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Charcoal production and household welfare in Uganda: a quantile regression approach

Published online by Cambridge University Press:  10 April 2013

John Herbert Ainembabazi ,
Gerald Shively  and
Arild Angelsen
John Herbert Ainembabazi
Affiliation:
UMB School of Economics and Business, Norwegian University of Life Sciences, P.O. Box 5003, N-1432 Ås, Norway; and International Institute of Tropical Agriculture (IITA), Uganda. E-mail: [email protected]
Gerald Shively
Affiliation:
UMB School of Economics and Business, Norwegian University of Life Sciences, Norway; and Department of Agricultural Economics, Purdue University, USA. E-mail: [email protected]
Arild Angelsen
Affiliation:
UMB School of Economics and Business, Norwegian University of Life Sciences, Norway. E-mail: [email protected]
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Abstract

Previous research suggests that forest-dependent households tend to be poorer than other groups, and that extreme reliance on forest resources might constitute a poverty trap. We provide an example in which a non-timber forest product – charcoal – appears to be providing a pathway out of poverty for some rural households in Uganda. Data come from households living adjacent to natural forests, some of whom engage in charcoal production. We use a semi-parametric method to identify the determinants of participation in charcoal production and a quantile regression decomposition to measure the heterogeneous effect of participation on household income. We find that younger households and those with few productive assets are more likely to engage in charcoal production. We also show that, as a result of their participation, charcoal producers are better off than non-charcoal producers in terms of income, even though they are worse off in terms of productive assets.

Type
Research Article
Information
Environment and Development Economics , Volume 18 , Issue 5 , October 2013 , pp. 537 - 558
Copyright
Copyright © Cambridge University Press 2013 

