Paris School of Economics - L'Agence Française de Développement

There are three kinds of combinations that
need to be examined in each circumstance: a)
how to combine information that is available
for each household member (as years of
schooling) and how to address “missing values”
in some responses or attribute scores to
ineligible respondents; b) how to attribute
household level data to individuals (taking
into account literature and empirical studies
on equivalence scales for income and on intrahousehold
inequalities in distribution), and c)
when and how it is justified to use a variable
from a single respondent or from a subset of
household members to represent all household
members. These may require detailed
expertise on each indicator.
The combination methods must also consider
biases due to differently sized households,
as well as households with different compositions.
In the 2010 MPI, larger households have
a greater probability of being deprived in the
health and school attendance indicators, and
less probability of being deprived in “years of
schooling” and at least the “asset” indicator
among the standard of living indicators.The
overall effect is not clear. Household composition
— the age and gender of household
members, as well as their relationships to one
another — also varies. A household of male
migrant workers will have a relatively low
probability of being deprived in nutrition
(most surveys lack male malnutrition data),
as well as child school attendance and child
mortality, whereas a household with a great
number of children will have a relatively larger
probability.
Studies are needed to enumerate alternative
methods of combining the data, what errors
may be introduced by different methodologies,
and how to check the robustness of
results to choices made. Empirical studies also
are needed to explore the magnitude of differences
introduced by different methodologies
and to generate examples of careful
and rigorously verified methods of combining
individual and household data. Alongside
quantitative work, qualitative and ethnographic
studies can be used to explore the
assumptions underlying different alternatives,
and consider which equivalence scales and
intra-household aggregation methods are
most accurate in a given context. [39]
Data
The data restrictions on the MPI or any other
global measure that requires internationally
comparable indicators are considerable, as
was detailed earlier. The data constraints at a
country level are less binding, but it can be
useful to itemize common constraints. Many
of these are well recognized. For example,
many household surveys omit institutionalized
populations such as the imprisoned, the
homeless, and the hospitalized; further, certain
surveys exclude key groups such as the
elderly or a gender group. The sampling frame,
periodicity, and quality of household surveys
are also regularly criticized. Multidimensional
measures raise a distinctive set of questions in
addition to these for two reasons.
Data on each variable must be available for the
same person. If a multidimensional poverty
measure follows Sen’s approach, and identi-
[39] For example, identifying a household as non-deprived if any member has 5 years of schooling, as the MPI, presumes that
education is shared across household members; in some cultural contexts or in some kinds of households, that assumption
may not be accurate.
December 2011 / Measure for Measure / How Well Do We Measure Development? / © AFD [ 77 ]