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Appraisal of natural resources rents and economic development

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  • Jović, Srđan
  • Maksimović, Goran
  • Jovović, David

Abstract

Economic development could be analyzed according to different inputs. As the inputs natural resources rents should be consider for the investigation of the economic development. In this study 5 natural resources rents were investigated in order to determine which of the natural resource rent has the highest influence on the economic development. The economic development was analyzed based on gross domestic product (GDP). Soft computing method was used to perform the sensitivity analysis of the GDP according to the 5 natural resources rents. Coal rents, forest rents, mineral rents, natural gas rents and oil rents were used as the inputs. According to results, forest rents has the highest dominance on the GDP. In other words GDP has highest variation with small changes in the forest rents.

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  • Jović, Srđan & Maksimović, Goran & Jovović, David, 2016. "Appraisal of natural resources rents and economic development," Resources Policy, Elsevier, vol. 50(C), pages 289-291.
    • Handle: RePEc:eee:jrpoli:v:50:y:2016:i:c:p:289-291
    DOI: 10.1016/j.resourpol.2016.10.012
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    1. Havranek, Tomas & Horvath, Roman & Zeynalov, Ayaz, 2016. "Natural Resources and Economic Growth: A Meta-Analysis," World Development, Elsevier, vol. 88(C), pages 134-151.
    2. Edward Barbier, 2007. "Frontiers and sustainable economic development," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 37(1), pages 271-295, May.
    3. Castaño, María Soledad & Méndez, María Teresa & Galindo, Miguel Ángel, 2016. "The effect of public policies on entrepreneurial activity and economic growth," Journal of Business Research, Elsevier, vol. 69(11), pages 5280-5285.
    4. Kapetanios, George & Marcellino, Massimiliano & Papailias, Fotis, 2016. "Forecasting inflation and GDP growth using heuristic optimisation of information criteria and variable reduction methods," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 369-382.
    5. Roberto Camagni, 2016. "Urban development and control on urban land rents," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 56(3), pages 597-615, May.
    6. Tom Crowards, 1996. "Natural resource accounting," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 7(3), pages 213-241, April.
    7. Brown, Jason P. & Fitzgerald, Timothy & Weber, Jeremy G., 2016. "Capturing rents from natural resource abundance: Private royalties from U.S. onshore oil & gas production," Resource and Energy Economics, Elsevier, vol. 46(C), pages 23-38.
    8. Kahia, Montassar & Ben Aïssa, Mohamed Safouane & Charfeddine, Lanouar, 2016. "Impact of renewable and non-renewable energy consumption on economic growth: New evidence from the MENA Net Oil Exporting Countries (NOECs)," Energy, Elsevier, vol. 116(P1), pages 102-115.
    9. Natalia Merkina, 2009. "Technological catch-up or resource rents?," International Economics and Economic Policy, Springer, vol. 6(1), pages 59-82, June.
    10. Agustin Alonso-Rodriguez, 1999. "Forecasting economic magnitudes with neural network models," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(4), pages 496-511, November.
    11. Agustin Alonso-Rodriguez, 1999. "Forecasting economic magnitudes with neural network models," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 5(2), pages 215-230, May.
    12. Vining, Aidan R. & Richards, John, 2016. "Indigenous economic development in Canada: Confronting principal-agent and principal–principal problems to reduce resource rent dissipation," Resources Policy, Elsevier, vol. 49(C), pages 358-367.
    13. Modis, Theodore, 2013. "Long-term GDP forecasts and the prospects for growth," Technological Forecasting and Social Change, Elsevier, vol. 80(8), pages 1557-1562.
    14. Modis, Theodore, 2013. "Long-Term GDP Forecasts and the Prospects for Growth," OSF Preprints aqcht, Center for Open Science.
    Full references (including those not matched with items on IDEAS)

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