Refining Targeting against Poverty Evidence from Tunisia*
| Published date | 01 June 2010 |
| Date | 01 June 2010 |
| DOI | http://doi.org/10.1111/j.1468-0084.2010.00583.x |
381
©Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2010. Published by Blackwell Publishing Ltd,
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OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 72, 3 (2010) 0305-9049
doi: 10.1111/j.1468-0084.2010.00583.x
Refining Targeting against Poverty Evidence from
TunisiaÅ
Christophe Muller† and Sami Bibi‡
†DEFI, University of Aix-Marseille, Marseille, France
(e-mail: christophe.muller@u-cergy.fr; christophe.muller@univmed.fr)
‡Poverty and Economic Policy, University of Laval, Quebec, Canada
(e-mail: samibibi@gnet.tn)
Abstract
Weintroduce a new methodology to target direct transfers against poverty. Our method
is based on estimation methods that focus on the poor. Using data from Tunisia, we
estimate ‘focused’transfer schemes that highly improve anti-poverty targeting perfor-
mances. Post-transfer poverty can be substantially reduced with the new estimation
method. For example, a one-third reduction in poverty severity from proxy-means
test transfer schemes based on OLS method to focused transfer schemes requires
only a few hours of computer work based on methods available on popular statistical
packages. Finally, the obtained levels of undercoverage of the poor are particularly
low.
I. Introduction
The issue
Transfer schemes are among the main policy tools against poverty.Cash transfers are
the provision of assistance in cash to the poor or to those who face a risk of falling
ÅWe are grateful to the INS (National Institute of Statistics of Tunisia) that provided us with the data.
This study is a revised version of the paper ‘Focused Targeting against Poverty. Evidence for Tunisia’. The
research has been supported by the ESRC Grant R000230326, which funded the employment of the second
author as a research assistant. The first author is also grateful for the financial support by Spanish Ministry of
Sciences and Technology,Project SEJ2005-02829/ECON, and by the Instituto Valenciano de Investigaciones
Econ´omicas.We thank G. Kingdon, J.-Y.Duclos, J.-M. Robin and participants in seminars in the Universities
of Oxford,Alicante, Nottingham, Sussex, GREQAM and DEFI at Aix-Marseille II, YonseiUniversity in Seoul,
University of Cergy-Pontoise, DIW in Berlin, University of Paris I and diverse conferences. Usual disclaimers
apply.
JEL Classification numbers: D12, D63, H53, I32, I38.
382 Bulletin
into poverty. Many of these schemes, called ‘proxy-means tests’ (PMT), are based
on predictions of household living standards used to calculate the transfers. Such
predictions are obtained by using household survey data for regressing the living
standard variable on household characteristics that are easy to observe. However, the
errors in using OLS for PMT against poverty are large, a key shortcoming of PMT.
In this study, we show how these errors can be substantially reduced by using other
statistical approaches.
Many countries have been using PMT to target transfers, particularly in (i) Latin
America and the Caribbean, such as Chile for many years under the Ficha CAS
system, Columbia under SISBEN , Mexico under the Oportunidades Program,
Nicaragua, Jamaica, etc.; and (ii) Asia, such as India, Indonesia, China, Thailand and
Philippines. In these countries, many theoretical and practical issues related to PMT
have been studied. The performance of the estimated transfer schemes is quite variable
(Coady, Grosh and Hoddinot, 2004). Raising their impact on poverty is of paramount
importance as stressed in De Janvry and Sadoulet (2006b). However, the statistical
foundations of these programmes have not received the attention that it deserves. We
fill this gap in this study.
Concerned with improving anti-poverty transfer schemes, we propose an estima-
tion method of anti-poverty PMT that focus on the poor and the near-poor, thereby
dramatically enhancing the scheme performance. We evaluate different approaches
to determine scores for PMT schemes. We apply our new method to Tunisia and find
significant improvement when compared with traditional methods.
What is targeting?
Although living standards are measured with household surveys, they are generally
badly known for the households that are not surveyed. Many authors have studied
assistance to poor people based on targeting when some characteristics of individuals
can be observed, but not income.1Recently, Coady, Grosh and Hoddinott (2004)
reviewed 122 targeted anti-poverty programmes in 48 countries. Cash transfers based
on PMT are generally found to provide the best results, although there is an enormous
variation in targeting performances. They also find that targeting performance is
better in rich countries and where governments are accountable. Lindert et al. (2005)
measure the redistributive power of 56 transfer programmes in eight countries. They
find that public transfers can be an efficient way of redistributing income, but often fail
to do so. Moreover, the coverage of the poor is found far from 100% for the studied
programmes. Some transfer programmes are conditional on prespecified behaviour
by beneficiaries (e.g. child school attendance or child vaccination). We do not deal
1For instance, see Ravallion (1991), Besley and Coate (1992), Glewwe (1992), Besley and Kanbur (1993),
Datt and Ravallion (1994), Slesnick (1996), Chakravarty and Mukherjee (1998), Ahmed and Bouis (2002),
Coady, Grosh and Hoddinott (2002), Schady (2002), Tabor (2002), Coady et al. (2004), Coady and Skoufias
(2004), Skoufias and Coady (2007), Datt and Joliffe (2005), Lindert, Skoufias and Shapiro (2005), Africa
Focus Bulletin (2006), DFID (2006) and Weiss (2005).
©Blackwell Publishing Ltd and the Department of Economics, University of Oxford 2010
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