Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa
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Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa

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Presented by Philip Osano, ILRI, Nairobi, 16 August 2011

Presented by Philip Osano, ILRI, Nairobi, 16 August 2011


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  • Rangelands comprise ¾ of SSA (made up of arid/semi-arid) lands and ¾ of Kenya’s terrestrial land area Production is limited by lack of plant-available water Cultivation, Offtake and livestock have increasing rates of production, per annum rates of production of wildlife is decreasing The high rate of losses in wildlife is of major concern because 70% of wildlife is found outside PA. Rates of wildlife loss is happening in both PA and outside PAs There are various reasons why this is happening (e.g. human population growth, expansion of domestic and international markets, improved transport networks and information networks (e.g. mobile phone coverage), improved access to financial services, increasing opportunities for off-farm jobs and investments). A potential cause in large wildlife declines could be because of habitat loss and fragmentation An important change, evident everywhere throughout rangelands is the rapid evolution of property rights from large parcels of land under communal ownership to small parcels of land under private ownership (this does not cover wildlife)
  • According to Okwi et al., (2007), the highest prevalence of poverty in Kenya is in the rangelands The costs include human-wildlife conflict, livestock predation, wildlife disease transmission etc
  • Need to define who the poor are; the following categories Supply side Poor environmental service sellers/providers) – this is the focus group of my study Demand side Poor environmental service (ES) buyers Poor environmental service (ES) free-riders
  • The
  • The ideal situation would be to compare program participants versus non-program participants (control group) to see if program participants are better off than non-participants. However, this is only possible if there is a baseline (at the start of the program) where participation is random (i.e. every household has a chance to participate in the program). In reality (for example in WLP case), there is self-selection bias because selection is not random Regression Analysis I intend to follow the method used by Pagiola et al. (2008) who assessed the intensity of participation of poor households in a PES program There is no control group, therefore the analysis focus entirely on participants (similarities difficult to establish as households are different in various aspects) Select a proxy for intensity of household participation (land allocated to PES or Proportion of land allocated to PES etc) and use as the dependent variable in regression analysis. The independent variables will include household and geographical factors
  • The

Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa Presentation Transcript

  • Payments to promote biodiversity conservation and implications for poverty reduction among pastoral communities in East Africa Philip Osano (PhD Candidate) Geography Department, McGill University, Canada Graduate Fellow, ILRI (PLE) August 16, 2011
  • Outline of Presentation
    • Problem Statements
    • Conceptual Framework of the study
    • Study Objectives and Questions
    • Study Design and Preliminary Results
  • Problem Statements
    • Severe declines in large mammal wildlife population in Kenyan rangelands
    Western et al. 2009; Ottichilo et al. 2000; Norton-Griffith, 2007; Norton-Griffith & Said, 2007 Source: Norton-Griffith, 2007
    • Habitat loss/fragmentation (pop. increase; agricultural expansion etc)
    • Poaching (e.g. illegal trade, local consumption etc)
    • Recurrent drought (and climate change)
  • 2. Pastoralists are becoming more poorer in rangelands Homewood et al. 2009; Reid et al. 2008; Okwi et al., 2007; UNDP, 2006; WRI, 2007; Norton-Griffith, 2007; Ferraro & Kiss, 2002; Pagiola et al. 2005; Grieg-Gran et al., 2005; Zilbermann et al., 2006; Barret & Arcese 1995; Horan et al., 2008 ; Pastoralists are diversifying income sources due to increase in poverty Wildlife payments (PES) can potentially provide stable , predictable and reliable income to pastoral landholders – future of wildlife conservation in private land (mostly pastoral) Past approaches (e.g. ICDPs, CBC, CBWM etc) insufficient: only 5% of wildlife tourism revenue accrue to local landowners in Kenya yet they bear heaviest costs of conservation Direct payments or (PES) can contribute to wildlife conservation and poverty reduction among pastoral communities e.g. Maasai in Amboseli (Bulte et al., 2008)
  • Conceptual Framework of the study
  • Wunder, 2008:287
  • Study Objectives, Hypothesis and Questions
  • Questions
    • What are the financial and non-financial benefits of PES on households?
      • What is the annual income benefit to provider households from PES
      • What is the income from PES to provider households used for?
      • What are the perceived social and cultural impacts of PES?
      • How are non-participants and potential providers affected by PES?
      • What are the potential drawbacks and obstacles of PES?
    • 2. What are the motivating factors driving household participation in PES?
      • How does household poverty status affect the intensity of participation in PES?
      • What are providers recommendations on PES design and features?
      • What are the reasons why potential providers want to participate in PES?
  • Questions
    • 3. How does formal and non-formal institutions contribute to PES implementation?
      • What are the roles of provider groups (e.g. landowners and wildlife associations), users, intermediaries and other stakeholders?
      • How does normative institutions (e.g. laws and policies on property rights) affect PES implementation?
      • How does PES fit with traditional non-formal institutions of Maasai community?
    • 4. What are the perceived risks and how are these mitigated in PES design?
      • What are the major perceived risks and threat to PES implementation?
      • What are stakeholders perceived future scenarios for household and wildlife conditions with and without PES?
  • Study Design and Preliminary Results
  • Data sources
    • Household surveys in 2 sites: ES providers, potential providers and non-providers
    • Interviews with Users, Intermediaries and Key Informants
    • Secondary data: past surveys, PES program database (contract/lease agreements, payment records, compliance /monitoring of conditionalities etc)
    • Review of legal, policy and development planning documents
    • GIS and spatial databases (ILRI)
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  • Olare-Orok Conservancy (OOC)
    • 130 HHs surveyed
    • Participants = 73
    • Non-participants = 57
    • Partnership between tourism private sector and pastoral landowners
    • Current payment rate of $ 43/ha/yr:
        • - Controlled livestock grazing
        • - No settlements in core conservation area
    Maasai Mara National Reserve
  • Proportion Below Poverty Line among Members and Non-Members of Olare Orok Conservancy (OOC) Ref: Osano et al. (in prep.) Year 2008 2009 Proportion below Poverty Line (US$ 1/capita/day OOC+PWC 24/72 22/73 33 % 30% OOC-PWC 26/52 22/51 50% 43 % OOC+PWC (Ex-Providers) 2/6 2/6 33% 33%
    • 164 HHs surveyed
    • Participants = 86
    • Non-participants = 78
    • PES funding from KWS, World Bank (GEF) and TNC (USA)
    • Current payment rate of $ 10/ha/yr:
        • - No plot fencing
        • - No land sub-division
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  • Thank You for Listening!
  • Add a trend line showing area of Nairobi National Park (ha) and area of Athi Kapiti Plains
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  • Study Design Data and Data Analysis Ferraro & Pattanayak, 2006; Pagiola et al. 2008 Question Data Requirements Data Analysis 1. Financial and non-financial benefits (profitability of land use) Household Income data (poor vs. non-poor) Household Socio-economic condition data Land prices/values Data on use of PES income Non-income benefits Land use data Access to social services
    • PES payments vs. household income/expenditure
    • Cost analysis:
    • - Start-up costs
    • - PES payments vs. Opportunity costs of land use (GIS maps)
    • PES payments vs. transaction costs
    • Qualitative analysis of interviews
    2. Motivating factors for HH participation Total land area of household Land area allocated to PES (in conservation) Household characteristics PES program characteristics Regression Analysis (dependent variable = proxy indicator intensity of HH participation) Independent variables: factors likely to affect participation of poor in PES Contingency Tables Qualitative analysis of interviews
  • Study Design Data and Data Analysis Question Data Requirements Data Analysis 3. Influence of Institutions Membership in local resource associations (e.g. land, wildlife) Implementation of laws and policies (e.g. land, wildlife, environment, etc) Qualitative analysis of household survey responses, users, intermediaries and key informants PES program reports Policy and Institutional Analysis (e.g. review of published /grey literature on local/national policy development planning) Policy screening 4. PES risks mitigation and future scenarios PES financing (short and long term) Data on use of PES income (displacement/leakage effect) Data on withdrawals/reasons Local drivers of change (land sales) National drivers of change (policy) Qualitative analysis Scenario analysis