Forest management decentralization in Kenya: Effects on household farm forestry decisions in Kakamega

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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.

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Forest management decentralization in Kenya: Effects on household farm forestry decisions in Kakamega

  1. 1. Forest management decentralization in Kenya:Effects on household farm forestry decisionsin 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. 2. Presentation Outline• Background to the study• Decentralized forest management &environmental outcomes• Methodology• Results and discussion•Policy implications
  3. 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. 4. Background……•The conflicts necessitated forest reform & in 2005,a new Forest Act was formulated•The new Act transformed FD into KFS to facilitatenew management arrangements•This marked the beginning of PFM withcommunities getting involved through CFAs. Theguiding principle is “integrated forest management”.•CFAs rely on membership fees & periodiccontributions to undertake their activities
  5. 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. 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. 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. 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. 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. 10. Results Determinants of CFA ParticipationVariable Marginal EffectsDistance 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. 11. Results….Propensity score distribution & common support0 .2 .4 .6 .8 1 Propensity Score Untreated: Off support Untreated: On support Treated: On support Treated: Off support
  12. 12. Results…. Average treatment effectsMatching Outcome ATT Critical Number of Number ofAlgorithm 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. 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. 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. 15. Thank you for listening

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