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References

Adhikari, B. (2005), ‘Poverty, property rights, and collective action: understanding the distributive aspects of common property resource management’, Environment and Development Economics 10: 7–31.Google Scholar
Albrecht, J., van Vuuren, A., and Vroman, S. (2009), ‘Counterfactual distributions with sample selection adjustments: econometric theory and an application to the Netherlands’, Land Economics 16: 383–396.Google Scholar
Ambrose-Oji, B. (2003), ‘The contribution of NTFPs to the livelihoods of the “forest poor”: evidence from the tropical forest zone of south-west Cameroon’, International Forestry Review 5: 106–117.Google Scholar
Angelsen, A. and Wunder, S. (2003), ‘Exploring the forest–poverty link: key concepts, issues and research implications’, CIFOR Occasional Paper No. 40, Center for International Forestry Research, Bogor, Indonesia.Google Scholar
Arnold, M., Köhlin, G., Persson, R., and Shepherd, G. (2003), ‘Fuelwood revisited: what has changed in the last decade?’, CIFOR Occasional Paper No. 39, Center for International Forestry Research, Bogor, Indonesia.Google Scholar
Belcher, B., Ruiz-Perez, M., and Achdiawan, R. (2005), ‘Global patterns and trends in the use and management of commercial NTFPs: implications for livelihoods and conservation’, World Development 33(9): 1435–1452.Google Scholar
Boucher, S., Carter, M.R., and Guirkinger, C. (2008), ‘Risk rationing and wealth effects in credit markets’, American Journal of Agricultural Economics 90(2): 409–423.Google Scholar
Campbell, B.M., Jeffrey, S., Kozanayi, W., Luckert, M., Mutamba, M., and Zindi, C. (2002), Household Livelihoods in Semi-Arid Regions: Options and Constraints, Bogor, Indonesia: Center for International Forest Research.Google Scholar
Carter, M.R. and May, J. (2001), ‘One kind of freedom: the dynamics of poverty in post-apartheid South Africa’, World Development 29(12): 1987–2006.Google Scholar
Cavendish, W. (2000), ‘Empirical regularities in the poverty–environment relationship of rural households: evidence from Zimbabwe’, World Development 28(11): 1979–2003.Google Scholar
Colfer, C.J., Peluso, N., and Chung, C.S. (1997), Beyond Slash and Burn, New York: New York Botanical Garden.Google Scholar
Debela, B., Shively, G., Angelsen, A., and Wik, M. (2012), ‘Economic shocks, diversification and forest use in Uganda’, Land Economics 88(1): 139–154.Google Scholar
Firpo, S. (2007), ‘Efficient semiparametric estimation of quantile treatment effects’, Econometrica 75(1): 259–276.Google Scholar
Fisher, M. (2004), ‘Household welfare and forest dependence in Southern Malawi’, Environment and Development Economics 9: 135–154.CrossRefGoogle Scholar
Fortin, N., Lemieux, T., and Firpo, S. (2011), ‘Decomposition methods in economics’, in Ashenfelter, O. and Card, D. (eds), Handbook of Labor Economics, Vol. 4A, Amsterdam: North-Holland, pp. 1–102.Google Scholar
Freedman, D.A. and Sekhon, J.S. (2010), ‘Endogeneity in probit response models’, Political Analysis 18: 138–150.Google Scholar
Frölich, M. and Melly, B. (2008), ‘Unconditional quantile treatment effects under endogeneity’, Discussion Paper Series IZA DP, No. 3288, Institute for the Study of Labor, Bonn.Google Scholar
Frölich, M. and Melly, B. (2010), ‘Estimation of quantile treatment effects with Stata’, Stata Journal 10(3): 423–457.Google Scholar
Fu, Y., Chen, J., Guo, H., Chen, A., Cui, J., and Hu, H. (2009), ‘The role of non-timber forest products during agroecosystem shift in Xishuangbanna, southwestern China’, Forest Policy and Economics 11: 18–25.Google Scholar
Golub, G.H. and Van Loan, C.F. (1996), Matrix Computations, 3rd edn, Baltimore, MD: Johns Hopkins University Press.Google Scholar
Hagenaars, A., de Vos, K., and Zaidi, M.A. (1994), Poverty Statistics in the late 1980s: Research Based on Micro-data, Luxembourg: Office for Official Publications of the European Communities.Google Scholar
Hirano, K. and Imbens, G.W. (2001), ‘Estimation of causal effects using propensity score weighting: an application to data on right heart catheterization’, Health Services and Outcomes Research Methodology 2: 259–278.Google Scholar
Horowitz, J.L. and Härdle, W. (1994), ‘Testing a parametric model against a semiparametric alternative’, Econometric Theory 10: 821–848.Google Scholar
Imbens, G.W. and Angrist, J.D. (1994), ‘Identification and estimation of local average treatment effects’, Econometrica 62(2): 467–475.Google Scholar
Khan, S.R. and Khan, S.R. (2009), ‘Assessing poverty–deforestation links: evidence from Swat, Pakistan’, Ecological Economics 68: 2607–2618.Google Scholar
Khundi, F., Jagger, P., Shively, G., and Sserunkuuma, D. (2011), ‘Income, poverty and charcoal production in western Uganda’, Forest Policy and Economics 13(3): 199–205.Google Scholar
Klein, W.R. and Spady, R.H. (1993), ‘An efficient semiparametric estimator of the binary response models’, Econometrica 61(2): 387–421.CrossRefGoogle Scholar
Klein, R. and Vella, F. (2009), ‘A semiparametric model for binary response and continuous outcomes under index heteroscedasticity’, Journal of Applied Econometrics 24(5): 735–762.Google Scholar
Klein, R. and Vella, F. (2010), ‘Estimating a class of triangular simultaneous equations models without exclusion restrictions’, Journal of Econometrics 154:154–164.Google Scholar
Koenker, R. and Bassett, G.B. (1978), ‘Regression quantiles’, Econometrica 46: 33–50.Google Scholar
Machado, J.A.F. and Mata, J. (2005), ‘Counterfactual decomposition of changes in wage distributions using quantile regression’, Journal of Applied Econometrics 20(4): 445–465.Google Scholar
McSweeney, K. (2004), ‘Forest product sale as natural insurance: the effects of household characteristics and the nature of shock in eastern Honduras’, Society and Natural Resources 17(1): 39–56.Google Scholar
McSweeney, K. (2005), ‘Natural insurance, forest access, and compounded misfortune: forest resources in smallholder coping strategies before and after Hurricane Mitch, Northeastern Honduras’, World Development 33(9): 1453–1471.Google Scholar
Melly, B. (2005), ‘Decomposition of differences in distribution using quantile regression’, Labour Economics 12: 577–590.Google Scholar
Millimet, D.L. and Tchernis, R. (2009), ‘On the specification of propensity scores: with applications to the analysis of trade policies’, Journal of Business and Economic Statistics 27: 397–415.Google Scholar
Millimet, D.L. and Tchernis, R. (2012), ‘Estimation of treatment effects without an exclusion restriction: with an application to the analysis of the school breakfast program’, Journal of Applied Econometrics; doi:10.1002/jae.2286.Google Scholar
Narain, U., Gupta, S., and van't Veld, K. (2008a), ‘Poverty and resource dependence in rural India’, Ecological Economics 66: 161–176.Google Scholar
Narain, U., Gupta, S., and van't Veld, K. (2008b), ‘Poverty and the environment: exploring the relationship between household incomes, private assets, and natural assets’, Land Economics 84(1): 148–167.Google Scholar
Neumann, R.P. and Hirsch, E. (2000), Commercialisation of Non-timber Forest Products: Review and Analysis of Research, Bogor, Indonesia: Center for International Forestry Research.Google Scholar
Newey, W.K., Powell, J.L., and Walker, J.R. (1990), ‘Semiparametric estimation of selection models: some empirical results’, American Economic Review 80(2): 324–328.Google Scholar
Pattanayak, S.K. and Sills, E.O. (2001), ‘Do tropical forests provide natural insurance? The microeconomics of non-timber forest product collection in the Brazilian Amazon’, Land Economics 77: 595–612.Google Scholar
Paumgarten, F. and Shackleton, C.M. (2009), ‘Wealth differentiation in household use and trade in non-timber forest products in South Africa’, Ecological Economics 68: 2950–2959.CrossRefGoogle Scholar
Shively, G., Jagger, P., Sserunkuuma, D., Arinaitwe, A., and Chibwana, C. (2010), ‘Profits and margins along Uganda's charcoal value chain’, International Forestry Review 12: 271–284.Google Scholar
Williams, R. (2009), ‘Using heterogeneous choice models to compare logit and probit coefficients across groups’, Sociological Methods and Research 37(4): 531–559.Google Scholar
Williams, R. (2010), ‘Fitting heterogeneous choice models with oglm’, Stata Journal 10(4): 540–567.Google Scholar
Wooldridge, J. (2010), Econometric Analysis of Cross Section and Panel Data, 2nd edn, Cambridge, MA: MIT Press.Google Scholar
Wooldridge, J. (2011), ‘Quasi-maximum likelihood estimation and testing for nonlinear models with endogenous explanatory variables’, Seminar Paper, Michigan State University, East Lansing, MI.Google Scholar
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