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SELLING OUR WAY INTO POVERTY: The Commercialisation of Poverty in Malawi

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Author Info
Bokosi, Fanwell Kenala

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Abstract

The aim of the article is to investigate the impact of commercialisation on household poverty in Malawi using the 1997/98 Integrated Household Survey data. The results indicate that overall those household who were more commercialised were better off than those who did not and thus commercialisation should be encouraged as a means of alleviating poverty. In terms of regional analysis the southern region and the central region results indicate that the more commercialised households were actually worse off. Furthermore, the livelihoods of the most vulnerable households (female headed and poor households) did not benefit from commercialisation. Therefore, in terms of policies, it is important that government should identify groups that are likely losers to commercialisation and hence the need for compensatory or socially protective policy design to socio-economic groups whose incomes have been reduced by commercialisation.

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File URL: http://mpra.ub.uni-muenchen.de/7087/
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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 7087.

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Date of creation: 14 Jan 2008
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Handle: RePEc:pra:mprapa:7087

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Related research
Keywords: Commercialisation; Poverty; Propensity Score Matching; Household Model; Malawi.;

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Find related papers by JEL classification:
I31 - Health, Education, and Welfare - - Welfare and Poverty - - - General Welfare
C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets

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  1. James Heckman & Salvador Navarro-Lozano, 2004. "Using Matching, Instrumental Variables, and Control Functions to Estimate Economic Choice Models," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 30-57, 08. [Downloadable!] (restricted)
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  2. Rajeev H. Dehejia & Sadek Wahba, 2002. "Propensity score matching methods for non-experimental causal studies," Discussion Papers 0102-14, Columbia University, Department of Economics. [Downloadable!]
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  3. Heckman, James J & Ichimura, Hidehiko & Todd, Petra, 1998. "Matching as an Econometric Evaluation Estimator," Review of Economic Studies, Blackwell Publishing, vol. 65(2), pages 261-94, April. [Downloadable!] (restricted)
  4. Michael Lechner, 2002. "Some practical issues in the evaluation of heterogeneous labour market programmes by matching methods," Journal Of The Royal Statistical Society Series A, Royal Statistical Society, vol. 165(1), pages 59-82. [Downloadable!] (restricted)
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  5. Sascha O. Becker & Andrea Ichino, 2002. "Estimation of average treatment effects based on propensity scores," Stata Journal, StataCorp LP, vol. 2(4), pages 358-377, November. [Downloadable!]
  6. Richard Blundell & Monica Costa Dias, 2002. "Alternative approaches to evaluation in empirical microeconomics," CeMMAP working papers CWP10/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies. [Downloadable!]
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  7. Jorge F. Balat & Guido Porto, 2005. "Globalization and Complementary Policies: Poverty Impacts in Rural Zambia," NBER Working Papers 11175, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  8. Dehejia, Rajeev, 2005. "Practical propensity score matching: a reply to Smith and Todd," Journal of Econometrics, Elsevier, vol. 125(1-2), pages 355-364. [Downloadable!] (restricted)
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