Dashboard of Indicators and Composite Indices

A starting point for measuring the multidimensionality of poverty is to assess the level of deprivation in dimensions separately, in other words, to apply a ‘standard unidimensional measure to each dimension' (Alkire, Foster, and Santos 2011).

Writing from within a basic needs approach framework, Hicks and Streeten proposed the use of dashboards: ‘as a first step, it might be useful to define the best indicator for each basic need.... A limited set of core indicators covering these areas would be a useful device for concentrating efforts' (1979: 577).^[78] A prominent implementation of a dashboard approach has been the Millennium Development Goals: a dashboard of 49 indicators was initially defined to monitor the eighteen targets to achieve the eight goals. Improvements in different aspects of poverty are evaluated with independent indicators, such as the proportion of people living below $1.25 a day, the fraction of children under 5 years of age who are underweight, the child mortality rate, the share of seats held by women in single or lower houses of national parliaments, and so on. This provides a rich and variegated profile of a population's achievements across a spectrum of dimensions and their changes over time. Furthermore, in many cases the indicators can be decomposed to illuminate disparities.

Observe that the different indicators in a dashboard are not necessarily based on the same reference population (section 2.2.4). In our notation, the n, population may be different for each j dimension.

An example of disjoint indicators is child malnutrition (computed using information for children under 5 years of age) and share of seats held by women in parliament (com­puted using information for men and women in the single or lower houses of national parliaments). If the indicators pertain to disjoint populations, there seems to be no need to consider joint deprivations. However, even in this case, joint deprivations could be relevant if the disjoint populations have something in common—such as belonging to the same household. Under such circumstances, the deprivation experienced by one individual (for example, a child who is malnourished) can affect others (like her mother). This is known as an intra-household negative externality. Thus, ignoring the joint distribution of a composite unit of analysis (households in the example) may obscure important aspects of poverty. An example of indicators with overlapping populations is the proportion of people living on less than $1.25 a day and the percentage of people without adequate sanitation. In this case, because both deprivations can be experienced by both groups of people, the information on the extent to which those living on less than $1.25 a day are also deprived in sanitation and vice versa may be relevant.

Dashboards have the advantage of broadening the set of considered dimensions, offering a rich amount of information, and potentially allowing the use of the best data source for each particular indicator and for assessing the impact of specific policies (such as nutritional or educational interventions).

Technically, a dashboard could also include a measure of correlation or association between every pair of dimensions, which may account for the joint distribution in some restricted sense. However, a large number of indicators in dashboards require an even larger number of pairwise correlations to be reported, which is definitely expected to increase complexity. Perhaps that is why such kinds of correlation indicators are not in practice included in dashboards. Even if bivariate associations/correlations are reported, they still do not account for the underlying multivariate joint distribution, and thus remain silent in identifying who the poor are. Secondly and relatedly, Th dashboards suffer because of their heterogeneity, at least in the case of very large and eclectic ones, and most lack indications about Th hierarchies among the indicators used. Furthermore, as communications instruments, one frequent criticism is that they lack what has made GDP a success: the powerful attraction of a single headline figure that allows simple comparisons of socio-economic performance over time or across countries' (Stiglitz et al. 2009: 63).

One way to overcome this heterogeneity and communications challenge is through composite indices.

There is a burgeoning literature on composite indices of poverty or well-being.^[79] Well-known indices include the Physical Quality of Life Index (Morris 1978), the Human Development Index (HDI) (Anand and Sen 1994), the Gender Empowerment Index (GEM) (UNDP 1995), and, within poverty measurement, the Human Poverty Index (HPI) (Anand and Sen 1997). These indices have been published in the global Human Development Reports for several years.^[80] A prominent policy index is the official EU-2020 measure of poverty and social exclusion, which uses a union counting approach across three dimensions: income poverty, joblessness, and material deprivation (Hametner etal. 2013).

Composite indices, like dashboards, can capture deprivations of different population subgroups and can combine distinct data sources. In contrast to dashboards, they impose relative weights on indicators, which govern trade-offs across aggregate dimensional dimensions. Such normative judgements are very demanding (Chapter 6) and have been challenged (Ravallion 2011b). In practice, they have catalysed expert, political, or public scrutiny of and debate about these trade-offs, facilitating a process of public reasoning as recommended by Sen (2009).

Like dashboards, composite indices do not reflect the joint distribution of deprivations. In fact, a composite index of the four dimensional deprivations presented in Table 2.2 would combine these indices with some aggregation formula, but would show the level of overall deprivation in the two distributions as being identical.

Moreover, composite indices like dashboards remain silent to one of the basic steps of poverty measurement: identification of the poor. Even when a composite index is constructed by considering all deprivations within a society in the selected dimensions, it fails to identify the set of the poor Z within the society. It may appear that, when the base population is the same for all considered dimensions, such composite indices follow the union criterion to identification as they consider all deprivations, but this notion is not correct because the identification of all deprivations does not ensure the identification of the set of poor. In fact as long as there is at least one person experiencing more than one deprivation, counting the deprived in each dimension would lead to a double counting of the number of the ‘union poor' (see Bourguignon and Chakravarty 2003: 28-9). Thus neither dashboards nor composite indices can answer the questions: Who is poor? How many poor people are there? How poor are they? (Alkire, Foster, and Santos 2011). In sum, the dashboard approach and composite indices represent important tools for understanding poverty based on multiple dimensions, and can be used with multiple data sources covering different reference populations. However, their inability to capture the joint distribution of multiple dimensions and to identify what proportion of the population are poor make them limited tools for multidimensional poverty measurement and analysis.^[81] In the following sections, we introduce approaches that address the joint distribution of deprivations.

3.2