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Forest reliance across poverty groups in Tanzania

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  • Dokken, Therese
  • Angelsen, Arild

Abstract

An emerging body of knowledge has established that poorer households in forest adjacent communities in developing countries are generally more forest reliant (higher forest income share) while richer households tend to extract more and generate higher absolute forest income. These studies commonly categorize households based on observed income in cross-section data, presenting a snap-shot reflecting both inter-household and inter-annual income variation. In this paper we introduce a new approach to categorize households based on a combination of the observed one-year income and predicted income by an augmented asset approach. Applying this approach on household data from Tanzania, we find forest reliance to be high among structurally poor households (low observed income and assets). The highest forest reliance is found among the stochastically non-poor households (high income and low assets), and this group also has the highest absolute forest income.

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  • Dokken, Therese & Angelsen, Arild, 2015. "Forest reliance across poverty groups in Tanzania," Ecological Economics, Elsevier, vol. 117(C), pages 203-211.
  • Handle: RePEc:eee:ecolec:v:117:y:2015:i:c:p:203-211
    DOI: 10.1016/j.ecolecon.2015.06.006
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    2. Pritchard, Rose & Grundy, Isla M. & van der Horst, Dan & Ryan, Casey M., 2019. "Environmental incomes sustained as provisioning ecosystem service availability declines along a woodland resource gradient in Zimbabwe," World Development, Elsevier, vol. 122(C), pages 325-338.
    3. Trædal, Leif Tore & Vedeld, Pål, 2018. "Cultivating forests: The role of forest land in household livelihood adaptive strategies in the Bac Kan Province of northern Vietnam," Land Use Policy, Elsevier, vol. 73(C), pages 249-258.
    4. Miller, Daniel C. & Hajjar, Reem, 2020. "Forests as pathways to prosperity: Empirical insights and conceptual advances," World Development, Elsevier, vol. 125(C).
    5. Lund, Jens Friis & Sungusia, Eliezeri & Mabele, Mathew Bukhi & Scheba, Andreas, 2017. "Promising Change, Delivering Continuity: REDD+ as Conservation Fad," World Development, Elsevier, vol. 89(C), pages 124-139.
    6. Grote, U. & Nguyen, T.T., 2018. "Natural resource extraction and household welfare in rural Laos," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277061, International Association of Agricultural Economists.
    7. Jiao, Xi & Walelign, Solomon Zena & Nielsen, Martin Reinhardt & Smith-Hall, Carsten, 2019. "Protected areas, household environmental incomes and well-being in the Greater Serengeti-Mara Ecosystem," Forest Policy and Economics, Elsevier, vol. 106(C), pages 1-1.
    8. Nguyen, Trung Thanh & Do, Truong Lam & Halkos, George & Wilson, Clevo, 2020. "Health shocks and natural resource extraction: A Cambodian case study," Ecological Economics, Elsevier, vol. 169(C).
    9. Rasmussen, Laura Vang & Watkins, Cristy & Agrawal, Arun, 2017. "Forest contributions to livelihoods in changing agriculture-forest landscapes," Forest Policy and Economics, Elsevier, vol. 84(C), pages 1-8.
    10. Noack,Frederik & Wunder,Sven & Angelsen,Arild & Börner,Jan, 2015. "Responses to weather and climate : a cross-section analysis of rural incomes," Policy Research Working Paper Series 7478, The World Bank.
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    More about this item

    Keywords

    Forest dependence; Poverty categories; Asset poverty; Cross-sectional data;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry

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