Composite poverty indices are derived in two main steps, i.e. the identification step to identify those who are poor, and the aggregation step to aggregate the individual poverty characteristics into the overall poverty index (Sen 1976). This section concentrates on the identification step. In a multidimensional framework, the identification of the poor requires two choices: i) the choice of poverty dimensions, indicators, thresholds and weights in order to identify those who are deprived, and ii) the choice of an appropriate method to identify the poor within the group of the deprived. Though the choices taken in the first step are crucial, they go well beyond the scope of this chapter whose theoretical approach can be applied to whatever poverty dimensions, indicators, weights and thresholds have been chosen in the first step. The following is a summary of the notations and definitions used throughout the chapter.
Let ℝk denote the Euclidean k-space, and ℝk+ ⊂ℝk the non-negative k-space. Further, let ℕ denote the set of positive integers. N={1,...,n}⊂ℕ represents the set of n individuals and
⊂
={2,...,d}
D ℕ the set of d poverty dimensions captured by a set of poverty attributes
⊂
={2,...,k}
K ℕ. Let a∈ℝK+ denote the weight vector for the different attributes with aj >0 for all j=1,...,k and 1.
∑
kj=1aj =I refer to the quantity of an attribute with which an individual is endowed as an achievement. The achievement vector of individual i is represented by xi⋅ =(xi1,...,xik). The achievement matrix of a society with n individuals is represented by X∈ℝNK+ where the ijth entry represents the achievement xijof individual i in attribute j. Let Xn be the set of possible achievement matrices of population size n and X=UN⊂ℕXn the set of all possible achievement matrices. I further refer to the poverty threshold of attribute j as zj. Thus, individual i is deprived in attribute j whenever her achievement falls short of the respective threshold, i.e.
whenever xij <zj. z∈ℝK++ represents the vector of the chosen poverty thresholds and Z the set of all possible vectors of poverty thresholds.
I define a poverty index as a function P: X ×Z→ℝ. For any poverty threshold vector Z
z∈ , society A has a higher poverty level than society B if and only if P
(
XA;z)
≥P(
XB;z)
for any XA,XB∈X.
The main methods for the identification of the poor are the aggregate poverty line approach and the component poverty approach. Under the aggregate poverty line approach, the identification of the poor is based upon a function ϕ:ℝk ×ℝk →ℝ that aggregates individual achievements in all poverty dimensions. A person is poor if and only if his/her aggregation function is negative, i.e. ϕ
(
xi⋅;z)
<0. The unique feature of this approach is that it allows compensation between attributes below and above threshold levels among those who are poor (Weak Focus Axiom).This limits the application of this approach to a cardinal context and does therefore not allow an ordinal-cardinal comparison as aspired in this volume.
Therefore, the following analysis is based upon the second method, the component poverty approach that builds upon an attribute-wise evaluation of poverty. All attributes are considered to be essential in the sense that a failure to achieve the threshold level automatically implies deprivation no matter what the achievements are in other dimensions, i.e. compensation is restricted to attributes below threshold levels (Strong Focus Axiom). In order to describe the component poverty approach, let ci⋅=(ci1,...,cik) represent the deprivation vector of individual i such that cij =1 if xij < zj and cij =0 if xij ≥zj. Further, let
{
∑
}=
∈
=
1 : ,..., 1 k cij j
j
i a
δ denote the sum of weighted deprivations suffered by individual i and let
( )
XSj – or simply Sj– denote the set of individuals who are deprived with respect to attribute j. The identification of the poor is based on a function ρ: ℝK+ ×ℝK++ →{0,1}, i.e. an identification function such that individual i is poor if ρ(ci⋅;z)=1 and not poor if
0 )
; (ci⋅ z =
ρ .
Three specifications of the identification function have been suggested so far. The union method is based on the (rather strong) assumption that all attributes are perfect complements7.
7 I follow the Auspitz-Lieben-Edgeworth-Pareto (ALEP) definition of substitutability and complementarity. The ALEP definition considers two attributes to be substitutes if their second cross partial derivatives are positive.
Intuitively, an increase in one attribute decreases poverty the less the higher the achievements in the second attribute. In the same way, attributes are considered to be complements, when the respective cross partial derivatives are negative and independent in case they are zero.
