant
effect on household land under farm forestry
• Households that participate in CFA have 0.428
acres more of land, on average, under tree growing
than their non-participating counterparts
• Sensitivity analysis indicates that even fairly large
unobserved heterogeneity would not alter the
inference

14. Policy implications
• PFM is the right direction for the country for
increasing forest cover. It may be enhanced through:
– Increased access to information particularly on the Forest
Act (2005)
– Opening channels for formal credit to forest communities
– Promoting formation of social groups among forest
communities
– Improving transport infrastructure linking communities
with the forests
– Increasing access to forests by communities to make
participation more rewarding

15. Thank you for listening