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Impact of Game Management Areas on Household Welfare

Impact of Game Management Areas on Household Welfare. Gelson Tembo Sushenjit Bandyopadhyay. Natural Resource Conservation Forum World Bank Zambia Wildlife Authority Central Statistical Office June, 2007. Plan of the presentation. Introduction Problem and rationale Objectives

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Impact of Game Management Areas on Household Welfare

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  1. Impact of Game Management Areas on Household Welfare Gelson Tembo Sushenjit Bandyopadhyay Natural Resource Conservation Forum World Bank Zambia Wildlife Authority Central Statistical Office June, 2007

  2. Plan of the presentation • Introduction • Problem and rationale • Objectives • Methods and procedures • Results • Concluding remarks

  3. Introduction • Tourism a potential growth frontier • GMA could foster tourism development • Wildlife protection • Public investment through VAGs/CRBs • Private investment (lodge, tour ops, etc)? • Goal: Foster more sustainable, nature- conserving livelihood systems

  4. Problem and rationale • Impact on welfare remains unknown • Such knowledge is important • Success and failure points • Strategies to further foster nature-based tourism • Bottom line: • Communities & households important part of the equation • Social contract to ensure welfare

  5. Objectives • Identify factors affecting participation in GMAs and CRBs/VAGs • Determine the impact of GMAs on the welfare of households living in those areas • Determine the distribution of the benefits between the poor and non-poor

  6. Methods and procedures • Multi-stage stratified cluster sampling • Park systems as reporting domains • Bangweulu (Kasanka, Lavushi, Isangano) • Kafue (Kafue, Blue Lagoon, Lochinvar) • Lower Zambezi • Luangwa (South Luangwa) • Standard enumeration areas as clusters • Sampled by PPS from within 4 strata • Strata: prime, secondary, specialized, under-stocked • HHs within clusters by systematic sampling

  7. Data collection • Two instruments, pretested in Luano, Chongwe • Household questionnaire • Community questionnaire • Implemented with help of CSO

  8. Analysis: Conceptual concerns • Many factors affect hh welfare, incl GMA • Need to separate them out • Major concern: ‘selection bias’ • Households self-select into GMA • Households self-select into VAGs/CRBs • ZAWA created GMAs based on certain criteria  Participation in GMA not random!

  9. Analysis: Two empirical models • Joint estimation of participation and outcome relationships, • Explicitly accounting and correcting for non-random selection • Propensity score matching • Ensures comparison within ‘common support’ • The comparison group is as close to the participating group as possible

  10. Analysis: Outcome variables • Per capita consumption expenditure • Overall • By park system • By asset-poverty status

  11. Results: Participation • Being in GMA is directly related to: • Being female headed • # of males 15-60 • Whether CRB is funded • km to main road • No significant differences in distance to basic schools, health centres

  12. Access to facilities

  13. Participation in GMAs • Being in GMA is inversely related to: • Age, education • Value of consumption assets • Participation in coops

  14. Coops more common in non-GMAs

  15. Participation in CRBs/VAGs • Directly related to: • Education • km to main road • Whether CRB is funded • # of participants • Participation in coops • # of projects • Inversely related to: • Being in Luangwa (relative to Bangweulu)

  16. Results: Impact of GMA on welfare • A naive comparison indicates that GMAs are • Slightly better off in Luangwa • Slightly worse off in others • Overall, not much of a difference!

  17. No impact?

  18. Impact there after all? • Impact much greater when other factors are controlled for!

  19. Vanishing benefits?

  20. Results: Distribution of benefits

  21. Who gets the benefits of participating in the CRB/VAG?

  22. Concluding remarks • GMA interventions have had some positive effects on consumption expenditure • Most visible when other factors are controlled for • There are other factors that would make GMA hhs worse off in the absence of the interventions • Greater gains lie in understanding and reducing the effects of such factors • Most benefits are captured by the non-poor • How can we make the poor benefit more?

  23. GMA hhs grow less crops

  24. Key issues • Why are there more female-headed households in the GMAs? • Participation appears to be restricted among the educated elites. • How can we make it more accessible? • Benefits are there but masked. Why? • Most of the benefits are captured by non-poor households?

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