Thus, deprivation in one attribute equals deprivation in all attributes so that each person who is deprived is considered to be poor:
In other words, already the loss in one attribute cannot be compensated, thus, there is no need to consider inequality.
The intersection method, on the other hand, is based on the (equally strong) assumption that all attributes to be perfect substitutes. Thus, poverty occurs solely in case of deprivation in all attributes so that only those individuals are considered poor who are deprived in every single attribute:
In other words, the loss in any attribute can always be completely compensated through other attributes – unless a person is deprived in all attributes. Therefore, only those persons are considered poor, who suffer the highest degree of deprivation. This resembles in a way a Rawlsian “maxi-max” principle (Rawls, 1971) which requires the maximum improvement of the situation of the worst off, i.e. those with the maximum number of deprivations (Jayaraj and Subramanian, 2010, p. 56).
Both approaches are obviously extreme cases, relying on extreme assumptions, and consequently yielding poverty rates that are plainly inapplicable, being either far too high or far too low (Bérenger and Bresson, 2010; Alkire and Foster, 2011).
Mack and Lindsay (1985) were the first to formulate the idea for an intermediate method as a kind of a loophole that was explicitly introduced as the dual cut-off method by Foster (2007) and Alkire and Foster (2007, 2011). The intermediate method defines a minimum level of weighted deprivation so that individual i is considered poor if his/ her sum of weighted
Please note that the intermediate method comprises union and intersection method as extreme cases, i.e. in case δIMmin =ˆ max
{ }
ci. =1 and δIMmin =ˆ min{ }
ci. =1, respectively.Though the intermediate method is a convenient way out of the dilemma of extreme poverty rates, its theoretical justification is questionable. Apart from the fact that the choice of
min
δIM is arbitrary, the whole method is based on the indirect assumption that up to δIMmin attributes are perfect substitutes whereas the same attributes are considered perfect complements from δIMmin onwards.
Figure 1.01 Existing Identification Methods
Figure 1 provides a graphical illustration of the different identification methods for the case of three equally weighted attributes. The upper part of the figure shows the different identification functions. The horizontal axis indicates the sum of weighted deprivations from which an individual suffers and the vertical axis whether the respective individual is poor
) 1
(ρ = or not (ρ =0). The identification function of the union method, ρU(c;z), equals one for any individual that is deprived, i.e. for any i with δi ≥13=δUmin. The intermediate method is illustrated for a cut-off level of two third, i.e. ρIM(c;z)=1 for any i with δi ≥2 3=δIMmin. The identification function of the intersection method, ρIS(c;z), equals one only in case an individual is deprived in all attributes, i.e. for any i with δi =1=δISmin.
The bottom part of the figure shows the attribute constellations according to which individuals are classified as poor. The three axes indicate the quantities for the three attributes
) , ,
(x1 x2 x3 and the respective threshold levels (z1,z2,z3). Shaded areas highlight those attribute constellations that indicate poverty. Obviously, the union method relates almost all, the intersection method only one attribute constellation to poverty. The intermediate approach is the only method that yields “reasonable” poverty rates – however based on a questionable assumption concerning the relationship between attributes.
Summarizing, all three methods rely on extreme assumptions regarding the relationship between attributes which are either all perfect substitutes or all perfect complements. These
1
0 1 δ
ρ
3
=1
min
δU
( )c;z ρU
1
0 δISmin=1 δ
ρ
( )c;z ρIS
1
0 δminIM =23 1 δ ρ
( )c;z ρIM
x3
z2
x1
x2
z1
z3
x3
z2
x1
z3
z1
x2
x3
z2
x1
x2
z1
z3
assumptions do not only exclude every other form of relationship between attributes, they also imply that inequality in the distribution of attributes between individuals is either irrelevant (in the case of perfect complements) or of utmost importance (in the case of perfect substitutes).
This chapter demonstrates that once the two concepts of distributive justice and efficiency are properly taken care of, they imply a new identification method which provides a theoretically-guided way out of the dilemma of inapplicable poverty rates. The argument is related to one made by Dasgupta and Ray in 1986 who observed that inequality issues can either be included in poverty reduction policies by formulating a respective axiom in the aggregation step or by choosing the “right” poverty line, i.e. by tailoring the poverty line to the size of the budget such that all who are poor according to this line are lifted out of poverty. They point out that though the two procedures are equivalent in an ‘arithmetical sense’, the former seems to be the more appropriate one to employ.