Targeting the poor and smallholder farmers : empirical evidence from Malawi

Houssou, Nazaire; Zeller, Manfred

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Houssou, Nazaire ; Zeller, Manfred

Targeting the poor and smallholder farmers : empirical evidence from Malawi

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URN: urn:nbn:de:bsz:100-opus-4161
URL: http://opus.uni-hohenheim.de/volltexte/2010/416/

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SWD-Schlagwörter:

Malawi , Kleinbauer , Armut , Prognose , Mathematische Modellierung

Freie Schlagwörter (Englisch):

Malawi , poverty targeting , proxy means tests , out-of-sample tests , bootstrap

Institut:

Institut für Agrar- und Sozialökonomie in den Tropen und Subtropen

DDC-Sachgruppe:

Landwirtschaft, Veterinärmedizin

Dokumentart:

ResearchPaper

Schriftenreihe:

Forschung zur Entwicklungsökonomie und -politik / Research in development economics and policy

Bandnummer:

2009,1

Sprache:

Englisch

Erstellungsjahr:

2009

Publikationsdatum:

17.02.2010

Lizenz:

Veröffentlichungsvertrag mit der Universitätsbibliothek Hohenheim ohne Print-on-Demand

Kurzfassung auf Englisch:

This paper develops low cost, reasonably accurate, and simple models for improving the targeting efficiency of development policies in Malawi. Using a stepwise logistic regression (weighted) along with other techniques applied in credit scoring, the research identifies a set of easily observable and verifiable indicators for correctly predicting whether a household is poor or not, based on the 2004-05 Malawi Integrated Household Survey data. The predictive power of the models is assessed using out-of-sample validation tests and receiver operating characteristic curves, whereas the model?s robustness is evaluated by bootstrap simulation methods. Finally, sensitivity analyses are performed using the international and extreme poverty lines.
The models developed have proven their validity in an independent sample derived from the same population. Findings suggest that the rural model calibrated to the national poverty line correctly predicts the status of about 69% of poor households when applied to an independent subset of surveyed households, whereas the urban model correctly identifies 64% of poor households. Increasing the poverty line improves the model?s targeting performances, while reducing the poverty line does the opposite. In terms of robustness, the rural model yields a more robust result with a prediction margin ±10% points compared to the urban model. While the best indicator sets can potentially yield a sizable impact on poverty if used in combination with a direct transfer program, some non-poor households would also be targeted as the result of model?s leakage. One major feature of the models is that household score can be easily and quickly computed in the field. Overall, the models developed can be potential policy tools for Malawi.